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Carbon-rich lime slag as a lime source in agriculture - effects on germination, plant growth and elemental content
Get PDFThe use of decarbonized virgin lime is connected to environmental problems like high CO2 emissions, high energy utilization and negative impact from open-pit lime mines. Recycling of used lime would reduce many of these problems. The effect of carbon-rich lime slag (CLS) from the metallurgical industry on germination, growth, and elemental content in barley, oilseed radish and sugar beet was investigated on two soils (clay and sand). CLS without and with water (CLSW) were compared to limestone in the sandy soil, primarily used to increase pH, and hydrated lime, primarily used to increase aggregation, in clay soils. CLS addition did not reduce the germination of the seeds as compared to the control. Including fertilization to the CLS treatment reduced the germination up to 23% in oilseed radish after 16 days. The germination of sugar beet seeds was delayed but had recovered after 16 days. Shoot biomass was higher in plants with CLS and CLSW, in both soils without fertilization. Plants grown in the CLS had lower Cd content compared to plants grown with hydrated lime. We can conclude that CLS show a high potential to be used on agricultural land from a crop growth perspective
Plant genotype-dependent biocontrol of wheat diseases
Get PDFSustainable crop production requires the reduction of chemical pesticides, and the use of beneficial microorganisms such as biological control agents (BCAs) is recommended as a sustainable alternative for disease management. However, the interaction between a host plant and a BCA can influence its biocontrol efficacy, which is currently not well understood. To better understand the role of plant genetic variation in influencing biocontrol efficacy, in this thesis, a winter wheat germplasm of approximately 200 genotypes was explored under controlled conditions for biocontrol efficacy of the BCA Clonostachys rosea during interactions with pathogens – Zymoseptoria tritici causing septoria tritici blotch (STB) and Fusarium graminearum causing fusarium foot rot (FFR). In both pathosystems, significant phenotypic variation was observed for disease susceptibility and C. rosea biocontrol efficacy. However, C. rosea efficacy varied in managing STB (positive effect: 7 genotypes, negative effect: 11 genotypes) and FFR (positive effect: 180 genotypes), suggesting that biocontrol efficacy can be specific not only to plant genotype but also to pathogen and/or plant tissue. Moreover, disease susceptibility and biocontrol efficacy were positively correlated, but distinct marker-trait associations were identified using genome-wide association studies (GWAS). The independent inheritance of disease susceptibility and C. rosea biocontrol efficacy offers the potential for simultaneous selection of these traits in future breeding programs. A few plant defence-related genes were co-localised in GWAS-identified regions for C. rosea biocontrol efficacy. To gain a deeper understanding, two genotypes with varying C. rosea biocontrol efficacy towards STB were used in a transcriptomic study, where differences in gene expression at early hours of inoculation were investigated in direct interaction with Z. tritici, C. rosea and their co-inoculation. The results showed a temporal difference between the genotypes, where the genotype with higher biological control efficacy showed a delayed but strong induction of the immune system by C. rosea. Overall, this thesis contributes towards advancing the knowledge of plants–BCA interaction in affecting biocontrol efficacy, which can aid future disease management and plant breeding efforts
Cumulative worries in Sápmi-the interplay between climate change and other threats to reindeer herding in Sweden and Norway
Get PDFCompeting land uses, climate change, and state regulations pose stress to Saami reindeer herders in Norway and Sweden. Saami reindeer herding is a nomadic tradition relying on huge natural pastures, often with long distance migration between seasonal pastures, and the foremost strategy to cope with changing environments has been flexible use of pastures. However, the adaptive space of reindeer herding is under pressure, which may threaten the sustainability of Saami reindeer herding both economically and culturally. The ability to adapt to external pressures has been of focus in several studies on reindeer herding, but few have analysed cumulative sources of worry as perceived by herders. Using data from a survey among reindeer herders in Norway and Sweden, we describe and analyse factors causing worry and cumulative concern. Overall, results show that differences in worry depend largely on country- and region-specific challenges, while other characteristics of the respondents, with some exceptions, do not significantly explain the degree of worry. A principal component analysis shows that underlying traits that could be interpreted as land use change have the highest factor loadings. Another principal component analysis of questions on the effects of climate change suggests that there are two groups of reactions among reindeer herders. One group of traits points to a general worry and insight that some undefined changes in management need to be done, while another set points to an insight that current reindeer husbandry is unsustainable, given the effects of climate change, and consequently a willingness to take concrete action
