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Article

Influence of No-Tillage on Soil CO2 Emissions Affected by Monitoring Hours in Maize in the North China Plain

by
Kun Du
1,
Fadong Li
2,
Peifang Leng
2 and
Qiuying Zhang
3,*
1
Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, College of Resources and Environment, Linyi University, Linyi 276000, China
2
Shandong Yucheng Agro-Ecosystem National Observation and Research Station, Yucheng Comprehensive Experiment Station, IGSNRR, Chinese Academy of Sciences, Beijing 100101, China
3
Chinese Research Academy of Environmental Sciences, Beijing 100101, China
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(1), 136; https://doi.org/10.3390/agronomy15010136
Submission received: 8 December 2024 / Revised: 25 December 2024 / Accepted: 3 January 2025 / Published: 8 January 2025
(This article belongs to the Section Agricultural Biosystem and Biological Engineering)
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Abstract

:
There is still controversy over the influence of no-tillage (NT) on CO2 emissions in farmland soil. Few studies focus on the impact of monitoring hours on the response of soil CO2 emissions to NT. Therefore, an in situ experiment was conducted in maize cropland in the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the North China Plain. The soil CO2 emissions, soil water content (SWC), and soil temperature (ST) were automatically monitored using the morning sampling (MonS) and continuous sampling (multi-hour sampling in one day, DayS) methods during the whole maize growth stages. The results showed that the MonS method decreased the sum of soil CO2 emissions by 146.39 g CO2 m−2 in the wet year 2018 and increased that by 93.69 g CO2 m−2 in the dry year 2019 when compared to the DayS method. The influence intensity of NT on soil CO2 effluxes was decreased with the MonS method. In contrast, the MonS method had no significant effect on the differences in SWC between NT and conventional tillage. However, the MonS method increased the variance in ST between NT and conventional tillage by 0.45 °C, which was higher than that with the DayS method (0.20 °C) across years. Compared to the DayS method, the MonS method increased the regression coefficient of soil CO2 emissions with SWC but decreased that with ST. This study is beneficial for reducing the artificial impact of monitoring hours on the data accuracy of soil CO2 effluxes and deepening the understanding of the influence of NT on soil CO2 emissions.

1. Introduction

Soil CO2 is a major source of terrestrial carbon flux, and it is beneficial in decreasing soil CO2 emissions to inhibit the increasing CO2 concentration in the atmosphere [1,2,3]. It has been reported that approximately 10~12% of total anthropogenic CO2 emissions result from agricultural activities [4]. As an effective agricultural management practice, no-tillage (NT) decreases soil CO2 emissions due to lower soil microbial communities and root activities and soil temperature (ST) than those in conventional tillage (CT) [5,6,7]. Nevertheless, some research has suggested that soil CO2 effluxes are higher in NT due to higher soil water content (SWC) and nutrients in the soil surface [8,9,10,11,12]. Therefore, there is still some dispute about the influence of NT on soil CO2 effluxes resulting from various phenomena, including soil texture, duration of NT, and crop types.
Soil water content and ST are the vital factors directly influencing soil CO2 emissions processes, including soil organic matter decomposition and root CO2 emissions [6]. It was reported that the relationships of SWC and ST with soil CO2 emissions were different in various soil conditions. In dry soil, SWC was more significant in influencing soil CO2 effluxes [8]. However, ST accounted for more contribution to the variation in soil CO2 emissions in wet soil [3,9]. Therefore, the influence of SWC and ST on soil CO2 effluxes was affected by the daily and hourly weather conditions [11].
The data accuracy of soil CO2 effluxes is critical to fully understanding the response of soil CO2 emissions to NT. In most previous studies on soil CO2 effluxes, sampling periods were not continuous due to the high cost of equipment and labor [5,11]. For instance, the sampling hours in most previous studies were 9–11 o’clock [10,13,14,15]. However, the data of soil CO2 effluxes monitored in the morning might have been different from the daily average soil CO2 emissions and led to misinterpretation of the soil CO2 emissions mechanism [16,17,18]. Therefore, it is vital to compare the difference in soil CO2 effluxes between the morning sampling method and the continuous sampling method.
However, few studies focus on the influence of sampling hours on the response of soil CO2 effluxes to NT. The North China Plain (NCP) is a critical long-term cultivated area in China. In this study, an experiment was conducted in a maize cropland in the NCP, and two sampling (morning sampling and continuous sampling) methods were chosen to evaluate the influence of sampling hours on the response of soil CO2 emissions, SWC, and ST to NT. The objectives of this study are (1) to determine the various responses of soil CO2 effluxes to NT between both sampling methods, (2) to identify the variance in SWC and ST resulting from NT using the two sampling methods, and (3) to quantify the different underlying contributions of SWC and ST to soil CO2 emissions between both sampling methods. This study will be helpful in guiding cropland management based on carbon emissions.

