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In sub-Saharan Africa, long-term maize cropping with low external inputs has been associated with the loss of soil fertility. While adding high-quality organic resources combined with mineral fertilizer has been proposed to counteract this fertility loss, the long-term effectiveness and interactions with site properties still require more understanding. This study used repeated measurements over time to assess the effect of different quantities and qualities of organic resource addition combined with mineral N on the change of soil organic carbon concentrations (SOC) over time (and SOC stocks in the year 2021) in four ongoing long-term trials in Kenya. These trials were established with identical treatments in moist to dry climates, on coarse to clayey soil textures, and have been managed for at least 16 years. They received organic resources in quantities equivalent to 1.2 and 4 t C ha−1 per year in the form of Tithonia diversifolia (high quality, fast turnover), Calliandra calothyrsus (high quality, intermediate turnover), Zea mays stover (low quality, fast turnover), sawdust (low quality, slow turnover) and local farmyard manure (variable quality, intermediate turnover). Furthermore, the addition or absence of 240 kg N ha−1 per year as mineral N fertilizer was the split-plot treatment. At all sites, a loss of SOC, rather than gain, was predominantly observed due to a recent conversion from permanent vegetation to agriculture. The average reduction of SOC concentration over 19 years in the 0 to 15 cm depth ranged from 42 % to 13 % of the initial SOC concentration for the control and the farmyard manure treatments at 4 t C ha−1 yr−1, respectively. Adding Calliandra or Tithonia at 4 t C ha−1 yr−1 limited the loss of SOC concentrations to about 24 % of initial SOC, while the addition of saw dust, maize stover (in 3 of 4 sites) and sole mineral N addition, showed no significant reduction in SOC loss over the control. Site specific analyses, however, did show, that at the site with the lowest initial SOC concentration (about 6 g kg−1), the addition of 4 t C ha−1 yr−1 farmyard manure or Calliandra plus mineral N led to a gain in SOC concentrations. All other sites lost SOC in all treatments, albeit at site specific rates. While subsoil SOC stocks in 2021 were little affected by organic resource additions (no difference in 3 of 4 sites), the topsoil SOC stocks corroborated the results for SOC concentrations. The relative annual change of SOC concentrations showed a higher site specificity in high-quality organic resource treatments than in the control, suggesting that the drivers of site specificity in SOC buildup (mineralogy, climate) need to be better understood for effective targeting of organic resources. Even though farmyard manure showed the most potential for reducing SOC loss, our results clearly show that maintaining SOC with external inputs only is not possible at organic resource rates that are realistic for small scale farmers. Thus, additional agronomic interventions such as intercropping, crop rotations or strong rooting crops may be necessary to maintain or increase SOC., EGUsphere

Models that simulate the dynamics of soil organic carbon (SOC) are crucial to understand the global carbon cycle, but current generation models are subject to major uncertainties due to two model shortcomings. Firstly, their different carbon pools are not connected to measurable SOC fractions. Secondly, there is uncertainty about the response of the different carbon pools to an increasing temperature. The aim of this thesis was thus to link the SOC model pools of the Daisy model to measurable proxies for SOC quality and pool specific temperature sensitivity. In the first study, the drying temperature for soil samples assessed by diffuse reflectance mid infrared Fourier transform spectroscopy (DRIFTS) was optimized to assure optimal representativeness of aliphatic and aromatic-carboxylate absorption bands as proxies for fast- and slow-cycling SOC pools. Their ratio was termed the DRIFTS stability index (DSI). In the second study, the DSI was used to distinguish fast- and slow-cycling SOC model pools at model initialization. In the third study, model initialization using DSI was performed to infer pool specific temperature sensitivities for the different Daisy carbon pools. Furthermore, it was tested whether the measured temperature sensitivities of different extracellular soil enzymes could be used as proxies for pool specific temperature sensitivity. Using a global collection of soil samples revealed that the absorption of all studied DRIFTS absorption bands increased significantly (p < 0.0001) with increasing drying temperature from 32°C to 105°C. This effect was disproportionally strong for the aliphatic absorption band. Due to the strong interference of the residual soil sample moisture content with the aliphatic absorption band, drying at 105°C and storage in a desiccator prior to measurement would be necessary for representative spectra for model pool initialization. In the following, a combination of medium to long-term bare fallow experiments was used, to test the utility of