Based on concepts for innovation processes and co-production of knowledge, approaches are investigated that address the urgent and complex problems related to climate change, because especially the participation of, and close collaboration with, practice partners is needed. The paper presents the agricultural knowledge management approach in the organic agriculture module of the R&D project INKA BB (Innovation Network for Climate Change Adaptation Brandenburg Berlin) in north-eastern Germany (Knierim et al. 2009). The methodology for the science-practice collaboration follows an action research approach that supports the communication and cooperation of researchers and practitioners. The framework is the action research cycle with iterative stages of planning, action, and reflection. The organic agriculture module, which addresses individual research questions on several farms, is presented as a good practice example for close transdisciplinary network cooperation. The workshop contribution will provide reflections on the innovation development process over two project years.
Impacts of climate change on agriculture have been predominantly analyzed by using biophysical and crop specific model applications. Vulnerability assessments which identify the vulnerability of regions with their farming systems are urgently required, because agricultural adaptations to climate change are related to regional specifics, and therefore research has to consider the regional level. Therefore sector- and system-specific approaches have to be developed. This paper presents the methodology of a vulnerability assessment for organic farming systems in the Brandenburg Region, which considers regional-specific climatic impact, as well as the regional-specific adaptive capacity. In this region, the cultivation and management of legume-grass swards have a key position, especially the climate change impact on legume symbiotic nitrogen fixation and nitrogen mineralization. Adaptation strategies of crop production systems include reduced soil tillage, which plays an important role also in organic farming systems (reducing soil erosion, improving water infiltration, reducing evaporation and improving soil structure, control of N-dynamics) are developed and tested by means of an action research approach.
“Adaptation to climate change” as a new field of knowledge challenges agricultural and horticultural (vocational) education and extension. Farmers and horticulturists are confronted with vague scientific findings at best. A broad variety of global climate scenarios is “projected” onto regions and exact predictions are usually not possible. Often, personal observations and experiences seem to contradict scientific assertions. Under this condition farmers and policy makers must decide about future land use.
What does this imply for capacity building? How to transform insecurity into concrete educational measures and programs?
The authors discuss their first experiences within a German R&D network (INKA BB) in which they develop capacity building programs. Two examples from urban agriculture / urban gardening will be used as case studies. Strengths and weaknesses of the development processes and their management will be discussed.
Since the topic is complex and adaptation is a continuous activity, learning in connection with climate change adaptation ideally begins on elementary level, continues in higher and vocational training, and does not end with extension. In other words: “learning chains” must be developed which enable life-long learning in formal, non-formal and informal learning environments.
Competencies are needed beyond classical technological and economic skills. Problem solving - from problem perception, analysis, generation of alternative solutions, to implementation and evaluation - with a key competence in critical analysis and reflection of contemporary research findings - gain in importance.
In INKA BB, participation is seen as axiomatic. As a consequence, an action-oriented, participatory approach has been chosen which enables mutual learning among partners from research, formal and informal, elementary, higher and vocational education.
A crucial point is the question of “Who could be the bridge between science and the educational practitioner?” In INKA BB, a specific working group (the subproject on “Knowledge Management and Transfer”) facilitates the development processes and therefore plays a liaison role between theory and practice. In the long-run, sustainable ownership of this process must be achieved. A combination of network building, mutual learning in permanent work groups, provision of technical trainings, and joint planning, testing, monitoring and evaluation is seen as a precondition.
Das Poster zur RADOST-Abschlusskonferenz im April 2014 fasst Ergebnisse des RADOST-Fokusthemas Erneuerbare Energien zusammen. Als Beispiel wird die oberflächennahe Geothermie besonders berücksichtigt.
A statistical downscaling method has been developed to produce highly resolved precipitation data from regional climate model (RCM) output, using the model CLM (2 runs, scenario A1B). The procedure is based on the analogue method with the predictors precipitation (daily sums on CLM grid points) and objective weather types (DWD). Analogue days of the time period 2001-2009 are searched using corrected and adjusted data of radar Essen and DWD measurements of objective weather types. The radar data is used to produce high-resolution precipitation data sets (1km², 5min) with realistic spatial and temporal correlations for three catchments in North Rhine-Westphalia. Results in
the reference period (1961 - 1990) are examined using extreme value statistics and compared to corrected station data. Data sets of the near and the far future (2021-2050, 2071-2100) are analysed with respect to future trends, and uncertainties of the downscaling procedure are discussed.
