Carbon -background and theory models represent temperature sensitivity expressed as Q1o differently. f.e.DAYCENT quickly increasing Q1o with low temperatures;RothC,CENTURY gradually increasing; pNET,TEM,Biome-BGC Q10=2. Organic matter decomposition as a,logistic"problem (Kleber 2010)as function of (i)microbial ecology; (ii)enzyme cinetics (iii)environment variables (Schmidt et al 2011) (iv)protection due to (soil)matrix -repercussions to models,model results ??! nstitut fur Waldokologie und Boden
Carbon – background and theory • models represent temperature sensitivity expressed as Q10 differently. • f.e. DAYCENT quickly increasing Q10 with low temperatures; RothC, CENTURY gradually increasing; pNET, TEM, Biome-BGC Q10=2. • Organic matter decomposition as a „logistic“ problem (Kleber 2010) as function of (i) microbial ecology; (ii) enzyme cinetics (iii) environment variables (Schmidt et al 2011) (iv) protection due to (soil)matrix → repercussions to models, model results ??!!
Carbon:temperature sensitivity,methods and so called,,confounding"factors(Smith et al 2008) Others Soil C stocks+modeling Soil Radioactive C across Oo against short- NPP term temperature Ses Resistant C Radioactive C at a single site Plant growth SOM Soil warming Mass loss in incubation Microbes Field litter bag (Field respiration Q1o against MAT Moisture Global data of soil respiration Temperature incubation Respiration in Labile C 10 102 103 Year Institut fur Waldokologie und Boden
Carbon: temperature sensitivity, methods and so called „confounding“ factors (Smith et al 2008)
How to measure changes in carbon stocks Long-time (experimental)plots or soil monitoring:long- time (at least 10,better 20 years)in order to assess changes in soil carbon content soil carbon stock [due to climate change /due to management practices) direct measurement small-scale variability;ressource intensive(many plots; intensive sampling),high precision necessary(small changes of a large pool),,confounding"factors 。 Warming/drought experiments:in situ experiments, where soil temperature and/or soil moisture (or other parameters)are changed -some variables may be controlled some variables controlled -see above;short-term B FW nstitut fur Waldokologie und Boden
How to measure changes in carbon stocks • Long-time (experimental) plots or soil monitoring: longtime (at least 10, better 20 years) in order to assess changes in soil carbon content / soil carbon stock [due to climate change // due to management practices) + direct measurement - small-scale variability; ressource intensive (many plots; intensive sampling), high precision necessary (small changes of a large pool), „confounding“ factors • Warming/drought experiments: in situ experiments, where soil temperature and/or soil moisture (or other parameters) are changed – some variables may be controlled + some variables controlled – see above; short-term
How to measure changes in carbon stocks Laboratory experiments to measure decomposition rates,respiration rates most variables may be controlled Many differing methods,unknown ,,confounding"factors 。 Models which simulate the effects of temperature and soil moisture (and other parameters to changes in soil carbon stock and fluxes prognostic,long-term projections possible,theories may be tested Data availability,no direct measurement institut fur Waldokologie und Boden
How to measure changes in carbon stocks • Laboratory experiments to measure decomposition rates, respiration rates + most variables may be controlled - Many differing methods, unknown „confounding“ factors • Models which simulate the effects of temperature and soil moisture (and other parameters to changes in soil carbon stock and fluxes + prognostic, long-term projections possible, theories may be tested - Data availability, no direct measurement
Results from Long-Term Soil Monitoring of Soil Carbon (Europe)-Results of a Meta analysis Study name Statistics for each study Std diff in means and 95%CI Std diff Standard Lower Upper in means error Variance limit limit Z-Value p-Value Bellamy -0.399 0.0140,000-0.426-0,372-28.875 0,000 Kirby 0.020 0,099 0,010-0,1730,2130,200 0,841 McGovern 0.080 0,167 0,028-0,2480,4070,477 0,633 Mutsch et al 2013,FBVA 1992 0,409 0,178 0,0320.0600,758 2,295 0,022 Mutsch et al 2013,FBVA1992 0.042 0,116 0,013-0,184 0,269 0.365 0,715 Reijneveld et al.2009Grass 0.403 0.008 0,0000,388 0,41851,468 0.000 Reijneveld et al.2009 Arable 0,000 0,008 0,000-0,017 0,017 0,000 1,000 0.071 0,143 0,020-0,2090,350 0.495 0,621 -1,00 -0,50 0,00 0,50 1,0 Favours A Favours B n=73300 nstitut fur Waldokologie und Boden
Results from Long-Term Soil Monitoring of Soil Carbon (Europe) – Results of a Meta analysis Study name Statistics for each study Std diff in means and 95% CI Std diff Standard Lower Upper in means error Variance limit limit Z-Value p-Value Bellamy -0,399 0,014 0,000 -0,426 -0,372 -28,875 0,000 Kirby 0,020 0,099 0,010 -0,173 0,213 0,200 0,841 McGovern 0,080 0,167 0,028 -0,248 0,407 0,477 0,633 Mutsch et al 2013,FBVA 1992 0,409 0,178 0,032 0,060 0,758 2,295 0,022 Mutsch et al 2013, FBVA1992 0,042 0,116 0,013 -0,184 0,269 0,365 0,715 Reijneveld et al. 2009 Grass 0,403 0,008 0,000 0,388 0,418 51,468 0,000 Reijneveld et al. 2009 Arable 0,000 0,008 0,000 -0,017 0,017 0,000 1,000 0,071 0,143 0,020 -0,209 0,350 0,495 0,621 -1,00 -0,50 0,00 0,50 1,00 Favours A Favours B n=73 300