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  • Rural Digital Europe
  • 2018-2022
  • Publications
  • Research software
  • Doctoral thesis
  • 2. Zero hunger

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lukas Wille;

    Pea (Pisum sativum L.) is a valuable and healthy protein source for food and feed. In addition to the nutritional benefits, pea is an invaluable agro-ecological asset for sustainable cropping systems through positive effects on soil fertility and soil microbial diversity. The symbiosis with nitrogen-fixing bacteria allows pea and other legume crops to supply the soil with nitrogen and, therefore, to significantly reduce the application of external nitrogen fertilisers. Therefore, pea plays an important role especially in low-input farming systems. The growing market for plant- based protein supply is likely to promote pea cultivation in the near future. However, pea production is severely challenged by various soil-borne pathogens that form a Pea Root Rot Complex (PRRC) causing root-rot diseases. Despite considerable progress in resistance breeding against individual pathogens, current pea varieties lack resistance against multiple interacting pathogens. The overall goal of this thesis was to contribute to the understanding of resistance against root rot pathogen complexes in pea. Chapter 1 gives an overview of the importance of pea as a future key player in agricultural systems and the food sector before introducing the pea root rot complex concept and its relevance for research on resistance. Furthermore, the most recent developments in molecular biology relevant for molecular plant breeding of pea are briefly summarised and an overview of quantitative real-time PCR relevant for research on microbial interactions in the pea root rot complex is given. Chapter 2 reviews the current knowledge of resistance against root- rot pathogens in major grain legumes, highlights the importance of the host genotype in determining the composition of plant-associated microbial communities and how the root associated microbiome relates to plant health. In addition, major findings on the role of root exudation in disease susceptibility and resistance of grain legumes are summarised. Finally, it delineates how this knowledge could be integrated in resistance breeding of grain legumes. In Chapter 3, a resistance screening assay was established based on infested soil from an agricultural field that showed severe pea root rot pressure. This approach was chosen in order to account for the whole rhizosphere microbiome - including the naturally occuring pathogen complex - in the assessment of root rot resistance in pea. The initial ITS- amplicon sequencing of the fungal rhizosphere community of diseased pea roots grown in the infested soil showed a root community of evenly abundant fungal taxonomic units not dominated by a few taxa. This finding points at complex interactions within the PRRC. Two hundred and sixty-one pea cultivars, landraces and breeding lines were screened for resistance on the naturally infested field soil in a controlled conditions experiment. The screening system allowed for a reproducible assessment of disease parameters among the tested genotypes. Broad sense heritabilities on the infested soil were H2 = 0.89 for plant emergence, H2 = 0.43 for root rot index and H2 = 0.51 for relative shoot dry weight. The resistance ranking was verified in an on-farm experiment with nine pea genotypes in two field sites: The controlled conditions root rot index showed a significant correlation with the resistance ranking in the field site with high PRRC infestation (Spearman's ρ = 0.73, p = .03). The screening system offers a tool for selection at early stages of the plant development, and for the study of plant resistance in the light of complex plant-microbe interactions. For Chapter 4, a subset of five resistant and three susceptible pea genotypes was selected based on the initial screening. In analogy to the previous experiment, a controlled conditions experiment was setup up in order to assess and validate resistance of the eight pea genotypes on four soils. Plant growth was significantly reduced on the three sick soils compared to the healthy soil. Despite the significantly different levels of disease pressure in the three infested soils (ANOVA: p < .001) and the strong genotype effect (p < .001), no significant soil × genotype interaction (p < .342) was found for plant growth reduction. In addition to disease assessments, ten key microbial taxa (eight putative pea pathogens and two putative beneficials) were quantified in the roots by quantitative real-time PCR (qPCR). Fusarium solani, F. oxysporum and Aphanomyces euteiches were the most abundant pathogens in diseased roots from the three sick soils. Further, various levels of the pathogens F. avenaceum, F. redolens, Rhizoctonia solani, D. pinodella and Pythium sp. as well as the potential antagonist Clonostachys rosea were quantified by qPCR. The contribution of individual pathogens to root rot and growth reduction differed among the three sick soils: F. solani and F. oxysporum showed significant correlations (Spearman correlations; p < 0.05) with root rot index and relative shoot dry weight in the two soils with the highest infestation level; A. euteiches showed significant relations with disease in two sick soils from Germany. The quantities of arbuscular mycorrhizal fungi were negatively correlated with root rot index and positively correlated with relative shoot dry weight in all sick soils. Furthermore, the root microbial composition differed significantly among the pea genotypes (PERMANOVA; p < .0001) and the soils (p < .0001) and a significant pea genotype × soil interaction was evidenced (p < .0001). In addition, resistant pea genotypes showed significantly lower F. solani and A. euteiches, and higher arbuscular mycorrhizal fungi abundance in the roots (Wilcoxon rank-sum test; p < .05). These results give insights into the complex interaction between key microorganisms of the PRRC and the plant, by pointing out potential key microorganisms in the root rot pathobiome. Further disentanglement of this complex and the validation of key microbial players can be harnessed by resistance breeding. Chapter 5 reviews the experimental approaches and results from the previous chapters before discussing the major findings and implications for future research and resistance breeding. I also raise the question if and how knowledge about complex soil microorganisms-plant feedbacks can be incorporated in resistance screenings and breeding efforts to conclude that today we are at a point where information on microbial complexes could indeed assist resistance breeding. However, our current state of knowledge does not yet allow to design specific microbiome-enabled selection-tools. This last chapter will also give short outlooks and indicate possible future lines of research in the field of microbe-mediated plant resistance.

