Else K. Bünemann1 and Christoph Müller2,3 A 33P tracing model for quantifying gross P transformation rates in soil Else K. Bünemann1 and Christoph Müller2,3   1 Institute of Agricultural Sciences, ETH Zurich, Eschikon 33, CH-8315 Lindau, Switzerland 2 School of Biology and Environmental Science, Earth Institute, University College Dublin, Dublin, Ireland 3 Department of Plant Ecology (IFZ), Justus-Liebig University Giessen, Germany

The challenge Soil P dynamics: dominance of physicochemical processes SOIL SOLUTION P (Pi + Po) MICROBIAL P (Pi + Po) INORGANIC P (Pi) ORGANIC P (Po) Pools Sorption/desorption Microbial processes Simultaneous to each other and to the physicochemical processes Soil P dynamics: dominance of physicochemical processes Main microbial processes: mineralization and immobilization => How can we quantify them?

N mineralization and immobilization Mineral N IMM. MICROBIAL and ORGANIC N t1 MIN. t2 For N: incubation and extraction (t1,t2); dNmin(t1,t2) = net N mineralization 15N isotopic dilution methods: gross mineralization - gross immobilization = net N mineralization For N: Net N mineralization can be measured by incubation and extraction (adsorption considered negligible) 15N isotopic dilution methods to go further into the processes Gross min – gross imm = net min (very simplified; in reality many simultaneous processes, including gaseous losses)

P mineralization and immobilization SOIL SOLUTION P IMM. MICROBIAL and ORGANIC P MIN. INORGANIC P (Pi) For P: incubation and extraction (t1,t2); no accumulation of soil solution P due to buffering 33P isotopic dilution technique: - assess gross rates (physicochemical and biological) - derive net organic P mineralization In most soils no change in soil solution P over time Need 33P isotopic dilution for gross rates and for net

33P isotopic dilution technique to measure gross and net organic P mineralization soil + H2O (1:10) 33P 100 min exp, extrapolate specific activity (33P/31P) in the soil solution => extrapolated SA (physicochemical processes) incubate 33P- labeled soils => measured SA (physicochemical+biological processes) net release of 31Pi to the soil solution by mineralization of 31Po gross Oehl et al. SSSAJ 2001; Bünemann et al. SBB 2007

Case study on organic P mineralization Else Bünemann-König: Summary of research presentation at MLU Mon Dec 2, 2013 Cambisol, pHH2O 5.5, 44% sand, 22% clay derived from: -isotopic dilution method -C mineralization Mineral P input Annual plant P uptake Microbial P immobili-zation Net P minerali-zation Treat- ment kg P ha-1 yr-1 --------- mg P kg-1 d-1 ---------------------- NK 6.2 b 5.5 a 2.7 a 0.36 ns NPK 17 16.6 a 2.2 b 0.9 b 0.39 ns Now to an interesting case study. Long-term grassland fertilization trial on a Cambisol (Braunerde bis Kalkbraunerde). Similar N and K inputs, different P levels. Plant P limitation clearly shown. Thus: even on our soils P deficiency likely in the absence of mineral P fertilizer inputs. Surprising result: very fast microbial P immobilization rate under P-limited conditions (high-affinity P transporters in action). Estimate of net P mineralization: high uncertainty because of incomplete extraction. Different approach: deduce from C mineralization? Benefit from progress made in data evaluation of 15N isotopic dilution experiments 45 kg N ha-1 yr-1 83 kg K ha-1 yr-1 Uncertainty due to incomplete extraction of microbial P Bünemann et al. SBB 2012 Bünemann et al. SBB 2012

A numerical 33P tracing model Conceptual model with 5 P pools and 9 P transformations Transformation rates: zero, first or second order (Michaelis Menten) kinetics Initial pool sizes and tracer distribution based on measured values In collaboration with Christoph Müller, Giessen: Developed P cycle model to simulate experimental data (31P and 33P) Explain P pools and transformations Kinetics of transformation rates can be selected, including MM which is relevant for microbial transformations Initial pools can be measured: e.g. Pif is the P isotopically exchangeable in 24 h, Pof is microbial P. Müller & Bünemann SBB 2014

Model parameter optimization Markov Chain Monte Carlo method: «random walk in the parameter space» Entrapment in local minima avoided, e.g. two parameter space: => Probability density function for each parameter (mean and stdev) Explain random walk: Improved parameters always accepted Degraded fit sometimes accepted, sometimes rejected Explain data output Müller et al. SBB 2007

Observed vs. modelled values Simulation of experimental data (31P and 33P in Pw and Pof): good agreement between measured and modeled values Each pool defined by differential equations for change in 31P and in 33P/31P => transformation rates Agreement between measured and modelled values Different kinetics tested, best fit evaluated by AIC Calculation of transformation rates Müller & Bünemann SBB 2014

Conclusions from the modelling approach =0.77 mg P kg-1 d-1 Net Po mineralization rate (NK) = 0.46 – 0.35 mg P kg-1 day-1 lower than estimate based on C mineralization 0.27 0.19 0.31 0.04 Dominance of microbial immobilization and remineralization over slow mineralization/immobilization Ratio of microbial to physicochemical processes in this soil (NK): about 1:2.5 0.84 0.0004 0.44 0.29 Allows us to put numbers to the transformation rates Here: Average over 32 days, but can also show rates for each time step Net Po mineralization: lower than estimate based on C mineralization, posing questions of mineralised substrates Microbial processes more important than mineralization of non-living SOM Microbial processes not negligible! (consequences for E-value concept…) 0.31 =1.88 mg P kg-1 d-1 Müller & Bünemann SBB 2014

Outlook Modelling approach allows application of isotopic dilution principles to non-steady-state conditions (baseline of extrapolated E-values not needed) Progress: application to non-steady-state conditions! (baseline not needed) e.g. Presence of growing plant, incorporation of plant residues