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.
2022
Abstract
Active crop sensor-based precision nitrogen (N) management can significantly improve N use efficiency but generally does not increase crop yield. The objective of this research was to develop and evaluate an active canopy sensor-based precision rice management system in terms of grain yield and quality, N use efficiency, and lodging resistance as compared with farmer practice, regional optimum rice management system recommended by the extension service, and a chlorophyll meter-based precision rice management system. Two field experiments were conducted from 2011 to 2013 at Jiansanjiang Experiment Station of China Agricultural University in Heilongjiang, China, involving four rice management systems and two varieties (Kongyu 131 and Longjing 21). The results indicated that the canopy sensor-based precision rice management system significantly increased rice grain yield (by 9.4–13.5%) over the farmer practice while improving N use efficiency, grain quality, and lodging resistance. Compared with the already optimized regional optimum rice management system, in the cool weather year of 2011, the developed system decreased the N rate applied in Kongyu 131 by 12% and improved N use efficiency without inducing yield loss. In the warm weather year of 2013, the canopy sensor-based management system recommended an 8% higher N rate to be applied in Longjing 21 than the regional optimum rice management, which improved rice panicle number per unit area and eventually led to increased grain yield by over 10% and improved N use efficiency. More studies are needed to further test the developed active canopy sensor-based precision rice management system under more diverse on-farm conditions and further improve it using unmanned aerial vehicle or satellite remote sensing technologies for large-scale applications.
Authors
Alejandro Belanche Alexander N. Hristov Henk Van Lingen Stuart J Denman Ermias Kebreab Angela Dagmar Schwarm Michael Kreuzer Maguy A. Eugène Mark McGee Christopher K. Reynolds Les A. Crompton Ali-Reza Bayat Zhongtang Yu André Bannink Jan Dijkstra Alex V. Chaves Harry Clark Stefan Muetzel Vibeke Lind Jon M. Moorby John Rooke Walter Antezana Mi Wang Roger Hegarty Jean Victor Savian Adibe Luiz Abdalla Alda Lucia Gomes Monteiro Juan Carlos Ku-Vera Gustavo Jaurena Carlos Gomez Olga Mayorga Guilhermo de Souza David R. Yáñez-RuízAbstract
No abstract has been registered
Abstract
Tree diameter increment (ΔDBH) and total tree height increment (ΔHT) are key components of a forest growth and yield model. A problem in complex, multi-species forests is that individual tree attributes such as ΔDBH and ΔHT need to be characterized for a large number of distinct woody species of highly varying levels of occurrence. Based on more than 2.5 million ΔDBH observations and over 1 million ΔHT records from up to 60 tree species and genera, respectively, this study aimed to improve existing ΔDBH and ΔHT equations of the Acadian Variant of the Forest Vegetation Simulator (FVS-ACD) using a revised method that utilize tree species as a random effect. Our study clearly highlighted the efficiency and flexibility of this method for predicting ΔDBH and ΔHT. However, results also highlighted shortcomings of this approach, e.g., reversal of plausible parameter signs as a result of combining fixed and random effects parameter estimates after extending the random effect structure by incorporating North American ecoregions. Despite these potential shortcomings, the newly developed ΔDBH and ΔHT equations outperformed the ones currently used in FVS-ACD by reducing prediction bias quantified as mean absolute bias and root mean square error by at least 11% for an independent dataset and up to 41% for the model development dataset. Using the revised ΔDBH and ΔHT estimates, greater prediction accuracy in individual tree aboveground live carbon mass estimation was also found in general but performance varied with dataset and accuracy metric examined. Overall, this analysis highlights the importance and challenges of developing robust ΔDBH and ΔHT equations across broad regions dominated by mixed-species, managed forests.