2. Materials and Methods

2.1. Site Description

This study was conducted on the Carbon–Nitrogen–Water Experiment field (established in 2014) at the Shandong Yucheng Agro-ecosystem National Observation and Research Station in the NCP (36°50′ N, 116°34′ E). This site is located in a temperate semi-arid climate with an annual mean temperature of approximately 13.3 °C and a mean precipitation of 625.4 mm in 1991–2020 [19]. The precipitation from June to September accounts for approximately 70% of the total annual precipitation [20]. The soil is classified as a calcaric Fluvisol, and the surface soil texture is silt loam (sand, 12%; silt, 66%; clay, 22%) [21]. Soil pH (soil: water, 1:5) was 8.3, total soil organic matter, total nitrogen, total soil phosphorus, and total soil potassium was 12.6 g kg−1, 0.89 g kg−1, 2.11 g kg−1, and 21.4 g kg−1 (0–20 cm), respectively [19].

2.2. Experimental Design and Management

This study was conducted in a winter wheat (Triticum aestivum L.)–summer maize (Zea mays L.) rotation farmland starting in 2014. Six 10 m × 5 m blocks with NT and CT (as CK plots) were replicated three times. The summer maize growth stages were from late June to early October. No-tillage and straw mulching management were conducted during the maize growth stages in all plots. Maize seeds (Denghai 605) were manually hole-sowed with 60 cm rows in the third week of June. No irrigation was applied during the maize growth stages in 2018, but 100 mm irrigation was applied before maize sowing to prompt seed establishment due to the continuous severe drought in the early summer in 2019. Nitrogen fertilizer (urea with 207 kg N ha−1, 46.4% nitrogen content) was applied once via top-dressing after the rain in late July. Maize was manually harvested on October 10th. Tillage (15 cm) was only conducted with a rotary tiller in CT treatments before winter wheat was sowed in late October. During the wheat growth stages, 105 kg N ha−1 nitrogen (as urea, 46.4% nitrogen content), 750 kg P2O5 ha−1 phosphorus, and 160 kg K2SO4 ha−1 potassium were applied at the sowing stage of wheat. Another 105 kg N ha−1 nitrogen was applied before a 100 mm irrigation at the greening stage in the next year. The field management was described in detail in our previous study [19,22].

2.3. CO2 Flux Sampling and Measurement

Soil CO2 emissions were monitored by an in situ auto sampling and measurement system, which was established in May 2018. A chamber (0.5 m × 0.5 m × 0.5 m) was installed in the center of the plots. Every chamber was automatically closed one by one for air sampling based on a certain schedule. After a chamber was closed for 200 S, it was opened to decrease the warming effect. Two fans were installed in the corners of each chamber to keep enough gas circulating during air sampling. A detailed description can be found in our previous study [19].
The gas from the chambers was rapidly transported to a Picarro G2508 analyzer (Picarro, Inc., Santa Clara, CA, USA) at sequence. After air CO2 concentrations were detected, data were collected and recorded with a datalogger (CR3000, Campbell Scientific, Inc., Logan, UT, USA).
The soil CO2 effluxes were calculated using the following equation according to a previous study [19,23]:
F = d C / d t × ρ × h × T 0 × P × 10 − 3 T × P 0
where F is the soil carbon flux rate (g CO2 m−2 d−1), dC/dt is the linear variance of the CO2 concentration (µL L−1 h−1) during the available sampling time 60–200 S (R2 > 0.9), h is the chamber height (0.5 m), ρ is the CO2 density (g C m−3) at T0 = 273 K and P0 = 1013 hPa, and T and P are the mean air temperature (K) and air pressure (hPa) during chamber closure, respectively. Data in 0–60 S were not available for gas flushing.