Rainfall statistics are composed based on data gained by precipitation measurements and from climate models. These statistics are carried out for both periods in the past and the future. When analysing the time series, different trends can be seen in the measured data of the past and the model data for future periods. Influences on the statistically determined precipitation amounts caused by changes can be neglected for past periods. However, significant increases of the statistical precipitation amounts can be observed for the future. Here a pragmatic approach is presented, showing how to consider possible increases in the statistical precipitation amounts – due to the climate change signal – in the dimensioning of water management systems.
The precipitation data of the Regional Climate Model CLM are used for the water management impact models within the dynaklim networking and research project. For this purpose, it is necessary to apply a bias correction to the CLM
precipitation data. First, the bias assessed for varying temporal resolutions and precipitation characteristics is described. Subsequently, a method for the bias correction is introduced. The developed methodology is a modified form of the socalled
quantile mapping. The focus lies on the corrections of the dry days and the heavy rainfall events. They are considered separately, deviating from other quantile mapping procedures.
The work presented here is part of the socioeconomic analysis that is carried out within the RADOST project. It has been the starting point of developing a dynamic regionalized Input-Output (IO) model that is used to assess the effects of climate change and adaptation strategies on the regional economy. In a first step the model has been set up for the tourist sector in Mecklenburg-Western Pomerania. The possible developments of the tourism demand – influenced by climate change and other factors – were represented in three scenarios, which in turn were used as input data for the IO model.
Im Zentrum dieser Arbeit stehen Bioindikatoren, die im begründeten Verdacht stehen, gleichzeitig auf die anthropogenen Belastungen „Eutrophierung“ und „Klimaerwärmung“ zu reagieren. Cyanobacteria werden als Bioindikatoren mit 42 Arten und mittels ihres gesamten Biomasseanteils am Phytoplankton unter weiteren Kenngrößen im neuen deutschen Seenbewertungssystem, dem Phyto-See-Index (PSI) zur Umsetzung der Wasserrahmenrichtlinie (WRRL) genutzt (Mischke et al. 2008). Die meisten Vertreter der Cyanobacteria profitieren in ihrem Wachstum sowohl von einer Erhöhung der Trophie, als auch von erhöhten Wassertemperaturen.
Für die Region Brandenburg wird nach Szenario B des PIC Potsdam mit einem Anstieg der Lufttemperatur um +1,5°C bis 2050 gerechnet (Jacob &Gerstengarbe. 2005). Dies hat eine Verlängerung der Schichtungsperiode (Adrian et al. 1993, Kirillin et al. 2008) in dimiktischen Seen, eine Annäherung der Wassertemperatur an die Optima vieler Arten und eine erhöhte hypolimnische P-Rücklösung (Adrian et al. 1993) zur Folge, was insgesamt einen höheren Trophiestatus der Seen einhergehend mit höheren Phytoplanktonbiomassen erwartet lässt. Es wird postuliert, dass die globale Erwärmung zur Verschiebung der Referenzzönosen („composition metrics“ wie PTSI und Algenklassen-Metrik) und der Biomasseausprägung („biomass metrics“) führt und damit die Bewertungsmatrix angepasst werden müsste.
Um den Effekt der prognostizierten Erhöhung von Cyanobacteria auf die Bewertung mittels Phyto-See-Index zu dokumentieren, wird in diesem Beitrag der Biomasseanteil dieser Gruppe in einem Szenario anhand realer Seendaten künstlich verdoppelt und der Bewertung „ohne potentiellen Klimaeinfluss“ gegenübergestellt.
Ein weiteres Phänomen aufgrund der Klimaerwärmung wird anhand eines Populationsmodells, welches zur Berücksichtigung der Überwinterung mittels Dauerzellen (Akineten) für eine Art der Nostocales (Cyanobacteria) entwickelt wurde, vorausgesagt (Wiedner et al. 2007): Es besagt, dass nostocale Arten mit einem Lebenszyklus bei Klimaerwärmung aufgrund der früheren Keimung höhere sommerliche Biomassen aufbauen werden. Um den Effekt einer Erhöhung der Lufttemperatur im vorausgehenden Winter oder Frühjahr auf die Nostocales in Freilanddaten zu beobachten, werden Langzeitdaten von 35 Seen mit kalten und warmen Jahren (-zeiten) ausgewertet.
Development of Agricultural Innovations in Organic Agriculture to adapt to Climate Change – Results from a Transdisciplinary R&D Project in Northeastern Germany
(2012)
