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
Authors
Cornelya KlutschAbstract
No abstract has been registered
Authors
Cornelya KlutschAbstract
No abstract has been registered
Authors
Cornelya KlutschAbstract
No abstract has been registered
Authors
Ammar Shihan Philippe Barre Venera Copani Rajae Kallida Liv Østrem Giorgio Testa Mark R. Norton Jean-Paul Sampoux Florence VolaireAbstract
1. The persistence of perennial herbaceous species is threatened by increasing aridity. However, summer dormancy is a strategy conferring superior survival to grasses adapted to hot and dry summers. The role of temperature on the induction of summer dormancy was investigated in the perennial grass Dactylis glomerata to analyse the potential expression of this strategy under warmer climates. 2. We tested seven populations of D. glomerata originating from Morocco to Norway across the same latitudinal gradient in a five-site experiment. One population of the highly summer-dormant grass Poa bulbosa was used as a reference. Plants were grown from autumn in pots under full irrigation for 1 year mostly under open-air shelters. Heading date (ear emergence preceding flowering) was recorded and foliage senescence was assessed from end of spring until autumn. The maximum plant senescence under summer irrigation indicated the level of dormancy expression. Summer dormancy onset, release, expression and duration were modelled as a function of climatic variables. 3. From north to south, the duration of summer dormancy of the Mediterranean populations of D. glomerata and P. bulbosa ranged from 0 to 122 days, and 79 to 200 days, respectively. P. bulbosa was always completely dormant, while dormancy expression of D. glomerata was positively correlated with the sum of temperatures from winter onset (R2 = 0.57) and with the mean of minimum temperatures in summer (R2 = 0.73). Dormancy onset, release and duration were also positively correlated with thermal time from winter onset, while the duration of summer dormancy was longer as maximum temperatures increased. Mapping the European regions with climates allowing the expression of summer dormancy in D. glomerata showed that the potentially inductive areas for this strategy may expand in parallel with increasing summer aridity under a future climate warming scenario. 4. Synthesis. The large phenotypic variability of the expression of summer dormancy in D. glomerata was driven by temperature, suggesting that this strategy may have a greater role in higher latitudes to increase plant survival over the predicted hotter and drier summers. Leveraging this strategy for the choice and selection of suitable populations could enhance future adaptation of major perennial grasses to climate change.
Authors
Boban Djordjević Dejan Djurović Gordan Zec Dragana Dabić Zagorac Maja Natić Mekjell Meland Milica Fotirić AkšićAbstract
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Authors
Christina Hoerterer Jessica Petereit Gisela Lannig Johan Johansen Gabriella V. Pereira Luis E. C. Conceição Roberto Pastres Bela H. BuckAbstract
No abstract has been registered
Authors
Diogo N. Cosenza Petteri Packalén Matti Maltamo Petri Varvia Janne Räty Paula Soares Margarida Tomé Jacob L. Strunk Lauri KorhonenAbstract
Semi- and nonparametric models are popular in the area-based approach (ABA) using airborne laser scanning. It is unclear, however, how many predictors and training plots are needed to provide accurate predictions without overfitting. This work aims to explore these limits for various approaches: ordinary least squares regression (OLS), generalized additive models (GAM), least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine (SVM), and Gaussian process regression (GPR). We modeled timber volume (m3·ha–1) for four boreal sites using ABA with 2–39 predictors and 20–500 training plots. OLS, GAM, LASSO, and SVM overfitted as the number of predictors approached the number of training plots. They required ≥15 plots per predictor to provide accurate predictions (RMSE ≤30%). GAM required ≥250 plots regardless of the number of predictors. The number of predictors only mildly affected RF and GPR, but they required ≥200 and ≥250 training plots, respectively. RF did not overfit in any circumstances, whereas GPR overfit even with 500 training plots. Overall, using up to 39 predictors did not generally result in overfit, and for most model types, it resulted in better accuracy for sufficiently large datasets (≥250 plots).
Abstract
No abstract has been registered