2.4. Auxiliary Measurements

The meteorological data were monitored by a weather station at the Yucheng experiment station. The cumulative precipitation during the maize growth stage (day 171 to 283) was 472.9 mm and 220.3 mm in 2018 and 2019 [19], respectively. Detailed air temperature and precipitation data can be found in Figure 1.
The soil volumetric water content (%) and soil temperature (°C) from a 0 to 20 cm depth in the chambers were automatically determined using sensors (CS655, Campbell Scientific, Inc., Logan, UT, USA) vertically inserted into the soil.

2.5. Data Analysis

We set the mean data of soil CO2 emissions, SWC, and ST in 9–12 o’clock as the data measured by the MonS method and set the mean data of soil CO2 emissions, SWC, and ST in 1–4, 9–12, and 17–20 o’clock as the data measured by DayS method. The total soil CO2 emissions were calculated using the sum of soil CO2 effluxes for every day. The mean SWC and ST were calculated using the average daily SWC and ST across the whole maize growth stage. No-tillage and sampling methods effects were tested using an analysis of variance with significance at p = 0.05. The relationships of soil CO2 efflux with SWC and ST were examined using a stepwise linear regression. All of the statistical analyses were conducted using SPSS 19.0 software (SPSS Inc., Chicago, IL, USA).

3. Results

3.1. Soil CO2 Emissions

Using the MonS method, NT promoted the sum of soil CO2 emissions during the whole maize growth period in 2018 (p < 0.05). However, there was no significant variance in soil CO2 effluxes of maize between NT and CT in 2019 with the MonS method (p > 0.05) (Figure 2, Table 1). Across years, the difference in soil cumulative CO2 emissions between NT and CT was reduced by 114.12 g CO2 m−2 using the MonS method when compared with the DayS method. Similarly, across NT and CT, the cumulative soil emissions in 2018 were reduced by 146.39 g CO2 m−2 and increased by 93.69 g CO2 m−2 in 2019 using the MonS method compared to that with the DayS method (Table 1).

3.2. Soil Water Content and Soil Temperature

Using the MonS method, NT increased the SWC of the whole maize growth stage in 2018 and 2019 (p < 0.05) (Table 2); however, there was no significant difference in SWC between NT and CT with the DayS method in 2018 (p > 0.05). In 2019, NT increased SWC by 0.70% with the MonS method, which is higher than that with the DayS method (0.36%). Across both years, the variance of SWC between NT and CT was similar to that with the DayS method.
Compared with CT, NT decreased the daily ST of the whole maize growth stage by 0.43 °C in 2018 and 0.66 °C in 2019 with the MonS method, respectively. However, with the DayS method, NT had no significant influence on the daily ST in 2018, and NT decreased by 0.23 °C in 2019. Compared with the DayS method, the MonS method increased the differences in ST between NT and CT across both years (0.25 °C).

3.3. Relationship Between Soil CO2 Emissions with SWC and ST

There were significant differences in the relationship between total CO2 emissions with SWC and ST (Table 3). Across both tillage practices, SWC made a greater contribution to soil CO2 effluxes than ST with the MonS method in both years (Table 3). Using the DayS method, ST and SWC were more significant in influencing soil CO2 emissions in 2018 and 2019, respectively. Compared to the DayS method, the MonS method increased the regression coefficient of SWC with soil CO2 emissions but decreased the regression coefficient of ST with soil CO2 effluxes across NT and CT every single year (Table 3).

