|
|
Brüser, K. (2015). Crop production..
|
|
|
|
Boote, K. J., Porter, C., Jones, J. W., Thorburn, P. J., Kersebaum, K. C., Hoogenboom, G., et al. (2015). Sentinel site data for crop model improvement – definition and characterization. In J. L. Hatfield, & D. Fleisher (Eds.), (Vol. Advances in Agricultural Systems Modeling (7)). Madison, WI: ASA, CSSA, and SSSA.
|
|
|
|
Bojar, W., Żarski, J., Knopik, L., Kuśmierek-Tomaszewska, R., Sikora, M., & Dzieża, G. (2015). Markov chain as a model of daily total precipitation and a prediction of future natural events.. Braunschweig (Germany).
Abstract: The size of arable crop yields depends on many weather factors, such as precipitation and air temperature during the vegetation period. When studying the relation between yields and precipitation, not only the total amount of precipitation, but also the occurrence of long periods without precipitation must be taken into account. The paper [Bojar et al., 2014] demonstrated that barley yield significantly statistically depends on the length of the series of days without precipitation. This paper attempts to analyse the statistical data on daily precipitation totals recorded during the January – December periods in the years 1971 – 2013 at the weather station of the University of Science and Technology in Bydgoszcz, Faculty of Agriculture and Biotechnology, in the Research Centre located in an agricultural area in the Mochle township, situated 17 kilometres from Bydgoszcz. The primary statistical operation in the study is an attempt to estimate the Markov chain order. To this end, two criteria of chain order determination are applied: BIC (Bayesian information criterion, Schwarz 1978) and AIC (Akaike information criterion, Akaike 1974). Both are based on the log-likelihood functions for transition probability of the Markov chain constructed on certain data series. Statistical analysis of precipitation totals data leads to the conclusion that both AIC and BIC indicate the 2nd order for the studied Markov chain. The proposed method of estimating the variability of precipitation occurrence in the future will be utilised to improve region-related bio-physical and economical models, and to assess the risk of extreme events in the context of growing climate hazards. It will serve as basis for a search in agriculture for solutions mitigating those hazards.
|
|
|
|
Abadie, L. M., Galarraga, I., Milford, A. B., & Gustavsen, G. W. (2015). Achieving Emission Reduction Targets by Changing Eating Habits in Norway.
|
|
|
|
Semenov, M. (2015). Local-scale climate scenarios based on ensembles of global/regional climate models for regional applications in Europe (Vol. 3).
Abstract: Local-scale climate scenarios based on ensembles of global/regional climate models for regional applications in Europe is a deliverable for WP4 ‘Scenario development and impact uncertainty evaluation’. We developed the integration of 21st century climate projections for Europe based on simulations carried out within the EU-ENSEMBLES and CMIP3 projects with the LARS-WG stochastic weather generator. The aim was to update ELPIS, a repository of local-scale climate scenarios, for use in impact assessment studies in Europe. No Label
|
|
