# Carbon Dioxide Efflux of Bare Soil as a Function of Soil Temperature and Moisture Content under Weather Conditions of Warm, Temperate, Dry Climate Zone

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## Abstract

**:**

_{2}efflux from soil with vegetation. Long-term experiments with bare soil will provide useful conclusions. In this study, we aimed to mathematize the effect of soil temperature and soil moisture content on bare soil CO

_{2}efflux in a four-season semiarid region to assess the adequacy of different models and to enable future predictions by seasons. We proved that the exponential model adequately described the relationship between the CO

_{2}efflux and the soil temperature. The model calculations showed no significant relationship in the case of an additional quadratic exponential function, while, in the case of the linear model, the homoscedasticity criteria were not met, and the accuracy of the estimation was found to be dependent on the level of CO

_{2}efflux. When the soil moisture content with either an exponential function or power was added to the exponential formula, the models did not provide more accurate results. Our findings confirm that the best-fitting models are dependent on the local environmental conditions, and there are areas in which the moisture content does not significantly affect the CO

_{2}efflux of bare soil. Using trends in historical hourly temperature data in the exponential model, the CO

_{2}emission was estimated to be in the range 772–898 g m

^{−2}y

^{−1}in 2050 in the location we used. Trends in climate change are expected to have considerable effects on the processes that govern the CO

_{2}emissions of soil.

## 1. Introduction

_{2}masks the contribution of soil–organic matter-derived CO

_{2}[2]. At the global scale, the mean soil CO

_{2}efflux of bare soil was calculated to be 282–476 g C m

^{−2}y

^{−1}[3].

_{2}efflux correlates with precipitation and temperature. However, the overall effect of the soil moisture content and temperature differs by climate zone and seasons. For example, in a short laboratory experiment, rewetting dry soil in a tropical forest did not affect the soil respiration rate [4]. Similarly, at the field scale, the CO

_{2}flux rate did not change significantly under the simulated rain conditions [5]. In dry areas, however, the soil’s microbiological activity quickly responds to changes in the soil moisture content, e.g., under laboratory conditions, the microbial biomass carbon level was found to be approximately two-fold higher within 3 h after rewetting soils in a hot, rain-free season [6]. Similarly, in a semiarid region, after a simulation of 24 mm of rainfall in the summer, the soil CO

_{2}efflux was 2.5 times higher [7]. The significance of the effect of the soil moisture content on the CO

_{2}efflux of bare soil can be assumed to be highly dependent on the climatic conditions.

_{2}production in bare soil to increases in the soil temperature was found to be more sensitive in wet soils [9]. In a study, variations in the soil water content were proven to have a stronger effect when the soil temperature was higher. The soil temperature was found to become a limiting factor of CO

_{2}efflux out of the growing season and in the dry season [10]. In a semiarid region, the soil temperature and moisture content were found to have greater impacts on soil respiration in the winter [11]. Based on these examples, it is shown that the contribution of the two differs by regions and seasons. When soil moisture is limited, soil respiration decreases considerably, and soil moisture exerts control over the CO

_{2}efflux [12]. A longer period with a relatively high moisture content can ensure more favorable living conditions for microbes. Sudden water input, i.e., natural precipitation and irrigation causing a sudden increase in the soil moisture content, generally does not result in the sudden propagation of soil microbes and, hence, does not increase CO

_{2}emissions in the short term [13].

_{2}efflux as the main controlling factors. An advantage of these parameters is that they can be continuously monitored in high time resolutions, providing adequate datasets for model calculations. The data gained under different environmental and experimental conditions have been described using many different mathematical models with variable results. For example, based on global, monthly climate data, a log-transformed and an untransformed model were suggested in which terrestrial soil CO

_{2}emissions significantly and linearly correlated with the published estimates of annual fluxes [3]. In another study, correlations between the CO

_{2}flux and soil temperature were found, but significant correlations in each year were not shown [11]. Some researchers concluded that the exponential and linear relationships between the soil temperature and soil respiration rate do not provide unbiased estimates [16]. In a further study, the soil CO

_{2}efflux was described using an exponential function of the soil temperature, and the temperature-normalized CO

_{2}fluxes were found to relate to the soil water content with a positive linear relationship [17]. When the correlation of soil respiration with the soil temperature and moisture contents was described by a two-variable exponential–power model, the soil respiration’s sensitivity to moisture was reported to increase with the increasing soil temperature [14]. In the winter period, the temperature sensitivity negatively correlated with the average soil temperature and moisture, described by exponential and power functions, respectively [11]. Under some circumstances, no significant correlation could be found between the soil CO

_{2}emissions and the soil temperature (e.g., [15]). In a further study, it was shown that exponential and Lloyd and Taylor functions relating CO

_{2}efflux to the soil temperature could be used to predict soil respiration when the soil water content was above 1/3 of the water-holding capacity. When combining the two parameters into one integrated model, four types of regression equations were successfully established for use in estimating the seasonal changes in CO

_{2}efflux [12].