The North American Greenhouse Gas Budget: Emissions, Removals, and Integration for CO2, CH4, and N2O (2010-2019): Results From the Second REgional Carbon Cycle Assessment and Processes Study (RECCAP2)
Get PDFAccurate accounting of greenhouse-gas (GHG) emissions and removals is central to tracking progress toward climate mitigation and for monitoring potential climate-change feedbacks. GHG budgeting and reporting can follow either the Intergovernmental Panel on Climate Change methodologies for National Greenhouse Gas Inventory (NGHGI) reporting or use atmospheric-based "top-down" (TD) inversions or process-based "bottom-up" (BU) approaches. To help understand and reconcile these approaches, the Second REgional Carbon Cycle Assessment and Processes study (RECCAP2) was established to quantify GHG emissions and removals for carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O), for ten-land and five-ocean regions for 2010-2019. Here, we present the results for the North American land region (Canada, the United States, Mexico, Central America and the Caribbean). For 2010-2019, the NGHGI reported total net-GHG emissions of 7,270 TgCO(2)-eq yr(-1) compared to TD estimates of 6,132 +/- 1,846 TgCO(2)-eq yr(-1) and BU estimates of 9,060 +/- 898 TgCO(2)-eq yr(-1). Reconciling differences between the NGHGI, TD and BU approaches depended on (a) accounting for lateral fluxes of CO2 along the land-ocean-aquatic continuum (LOAC) and trade, (b) correcting land-use CO2 emissions for the loss-of-additional-sink capacity (LASC), (c) avoiding double counting of inland water CH4 emissions, and (d) adjusting area estimates to match the NGHGI definition of the managed-land proxy. Uncertainties remain from inland-water CO2 evasion, the conversion of nitrogen fertilizers to N2O, and from less-frequent NGHGI reporting from non-Annex-1 countries. The RECCAP2 framework plays a key role in reconciling independent GHG-reporting methodologies to support policy commitments while providing insights into biogeochemical processes and responses to climate change
Characterization and identification of hot pepper-associated endospore-forming bacteria with potential applications as biofertilizers and in biocontrol of pepper wilt pathogens
Get PDFBackgroundAlthough hot pepper contributes significantly to Ethiopia's national economy, its production is hindered by devastating outbreaks of phytopathogens such as Fusarium wilt and Meloidogyne incognita disease complexes. It is known that bacteria in the pepper rhizosphere can promote plant growth by suppressing soil-borne pathogens and producing growth-promoting substances. Therefore, hot pepper-associated endospore-forming bacteria were evaluated for plant growth-promoting traits and in vitro antagonism to pepper wilt-causing pathogens, revealing some potentially valuable isolates.ResultsOne hundred and forty-seven heat-resistant endospore-forming rhizobacteria were recovered from 48 rhizosphere samples. Thirty-five of these isolates solubilized phosphate efficiently with solubilization index values of 2.8-10, and produced indole acetic acid (27. 31-59.16 mu g/ml). Moreover, 20 isolates hydrolyzed chitin effectively, 21 of them reduced the radial growth of three pathogenic Fusarium oxysporum strains by between 26.7% and 79.2%, and cell-free supernatants of 12 isolates reduced the hatching of M. incognita eggs by 51-96.4% while also increasing juvenile mortality by 45-98.7%. After 16S rRNA gene sequence analysis, 31 of the isolates were identified as Bacillus spp. (B. siamensis, B. velezensis, and B. cereus; n = 26) and Paenibacillus polymyxa (n = 5).ConclusionsThe bacterial strains JUBC7 (B. cereus) and JUBC12 (B. siamensis) have multiple phytobeneficial traits that make them promising microbial inoculants for protecting high value crops against phytopathogens
Per- and polyfluoroalkyl substances (PFAS) in drinking water, gestational diabetes mellitus, hypertension and preeclampsia: A nation-wide register-based study on PFAS in drinking water