the DSI for SOC pool initialization. Pool partitioning by the DSI was superior to using a fixed pool partitioning under the assumption that SOC was at steady state. The DSI contained robust information on SOC quality across sites. Therefore, in the majority of cases, the application of the DSI led to significantly lower model errors than the steady state assumption. Furthermore, the application of the DSI in Bayesian calibration led to a reduced parameter uncertainty for the turnover of the slow-cycling SOC pool and the humification efficiency. The 95% credibility interval of the slow-cycling SOM pools’ half-life between 278 and 1095 years suggested faster SOC turnover than earlier studies. The DSI used for SOC model pool initialization was then combined with the lignin-to-nitrogen ratio for litter pool initialization to infer pool specific temperature sensitivities. The simulations of five field studies and laboratory incubations with fallow soil and crop-litter inputs were combined. Based on a clear pool definition, pool specific temperature sensitivities could be inferred by Bayesian calibration. However, differences in temperature sensitivities of the same pools between experiments suggested that carbon stability was not the main driver of temperature sensitivities. Instead, the main difference was found between the laboratory incubations (higher Q10 values up to 3) and the field (lower Q10 values centered around 2). In a second approach, the measured Q10 value of phenoloxidase (1.35) was used as Q10 value of the temperature function of both SOM pools and the slow crop-litter pool while ß glucosidase (1.82) was used for the fast crop litter pool. This improved field simulations by 3 to 10% compared to assuming a standard Q10 of 2 for all pools. Thus, site specific Q10 of different soil enzymes showed potential as proxy for site and pool specific temperature sensitivities. Important state variables that explain the observed Q10 value differences between experiments were identified as physical protection of SOC, substrate availability and environmental stress for microorganisms due to fluctuating state variables in the field. In conclusion, the usefulness of the DSI as an indicator of SOC stability and proxy for pool initialization was demonstrated for several soils in central Europe. In addition, it was shown that pool partitioning proxies can help to infer pool specific temperature sensitivity by Bayesian calibration. However, temperature sensitivity was not mainly a function of carbon stability. Modelle, welche die Flüsse des organischen Bodenkohlenstoffes (OBK) simulieren, sind zum Verständnis des globalen Kohlenstoffkreislaufs entscheidend. Modelle der heutigen Generation haben wegen zwei Hauptschwächen große Unsicherheiten. Zum einen sind die unterschiedlichen Kohlenstoffpools nicht mit messbaren OBK Fraktionen verknüpft. Zum anderen besteht Unsicherheit darüber, wie die verschiedenen Kohlenstoffpools auf steigende Temperaturen reagieren. Das Ziel dieser Arbeit war es deshalb, die OBK Pools des Daisy Modelles mit messbaren Proxies für Kohlenstoffqualität und poolspezifischen Temperatursensitivitäten zu verknüpfen. In der ersten Studie wurde die Vorbehandlung von Bodenproben zur Messung mit diffuser Reflexions-Fourier-Transformations-Infrarotspektroskopie (DRIFTS) optimiert. Die Trocknungstemperatur der Vorbehandlung wurde angepasst, um die Repräsentativität von aliphatischen sowie aromatisch-carboxylischen Absorptionsbändern zu verbessern. Diese Bänder wurden respektive als Proxies für sich schnell und langsam umsetzende OBK Pools, verwendet. Ihr Verhältnis wurde als DRIFTS Stabilitätsindex (DSI) bezeichnet. In der zweiten Studie wurde der DSI genutzt, um sich schnell und langsam umsetzende OBK Modellpools bei der Modellinitialisierung zu unterteilen. In der dritten Studie wurde die entwickelte Modellinitialisierung mittels des DSI eingesetzt, um poolspezifische Temperatursensitivitäten für unterschiedliche Daisy Kohlenstoffpools abzuleiten. Zusätzlich wurden gemessene Temperatursensitivitäten unterschiedlicher extrazellulärer Bodenenzyme als Proxies für poolspezifische Temperatursensitivitäten getestet. Unter Verwendung einer globalen Sammlung von Bodenproben wurde festgestellt, dass die Absorption der untersuchten DRIFTS Absorptionsbänder mit der Trocknungstemperatur zwischen 32°C und 105°C signifikant zunahm (p < 0.0001). Wegen starker Interferenz der Restfeuchte von Bodenproben mit dem aliphatischen Absorptionsband ist daher die Trocknung bei 105°C und die Aufbewahrung im Exsikkator notwendig, um repräsentative Spektren für die Modellinitialisierung zu messen. Im Folgenden wurde eine Kombination aus Mittel- bis Langzeitversuchen mit Bodenbrache verwendet, um den DSI für die OBK Poolinitialisierung zu testen. Der DSI war einer fixen Poolaufteilung, unter der Annahme eines Gleichgewichtszustandes des OBK, überlegen. Der DSI enthielt über mehrere Standorte hinweg belastbare Informationen zur OBK Qualität und seine Anwendung führte in der Mehrzahl der Fälle zu einem signifikant niedrigeren Modellfehler als die Annahme eines Gleichgewichtszustandes der OBK. In der Bayesschen Kalibrierung führte der DSI zu einer