4. Discussion

4.1. Influence of Sampling Method on the Response of Soil CO2 Emissions to NT

In this study, compared to CT, NT increased the total soil CO2 emissions in 2018 (a wet year) with both monitoring methods (Figure 2, Table 1). Those were in line with the studies conducted by Fan et al. [24] and Feiziene et al. [5]. This could be because, on the one hand, NT increased SWC in this study across years (Table 2), as Farhate et al. [5] and Rutkowska et al. [6] suggested; on the other hand, we found that SWC was significantly correlated with soil CO2 emissions across both tillage practices (Table 3), similar to the research conducted by Pabst et al. [25] and Inglima et al. [26]. However, it was found that NT had no significant influence on soil CO2 effluxes in 2019 (a severe drought year) with the MonS method, in contrast with that with the DayS method in this study (Table 1). This was because the ST was too low (Table 2) and exhibited the prompt effect of the increasing SWC in NT with the MonS method in 2019.
Wu et al. [27] highlighted that sampling at the daytime peaking and non-diurnal period caused variance in greenhouse gas effluxes by 60% and 20% when compared with a common sampling time around 10 a.m. Xian et al. [18] suggested that the motoring hours underestimated soil CO2 emissions by 25% in the agroforestry system. In this study, soil CO2 emissions were decreased under the MonS method when compared with the DayS method in 2018, especially in NT (Figure 2, Table 1). This could be because the MonS method decreased SWC and ST in NT in 2018 (Table 2). Pabst et al. [25] suggested that SWC and ST were critical soil characteristics influencing the CO2 emissions from the process of soil microbial and root respiration. However, it was found that MonS increased soil CO2 effluxes in both tillage practices in 2019 (Table 3). This may be because the ST was decreased by the MonS method (Table 2). But, there was no significant variance in SWC between both sampling methods (Table 2). Therefore, soil CO2 emissions were decreased by lower ST in the morning monitoring in 2019 (Table 2). Those were in line with the previous study, which suggested that varying annual weather conditions, including solar radiation, precipitation patterns, and air temperature, had a different influence on the accuracy of daily GHG emissions data [28].
Soil CO2 emissions were from soil heterotrophic respiration and autotrophic respiration [4,5]. Both respiration processes of soil CO2 emissions were sensitive to dynamic hourly weather conditions, and the response mechanism was varying and complicated [13,26]. For instance, soil respiration was sensitive to higher temperatures, and soil heterotrophic respiration was more sensitive to rain events than soil autotrophic respiration [3,4]. Therefore, it is essential to explore the response mechanism of soil heterotrophic and autotrophic respiration to gain a deep understanding of soil CO2 emissions in terms of sampling hours and tillage practices.

4.2. Influence of Sampling Method on the Response of SWC and ST to NT

The no-tillage practice increased SWC in this study (Table 2); this was in line with the study conducted by Pareja-Sanchez et al. [25]. This resulted from NT, which could reserve SWC by soil crust and decrease soil evaporation in the topsoil [5]. Across the years, there was no significant difference in SWC between both sampling methods (Table 2). This could be explained by the fact that the daily dynamics of SWC are relatively less affected by atmospheric temperature, and the difference in daily SWC values between the MonS and DayS methods ranged from 0 to 0.6% in bright days in this study (Figure 3). Across NT and CT, the variance in SWC between both sampling methods was greater in 2018 than in 2019 (Table 2). This resulted from the high frequency of rainfall (Figure 1) and relatively high SWC during the maize growth stages in 2018 (Table 2), as Lammirato et al. [28] suggested.
This study suggests that the MonS method decreased ST in NT but increased ST in CT (Table 2). This may result from CT management increased air permeability due to greater macropores [14]. Therefore, this change in the soil led to a more significant increase in the response of ST to atmospheric temperature and solar radiation and caused a faster increase in ST in the morning (Figure 3).

4.3. Influence of Sampling Method on the Relationship of Soil CO2 Emissions with SWC and ST

In this study, SWC was more significant in influencing soil CO2 emissions with MonS, the method across both tillage practices every single year, as Silva et al. [29] and Poll et al. [30] suggested. However, SWC or ST had no significant relationship with soil effluxes in NT in 2018 (Table 3). It was reported that roots in NT were more sensitive to strong wind and heavy rain due to NT-limited root growth in surface soil [15,31]. Therefore, the heavy rain events (Figure 1) caused severe lodging in NT in August 2018 and led to the inconsistent variance in soil CO2 effluxes, SWC and ST in NT in this study. In contrast, ST accounted for more contribution to soil CO2 emissions in CT across both years (Table 3). This may be caused by the dramatic increase in ST resulting from solar radiation, which was vital to soil CO2 emissions baseline in summer morning [27].
It was suggested that the relationship of soil CO2 emissions with SWC was increased, but that with ST was decreased using the MonS method (Table 3). Some previous studies suggested that the SWC in surface soil (0–20 cm) topsoil layer was significantly related to air humidity, especially under relatively higher humidity in arid circumstances [16,17]. Furthermore, Xian et al. [18] highlighted that the relationship between soil CO2 emissions was increased with air humidity but decreased with air temperature and ST under higher air humidity. In this study, the humidity was higher in summer mornings when compared with other diurnal times in the NCP (typical temperate semi-arid climate). Therefore, the correlation between ST and soil CO2 emissions was weakened, but the effect of SWC on soil CO2 effluxes was relatively enhanced across both tillage practices (Table 3).
Soil CO2 emissions mechanism to NT was increasingly complicated with varying weather conditions, especially under high temperatures and extreme precipitation [1]. Therefore, we suggested that it is necessary to strengthen the understanding of the interannual and daily dynamic variances in meteorological factors to reduce the impact of sampling hours on the response of soil CO2 emissions to NT and to prompt agricultural management to inhibit soil carbon emissions from cropland.