_{2}efflux in different climate zones are necessary to draw realistic conclusions. Furthermore, the systematic comparison of the adequacy of different models, as highlighted decades ago [16], remains of scientific interest. In addition, to the best of our knowledge, seasonal predictions of future CO

_{2}emissions using historical, long-term, daily weather data have not yet been published.

_{2}efflux of bare soil using long-term field data gained under natural environmental conditions in a continental region with a four-season climate in Hungary; (2) to assess the adequacy of different mathematical models appearing in the literature, such as linear, exponential, quadratic exponential, and exponential combined with power function; and (3) to provide estimations of CO

_{2}emissions of bare soil using the model found adequate for use in this region.

## 2. Materials and Methods

#### 2.1. Experimental Site and Measurement Techniques

_{2}efflux of undisturbed bare soil under field conditions in an area with a warm, dry, temperate climate zone. The experimental site was situated in Karcag in one of the driest areas of Hungary with high fluctuations in the temperature and the most continental characteristic. The summers are dry and warm with low cloud cover. The total number of annual sunshine hours is in the range of 1970–2020. The annual mean temperature is 10.2 °C. The annual mean precipitation is 503 mm. For the characterization of the weather conditions in the years we investigated, the comparison of the monthly mean air temperature and precipitation to the 50-year averages is shown in Figure 1 and Figure 2, respectively.

_{2}efflux, the soil moisture content, and the temperature were monitored at the lysimeter station (47°17′29.3″ N, 20°53′23.3″ E) at the Research Institute of Karcag (RIK), Hungarian University of Agriculture and Life Sciences between June of 2018 and March of 2022. Measurements were carried out weekly, providing data for a total of 170 dates. Using the lysimeter controlled and precisely determined the factors affecting the CO

_{2}efflux from the soil under field conditions.

_{2}efflux was measured with the method developed and described by Zsembeli et al. [18]. The tool consisted of a plastic bowl that was 8000 cm

^{3}and a metal frame that was 40 cm in diameter and 8 cm in height (Figure 3). The CO

_{2}concentration was measured using a Testo 535 infrared gas analyzer after an incubation time of 10 min.

_{2}concentration was converted to g m

^{−2}h

^{−1}based on the unified gas law (Equation (1)) with transformation according to Equation (2) to obtain the efflux in gram per unit area (m

^{−2}) and time (h

^{−1}).

^{3}), n is the amount of chemical substance of the gas (mol), R is the universal gas constant (8.314 J mol

^{−1}K

^{−1}), and T is the absolute temperature (Kelvin).

_{CO2}is the CO

_{2}efflux (g m

^{−2}h

^{−1}), ΔC is the change in CO

_{2}concentration (mol mol

^{−1}), l is the height of the measuring cylinder (m), M is the molar mass of CO

_{2}(44.01 g mol

^{−1}), and Δt is the measurement time (h).

_{2}concentration, soil temperature, and soil moisture content used for modeling are summarized in Table S1.

#### 2.2. Model Calculations

_{2}efflux. The linear and the exponential models with quadratic function, which are the simplest and the most complex models, were used for the evaluation of the contribution of the environmental factors (Equations (3) and (4), respectively). The latter was used in a study for the evaluation of the maximum substrate-limited respiration rate of litter but not for bare soil [19].

_{CO2}is the CO

_{2}efflux (g m

^{−2}h

^{−1}), T is the soil temperature (°C); and a, b, and c are the regression coefficients determined by a true nonlinear regression analysis using the method of least squares.

_{2}efflux was analyzed using linear and nonlinear regression analyses. The adequacies of both the exponential and the power functions of the soil moisture content were examined.

^{2}), the root mean square error (RMSE), and the Akaike Information Criterion (AIC) (Equation (5)). The minimal AIC value represents the optimal model.

_{res}/n) + 2p

_{res}is the residual sum of squares, and p is the number of parameters (also the constant).