reduzierten Parameterunsicherheit für die Umsatzrate des sich langsam umsetzenden OBK Pools sowie der Humifizierungseffizienz. Das 95% Glaubwürdigkeitsintervall der Halbwertszeit des sich langsam umsetzenden OBK Pools betrug 278 bis 1095 Jahre. Im Anschluss daran wurde die Verwendung des DSI für OBK Poolinitialisierung mit der Verwendung des Lignin-zu-Stickstoff Verhältnisses für die Poolinitialisierung der Pflanzenstreu-Pools kombiniert, um nachfolgend Pool spezifische Temperatursensitivitäten abzuleiten. Es wurden fünf Versuche, bestehend aus Feldstudien und Laborinkubationen mit Brachflächen sowie Streueinarbeitung, kombiniert. Dabei konnten poolspezifische Temperatursensitivitäten durch Bayessche Kalibrierung abgeleitet werden. Wegen unterschiedlicher Temperatursensitivitäten derselben Pools in verschiedenen Experimenten war die Kohlenstoffstabilität jedoch nicht die Hauptursache der beobachteten Temperatursensitivitäten. Der Hauptunterschied bestand zwischen Laborinkubationen (höhere Q10 Werte bis zu 3) und Feldversuchen (niedrigere Q10 Werte um 2). In einem zweiten Ansatz wurden gemessene Q10 Werte der Phenoloxidase (1.35) als Q10 Wert der der beiden OBK-Pools und des langsamen Pflanzenstreu-Pools, und ß-Glucosidase (1.82) für den schnellen Pflanzenstreu-Pool verwendet. Dies verbesserte die Simulationen der Feldversuche um 3 bis 10% im Vergleich zum Standard-Q10 von 2 für alle Pools. Standortspezifische Q10 Werte verschiedener Bodenenzyme bewiesen somit Potenzial als Proxies für standort- und poolspezifische Temperatursensitivitäten. Als wichtige Zustandsvariablen zur Erklärung der beobachteten Q10 Wert-Unterschiede zwischen Experimenten wurden physikalischer Schutz von OBK, Substratverfügbarkeit und Umweltstress für Mikroorganismen infolge sich ständig ändernder Zustandsvariablen im Feld identifiziert. Zusammenfassend konnte in dieser Arbeit der Mehrwert des DSI als Indikator der OBK Stabilität und als Proxy für die Poolinitialisierung für eine Reihe von Böden in Mitteleuropa demonstriert werden. Darüber hinaus konnte gezeigt werden, dass Poolpartitionierungs-Proxies helfen können, die poolspezifische Temperatursensitivität durch Bayessche Kalibrierung abzuleiten. Die Temperatursensitivität konnte jedoch nicht primär durch die Kohlenstoffstabilität erklärt werden.

Soil organic matter (SOM) turnover models predict changes in SOM due to management and environmental factors. Their initialization remains challenging as partitioning of SOM into different hypothetical pools is intrinsically linked to model assumptions. Diffuse reflectance mid-infrared Fourier transform spectroscopy (DRIFTS) provides information on SOM quality and could yield a measurable pool-partitioning proxy for SOM. This study tested DRIFTS-derived SOM pool partitioning using the Daisy model. The DRIFTS stability index (DSI) of bulk soil samples was defined as the ratio of the area below the aliphatic absorption band (2930 cm(-1)) to the area below the aromatic- carboxylate absorption band (1620 cm(-1)). For pool partitioning, the DSI (2930 cm(-1) / 1620 cm(-1)) was set equal to the ratio of fast-cycling / slow-cycling SOM. Performance was tested by simulating long-term bare fallow plots from the Bad Lauchstadt extreme farmyard manure experiment in Germany (Chernozem, 25 years), the Ultuna continuous soil organic matter field experiment in Sweden (Cambisol, 50 years), and 7 year duration bare fallow plots from the Kraichgau and Swabian Jura regions in southwest Germany (Luvisols). All experiments were at sites that were agricultural fields for centuries before fallow establishment, so classical theory would suggest that a steady state can be assumed for initializing SOM pools. Hence, steady-state and rameter sets that differed in turnover rates and humification efficiency. Initialization using the DSI significantly reduced Daisy model error for total soil organic carbon and microbial carbon in cases where assuming a steady state had poor model performance. This was irrespective of the parameter set, but faster turnover performed better for all sites except for Bad Lauchstadt. These results suggest that soils, although under long-term agricultural use, were not necessarily at a steady state. In a next step, Bayesian-calibration-inferred best-fitting turnover rates for Daisy using the DSI were evaluated for each individual site or for all sites combined. Two approaches significantly reduced parameter uncertainty and equifinality in Bayesian calibrations: (1) adding physicochemical meaning with the DSI (for humification efficiency and slow SOM turnover) and (2) combining all sites (for all parameters). Individual-site-derived turnover rates were strongly site specific. The Bayesian calibration combining all sites suggested a potential for rapid SOM loss with 95 % credibility intervals for the slow SOM pools' halflife being 278 to 1095 years (highest probability density at 426 years). The credibility intervals of this study were consistent with several recently published Bayesian calibrations of similar two-pool SOM models, i.e., with turnover rates being faster than earlier model calibrations suggested; hence they likely underestimated potential SOM losses.