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    Doctoral thesis . 2020
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    Authors: Bongiorno, Giulia;

    Developments in soil biology and methods to characterize soil organic carbon have the potential to deliver novel soil quality indicators that can help to identify soil management practices that sustain soil productivity and environmental resilience. This thesis aimed at investigating the suitability of a range of soil biological and biochemical parameters as novel soil quality indicators for agricultural management. The soil parameters, selected through a literature review, comprised different labile organic carbon fractions (hydrophilic dissolved organic carbon (Hy-DOC), dissolved organic carbon (DOC), permanganate oxidizable carbon (POXC), hot water extractable carbon (HWEC) and particulate organic matter carbon (POMC), ordered here from the smallest to the largest proportion of the total organic carbon), soil disease suppressiveness measured with a Pythium-Cress bioassay, nematode communities characterized with amplicon sequencing and qPCR, and microbial community level physiological profiling (CLPP) measured with MicroRespTM. We tested the sensitivity of the novel indicators to tillage and organic matter addition in 10 European long-term field experiments, and assessed their relationship with already existing soil quality indicators linked to soil functioning. Lastly, the results of these experimental chapters are interpreted relative to each other and to the broader body of literature on soil quality assessments. Moreover, pros and cons of the novel indicators are discussed, and possibilities and needs for future research are outlined. Reduced tillage increased carbon availability, disease suppressiveness, nematode richness and diversity, the stability and maturity of the food web, and microbial activity and functional diversity. Organic matter addition had a weaker role in sustaining soil quality, possibly due to the different compositions of the organic matter inputs in the long-term field experiments that were sampled. Random forest analysis showed that POXC was the indicator that discriminates soil management most, and structural equation modelling showed its central role in nutrient cycling, carbon sequestration, biodiversity conservation, erosion control and disease regulation/suppression. The novel indicators proposed here have great potential to improve existing soil quality assessment schemes, but their usefulness is still to be validated and optimized.

