|
|
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.
|
|
|
|
van Bussel, L. G. J., Stehfest, E., Siebert, S., Müller, C., & Ewert, F. (2015). Simulation of the phenological development of wheat and maize at the global scale. Glob. Ecol. Biogeogr., 24(9), 1018–1029.
Abstract: AimTo derive location-specific parameters that reflect the geographic differences among cultivars in vernalization requirements, sensitivity to day length (photoperiod) and temperature, which can be used to simulate the phenological development of wheat and maize at the global scale. LocationGlobal. Methods Based on crop calendar observations and literature describing the large-scale patterns of phenological characteristics of cultivars, we developed algorithms to compute location-specific parameters to represent this large-scale pattern. Vernalization requirements were related to the duration and coldness of winter, sensitivity to day length was assumed to be represented by the minimum and maximum day lengths occurring at a location, and sensitivity to temperature was related to temperature conditions during the vegetative development phase of the crop. Results Application of the derived location-specific parameters resulted in high agreement between simulated and observed lengths of the cropping period. Agreement was especially high for wheat, with mean absolute errors of less than 3 weeks. In the main maize cropping regions, cropping periods were over- and underestimated by 0.5-1.5 months. We also found that interannual variability in simulated wheat harvest dates was more realistic when accounting for photoperiod effects. Main conclusions The methodology presented here provides a good basis for modelling the phenological characteristics of cultivars at the global scale. We show that current global patterns of growing season length as described in cropping calendars can be largely reproduced by phenology models if location-specific parameters are derived from temperature and day length indicators. Growing seasons can be modelled more accurately for wheat than for maize, especially in warm regions. Our method for computing parameters for phenology models from temperature and day length offers opportunities to improve the simulation of crop productivity by crop simulation models developed for large spatial areas and for long-term climate impact projections that account for adaptation in the selection of varieties
|
|
|
|
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.
|
|
|
|
Webber, H., Zhao, G., Wolf, J., Britz, W., Vries, W. de, Gaiser, T., et al. (2015). Climate change impacts on European crop yields: Do we need to consider nitrogen limitation. European Journal of Agronomy, 71, 123–134.
Abstract: Global climate impact studies with crop models suggest that including nitrogen and water limitation causes greater negative climate change impacts on actual yields compared to water-limitation only. We simulated water limited and nitrogen water limited yields across the EU-27 to 2050 for six key crops with the SIMPLACE<LINTUL5, DRUNIR, HEAT> model to assess how important consideration of nitrogen limitation is in climate impact studies for European cropping systems. We further investigated how crop nitrogen use may change under future climate change scenarios. Our results suggest that inclusion of nitrogen limitation hardly changed crop yield response to climate for the spring-sown crops considered (grain maize, potato, and sugar beet). However, for winter-sown crops (winter barley, winter rapeseed and winter wheat), simulated impacts to 2050 were more negative when nitrogen limitation was considered, especially with high levels of water stress. Future nitrogen use rates are likely to decrease due to climate change for spring-sown crops, largely in parallel with their yields. These results imply that climate change impact studies for winter-sown crops should consider N-fertilization. Specification of future N fertilization rates is a methodological challenge that is likely to need integrated assessment models to address.
|
|
|
|
Zander, P. (2015). Modelling regional agricultural land use and climate change adaptation strategies in 4 case study regions Northern Germany (Vol. 4).
|
|