Get PDFBackground: There is inconclusive evidence of associations between exposure to per- and polyfluoroalkyl substances (PFAS) and diabetes and hypertensive disorders during pregnancy. Objectives: We conducted a nation-wide register-based cohort study to assess the associations of the estimated maternal drinking water exposure to the sum of four major PFAS (PFAS4; perfluorooctane sulfonate (PFOS), perfluorooctanoate (PFOA), perfluorononanoate (PFNA) and perfluorohexanoate (PFHxS)) with gestational diabetes mellitus (GDM), hypertension and preeclampsia. Materials and methods: We included nulliparous women giving birth in Sweden during 2012-2018 in large localities served by municipal drinking water where PFAS were measured in raw and drinking water. Using a onecompartment toxicokinetic model, we estimated cumulative maternal blood levels of PFAS4 during pregnancy considering residential history, municipal PFAS water concentration and year-specific maternal PFAS background serum levels. The outcomes and individual covariates were ascertained via register linkage. Mean values and 95% Confidence Intervals (CI) of Odds Ratios (OR) were estimated by logistic regression. Results: Among the 109,031 nulliparous women included, with an estimated average 7.8 ng PFAS4/mL serum (standard deviation: 2.0 ng/mL), there were indications of a non-monotonic inverse association for PFAS4 and GDM, corresponding to multivariable-adjusted OR 0.72 (95 % CI: 0.61-0.84) when comparing extreme quartiles. An inverse association were also seen for each PFAS individually. No clear associations were seen for hypertension or preeclampsia, although individual PFAS indicated significant associations, both inverse (PFAS and PFHxS) and direct (PFOS and PFNA) for hypertension. Conclusion: In the present study, we observed indications of inverse, non-monotonic associations for PFAS4 and GDM. Some individual PFAS were also associated with hypertension, both direct and inverse. The limitations linked to the exposure assessment still require caution in the interpretation
Re-Thinking People and Nature Interactions in Urban Nature-Based Solutions
Get PDFPeople-environment interactions within nature-based solutions (NBS) are not always understood. This has implications for communicating the benefits of NBS and for how we plan cities. We present a framework that highlights a duality in NBS. The NBS as an asset includes both natural capital and human-centred capital, including organisational structures. NBS also exist as a system within which people are able to interact. Temporal and spatial scales moderate the benefits that NBS provide, which in turn are dependent on the scale at which social processes operate. Co-production and equity are central to the interactions among people and institutions in the design, use and management of NBS, and this requires clear communication. Drawing on ideas from culture-based development (CBD), we suggest an approach to communicate the benefits of NBS in a neutral but effective way. We propose guidelines for planning NBS that allow the optimisation of NBS locations and designs for particular outcomes
Artificial intelligence meets genomic selection: comparing deep learning and GBLUP across diverse plant datasets
Get PDFTo enhance the implementation of genomic selection (GS) in plant breeding, we conducted a comprehensive comparative analysis of deep learning (DL) models and genomic best linear unbiased predictor (GBLUP) methods across 14 real-world datasets derived from diverse plant breeding programs. We evaluated model performance by meticulously tuning hyperparameters specific to each dataset, aiming to maximize predictive accuracy and reliability. Our results demonstrated that DL models effectively captured complex, non-linear genetic patterns, frequently providing superior predictive performance compared to GBLUP, especially in smaller datasets. However, neither method consistently outperformed the other across all evaluated traits and scenarios. The analysis revealed that the success of DL models significantly depended on careful parameter optimization, reinforcing the importance of rigorous model tuning procedures. In the discussion, we emphasize the complementary nature of DL and GBLUP methods, highlighting that the choice between these models should be driven by the specific characteristics of the traits under study and the evaluation metrics prioritized in breeding programs. These insights contribute practical guidelines for selecting and optimizing genomic prediction models to achieve robust outcomes in plant breeding contexts
Using the Newcomb–Benford law to detect species misreporting in mixed pelagic catches
Get PDFModern stock assessment models used to provide management advice on sustainable catches rely on unbiased catch data. Distortion of this data, intentional or not, may increase the uncertainty in the stock perception, jeopardize the assessment of marine resources, and compromise their sustainable management with negative ecological and socio-economic effects. In this study, we apply an analysis of anomalous numbers based on the Newcomb–Benford law (NBL) to test for fisheries catch misreporting. We focus on the Swedish small pelagic fisheries targeting herring and sprat in the Baltic Sea, which are known to be highly problematic due to the pronounced mixing of the two species in their catches and the existence of potential incentives for misreporting. The analyses also include fishery-independent data from international scientific surveys, which are used as standards for the interpretation of the anomalies