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    Article . 2020
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    Other literature type . Doctoral thesis . Thesis . 2020 . Peer-reviewed
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    Authors: Barth, Ruud;

    The objective of this work was to further advance technology in agriculture, specifically by pursuing the research direction of agricultural robotics for harvesting in greenhouses, with the specific use-case of Capsicum annuum, also known as sweet or bell pepper. Within this scope, it was previously determined that the primary cause of agricultural robotics not yet maturing was the complexity of the tasks due to inherent variations of the crops, in turn limiting performance in harvest success and time. As a solution, it was suggested to further enhance robotic systems with sensing, world modelling and reasoning, for example by pursuing approaches like machine learning and visual servo control. In this work, we have followed this suggestion. It was identified that facilitating new levels of artificial intelligence in the domains of sensing and motion control would be one of the ways to improve upon classical mechanization. Specifically, we investigated the means of using machine learning based computer vision guided manipulation towards a basic form of world representation and autonomy. For this, in Chapter 2 we developed an eye-in-hand sensing and visual control framework for dense crops with the goal to overcome issues of occlusion and image registration that were previously introduced when sensing was performed externally from the robot manipulator. Additionally, simultaneous localization and mapping was investigated to aid in forming a world model. In Chapter 3 we aimed to reduce the requirement of annotating empirical images by providing a method to synthetically generate large sets of automatically annotated images as input for convolutional neural network (CNN) based segmentation models. An annotated dataset was created of 10,500 synthetic and 50 empirical images. In Chapter 4 we further investigated how synthetic images can be used to bootstrap CNNs for successful learning of empirical images. We provided computer vision in agriculture a pioneering machine learning based methodology for state-of-the-art plant part segmentation performance, whilst simultaneously reducing the reliance on labor intensive manual annotations. Chapter 5 explored applying a cycle consistent generative adversarial network to our dataset with the objective to generate more realistic synthetic images by translating them to the feature distribution of the empirical domain. We show that this approach can further improve segmentation performance whilst further reducing the requirement of annotated empirical images. In Chapter 6 we aimed to bring all previous chapters into practice. The objective was to estimate angles between fruit and stems from image segmentations to support visual servo control grasping in a sweet-pepper harvesting robot. Our approach calculated angles under unmodified greenhouse conditions that met the accuracy requirement of 25 degrees for 73% of the cases. Combined, the work shows a promising stepping stone towards agricultural robotics which could ensure the quality of meals and nourishment of a growing population. Furthermore, it can become an important technology for societal issues in developed nations, e.g. by solving current labor problems. It can further improve upon the quality of life and contribute to reaching an exemplary equilibrium of sustainable agricultural production.

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  • image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
    Authors: Lukas Wille;

