Name: Temperature Response of Soil Organic Matter Decomposition Rates: Construction and Applications of a Temperature Gradient Block (Video) | JoVE Journal
Uploaded: 2026-01-30T13:30:00.000+00:00
Duration: 7 min 46 s
Description: Learn to measure soil organic matter decomposition rates using a temperature gradient block with 22 discrete temperatures. This method improves carbon climate feedback predictions and enhances mechanistic understanding of soil processes.

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We acknowledge Pieter van de Broek for helping with the programming, electronics, and testing of the block. We thank Christopher Wedde for helping with writing the electronic unit protocol. We thank Henk Martens for the assistance in ordering material for this setup. We also thank Tamas Salanki, Paolo Di Lonardo, and Willeke van Tintelen for their assistance and facilitation during the laboratory placement of the block. We also thank the Joint Research Center for granting access to historical samples presented as a performance test of the temperature block. Finally, we thank Antoine Moinet and Tullia Calogiuri for their help during the soil sampling campaign to test this protocol. This project was funded by the Soil Science Cluster from Wageningen University and Research.

The authors have nothing to disclose.

Using a temperature gradient block for measuring the short-term temperature response of SOM decomposition rates presents challenges, especially in detecting respiration rates at low temperatures. Low temperatures can directly inhibit the catalytic activities of intracellular and extracellular enzymes46,47. Therefore, the incubation time must be long enough to ensure that the low respiration rates at the low end of the temperature range allow for a sufficient build-up of CO2 in the glass vials to fall within the detection range of the gas analyzer. In this experimental setup, the lowest temperature was 4.1 &#176;C; these samples were the first ones to be loaded into the block and last to be analyzed, giving extra incubation time. The ~3.5 h of block incubation time were found to be sufficient to yield detectable changes in CO2 concentration in the samples. This incubation length is likely to depend on soil type and particularly organic matter content. For example, it may be that sandy soils with low organic matter content may require longer incubation times than carbon-rich soils. However, in the tests here, the incubation time of 3.5 h was found to be generally sufficient for most soil types and organic content levels.
In addition to optimizing incubation time for CO2 detection at low temperatures, it is equally important to minimize moisture loss at the higher end of the temperature gradient. Soil moisture plays a crucial role in transportation, diffusion, and dissolution of substrates to microbial communities48. For instance, soil moisture can limit respiration rates by restricting substrate diffusion at low moisture levels and reducing oxygen availability at high moisture levels45,49. Thus, minimizing moisture loss at high temperatures is relevant to prevent moisture from limiting microbial access to substrates. Although the vials in this protocol were completely sealed, water condensed on the caps and inner walls of the vials. This condensation was considered as moisture lost from the soil. Thus, we reduced the incubation time from 4 h, as in Robinson et al. 201741, to 3.5 h. While it was not possible to maintain completely constant moisture levels across all temperature treatments, soils were kept at optimal moisture levels during the 3.5 h incubation period. Even at high temperatures, the WHC of the soil remained above 50%, ensuring that water availability did not limit microbial access to substrates44,45.
Another important consideration is the temperature range of the incubation. While the cooling system can reach temperatures as low as -30 &#176;C and the heating system up to 320 &#176;C, the insulation material has a temperature tolerance of -183 &#176;C to 148 &#176;C. The glass vials used in this setup can withstand 500 &#176;C. Although these ranges are wide, incubating samples above 100 &#176;C possesses a safety concern for the operator. To prevent overheating, a thermal resistor was added, ensuring the heating system automatically shuts down if the aluminum block reaches 90 &#176;C. The inclusion of this thermal resistor is crucial due to the observed initialization overshoot (Figure 13A). If the temperature during the initialization overshoots above 90 &#176;C the system will be disabled. Given these limitations, we recommend operating this setup to a maximum temperature of 60 &#176;C for safe and reliable performance. Additional testing is required for incubating soil samples below 0 &#176;C to ensure reliable CO2 detection, as condensation and associated moisture variability may affect measurements at subzero temperatures. For this protocol, we chose a lower temperature limit of 4 &#176;C, at which no condensation was observed. If the system is used at wider temperature ranges, we recommend implementing additional thermal safety guidelines.
Another potential constraint of this method is that the relatively short incubation period does not allow microbial communities to adapt to changes in temperature. The temperature response of soils exposed to sustained warming has been the focus of many studies, particularly with the aim of testing microbial adaptation theories27,43,47,50,51. Microbial adaptation has been proposed to explain the diminishing effect over time of experimental warming on soil respiration rates47. However, the methodology presented here was developed to measure the short-term temperature response of SOM decomposition rates. While microbial adaptation is unlikely to occur in short periods of time47, a recent study used a similar methodology to quantify the rate of microbial adaptation by incubating soils from a geothermal gradient. In this study, MMRT was fitted to estimate key parameters, including the temperature optima and inflection points. By comparing these parameters across soils from the geothermal gradient, the findings show that the rate of microbial adaptation is significantly lower than the rate of warming52. This study highlights the importance of understanding the whole temperature response curve, particularly capturing the temperature optimum and inflection points of biological soil processes (Figure 19C,D). In light of these findings, we recommend sampling microbial communities at multiple temperature points during block incubation to assess how microbial community composition and functions correlate with respiration rates and temperature sensitivity parameters.
The temperature gradient block outlined in this paper offers significant advantages over traditional methodologies. One major benefit is that it enables researchers to measure SOM decomposition rates efficiently and accurately at 22 discrete temperatures with small amounts of soil. This represents a methodological improvement compared to incubating soil samples at a few discrete temperatures, which compels researchers to apply the Q10 temperature coefficient -&#160;a method that many studies have challenged12,13,15. Another significant benefit is that this setup collects data that clearly captures the shape and steepness of the temperature response curve. The number of discrete temperature treatments along a wide temperature gradient allows us to identify the inflection point temperature and optimum of biological processes, allowing comparisons between treatments. The examples presented in this manuscript show two distinct patterns (Figure 19A,B vs. Figure 19C,D), which researchers can further analyze using different temperature sensitivity theories. Some examples support the Arrhenius theory, where reaction rates increase exponentially with temperature (Figure 19A,B). In contrast, other examples align with the MMRT theory, showing a unimodal response with a clear temperature optimum (Figure 19C,D). These two different patterns indicate that the presented temperature block incubation protocol is capable of reproducing both types of response curves, which can be used to test theories of the temperature sensitivity of SOM decomposition rates, thereby deepening our understanding of thermodynamics, biology, and chemistry.

