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
Urban Česnik Mitja Martelanc Ingunn Øvsthus Tatjana Radovanovic Vukajlović Ahmad Hosseini Branka Mozetič Vodopivec Lorena ButinarAbstract
Saccharomyces cerevisiae is commonly used for the production of alcoholic beverages, including cider. In this study, we examined indigenous S. cerevisiae and S. uvarum strains, both species commonly found in cider from Hardanger (Norway), for their strain-specific abilities to produce volatile and non-volatile compounds. Small-scale fermentation of apple juice with 20 Saccharomyces strains was performed to evaluate their aroma-producing potential as a function of amino acids (AAs) and other physicochemical parameters under the same experimental conditions. After fermentation, sugars, organic acids, AAs, and biogenic amines (BAs) were quantified using the HPLC–UV/RI system. A new analytical method was developed for the simultaneous determination of nineteen AAs and four BAs in a single run using HPLC–UV with prior sample derivatization. Volatile compounds were determined using HS-SPME-GC-MS. Based on 54 parameters and after the removal of outliers, the nineteen strains were classified into four groups. In addition, we used PLS regression to establish a relationship between aroma compounds and predictor variables (AAs, BAs, organic acids, sugars, hydrogen sulfide (H2S) production, CO2 release) of all 19 strains tested. The results of the VIP show that the main predictor variables affecting the aroma compounds produced by the selected yeasts are 16, belonging mainly to AAs.
Authors
Ingunn ØvsthusAbstract
No abstract has been registered
Authors
Lampros LamprinakisAbstract
Purpose The purpose of this article is to introduce identity economics in the individual and organizational level, and highlight its impact on organizational performance, especially when dealing with foundational issues such as sustainability. Design/methodology/approach Identity, sustainability, and the role of personal values in organizational performance are well researched topics that have been typically addressed on different literature streams. The article draws from this diverse literature to introduce identity effects in individual and organizational levels, and further explore how such elements link to the rising popularity of the sustainability discourse and how they can affect operations in an organization. Findings The article highlights the importance of identity effects on organizational operations and performance, especially when dealing with foundational issues such as sustainability perceptions. Originality/value The article builds upon the recent developments in the field of identity economics to introduce identity in the sustainability discourse and explore how (mis)alignment between identity values on sustainability and organizational culture can affect organizational operations and individual performance.
Authors
Lampros LamprinakisAbstract
No abstract has been registered
Authors
Ingunn ØvsthusAbstract
No abstract has been registered
Authors
Lampros LamprinakisAbstract
No abstract has been registered
Abstract
No abstract has been registered
Abstract
This chapter describes the work performed within the Sinograin II project on implementation of new precision nitrogen management technologies in three regions of North China. Each of the analyzed regions represents a different crop and scale of a farming system: large-scale rice farming system in Heilongjiang province, medium-scale maize farming system in Jilin province, and small-scale wheat farming system in the North China Plain. A village was selected in each region to represent the agricultural practices and current nutrient and crop management strategies of the tested region. Moreover, the initial regional optimum crop management, the current agricultural extension, as well as the precision nitrogen technologies implemented in the respective regions are described. During the course of the project, a number of novel tools and strategies for precision nitrogen management were developed for the respective regions and published in scientific papers. This chapter reviews and discusses the selected findings and indicates directions of the upcoming research.
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
Active canopy sensors (ACSs) are great tools for diagnosing crop nitrogen (N) status and grain yield prediction to support precision N management strategies. Different commercial ACSs are available and their performances in crop N status diagnosis and recommendation may vary. The objective of this study was to determine the potential to minimize the differences of two commonly used ACSs (GreenSeeker and Crop Circle ACS-430) in maize (Zea mays L.) N status diagnosis and recommendation with multi-source data fusion and machine learning. The regression model was based on simple regression or machine learning regression including ancillary information of soil properties, weather conditions, and crop management information. Results of simple regression models indicated that Crop Circle ACS-430 with red-edge based vegetation indices performed better than GreenSeeker in estimating N nutrition index (NNI) (R2 = 0.63 vs. 0.50–0.51) and predicting grain yield (R2 = 0.56–0.57 vs. 0.49). The random forest regression (RFR) models using vegetation indices and ancillary data greatly improved the prediction of NNI (R2 = 0.81–0.82) and grain yield (R2 = 0.87–0.89), regardless of the sensor type or the vegetation index used. Using RFR models, moderate degree of accuracy in N status diagnosis was achieved based on either GreenSeeker or Crop Circle ACS-430. In comparison, using simple regression models based on spectral data only, the accuracy was significantly lower. When these two ACSs were used independently, they performed similarly in N fertilizer recommendation (R2 = 0.57–0.60). Hybrid RFR models were established using vegetation indices from both ACSs and ancillary data, which could be used to diagnose maize N status (moderate accuracy) and make side-dress N recommendations (R2 = 0.62–0.67) using any of the two ACSs. It is concluded that the use of multi-source data fusion with machine learning model could improve the accuracy of ACS-based N status diagnosis and recommendation and minimize the performance differences of different active sensors. The results of this research indicated the potential to develop machine learning models using multi-sensor and multi-source data fusion for more universal applications.