    Pea (Pisum sativum L.) is a valuable and healthy protein source for food and feed. In addition to the nutritional benefits, pea is an invaluable agro-ecological asset for sustainable cropping systems through positive effects on soil fertility and soil microbial diversity. The symbiosis with nitrogen-fixing bacteria allows pea and other legume crops to supply the soil with nitrogen and, therefore, to significantly reduce the application of external nitrogen fertilisers. Therefore, pea plays an important role especially in low-input farming systems. The growing market for plant- based protein supply is likely to promote pea cultivation in the near future. However, pea production is severely challenged by various soil-borne pathogens that form a Pea Root Rot Complex (PRRC) causing root-rot diseases. Despite considerable progress in resistance breeding against individual pathogens, current pea varieties lack resistance against multiple interacting pathogens. The overall goal of this thesis was to contribute to the understanding of resistance against root rot pathogen complexes in pea. Chapter 1 gives an overview of the importance of pea as a future key player in agricultural systems and the food sector before introducing the pea root rot complex concept and its relevance for research on resistance. Furthermore, the most recent developments in molecular biology relevant for molecular plant breeding of pea are briefly summarised and an overview of quantitative real-time PCR relevant for research on microbial interactions in the pea root rot complex is given. Chapter 2 reviews the current knowledge of resistance against root- rot pathogens in major grain legumes, highlights the importance of the host genotype in determining the composition of plant-associated microbial communities and how the root associated microbiome relates to plant health. In addition, major findings on the role of root exudation in disease susceptibility and resistance of grain legumes are summarised. Finally, it delineates how this knowledge could be integrated in resistance breeding of grain legumes. In Chapter 3, a resistance screening assay was established based on infested soil from an agricultural field that showed severe pea root rot pressure. This approach was chosen in order to account for the whole rhizosphere microbiome - including the naturally occuring pathogen complex - in the assessment of root rot resistance in pea. The initial ITS- amplicon sequencing of the fungal rhizosphere community of diseased pea roots grown in the infested soil showed a root community of evenly abundant fungal taxonomic units not dominated by a few taxa. This finding points at complex interactions within the PRRC. Two hundred and sixty-one pea cultivars, landraces and breeding lines were screened for resistance on the naturally infested field soil in a controlled conditions experiment. The screening system allowed for a reproducible assessment of disease parameters among the tested genotypes. Broad sense heritabilities on the infested soil were H2 = 0.89 for plant emergence, H2 = 0.43 for root rot index and H2 = 0.51 for relative shoot dry weight. The resistance ranking was verified in an on-farm experiment with nine pea genotypes in two field sites: The controlled conditions root rot index showed a significant correlation with the resistance ranking in the field site with high PRRC infestation (Spearman's ρ = 0.73, p = .03). The screening system offers a tool for selection at early stages of the plant development, and for the study of plant resistance in the light of complex plant-microbe interactions. For Chapter 4, a subset of five resistant and three susceptible pea genotypes was selected based on the initial screening. In analogy to the previous experiment, a controlled conditions experiment was setup up in order to assess and validate resistance of the eight pea genotypes on four soils. Plant growth was significantly reduced on the three sick soils compared to the healthy soil. Despite the significantly different levels of disease pressure in the three infested soils (ANOVA: p < .001) and the strong genotype effect (p < .001), no significant soil × genotype interaction (p < .342) was found for plant growth reduction. In addition to disease assessments, ten key microbial taxa (eight putative pea pathogens and two putative beneficials) were quantified in the roots by quantitative real-time PCR (qPCR). Fusarium solani, F. oxysporum and Aphanomyces euteiches were the most abundant pathogens in diseased roots from the three sick soils. Further, various levels of the pathogens F. avenaceum, F. redolens, Rhizoctonia solani, D. pinodella and Pythium sp. as well as the potential antagonist Clonostachys rosea were quantified by qPCR. The contribution of individual pathogens to root rot and growth reduction differed among the three sick soils: F. solani and F. oxysporum showed significant correlations (Spearman correlations; p < 0.05) with root rot index and relative shoot dry weight in the two soils with the highest infestation level; A. euteiches showed significant relations with disease in two sick soils from Germany. The quantities of arbuscular mycorrhizal fungi were negatively correlated with root rot index and positively correlated with relative shoot dry weight in all sick soils. Furthermore, the root microbial composition differed significantly among the pea genotypes (PERMANOVA; p < .0001) and the soils (p < .0001) and a significant pea genotype × soil interaction was evidenced (p < .0001). In addition, resistant pea genotypes showed significantly lower F. solani and A. euteiches, and higher arbuscular mycorrhizal fungi abundance in the roots (Wilcoxon rank-sum test; p < .05). These results give insights into the complex interaction between key microorganisms of the PRRC and the plant, by pointing out potential key microorganisms in the root rot pathobiome. Further disentanglement of this complex and the validation of key microbial players can be harnessed by resistance breeding. Chapter 5 reviews the experimental approaches and results from the previous chapters before discussing the major findings and implications for future research and resistance breeding. I also raise the question if and how knowledge about complex soil microorganisms-plant feedbacks can be incorporated in resistance screenings and breeding efforts to conclude that today we are at a point where information on microbial complexes could indeed assist resistance breeding. However, our current state of knowledge does not yet allow to design specific microbiome-enabled selection-tools. This last chapter will also give short outlooks and indicate possible future lines of research in the field of microbe-mediated plant resistance.