5. Conclusions

Using a field experiment from 2018 to 2019, we quantified the performance in terms of soil CO2 emissions, soil water content, and soil temperature according to the morning sampling and continuous sampling methods in a maize field in the North China Plain. This study showed that the morning sampling method returned a decreased sum of soil CO2 emissions across maize growth stages in the wet year 2018 and an increased sum in the dry year 2019 when compared to the continuous sampling method. The influence of no-tillage on soil CO2 effluxes was weakened under the morning sampling method. Compared with the continuous sampling method, the morning sampling method returned increased differences in soil temperature between both tillage practices. The morning sampling method overestimated the relationship of soil water content with soil CO2 effluxes but decreased the regression coefficient of soil temperature with soil CO2 emissions. Therefore, we suggest that the response of soil CO2 emissions to no-tillage was affected by sampling hours (especially under wet conditions), and it is necessary to increase data accuracy of soil carbon emissions from cropland with proper monitoring hours.

Author Contributions

Conceptualization, K.D., F.L. and Q.Z.; methodology, K.D. and P.L.; software, K.D. and P.L.; validation, K.D. and F.L.; formal analysis, K.D. and Q.Z.; investigation, K.D. and P.L.; resources, F.L. and Q.Z.; data curation, K.D.; writing—original draft preparation, K.D. and F.L.; writing—review and editing, F.L. and K.D.; supervision, K.D., F.L. and Q.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by Shandong Provincial Natural Science Foundation (ZR2022QC238), the Open Fund of Shandong Provincial Key Laboratory of Water and Soil Conservation and Environmental Protection, Linyi University (STKF202308), the Key Program of the National Natural Science Foundation of China (No. 42430508), and the National Natural Science Foundation of China (No. U2006212).

Data Availability Statement

Data will be made available on request.

Acknowledgments

We would like to thank colleagues at the Yucheng experimental station for experimental support and constructive advice on this manuscript.

Conflicts of Interest

The authors declare no conflicts of interest.

