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.
2023
Authors
Yi Zhang Zhangmu Jing Yijing Feng Shuo Chen Yeqing Li Yongming Han Lu Feng Junting Pan Mahmoud Mazarji Hongjun Zhou Xiaonan Wang Chunming XuAbstract
Exploring key factors has important guidance for understanding complex anaerobic digestion (AD) systems. This study proposed a multi-layer automated machine learning framework to understand the complex interactions in AD systems and explore key factors at the environmental factor, microorganisms and system levels. The first layer of the framework identified hydraulic residence time (HRT) as the most important environmental factor, with an optimal range of 33–45 d. In the second layer of the framework, Methanocelleus (optimal relative abundance (ORA) = 3.0%) and Candidatus_Caldatribacterium (ORA = 1.7%) were found to be the key archaea and bacteria, respectively. Furthermore, the prediction of key microorganisms based on environmental factors and remaining microbial data showed the essential roles of Methanothermobacter and Acetomicrobium. The third layer for finding the optimal combination of data variables for predicting biogas production demonstrated that combined Archaea genera and environmental factors should be achieved for the most accurate prediction (root mean square error (RMSE) = 84.21). GBM had the best model performance and prediction accuracy among all the built-in models. Based on the optimal GBM model, the analysis at the system level showed that HRT was the most important variable. However the most important microorganism, Methanocelleus, within the appropriate survival range is also essential to achieve optimal biogas production. This research explores key parameters at various levels through automated machine learning techniques, which are expected to provide guidance in understanding the complex architecture of industrial and laboratory AD systems.
Authors
Rui Dong Yuxin Miao Pete Berry Xinbing Wang Fei Yuan Krzysztof Kusnierek Chris Baker Mark SterlingAbstract
Lodging is a major problem in maize (Zea mays L.) production worldwide. An analytical lodging model has previously been established. However, some of the model inputs are time consuming to obtain and require destructive plant sampling. Efficient prediction of lodging risk early in the season would be beneficial for management decision-making to reduce lodging risks and ensure high yield potential. Remote sensing technology provides an alternative method for fast and nondestructive measurements with the potential for efficient prediction of lodging risks. The objective of this study was to explore the potential of using an active canopy sensor for the early prediction of maize stem lodging risk using simple regression and multiple linear regression (MLR) models. The results indicated that the MLR models using active canopy sensor data together with weather and management factors performed better than simple regression models using only sensor data for predicting maize stem lodging indicators. Similar results were achieved either using regression models to predict the maize stem lodging risk indicators directly or using the regression models to predict lodging related plant parameters as inputs to a process-based lodging model to predict lodging risk indicators indirectly, although the latter approach using MLR models performed slightly better. A medium planting density (7.0 plants m-2) and 240 kg ha-1 N rate would be suitable in the study region, and the recommendations may be adjusted according to different weather conditions. It is concluded that maize stem lodging risks can be predicted using active canopy sensor data together with weather and management information at V8 stage, which can be used to guide in-season management decisions. Additional research is needed to evaluate the potential of using unmanned aerial vehicles and satellite remote sensing technologies in conjunction with machine learning methods to improve the prediction of lodging risks for large scale applications.
Abstract
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Authors
Nhat Strøm-AndersenAbstract
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Authors
Lone Ross Lizhen Huang Erik Trømborg Roja Modaresi Hanne Kathrine Sjølie Kristina Bringedal GeddeAbstract
No abstract has been registered
Authors
Nhat Strøm-AndersenAbstract
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Authors
Damaris Marina Matten Ida Marielle Mienna Vanessa Carina Bieker Brent D. Mishler Victoria Stornes Moen Malene Østreng Nygård Katariina Elsa M Vuorinen Mika Bendiksby Michael David Martin James David Mervyn SpeedAbstract
Paid open access
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Abstract
The fungus Neonectria ditissima causes Fruit Tree Canker on apple and pear. In the past years the disease has become a threat for Swedish and Northern European apple production since devastating outbreaks destroy large numbers of trees. To date, no complete genetic resistance to N. ditissima is known in apple but genotypes (scion cultivars and rootstocks) differ greatly in their level of partial resistance. Furthermore, the degree of susceptibility of a scion cultivar may be influenced by the rootstock it is grafted to. Thus, we aimed to improve our understanding of genetically determined differences in resistance among rootstocks and clarify cultivar/rootstock interactions with regards to canker resistance. For that, we evaluated differences in resistance to fruit tree canker in 24 rootstocks (including two M9 clones). We also evaluated differences in resistance of four most widely grown in Sweden scion cultivars grafted to four common rootstocks differing in vigour. The new knowledge will be useful for growers and breeders to minimize canker damages, prevent loss of the fruit-bearing surface in the orchards, save time and money for the growers.
Abstract
No abstract has been registered