Increasing carbon dioxide (CO2) concentrations in the atmosphere lead to higher temperatures on Earth1. This phenomenon was first reported in 1856 by Eunice Foote2, who conducted a simple yet groundbreaking experiment. She filled one cylinder with ambient air and another with CO2 and recorded the temperatures in both1. The results were clear: cylinders with higher CO2 concentrations exhibited higher temperatures, demonstrating the warming effect of increased atmospheric CO21. This observation has significant implications for climate change. Today, Foote&#39;s early insights continue to inform contemporary climate science, guiding research on how rising CO2 levels affect global warming and influence various ecosystems.
Soils store nearly twice as much carbon (C) as the atmosphere in the first meter, playing a crucial role in the global C cycle3. A key process influencing soil C stocks is soil organic matter (SOM) decomposition, which determines the rate at which C is released back into the atmosphere4,5. Higher mean annual temperatures, driven by climate change, have the potential to stimulate soil organic matter (SOM) decomposition, accelerating the release of CO2 into the atmosphere6,7. This represents a positive feedback mechanism to climate change, particularly when higher temperatures accelerate C losses through SOM decomposition more than they stimulate photosynthetic C inputs6. Despite extensive research on the topic, the magnitude and direction of the so-called land-carbon-climate feedback remains widely debated8,9,10,11. This uncertainty is largely due to fundamental knowledge gaps regarding the temperature response of SOM decomposition rates. Addressing these gaps is essential for refining Earth system models and incorporating theoretically grounded representations of the temperature response of SOM decomposition rates.
The temperature response of SOM decomposition rates is commonly represented by the Q10 value and the Arrhenius equation. The Q10&#160;value is defined as the factor by which a rate is multiplied when the temperature increases by&#160; 10 &#176;C. Despite challenges to its validity12,13,14,15, Q10 has become the default approach for comparing the temperature response of different treatments and soils. The Arrhenius theory suggests an exponential increase in process rates, such as those of SOM decomposition, with increasing temperature16. However, actual enzyme activities tend to follow a unimodal form with a distinct thermal optimum and decline at higher temperatures17,18,19. Arrhenius-based equations attempt to account for this decline by suggesting enzyme denaturation beyond the thermal optimum6. However, enzyme denaturation occurs at higher temperatures than those commonly found in soils, indicating that enzymes reach a thermal optimum before denaturation occurs19,20. Moreover, simply adding a second function to the Arrhenius equation to account for the collapse of respiration rates due to denaturation does not allow estimation of an inflection point temperature - the temperature at which the rate of change in respiration is maximized21,22. Consequently, Arrhenius-based equations fail to fully capture the commonly measured shape of the overall temperature response of SOM decomposition rates within the range of temperatures generally observed in soils14,23.
To address the limitations of Arrhenius theory, multiple equations have been developed to represent the temperature sensitivity of biological processes; however, few are grounded in biological theory or have been applied to soils24,25. Among the biologically-based approaches, the macromolecular rate theory (MMRT) overcomes some of the Arrhenius equation&#39;s limitations by incorporating the thermodynamic properties of biological macromolecules, such as enzymes26. Similarly, the Enzyme-Assisted Arrhenius theory (EAAR) accounts for the temperature-dependent role of enzymes in lowering the activation energy of the reaction24. Both equations have been successfully applied to studies of the temperature response of biochemical reactions in soils and microorganisms17,24,27,28. These equations provide valuable mechanistic insights into the temperature response of SOM decomposition and underlying reaction steps. However, their broad adoption and parametrization are often hindered by experimental challenges.
Both field and laboratory studies have advantages and limitations for understanding the temperature response of SOM decomposition rates. Field experiments provide valuable insights on the long-term temperature response of SOM decomposition rates. However, interpreting CO2 flux measurements in the field can be challenging due to confounding variables such as autotrophic respiration, moisture variability, and substrate accessibility29,30,31,32,33. While these interactions are essential for quantifying real-world processes, such studies are generally focused on long-term ecosystem responses rather than the measurements of short-term mechanistic processes. Understanding the short-term temperature response is crucial for parameterizing equations that are defined by recent theories, like MMRT and EAAR, and for this, laboratory studies are key. Many laboratory studies typically suffer from confounding effects of substrate depletion, which increase&#160;with incubation time and temperature treatments34,35,36. Additionally, most laboratory experiments use only a few temperature treatments37,38,39,40, resulting in fitting simple exponential equations or calculating Q10 ratios instead of more advanced equations, which require rates to be measured at a larger number of discrete temperatures41. To address these issues, it is crucial to develop new experimental approaches that enable accurate measurements of SOM decomposition rates across a broader range of temperatures, thereby supporting the rigorous testing and development of SOM decomposition theories in relation to temperature response.
In this paper, we describe the construction and utilization of a temperature gradient block, which not only addresses current experimental limitations but also provides a state-of-the-art study to demonstrate the short-term response of SOM decomposition to temperature. The design presented here is an adaptation of the methodology first developed by Thomas et al. (1963)42 and later modified by Robinson et al. (2017)41. The main differences include 1) an improvement of the cooling system, 2) a reduced operating time by coupling the block to a CO2 analyzer for direct measurement of CO2 concentrations, and 3) eliminating the need for transporting vials during the experiment. While previous reports21,41,43 have provided a broad description of the temperature block, the detailed design, construction methods, and testing were minimal, limiting the ability for other researchers to utilize this approach. Here, we provide complete details of both the design and construction, and present results from multiple tests to demonstrate the proper functionality of the setup for different soil samples.