_{2}emissions, historical seasonal trends in the air temperature were analyzed by using local hourly meteorological data of the period 1990–2019 from the database of the National Meteorological Service of Hungary. The standard error (SE) of the trend in the changes was used to calculate the 95% confidence interval by seasons (SE × z

_{95%}). Scenarios of the estimated minimum and maximum levels of temperature increase were considered for the prediction of CO

_{2}efflux in 2050. The hourly CO

_{2}efflux was estimated by using the best-fitting model based on hourly temperature data, and this was aggregated to obtain the daily and yearly emissions for a given area.

## 3. Results

#### 3.1. Linear Model for the Description of CO_{2} Efflux as a Function of Soil Temperature

_{2}efflux from bare soil as a function of the soil temperature in the study area during the experimental time period are given in Table 2.

_{2}efflux by nearly 40%. The residual standard error was found to be 0.05737. Figure S1 shows the linear model fitted to the soil temperature and the CO

_{2}efflux, while Figure 4 shows the CO

_{2}efflux by time.

^{−12}. Those of the studentized Breusch–Pagan test were BP = 8.5147; df = 1; p-value = 0.00352. The homoscedasticity criteria were not met in the regression analysis. However, the estimation by using the linear model was unbiased, and the expected value of the residues was zero.

_{2}efflux resulted in a greater error in the estimation at a higher level of CO

_{2}efflux. The accuracy of the estimation was found to be dependent on the level of CO

_{2}efflux. The linear model outputs were negative when the soil temperature was low.

#### 3.2. Quadratic Exponential Model for the Description of CO_{2} Efflux as a Function of Soil Temperature

#### 3.3. Exponential Model for the Description of CO_{2} Efflux as a Function of Soil Temperature

_{CO2}is the CO

_{2}efflux (g m

^{−2}h

^{−1}), T is the air temperature (°C), and a and b are the regression coefficients.

#### 3.4. Combined Exponential and Powered Model for the Description of CO_{2} Efflux as a Function of Air Temperature and Soil Moisture Content

_{2}efflux was not shown.

#### 3.5. Extended Exponential Model for the Description of CO_{2} Efflux as a Function of Air Temperature and Soil Moisture Content

#### 3.6. CO_{2} Efflux Predictions of Bare Soil for the Year 2050

_{2}efflux with the same level of accuracy. The best model was the simplest, with the least variables. In our case, this was the exponential formula with the lowest AIC, in which only the soil temperature was considered as a variable.

_{2}m

^{−2}y

^{−1}(452 ± 29 g CO

_{2}m

^{−2}y

^{−1}) within the period 2018–2021, which means bare soil contributed nearly 4.5–5 tons per hectare to CO

_{2}emissions yearly.

_{2}efflux exponentially increases with the increasing temperature. By aggregating the hourly data calculated by using the exponential model, the mean CO

_{2}efflux is estimated to reach 834 g m

^{−2}y

^{−1}in 2050. With the consideration of the 95% confidence interval, the range was calculated as 772–898 g m

^{−2}y

^{−1}(Figure 8).

^{−1}y

^{−1}(7.7–9.0 Mg ha

^{−1}y

^{−1}with a 95% confidence interval) to the overall CO

_{2}emissions, which will result from soil respiration alone, with the assumption that the soil organic carbon content will remain constant. Its decrease by time will lower the level of emissions.

_{2}emissions from soil. Resulting from the chaotic character of weather parameters, estimations for future trends hold high uncertainty, e.g., with the consideration of 19 scenarios for a 1% year

^{−1}CO

_{2}concentration increase, the global temperature change was estimated for 2050 within the range 0.7–2.7, approximately [20]. At the global scale, alterations in CO

_{2}concentrations and temperature resulting from climate change alter the soil respiration, soil carbon dynamics, and microbial community structures [21]. Microbial variables such as enzymatic activities, hyphal lengths, and bacterial substrate assimilation have been proven to significantly and substantially increase under elevated CO

_{2}conditions [22].

## 4. Discussion

_{2}efflux based on laboratory measurements, as well as field experiments over periods varying from a few months to several years. Specifically, they have been carried out in different climatic zones [5,7,9,10,11,12,23,24].