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Agronomy 15 00136 g001
Figure 1. Mean, min, and max air temperature (°C) and precipitation (mm) during the growth stages of maize at Yucheng experiment station in 2018–2019. Maize growth stages: days 171–283. Data on air temperature and precipitation were from our previous study [19].
Figure 1. Mean, min, and max air temperature (°C) and precipitation (mm) during the growth stages of maize at Yucheng experiment station in 2018–2019. Maize growth stages: days 171–283. Data on air temperature and precipitation were from our previous study [19].
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Figure 2. Mean (±SE) soil CO2 emissions (g CO2 m−2 d−1) by the MonS method during the maize growth stages in 2018–2019. Data with the DayS method were from our previous study [19].
Figure 2. Mean (±SE) soil CO2 emissions (g CO2 m−2 d−1) by the MonS method during the maize growth stages in 2018–2019. Data with the DayS method were from our previous study [19].
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Figure 3. The difference in SWC and ST between the MonS and DayS methods in both tillage practices in 2018.
Figure 3. The difference in SWC and ST between the MonS and DayS methods in both tillage practices in 2018.
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Table 1. The cumulative (±SE) soil CO2 emissions (g CO2 m−2) by the MonS and DayS methods during the maize growth stages in 2018–2019.
Table 1. The cumulative (±SE) soil CO2 emissions (g CO2 m−2) by the MonS and DayS methods during the maize growth stages in 2018–2019.
YearsTreatmentsMonS MethodDayS Method
2018NT1958 ± 11 Ac2189 ± 21 Bd
CT1584 ± 43 Aa1610 ± 23 Aa
2019NT1932 ± 27 Bbc1864 ± 14 Ac
CT1883 ± 37 Bb1755 ± 11 Ab
Across yearsNT1940 ± 152027 ± 164
CT1716 ± 1641688 ± 74
Across NT and CT20181753 ± 2011899 ± 290
20191903 ± 1491809 ± 56
Data with the DayS method were from our previous study [19]. Values with the same lowercase letter in a column are not significantly different between NT and CT, and values with the same capital letter in a row are not significantly different between the MonS and DayS methods at p < 0.05.
Table 2. Mean (±SE) daily SWC (%) and ST (°C) by the MonS and DayS methods during the maize growth stages in 2018–2019.
Table 2. Mean (±SE) daily SWC (%) and ST (°C) by the MonS and DayS methods during the maize growth stages in 2018–2019.
YearsTreatmentsSWC (%)ST (°C)
MonS MethodDayS MethodMonS MethodDayS Method
2018NT22.70 ± 0.04 Ad22.81 ± 0.06 Ac25.57 ± 0.09 Ac25.83 ± 0.17 Bc
CT22.60 ± 0.13 Ac22.70 ± 0.07 Ac26.00 ± 0.11 Ad25.76 ± 0.16 Ac
2019NT21.20 ± 0.02 Ab21.22 ± 0.08 Ab24.54 ± 0.06 Aa25.04 ± 0.06 Ba
CT20.50 ± 0.04 Aa20.58 ± 0.11 Aa25.20 ± 0.10 Ab25.27 ± 0.07 Ab
Across yearsNT21.95 ± 0.1522.01 ± 0.0225.05 ± 0.1225.46 ± 0.08
CT21.55 ± 0.0621.64 ± 0.0425.60 ± 0.0425.26 ± 0.08
Across NT and CT201822.65 ± 0.0522.75 ± 0.0525.78 ± 0.2125.80 ± 0.16
201920.85 ± 0.1220.90 ± 0.0124.87 ± 0.1324.93 ± 0.02
Data with the DayS method were from our previous study [19]. Values with the same lowercase letter in a column are not significantly different between NT and CT, and values with the same capital letter in a row are not significantly different between the MonS and DayS methods at p < 0.05.
Table 3. Multiple linear stepwise regression of soil CO2 emissions with SWC and ST with the MonS and DayS method in 2018–2019.
Table 3. Multiple linear stepwise regression of soil CO2 emissions with SWC and ST with the MonS and DayS method in 2018–2019.
YearsNT/CTMonS MethodDayS Method
Regression CoefficientsR2Regression CoefficientsR2
SWCSTSWCST
2018 + 2019NTNSNSNSNSNSNS
2018 + 2019CT−0.4451.0860.939 **2.8931.9880.923 **
2018NT + CT0.953−0.0430.851 *0.5010.5290.832 **
2019NT + CT1.0320.1420.993 **0.529−0.4520.905 **
2018 + 2019NT + CTNSNSNS2.7322.3060.669 **
Notes: NS, not significant; ** p < 0.01, * p < 0.05. Data from the DayS method were from our previous study [19].
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Du, K.; Li, F.; Leng, P.; Zhang, Q. Influence of No-Tillage on Soil CO2 Emissions Affected by Monitoring Hours in Maize in the North China Plain. Agronomy 2025, 15, 136. https://doi.org/10.3390/agronomy15010136

AMA Style

Du K, Li F, Leng P, Zhang Q. Influence of No-Tillage on Soil CO2 Emissions Affected by Monitoring Hours in Maize in the North China Plain. Agronomy. 2025; 15(1):136. https://doi.org/10.3390/agronomy15010136

Chicago/Turabian Style

Du, Kun, Fadong Li, Peifang Leng, and Qiuying Zhang. 2025. "Influence of No-Tillage on Soil CO2 Emissions Affected by Monitoring Hours in Maize in the North China Plain" Agronomy 15, no. 1: 136. https://doi.org/10.3390/agronomy15010136

APA Style

Du, K., Li, F., Leng, P., & Zhang, Q. (2025). Influence of No-Tillage on Soil CO2 Emissions Affected by Monitoring Hours in Maize in the North China Plain. Agronomy, 15(1), 136. https://doi.org/10.3390/agronomy15010136

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