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m/us/en/product/39213360/mast&lt;br/&gt; erflex-transfer-tubing-flexible-&lt;br/&gt; vinyl-avantor</td></tr><tr><td>M6 Screws, Hex</td><td>RS</td><td>281-120</td><td>https://nl.rs-online.com/web/p/so&lt;br/&gt; cket-screws/0281120?srsltid=Afm&lt;br/&gt; BOorVGq-ZyA2yrJG1kU7SHGiZ&lt;br/&gt; EblaRz0nDmX7MwhzIRjcc6P4YIYE</td></tr><tr><td>Main junction box</td><td>RS</td><td>Check Dia.</td><td>https://nl.rs-online.com/w&lt;br/&gt; eb/p/general-purpose-enclo&lt;br/&gt; sures/2896320?gb=s</td></tr><tr><td>Male connector</td><td>qosina</td><td>12090</td><td>https://www.qosina.com/male-&lt;br/&gt; to-male-luer-lock-connector-12090</td></tr><tr><td>Male Luer</td><td>Walternedical</td><td>BPC04M</td><td>https://www.waltersmedical.co.&lt;br/&gt; uk/product/nibp-connector-male-luer-slip/</td></tr><tr><td>MCB - Siemens SENTRON</td><td>RS</td><td>3RF2320-1AA02</td><td>https://nl.rs-online.com/we&lt;br/&gt; b/p/mcbs/2383188?gb=s</td></tr><tr><td>PE/DND feedtrhough</td><td>RS</td><td>687-9411</td><td>https://uk.rs-online.com/we&lt;br/&gt; b/p/din-rail-terminal-blocks/&lt;br/&gt; 6879411?searchId=c9a9c1&lt;br/&gt; b1-3def-40d0-8b14-b792f8269615&amp;amp;gb=s</td></tr><tr><td>Power Indication LED</td><td>RS</td><td>909-2437</td><td>https://uk.rs-online.com/&lt;br/&gt; web/p/pilot-lights/9092437?gb=s</td></tr><tr><td>Power supply</td><td>Antratek</td><td>X-2902992</td><td>https://nl.rs-online.com/web/&lt;br/&gt; p/din-rail-power-supplies/054&lt;br/&gt; 0731?srsltid=AfmBOoqMPIO&lt;br/&gt; Av7bZd1Qu_Acy3KMFR0-x&lt;br/&gt; OD_Hkw3NeQCYyzKJLzEGXzxQ</td></tr><tr><td>Quintople conductor bridges</td><td>RS</td><td>803-6713</td><td>https://uk.rs-online.com/web&lt;br/&gt; /p/din-rail-terminal-blocks/80&lt;br/&gt; 36713?searchId=7de6627a-&lt;br/&gt; 3634-42ed-a600-2e3ce8974fe7&amp;amp;gb=s</td></tr><tr><td>Red wire</td><td>RS</td><td>361-822</td><td>https://nl.rs-online.com/web&lt;br/&gt; /p/hook-up-wire/0361822</td></tr><tr><td>Ring Terminal 2.5 mm&amp;sup2;</td><td>RS</td><td>534-569</td><td>https://uk.rs-online.com/&lt;br/&gt; web/p/ring-terminals/0534569</td></tr><tr><td>Sensors</td><td>Antratek</td><td>X-DTS-S3C</td><td>https://www.antratek.nl/dig&lt;br/&gt; 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schappen-verspanende-geree&lt;br/&gt; dschappen-boren-hss-en-hsse&lt;br/&gt; -boren-cilindrische-schacht/spir&lt;br/&gt; aalboor-din340-hsse-geslepen-&lt;br/&gt; type-ti-5-80mm-12592599/?srslt&lt;br/&gt; id=AfmBOoopMLgX1mtyuUxkU&lt;br/&gt; KU7jW4fYY1bY5wSTvSGQUkO&lt;br/&gt; zTc6E2wdxUZQ</td></tr><tr><td>Stainless steel housing and lid</td><td>247 Tailore steel</td><td></td><td>https://247tailorsteel.com/nl/?ut&lt;br/&gt; m_term=24%207%20steel&amp;amp;utm&lt;br/&gt; _campaign=B+-+NL+-+247tailo&lt;br/&gt; rsteel+-+NL+-+Doelvertonings&lt;br/&gt; percentage+%3E+Conv.