Authors
Mladen Cucak Dalphy Ondine Camira Harteveld Lisa Wasko DeVetter Tobin L. Peever Rafael de Andrade Moral Chakradhar MattupalliAbstract
No abstract has been registered
Authors
Giovanna Ottaviani Aalmo Beniamino Gioli Divina Gracia P. Rodriguez Diana Tuomasjukka Hai-Ying Liu Maria Chiara Pastore Fabio Salbitano Peter Bogetoft Arne Sæbø Cecil KonijnendijkAbstract
The greenhouse gases (GHG) emissions in the European Union (EU) are mainly caused by human activity from five sectors—power, industry, transport, buildings, and agriculture. To tackle all these challenges, the EU actions and policies have been encouraging initiatives focusing on a holistic approach but these initiatives are not enough coordinated and connected to reach the much needed impact. To strengthen the important role of regions in climate actions, and stimulate wide stakeholders’ engagement including citizens, a conceptual framework for enabling rapid and far-reaching climate actions through multi-sectoral regional adaptation pathways is hereby developed. The target audience for this framework is composed by regional policy makers, developers and fellow scientists. The scale of the framework emphasizes the regional function as an important meeting point and delivery arena for European and national climate strategies and objectives both at urban and rural level. The framework is based on transformative and no-regret measures, prioritizing the Key Community Systems (KCS) that most urgently need to be protected from climate impacts and risks.
Abstract
No abstract has been registered
Abstract
No abstract has been registered
Authors
Mateusz Liziniewicz Mats Berlin Thomas Solvin Henrik R. Hallingbäck Matti Haapanen Seppo Ruotsalainen Arne SteffenremAbstract
Norway spruce is a major industrial tree species in Fennoscandia and future productivity of the species must be secured by matching the variation in adaptation of the species with suitable sites for optimized performance. An appropriate transfer model for forest reproductive material (FRM) is crucial for regeneration of productive forests in the changing climatic conditions that are predicted to occur in Fennoscandia. We have developed a transfer model for prediction of height of Norway spruce in Norway, Sweden, and Finland, using data acquired from 438 progeny and provenance trials with 1919 genetic entries of local and transferred origins. Transfer of genetic material at a given site was expressed in terms of the difference in daylength (photoperiod) between the site and its origin. This variable best reflected the nonlinear response to transfer that has been commonly reported in previous studies. Apart from the transfer variable, the height prediction model included the age of material when height measurements were acquired, annual temperature sum over 5 °C, precipitation during the vegetation period, and interaction terms between test site and transfer variables. The results show that long northward transfers (4-5° latitude) seem to be optimal for relatively mild sites in southern parts of the countries where growing season is longer, and shorter northward transfers (2-4° latitude) for harsher northern sites with shorter growing seasons. The transfer model also predicts that southward transfers of Norway spruce would result in height growth reductions. The developed model provides foundations for development of common or national recommendations for genetically improving Norway spruce material in Fennoscandia.
Abstract
No abstract has been registered
Authors
Vincent Baillet Lorraine Balaine Xabier Diaz De Otalora Divina Gracia P. Rodriguez Joanna Frątczak-Müller Bjørn Egil Flø Habtamu Alem Barbara Amon Vasileios Anestis Thomas BartzanasAbstract
The MilKey project aims at assessing the environmental, economic, and social sustainability of European dairy production systems, and at identifying ‘win-win’ farming practices for sustainable and greenhouse gas (GHG) optimised milk production. In this context, a holistic model was developed to evaluate the sustainability of specialised dairy farms and was entitled DEXi-Dairy. This model has the potential of aiding the identification of GHG and nitrogen (N) emission mitigation options and assessing their effects across multiple sustainability aspects. DEXi-Dairy covers the three sustainability pillars, i.e., environmental, economic, and social. Based on the ‘DEX’ multi-criteria methodology, the model is detailed under the form of a tree structure represented by four main hierarchical layers, i.e., branches, principles, criteria, and indicators. DEXi-Dairy was built following a participatory and interdisciplinary approach by MilKey project partners. It was then tested on three case study farms from Ireland, France, and Germany, respectively, using data from 2020. The DEXi-Dairy indicator handbook describes the sustainability tree and selected indicators to assess dairy production systems over a production year. Overall, this document can be used as a basis to replicate and expand the sustainability assessment framework developed as part of the MilKey project.