_{2}efflux is mainly determined by the soil status and weather conditions. CO

_{2}efflux can be expected to closely correlate with the preserved soil moisture content. In a crop year, before and after the vegetation period, and even at the beginning and end of the period, bare soils experience higher evaporation loss due to the lack of soil surface cover. In such a period, the soil moisture content is often a limiting factor in soil respiration [13]. A longer period with a relatively high moisture content could ensure more favorable living conditions for microbes. Generally, sudden water input such as precipitation, which causes a sudden increase in the soil moisture content, does not result in the sudden propagation of soil microbes; hence, it does not increase CO

_{2}emissions in the short term. Soil moisture does not correlate strongly with the rates of soil respiration [4]. Contrary to this, under optimal circumstances for microbiological activity, when neither the soil temperature nor water content are limiting factors, high soil CO

_{2}emissions can be observed [24]. In experiments, weather conditions showed high fluctuations in the temperature and the unequal distribution of rain. At our experimental site, however, the soil moisture content was found to be very low and varied in a relatively narrow range (Table S1), resulting from both the local weather- and soil-type characteristics. Under these circumstances, soil microbes could be expected to be less responsive to any changes in the moisture content measured in the field [25].

_{2}efflux when the soil temperature was considered alone [16]. For bare soils, however, exponential models have been widely used for the description of the relationship between soil CO

_{2}efflux and soil temperature. Some studies did not prove a direct relationship (e.g., [9,15]), while others did, e.g., [11,12,14,17,23]. For example, in a dataset related to a semiarid area, the soil temperature as a variable explained 46% of the seasonal changes [11]. The R

^{2}for bare soil was found to be similar to our findings. In a highland area, the R

^{2}was found to be 0.44 and increased to 0.63 when drought-affected dates were excluded [12]. In our study, data representing the whole year included predominantly dry conditions (Table S1). Interestingly, at a seasonal scale in a similar location, the R

^{2}was 0.124, 0.000, 0.447, and 0.002 in the spring, summer, autumn, and winter, respectively [14].

_{2}efflux was given as the product of two exponential functions [19]. Some researchers combined the exponential and power functions of soil temperature and the soil water content, respectively. In one study, with the combination of the exponential function of soil temperature and the power function of the moisture content, the R

^{2}was 0.82 in the case of bare soil [12]. For bare lands within a 11-year period, the R

^{2}was 0.62 [14]. On a seasonal scale, for bare soil, the R

^{2}was 0.24, 0.608, 0.59, and 0.11 in the spring, summer, autumn, and winter, respectively [14]. In one study, bare soil’s CO

_{2}efflux was described by the product of the power functions of the two variables, and the R

^{2}was found to be 0.79 [12]. In another study using the same model, the R

^{2}was calculated as 0.553 [11]. As suggested, the moisture content can be expected to have significant effects above one-third of the water-holding capacity. This explains our findings in which the soil moisture did not improve the accuracy of the best-fitting model. Using the power function for the soil water content alone, some researchers found an R

^{2}of 0.15 for bare soil [11], while others reported an R

^{2}of 0.71 [12]. On a seasonal scale, the R

^{2}was 0.005, 0.566, 0.062, and 0.11 in the spring, summer, autumn, and winter, respectively [14]. In one study, temperature-normalized CO

_{2}fluxes were found to relate to the soil water content with a positive linear relationship [17]. Based on the findings of these case studies, the contribution of the soil moisture content to the overall effect of weather conditions on the CO

_{2}efflux of bare soil differs widely, supporting the need for further field experiments, especially in situ, long-term studies continuously monitoring weather and soil parameters.

## 5. Conclusions

_{2}emissions from bare soil remain of considerable scientific interest. Several physical, chemical, and biological properties; meteorological parameters (e.g., temperature and precipitation); and hydrologic parameters (e.g., soil moisture content) determine the spatial and temporal variability in CO

_{2}emissions from bare soils. Nevertheless, bare soil surfaces with no vegetation provide suitable environments to study the microbiological activity of soil, because root respiration, as another source of CO

_{2}emissions, is excluded. We determined the validity and accuracy of different mathematical models based on daily data regarding the soil carbon dioxide efflux and soil temperature and extended the best-fitting formula with the soil moisture content. The data were recorded within the period of June 2018–March 2022 under natural field conditions, characterizing undisturbed bare chernozem soil in Karcag, Hungary, a semiarid region with four seasons.

_{2}efflux and the soil temperature, the linear model was not adequate, as the homoscedasticity criteria were not met. The exponential model with quadratic function did not provide more accurate results compared to those of the simplified exponential model. The addition of the soil moisture content to the simplified exponential formula did not improve the accuracy, suggesting that the moisture content under the given environmental circumstances within the investigated time period and location was not considerable. We have found further evidence that the best-fitting models are dependent on the local environmental conditions of the fields.