+Waa&lt;br/&gt; rde+met+Doel-ROAS&amp;amp;utm_so&lt;br/&gt; urce=adwords&amp;amp;utm_medium=&lt;br/&gt; ppc&amp;amp;hsa_acc=2007690635&amp;amp;h&lt;br/&gt; sa_cam=783787418&amp;amp;hsa_grp&lt;br/&gt; =118504886580&amp;amp;hsa_ad=67&lt;br/&gt; 1662182959&amp;amp;hsa_src=g&amp;amp;hsa&lt;br/&gt; _tgt=kwd-374862287021&amp;amp;hs&lt;br/&gt; a_kw=24%207%20steel&amp;amp;hsa&lt;br/&gt; _mt=p&amp;amp;hsa_net=adwords&amp;amp;hsa&lt;br/&gt; _ver=3&amp;amp;gad_source=1&amp;amp;gclid=&lt;br/&gt; Cj0KCQjw8MG1BhCoARIsAHx&lt;br/&gt; SiQnhmktBk_3c_wobSJobrTVp4&lt;br/&gt; _nqiW26Zt7XzSkHdK-htW8medy&lt;br/&gt; NCcQaAjbrEALw_wcB</td></tr><tr><td>Switch 2-way</td><td>RS</td><td>746-9336</td><td>https://nl.rs-online.com/web/p&lt;br/&gt; /cam-switch-handles/7469336</td></tr><tr><td>Terminal bridge</td><td>Elektramat</td><td>401043503</td><td>https://www.elektramat.nl/phoenix-c&lt;br/&gt; ontact-doorverbindingsbrug-voor-rijg&lt;br/&gt; klem-2-polig-5-2-mm-rood-fbs-2-5/?c&lt;br/&gt; hannable=0141ac6964003330383936&lt;br/&gt; ec&amp;amp;utm_source=google-surfaces&amp;amp;utm&lt;br/&gt; _medium=organic&amp;amp;bg_source=ga&amp;amp;bg_&lt;br/&gt; campaign=19011971421&amp;amp;bg_kw=-mi-9&lt;br/&gt; 214030-pi-401043503-ppi-&amp;amp;bg_source&lt;br/&gt; _id=&amp;amp;gad_source=1&amp;amp;gclid=CjwKCAjw&lt;br/&gt; 8fu1BhBsEiwAwDrsjE2kRcDZUHPupfy&lt;br/&gt; haLoTzWLqM9v8HKaiI67st4UOmUXIN&lt;br/&gt; 9OaI25ALRoCvmUQAvD_BwE</td></tr><tr><td>Thermostat DAQ</td><td>Antratek</td><td>X-300-I</td><td>Temperatur&amp;uuml;berwachung &amp;amp; Thermostat (+X-DTS-S3C) von ControlByWeb (antratek.nl)</td></tr><tr><td>Universal Milling Machine</td><td>Mach4Metal</td><td>MACH-ID 7580</td><td>https://mach4metal.com/en&lt;br/&gt; /milling-machines/cnc/fehlm&lt;br/&gt; ann-picomax-p54-2</td></tr><tr><td>Wago 3 conductor feedthrough</td><td>RS</td><td>803-6713</td><td>https://nl.rs-online.com/web/&lt;br/&gt; p/din-rail-terminal-blocks/80&lt;br/&gt; 36713?cm_mmc=NL-PLA-D&lt;br/&gt; S3A-_-google-_-CSS_NL_N&lt;br/&gt; L_Pmax_0525-_--_-803671&lt;br/&gt; 3&amp;amp;matchtype=&amp;amp;&amp;amp;gclsrc=aw.d&lt;br/&gt; s&amp;amp;gad_source=1&amp;amp;gad_campa&lt;br/&gt; ignid=22555696009&amp;amp;gbraid=0&lt;br/&gt; AAAAADkeWNNXeREaDmJ5n&lt;br/&gt; yYqYMqp-c7A6&amp;amp;gclid=CjwKCAj&lt;br/&gt; wtfvEBhAmEiwA-DsKjilXxL8uO&lt;br/&gt; 4Jo6XyDesHGaQkoz6Ggi4ATI6&lt;br/&gt; FUInWV9TxmIX519DGIZBoCh9&lt;br/&gt; YQAvD_BwE</td></tr><tr><td>Wago connector</td><td>RS</td><td>883-7557</td><td>https://uk.rs-online.com/&lt;br/&gt; web/p/standard-terminal-b&lt;br/&gt; locks/8837557?gb=s</td></tr><tr><td>Wire end sleeves (100 pc)</td><td>Elektramat</td><td>401173609</td><td>https://www.elektramat.nl/solar&lt;br/&gt; -adereindhuls-geisoleerd-2-5mm&lt;br/&gt; 2-hulslengte-8mm-blauw-per-100&lt;br/&gt; -stuks-1505145/?channable=014&lt;br/&gt; 1ac696400343034a4&amp;amp;utm_source&lt;br/&gt; =google-surfaces&amp;amp;utm_medium&lt;br/&gt; =organic&amp;amp;bg_source=ga&amp;amp;bg_c&lt;br/&gt; ampaign=17673967332&amp;amp;bg_kw=&lt;br/&gt; -mi-9214030-pi-401173609-ppi-&amp;amp;bg&lt;br/&gt; _source_id=&amp;amp;gad_source=1&amp;amp;gclid=&lt;br/&gt; CjwKCAjw8fu1BhBsEiwAwDrsjNms&lt;br/&gt; VYTNZ5TuXEHtsF34hcY-T5kLFnhi&lt;br/&gt; zir1fUsR3UYcG9bxmugqJRoCp_Y&lt;br/&gt; QAvD_BwE</td></tr></tbody></table>