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    Authors: Bongiorno, Giulia;

    Developments in soil biology and methods to characterize soil organic carbon have the potential to deliver novel soil quality indicators that can help to identify soil management practices that sustain soil productivity and environmental resilience. This thesis aimed at investigating the suitability of a range of soil biological and biochemical parameters as novel soil quality indicators for agricultural management. The soil parameters, selected through a literature review, comprised different labile organic carbon fractions (hydrophilic dissolved organic carbon (Hy-DOC), dissolved organic carbon (DOC), permanganate oxidizable carbon (POXC), hot water extractable carbon (HWEC) and particulate organic matter carbon (POMC), ordered here from the smallest to the largest proportion of the total organic carbon), soil disease suppressiveness measured with a Pythium-Cress bioassay, nematode communities characterized with amplicon sequencing and qPCR, and microbial community level physiological profiling (CLPP) measured with MicroRespTM. We tested the sensitivity of the novel indicators to tillage and organic matter addition in 10 European long-term field experiments, and assessed their relationship with already existing soil quality indicators linked to soil functioning. Lastly, the results of these experimental chapters are interpreted relative to each other and to the broader body of literature on soil quality assessments. Moreover, pros and cons of the novel indicators are discussed, and possibilities and needs for future research are outlined. Reduced tillage increased carbon availability, disease suppressiveness, nematode richness and diversity, the stability and maturity of the food web, and microbial activity and functional diversity. Organic matter addition had a weaker role in sustaining soil quality, possibly due to the different compositions of the organic matter inputs in the long-term field experiments that were sampled. Random forest analysis showed that POXC was the indicator that discriminates soil management most, and structural equation modelling showed its central role in nutrient cycling, carbon sequestration, biodiversity conservation, erosion control and disease regulation/suppression. The novel indicators proposed here have great potential to improve existing soil quality assessment schemes, but their usefulness is still to be validated and optimized.

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    Other literature type . Doctoral thesis . Thesis . 2020 . Peer-reviewed
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    Authors: Barth, Ruud;

    The objective of this work was to further advance technology in agriculture, specifically by pursuing the research direction of agricultural robotics for harvesting in greenhouses, with the specific use-case of Capsicum annuum, also known as sweet or bell pepper. Within this scope, it was previously determined that the primary cause of agricultural robotics not yet maturing was the complexity of the tasks due to inherent variations of the crops, in turn limiting performance in harvest success and time. As a solution, it was suggested to further enhance robotic systems with sensing, world modelling and reasoning, for example by pursuing approaches like machine learning and visual servo control. In this work, we have followed this suggestion. It was identified that facilitating new levels of artificial intelligence in the domains of sensing and motion control would be one of the ways to improve upon classical mechanization. Specifically, we investigated the means of using machine learning based computer vision guided manipulation towards a basic form of world representation and autonomy. For this, in Chapter 2 we developed an eye-in-hand sensing and visual control framework for dense crops with the goal to overcome issues of occlusion and image registration that were previously introduced when sensing was performed externally from the robot manipulator. Additionally, simultaneous localization and mapping was investigated to aid in forming a world model. In Chapter 3 we aimed to reduce the requirement of annotating empirical images by providing a method to synthetically generate large sets of automatically annotated images as input for convolutional neural network (CNN) based segmentation models. An annotated dataset was created of 10,500 synthetic and 50 empirical images. In Chapter 4 we further investigated how synthetic images can be used to bootstrap CNNs for successful learning of empirical images. We provided computer vision in agriculture a pioneering machine learning based methodology for state-of-the-art plant part segmentation performance, whilst simultaneously reducing the reliance on labor intensive manual annotations. Chapter 5 explored applying a cycle consistent generative adversarial network to our dataset with the objective to generate more realistic synthetic images by translating them to the feature distribution of the empirical domain. We show that this approach can further improve segmentation performance whilst further reducing the requirement of annotated empirical images. In Chapter 6 we aimed to bring all previous chapters into practice. The objective was to estimate angles between fruit and stems from image segmentations to support visual servo control grasping in a sweet-pepper harvesting robot. Our approach calculated angles under unmodified greenhouse conditions that met the accuracy requirement of 25 degrees for 73% of the cases. Combined, the work shows a promising stepping stone towards agricultural robotics which could ensure the quality of meals and nourishment of a growing population. Furthermore, it can become an important technology for societal issues in developed nations, e.g. by solving current labor problems. It can further improve upon the quality of life and contribute to reaching an exemplary equilibrium of sustainable agricultural production.

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