_{2}efflux from undisturbed bare soil for the year 2050 by season, with the consideration of historical trends in the daily mean temperature in the last 30 years. Based on our calculations, in the future, an increase in the CO

_{2}efflux of bare soil can be expected in the warm, dry, temperate climate zone.

## Supplementary Materials

_{2}efflux (g m

^{−2}h

^{−1}) from undisturbed bare soil as a function of the soil temperature. Measured and calculated data are represented by black and red circles, respectively; Figure S2. Residuals in the linear model describing the CO

_{2}efflux (g m

^{−2}h

^{−1}) from undisturbed bare soil as a function of the estimated results; Figure S3. Exponential model fitted to the CO

_{2}efflux (g m

^{−2}h

^{−1}) from undisturbed bare soil as a function of the soil temperature. Measured and calculated data are represented by black and red circles, respectively; Figure S4. Hourly CO

_{2}efflux (g m

^{−2}h

^{−1}) of undisturbed bare soil, calculated with the exponential model with the consideration of soil temperature, for the years of 2018—2021; Table S1. Mean and standard deviation of measured soil temperature, soil moisture content, and CO

_{2}efflux data by years used as inputs for model calculations.

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

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**Figure 3.**The setup of the frame–bowl method to measure the CO

_{2}efflux from undisturbed soil under field conditions.

**Figure 4.**Outputs of the linear model for the CO

_{2}efflux (g m

^{−2}h

^{−1}) from undisturbed bare soil within the period of 2018–2022. Measured and calculated data are represented by black and red circles, respectively.

**Figure 5.**Outputs of the exponential model for the CO

_{2}efflux (g m

^{−2}h

^{−1}) from undisturbed bare soil within the period of 2018–2022. Measured and calculated data are represented by black and red circles, respectively.

**Figure 6.**Outputs of the extended exponential model for the CO

_{2}efflux (g m

^{−2}h

^{−1}) from undisturbed bare soil within the period of 2018–2022. Measured and calculated data are represented by black and red circles, respectively.

**Figure 7.**Daily CO

_{2}efflux (g m

^{−2}d

^{−1}) of undisturbed bare soil, calculated with the exponential model with the consideration of the soil temperature, for the years of 2018–2021.

**Figure 8.**Daily CO

_{2}efflux (g m

^{−2}d

^{−1}) expected in 2050 in Karcag, Hungary, calculated with the exponential model. With the consideration of the 95% confidence interval, the highest and the lowest estimations are indicated with blue and red lines, respectively.

Depth cm | pH (KCl) | Salt Cont. m/m% | CaCO_{3} % | Organic Matter m/m% | NO_{3}-N mg/kg | P_{2}O_{5} mg/kg | K_{2}O mg/kg |
---|---|---|---|---|---|---|---|

0–20 | 5.21 | 0.02 | <0.05 | 3.22 | 9.7 | 86 | 338 |

20–40 | 5.27 | 0.02 | 1.09 | 3.17 | 18.5 | 40 | 299 |

40–60 | 6.14 | 0.02 | 10.88 | 2.68 | 8.5 | 33 | 245 |

Particle size distribution (%) | |||||||

Depth cm | >0.25 mm | 0.25–0.05 | 0.05–0.02 | 0.02–0.01 | 0.01–0.005 | 0.005–0.002 | <0.002 mm |

0–20 | 0.5 | 6.4 | 11.4 | 15.4 | 13.6 | 11.5 | 41.2 |

20–40 | 0.2 | 5.8 | 7.2 | 16.3 | 14.1 | 10.6 | 45.9 |

40–60 | 0.1 | 3.5 | 9.0 | 13.4 | 16.0 | 11.0 | 47.0 |

**Table 2.**Parameters of the linear model describing the effects of soil temperature on the CO

_{2}efflux from undisturbed bare soil under field conditions in Karcag, Hungary.

Coefficient | Estimate | Standard Error | t Value | Pr (>|t|) |
---|---|---|---|---|

Intercept | 0.005875 | 0.008419 | 0.698 | 0.48615 |

Soil temperature | 0.005320 | 0.000515 | 10.330 | <20^{−16} *** |

**Table 3.**Parameters of the quadratic exponential model describing the effect of soil temperature on the CO

_{2}efflux from undisturbed bare soil under field conditions in Karcag, Hungary.

Constant | Estimate | Standard Error | t Value | Pr (>|t|) |
---|---|---|---|---|

a | 0.0285905 | 0.0049207 | 5.810 | 3.08 × 10^{−8} *** |

b | 0.0511563 | 0.0143776 | 3.558 | 0.000487 *** |

c | 0.0004216 | 0.0003250 | 1.297 | 0.196298 |

**Table 4.**Parameters of the exponential model describing the effect of soil temperature on the CO

_{2}efflux from undisturbed bare soil under field conditions in Karcag, Hungary.