temperature response of soil organic matter decomposition rates: construction and applications of a temperature gradient block

Temperature Response of Soil Organic Matter Decomposition Rates: Construction and Applications of a Temperature Gradient Block

Soil is the Earth's largest carbon (C) reservoir, holding nearly twice as much C as the atmosphere. Climate change-induced temperature increases could enhance soil organic matter (SOM) decomposition, potentially releasing carbon dioxide (CO2) into the atmosphere, thus resulting in a positive feedback to climate change. This presents the question: how can we define the response of SOM decomposition rates to short-term changes in temperature? Can we utilize this data to fit mechanistically defined equations in Earth system models for climate predictions? Most research to date has focused on laboratory soil incubations at different discrete temperatures. This, however, does not result in a detailed temperature response curve, which is required to test novel hypotheses imposed by the latest developments in the field, such as the macromolecular rate theory (MMRT). In this paper, we introduce a temperature gradient block, modified and improved from earlier work, detail its construction process, and provide a detailed explanation for its utilization. The temperature gradient block is an aluminum block with four rows of 22 discrete holes, allowing the simultaneous incubation of 88 microcosms (60 mL vials). The block is cooled at one end and heated at the other, creating a linear temperature distribution. It provides 22 discrete temperature points along a user-defined range within limits of 0 to 90 &#176;C. After 3.5 h of incubation, CO2 production is measured from each of the 88 microcosms. Results demonstrate that the block produces the user-defined temperature gradient and facilitates the construction of temperature response curves of SOM decomposition. Additionally, the data collected from this setup can be used to test and develop new hypotheses and theories regarding the impact of temperature changes on SOM decomposition rates, an area of increasing importance in the context of climate change.