in the commercial catch data. We demonstrate that data from two Baltic fishery independent surveys conformed to the NBL, while Swedish commercial catch data recorded at sea (logbooks) and onshore (landing declarations) did not, indicating inaccurate reporting of commercial catches. While non-conformity to the NBL may not be considered as proof of misreporting, and to determine the intentionality of misreporting, if any, goes beyond the scope of the paper, we discuss the possible reasons for the observed deviations from the model and recommend the application of this method for quality control of fishery data. Further research (i.e. testing new tools both for detection and estimation of misreporting) should be carried on this fishery with the aim of improving the accuracy of the reported catches. Furthermore, we open the discussion to whether the management should rely on less accurate but more spatially resolved or more accurate but spatially unresolved commercial data. The application of the NBL presented in this study can be readily implemented to other stocks and fishery as a supporting tool to investigate potential misreporting and contribute to improve our understanding of self-reported fisheries data
Improving the performance of machine learning algorithms for detection of individual pests and beneficial insects using feature selection techniques
Get PDFTo reduce damage caused by insect pests, farmers use insecticides to protect produce from crop pests. This practice leads to high synthetic chemical usage because a large portion of the applied insecticide does not reach its intended target; instead, it may affect non-target organisms and pollute the environment. One approach to mitigating this is through the selective application of insecticides to only those crop plants (or patches of plants) where the insect pests are located, avoiding non-targets and beneficials. The first step to achieve this is the identification of insects on plants and discrimination between pests and beneficial non-targets. However, detecting small-sized individual insects is challenging using image-based machine learning techniques, especially in natural field settings. This paper proposes a method based on explainable artificial intelligence feature selection and machine learning to detect pests and beneficial insects in field crops. An insect-plant dataset reflecting real field conditions was created. It comprises two pest insects-the Colorado potato beetle (CPB, Leptinotarsa decemlineata) and green peach aphid (Myzus persicae)-and the beneficial seven-spot ladybird (Coccinella septempunctata). The specialist herbivore CPB was imaged only on potato plants (Solanum tuberosum) while green peach aphids and seven-spot ladybirds were imaged on three crops: potato, faba bean (Vicia faba), and sugar beet (Beta vulgaris subsp. vulgaris). This increased dataset diversity, broadening the potential application of the developed method for discriminating between pests and beneficial insects in several crops. The insects were imaged in both laboratory and outdoor settings. Using the GrabCut algorithm, regions of interest in the image were identified before shape, texture, and colour features were extracted from the segmented regions. The concept of explainable artificial intelligence was adopted by incorporating permutation feature importance ranking and Shapley Additive explanations values to identify the feature set that optimized a model's performance while reducing computational complexity. The proposed explainable artificial intelligence feature selection method was compared to conventional feature selection techniques, including mutual information, chi-square coefficient, maximal information coefficient, Fisher separation criterion and variance thresholding. Results showed improved accuracy (92.62 % Random forest, 90.16 % Support vector machine, 83.61 % Knearest neighbours, and 81.97 % Na & iuml;ve Bayes) and a reduction in the number of model parameters and memory usage (7.22 x 107 Random forest, 6.23 x 103 Support vector machine, 3.64 x 104 K-nearest neighbours and 1.88 x 102 Na & iuml;ve Bayes) compared to using all features. Prediction and training times were also reduced by approximately half compared to conventional feature selection techniques. This demonstrates a simple machine learning algorithm combined with an ideal feature selection methodology can achieve robust performance comparable to other methods. With feature selection, model performance can be maximized and hardware requirements reduced, which are essential for real-world applications with resource constraints. This research offers a reliable approach towards automatic detection and discrimination of pest and beneficial insects which will facilitate the development of alternative pest control approaches and other targeted pest removal methods that are less harmful to the environment than the broad-scale application of synthetic insecticides.(c) 2025 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co., Ltd. This is an ope