Constant | Estimate | Standard Error | t Value | Pr (>|t|) |
---|---|---|---|---|

a | 0.026743 | 0.004583 | 5.835 | 2.70 × 10^{−8} *** |

b | 0.067437 | 0.007491 | 9.002 | 4.63 × 10^{−16} *** |

**Table 5.**Parameters of the extended exponential model describing the CO

_{2}efflux (g m

^{−2}h

^{−1}) from undisturbed bare soil.

Constant | Estimate | Standard Error | t Value | Pr (>|t|) |
---|---|---|---|---|

a | 0.018790 | 0.006062 | 3.100 | 0.00227 ** |

b | 0.065668 | 0.007499 | 8.757 | 2.15 × 10^{−15} *** |

c | 0.157738 | 0.121944 | 1.294 | 0.198 |

**Table 6.**Parameters of the extended exponential model describing the CO

_{2}efflux (g m

^{−2}h

^{−1}) from undisturbed bare soil.

Constant | Estimate | Standard Error | t Value | Pr (>|t|) |
---|---|---|---|---|

a | 0.023405 | 0.004573 | 5.118 | 8.40 × 10^{−7} *** |

b | 0.066250 | 0.007460 | 8.881 | 1.01 × 10^{−15} *** |

c | 0.012325 | 0.009166 | 1.3453 | 0.181 |

**Table 7.**Summary of the statistics of the models tested for the description of the CO

_{2}efflux (g m

^{−2}h

^{−1}) from undisturbed bare soil as a function of the soil temperature

^{(1–3)}and soil moisture contents

^{(4,5)}.

Model | R^{2} | RSE | AIC |
---|---|---|---|

Linear ^{1} | 0.37 | 0.058 | −964.89 |

Quadratic exponential ^{2} | 0.41 | 0.057 | −973.19 |

Exponential ^{3} | 0.40 | 0.057 | −973.28 |

Combined exponential and powered ^{4} | 0.41 | 0.057 | −973.06 |

Extended exponential ^{5} | 0.41 | 0.057 | −973.02 |

^{2}: determination coefficient; RSE: root mean square error; AIC: Akaike Information Criterion.

**Table 8.**Yearly seasonal increase in the temperature within the period 1990–2019 based on a linear model, and the estimated change until 2050 compared to the temperature in 2019 in Karcag, Hungary. Standard error (SE) × z

_{95%}indicates the 95% confidence interval.

Season | Temperature Increase °C y^{−1} ± SE × z_{95%} | Estimated Change until 2050 Compared to 2019 °C ± SE × z _{95%} |
---|---|---|

winter | 0.069 ± 0.060 | 2.139 ± 1.860 |

spring | 0.063 ± 0.033 | 1.953 ± 1.023 |

summer | 0.059 ± 0.033 | 1.829 ±1.023 |

autumn | 0.069 ± 0.037 | 2.139 ± 1.147 |

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## Share and Cite

**MDPI and ACS Style**

Juhász, C.; Huzsvai, L.; Kovács, E.; Kovács, G.; Tuba, G.; Sinka, L.; Zsembeli, J.
Carbon Dioxide Efflux of Bare Soil as a Function of Soil Temperature and Moisture Content under Weather Conditions of Warm, Temperate, Dry Climate Zone. *Agronomy* **2022**, *12*, 3050.
https://doi.org/10.3390/agronomy12123050

**AMA Style**

Juhász C, Huzsvai L, Kovács E, Kovács G, Tuba G, Sinka L, Zsembeli J.
Carbon Dioxide Efflux of Bare Soil as a Function of Soil Temperature and Moisture Content under Weather Conditions of Warm, Temperate, Dry Climate Zone. *Agronomy*. 2022; 12(12):3050.
https://doi.org/10.3390/agronomy12123050

**Chicago/Turabian Style**

Juhász, Csaba, László Huzsvai, Elza Kovács, Györgyi Kovács, Géza Tuba, Lúcia Sinka, and József Zsembeli.
2022. "Carbon Dioxide Efflux of Bare Soil as a Function of Soil Temperature and Moisture Content under Weather Conditions of Warm, Temperate, Dry Climate Zone" *Agronomy* 12, no. 12: 3050.
https://doi.org/10.3390/agronomy12123050