Stability and distribution of the block temperature 
During the development of this protocol, we tested the linearity and temporal stability of the temperature inside the block. After switching the cooling system and the heater ON, the initialization time of the block is approximately one hour, during which time most of the eight evenly distributed temperature sensors reach the target temperature and then remain constant after an overshoot (Figure 13A). After this period, the temperatures recorded by the eight sensors confirm a perfect linear distribution of the temperature along the aluminum block (Figure 13B).
<img alt="Temperature vs. time and distance graphs; sensor data analysis; incubation process; correlation plot." class="xfigimg" src="/files/ftp_upload/69785/69785fig13.jpg" data-altupdatedat="1773720005000" data-alt="Figure 13"> 
Figure 13: Linearity and temporal stability inside the temperature gradient block. (A) Temporal stability of the temperatures inside the block over the incubation period, and (B) temperature recorded by the sensors at the starting time of the incubation. <a href="https://www.jove.com/files/ftp_upload/69785/69785fig13large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Block temperature and soil temperature 
We tested the time required for the soil sample inside the block to reach the temperature recorded by the eight sensors (Figure 14). In this study, we measured the temperature of soil samples at eight different positions within the block, located near the sensors. The sensors, positioned inside the block were pre-stabilized at their target temperature. Following the above-described protocol, once the block temperatures were stable, soil samples were placed inside the block. For this test, we used extra sensors that are separated from the block. We used 15 g of soil, ensuring that the sensor probe was in full contact with the soil. The soil samples were left uncapped, and the temperature was recorded every minute for a total of 30 min. During this test, the block was slightly closed to minimize temperature variation and reproduce as much as possible real utilization conditions (completely closed). We found that it takes approximately 12 min for the soil samples to stabilize at the target temperature. We deemed this stabilization time to be acceptable, given that the incubations last for a minimum of 3.5 h. Additionally, we assessed the linearity of soil temperature after 12 min of block incubation. The results indicate that the soil temperatures exhibit a linear relationship, closely aligning with the linearity observed in the eight sensors (Figure 15).
<img alt="Soil temperature vs. time graphs; eight vials; data analysis of thermal response; research results." class="xfigimg" src="/files/ftp_upload/69785/69785fig14.jpg" data-altupdatedat="1773720005000" data-alt="Figure 14"> 
Figure 14: Recorded soil temperature incubated at different temperatures. Samples A, B, C, and D were incubated in vials positioned near the corresponding sensors (see Figure 10 for reference). The soil temperature was recorded by a thermocouple inserted directly into the soil samples. The specific temperatures monitored by each sensor are as follows: (A) Sensor 1 (S1) at 4.1 °C , (B) Sensor 2 (S2) at 12.5 °C, (C) Sensor 3 (S3) at 20.1 °C, (D) Sensor 4 (S4) at 28.1 °C, (E) Sensor 5 (S5) at 36.1 °C, (F) Sensor 6 (S6) at 43.8 °C, (G) Sensor 7 (S7) at 51.9 °C, and (H) Sensor 8 (S8) at 60 °C. After placing the soil samples into the block, it takes approximately 12 min to reach the target temperature. <a href="https://www.jove.com/files/ftp_upload/69785/69785fig14large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Temperature correlation graph; soil vs sensor, linear fit, R²=0.9999, Celsius, experimental data." class="xfigimg" src="/files/ftp_upload/69785/69785fig15.jpg" data-altupdatedat="1773720005000" data-alt="Figure 15"> 
Figure 15: Strong linear correlation between soil temperature and sensor temperature. Four vials with soil were placed close to the eight sensors in block positions A, B, C, and D. The soil temperature was recorded by a thermocouple inserted directly into the soil samples. <a href="https://www.jove.com/files/ftp_upload/69785/69785fig15large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Temperature-moisture interaction 
There is an inevitable interaction between temperature and moisture that needs to be assessed during the incubation period. For instance, high temperatures can accelerate evaporation, while low temperatures can cause water condensation. For this test, we used four randomly selected soil samples with different textures (Supplementary Table 2). According to the protocol, each soil sample was placed in the same block position across all 22 temperatures. The results show that after an average incubation time of 3.5 h, higher temperatures resulted in greater moisture loss compared to lower temperatures, regardless of soil texture (Figure 16). Soils incubated at 60 °C lost on average 12% of the total water added, whereas soils incubated at 4.1 °C lost on average only 1.88% of the total water added. This means that during the 3.5 h of block incubation, soils were maintained within optimal moisture conditions, between 50% and 60% water holding capacity, irrespective of incubation temperature44,45 (Supplementary Figure 6).
<img alt="Moisture loss vs temperature graph; soil texture types: loam, silt Loam, clay; data analysis." class="xfigimg" src="/files/ftp_upload/69785/69785fig16.jpg" data-altupdatedat="1773720005000" data-alt="Figure 16"> 
Figure 16: Relative loss of soil moisture during block incubation in four different samples. Moisture loss was calculated relative to the total amount of water added to adjust for WHC. <a href="https://www.jove.com/files/ftp_upload/69785/69785fig16large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
CO2 concentration measurements 
To test the accuracy and precision of the gas analyzer, we carried out four measurements of a certified reference gas (413 ± 5 ppm CO2), for which the analyzer recorded 413.71 ± 0.60 (ppm CO2, mean ± SD). This confirmed the high accuracy and precision of the CO2 measurements recorded by the gas analyzer. In addition, to adhere to the manufacturer's specifications, a yearly test using a certified reference gas should be done.
In view of the lag time caused by the injection line and the flow reduction rate kit to the gas analyzer, multiple tests were performed to determine the optimal measuring time. Results show that the injection line causes a delay of approximately five seconds. In this setup, the ideal measuring time is 20 s, allowing sufficient time to fill the CO2 detection unit and reach an equilibrium for 5 s (Figure 17). Given that the flow reduction rate kit measures at a rate of 1.17 mL/s, this measuring time corresponds to sampling 39% of the air content in the 60 mL vials. Additionally, we observed that when the gas analyzer is used as a closed system, a pressure drop occurs. This drop arises during the one-second interval when there is no air injection as the needle is extracted from the vial. To account for this drop in pressure during data processing, we excluded CO2 values that are slightly lower (between 3% and 5%) than the previous or next value.
<img alt="CO2 concentration vs. time graph; needle injection/extraction; shows equilibrium, pressure drop." class="xfigimg" src="/files/ftp_upload/69785/69785fig17.jpg" data-altupdatedat="1773720005000" data-alt="Figure 17"> 
Figure 17: Optimal measuring time for an individual soil sample incubated at three different temperatures. This graph shows the injection and extraction time of the needle, the equilibrium of CO2 for 5 s, and the pressure drop associated with the selected time frame. Incubation_ID shows block position (C) and vial positions (5, 10, and 15). <a href="https://www.jove.com/files/ftp_upload/69785/69785fig17large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Testing the full protocol 
To evaluate the functionality and reproducibility of the protocol, we incubated two different samples, each with four technical replicates, across the 22 discrete temperatures ranging from 4 °C to 60 °C. These soil samples were chosen to demonstrate the type of data generated with this setup and its performance. Specific sample characteristics can be found in Supplementary Table 2. The temperature gradient block reliably captured the soil respiration rates of the different soil samples at each temperature point, allowing us to visualize the temperature response curve. Additionally, the resulting temperature response curves for all replicates were nearly identical, as reflected by the small standard deviation around the mean respiration rates across all temperatures (Figure 18). These results demonstrate high reproducibility and confirm the precision of the protocol.
<img alt="Temperature dependence of respiration rate \(Rs\) vs temperature graph; data analysis in soil science." class="xfigimg" src="/files/ftp_upload/69785/69785fig18.jpg" data-altupdatedat="1773720005000" data-alt="Figure 18"> 
Figure 18: Representative SOM decomposition rates (Rs) for two soils, each measured with four technical replicates incubated across the full temperature gradient. Points represent the mean of respiration rates at each temperature, and error bars indicate the standard deviation among technical replicates. <a href="https://www.jove.com/files/ftp_upload/69785/69785fig18large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
A second test was performed to demonstrate the scientific applicability of the protocol. We measured soil respiration rates across the full temperature gradient and fitted the data using the Arrhenius or MMRT model (Figure 19). These soil samples were chosen to demonstrate the system performance for contrasting temperature response curves (sample characteristics are provided in Supplementary Table 2). Results show that the data collected with the temperature gradient block can be further analyzed by testing different theories about the temperature sensitivity of SOM decomposition rates. However, challenges arise when working with fresh soil in any experiment. During the 10 days pre-incubation period, we observed fungal communities growing inside some of the vials in soil samples beyond the set of representative samples. Despite our efforts to carefully remove the fungal communities without disturbing the soil, this growth impacted the results of some samples (Figure 20). Consequently, we decided to discard the affected samples by removing the entire sample, not just the individual vials impacted by fungal growth.
<img alt="Temperature vs. reaction rate graphs; Arrhenius, MMRT models; experimental data fitting." class="xfigimg" src="/files/ftp_upload/69785/69785fig19.jpg" data-altupdatedat="1773720005000" data-alt="Figure 19"> 
Figure 19: Representative results and respiration rates (Rs) of four different soil samples incubated in the temperature gradient block. The red line represents the (A,B) Arrhenius fit, and the blue line represents the (C,D) MMRT fit. <a href="https://www.jove.com/files/ftp_upload/69785/69785fig19large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Soil respiration vs. temperature graph, data analysis of Rs (gC/kgsoil/hour) at varying temperatures." class="xfigimg" src="/files/ftp_upload/69785/69785fig20.jpg" data-altupdatedat="1773720005000" data-alt="Figure 20"> 
Figure 20: Respiration rate patterns for some samples affected by fungal growth. These results are examples of (A,B) two contaminated samples that were excluded from further analysis. <a href="https://www.jove.com/files/ftp_upload/69785/69785fig20large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplementary Figure 1: Basic NO/NC circuit. "C" may also be labeled as "COM" for Common. <a href="https://www.jove.com/files/ftp_upload/69785/Figure%20S1.png" target="_blank">Please click here to download this File.</a>
Supplementary Figure 2: Cooling system control panel. <a href="https://www.jove.com/files/ftp_upload/69785/Figure_S2.png" target="_blank">Please click here to download this File.</a>
Supplementary Figure 3: System switch and light indicator. (A) Temperature gradient block control system switch; and (B) green light indicating the sensors and heating system are ON. <a href="https://www.jove.com/files/ftp_upload/69785/Figure%20S3.png" target="_blank">Please click here to download this File.</a>
Supplementary Figure 4: Temperature gradient block heater control in the online interface. <a href="https://www.jove.com/files/ftp_upload/69785/Figure%20S4.png" target="_blank">Please click here to download this File.</a>
Supplementary Figure 5: Diagnostic section of the gas analyzer. Everything is green means that the instrument is ready for measurements. <a href="https://www.jove.com/files/ftp_upload/69785/Figure%20S5.png" target="_blank">Please click here to download this File.</a>
Supplementary Figure 6: Water holding capacity of soils incubated at different temperatures, measurements recorded after 3.5 h of block incubation. <a href="https://www.jove.com/files/ftp_upload/69785/Figure%20S6.png" target="_blank">Please click here to download this File.</a>
Supplementary File 1: Detailed wiring instructions. <a href="https://www.jove.com/files/ftp_upload/69785/Supplementary%20File%201.docx" target="_blank">Please click here to download this File.</a>
Supplementary Table 1: List of materials with the required quantity. <a href="https://www.jove.com/files/ftp_upload/69785/Supplementary%20Table%201.xlsx" target="_blank">Please click here to download this File.</a>
Supplementary Table 2: Characteristics of soil samples used for the moisture loss test and full protocol. These samples were chosen to demonstrate the functionality of the protocol. Texture classification follows the USDA system, with coarse, clay, silt, and sand expressed as percentages. OC refers to organic carbon content (g C·kg-1 soil), pH was measured in water, and CaCO3 represents calcium carbonate content (g·kg-1 soil). <a href="https://www.jove.com/files/ftp_upload/69785/Supplementary%20Table%202.xlsx" target="_blank">Please click here to download this File.</a>

We introduce a temperature gradient block and detail its construction and utilization. The block provides 22 discrete temperatures, linearly distributed between a user-defined range from 0 to 90 &#176;C. Data collected from this setup enables testing and developing new theories on the temperature response of soil organic matter decomposition rates.

Soil is the Earth's largest carbon (C) reservoir, holding nearly twice as much C as the atmosphere. Climate change-induced temperature increases could ...

Our research examines how changing temperatures influence soil organic matter decomposition. And our aim is to define the temperature response and help improve carbon climate feedback predictions. Recent studies reveal broadly varying temperature responses for soil organic matter decomposition, but oversimplified matrix limit insight.Improved methodology will allow collecting data for better mechanistic understanding. To begin, place the soil sample in a metal tin and mix it thoroughly until homogeneous. Distribute the mixed soil into 22 pre-labeled 60 milliliter glass vials.Using weighing paper, add about three grams of soil into each 60 milliliter vial. With a pipette, adjust the soil in each vial to the desired water holding capacity based on the funnel method. After adjusting the water content to the target percentage water holding capacity, record the exact weight of each vial.Cap each vial using cotton wool to allow ambient air exchange. Pre-incubate the vials in a temperature-controlled laboratory set to 20 degrees Celsius for 10 days. To begin preparation for loading, adjust the water holding capacity one hour before starting the incubation.Check the heater control to ensure it has reached the desired temperature 10 minutes before placing samples in the block. Then turn on the ambient conductor located in the laboratory. Connect the flushing system and connect the gas analyzer to the computer.To begin collecting blanks, connect the flushing line to the injection system of the gas analyzer and record the carbon dioxide concentration in parts per million directly from the flushing line. For a second blank measurement, place five 60 milliliter glass vials in an empty holder. Insert one flushing line into each vial.A minute later, remove the flushing line and cap each vial. Measure the carbon dioxide inside the vials after four hours. Next, open the temperature gradient block and place the computer and flushing system on top of the block lid.Place the sample boxes on a movable table. Prepare a spreadsheet to record the closing time of each vial and simultaneously visualize the time displayed on the gas analyzer. Start loading samples into the temperature gradient block in batches of eight.Place uncapped samples inside the block, starting from the cooler end. Insert the flushing line into the eight vials. Remove the flushing line after a minute and immediately cap each vial.Record the exact closing time in the spreadsheet. While the first batch is flushing, load the next batch of samples into the block and insert the flushing line into this next batch. Repeat the loading and flushing procedure until the temperature gradient block is fully loaded.Close the temperature gradient block and wait 3.5 hours from the moment the last vial was closed. To measure the carbon dioxide, turn on the flushing system and place the gas analyzer onto the movable table. After 3.5 hours of incubation, begin measuring samples, starting with those at the highest temperatures.Connect the injection needle of the analyzer to a flushing line between samples. In the gas analyzer software, create a unique and simple remark before each measurement using the remark function. Insert the analyzer needle into a 60 milliliter sample vial for 20 seconds, then remove the needle from the vial.Start the remark at the zero seconds. Insert the needle at 10th seconds. Then remove the needle at the 30th second.And stop the remark at the 45th second. Replace the 0.45 micrometer filter connected to the injection needle every 16 samples. To unload samples, remove each vial one by one from the temperature gradient block and place it into a box while continuing carbon dioxide measurements.Most of the eight temperature sensors reach their target temperature within one hour after switching the cooling system and heater on and remained stable after an initial overshoot during the incubation period. After stabilization, the temperatures recorded by the eight sensors showed a perfectly linear distribution along the length of the aluminum block. Soil samples placed inside the stabilized block reached the target temperatures recorded by the sensors within approximately 12 minutes.After 12 minutes of incubation, soil temperatures displayed a strong linear relationship with the sensor temperatures across the block. With an average incubation time of 3.5 hours, higher incubation temperatures resulted in greater soil moisture loss regardless of soil texture. An optimal measuring time of 20 seconds was identified, allowing the carbon dioxide signal to reach equilibrium for approximately five seconds.When the gas analyzer was used as a closed system, a pressure drop occurred. Soil respiration rates measured across 22 discrete temperatures produced consistent temperature response curves with small standard deviations. Soil respiration data collected with the temperature gradient block were successfully fitted using Arrhenius and macromolecular rate theory models.Some soil samples exhibited altered respiration patterns associated with fungal growth and were excluded from further analysis. Our protocol uses an advanced temperature gradient block that measure soil respiration at 22 temperatures, enabling a detailed diverse response curves for soil organic matter decomposition rates. This method improves mechanistic understanding of the temperature effects on soil organic matter decomposition, revealing roles of soil properties and land management.

temperature-response-soil-organic-matter-decomposition-rates

Research

JoVE Journal

Environment

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