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Article

Contrasting Soil Organic Carbon Concentrations and Mass Storage Between Conventional Farming and Organic Farming: A Meta-Analysis

by
Tingxuan Zhao
1,
Hiroshi Kubota
2 and
Guillermo Hernandez-Ramirez
1,*
1
Department of Renewable Resources, University of Alberta, Edmonton, AB T6G 2R3, Canada
2
Lacombe Research and Development Centre, Agriculture and Agri-Food Canada, Lacombe, AB T4L 1W1, Canada
*
Author to whom correspondence should be addressed.
Sustainability 2024, 16(24), 11260; https://doi.org/10.3390/su162411260
Submission received: 11 November 2024 / Revised: 3 December 2024 / Accepted: 3 December 2024 / Published: 22 December 2024
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Abstract

:
This meta-analysis studied the impact of conventional versus organic farming practices on soil organic carbon (SOC) concentrations and mass storage. We aimed to understand the carbon dynamics associated with adopting organic agricultural practices by reviewing and synthesizing data from 1950 to 2023. We analyzed data from 64 experimental field comparisons that involved SOC concentration and mass storage measurements, covering a wide range of studies selected for soil depth analyses, carbon concentration measurements, and comparative methods between conventional and organic systems. Our results indicated a significant increase in SOC under organic agriculture. An analysis of the response ratios (LnRR) for SOC concentration showed variability, with a 95% confidence interval of 0.089–0.149. Similarly, the analysis of carbon storage data indicated a 95% confidence interval of LnRR of 0.053–0.205. These increases in SOC concentration and mass storage reflect the variable but statistically positive impact of organic farming on SOC. Fine soil textures demonstrated the highest mean LnRR for both SOC concentration (0.163) and mass storage (0.173), suggesting the role of soil texture in mediating the effects of organic agriculture on SOC dynamics. Furthermore, there was a significant negative correlation between soil pH and SOC mass storage, with a regression coefficient of −0.174 (p < 0.039). Although a slight positive correlation was observed between temperature and SOC mass storage as LnRR, other environmental factors likely play a critical role in SOC dynamics. These findings emphasize the complexity of SOC dynamics and the significant impact of organic agriculture on increasing SOC concentrations and mass storage. This study broadly contributes to the debate in sustainable agriculture by providing quantitative evidence of the role and benefits of organic agriculture on climate change mitigation. The results also emphasize the importance of adopting organic farming practices for broadly enhancing ecosystems’ resilience and long-term food security.

1. Introduction

Modern agricultural practices have long been the cornerstone of global food production [1]. However, there is a growing demand for environmentally friendly and economically viable long-term food production systems. As such, organic agriculture can play an important role in sustainable crop production by offering ways to promote ecological balance, conserve natural resources, and improve the soil’s fertility while minimizing the use of synthetic chemicals. A shift from conventional to organic farming may contribute to advancing sustainable agriculture.
Certain consequences of current conventional agriculture, including soil degradation, erosive processes, salinization, biodiversity loss, and environmental pollution, have highlighted the need for more sustainable approaches [2,3]. Organic agriculture, characterized by excluding synthetic chemicals and promoting natural processes for nutrient cycling, is currently a promising alternative in areas where soil and environmental degradation has been observed [4]. This shift could address environmental concerns and the growing demand for nutritious and safe food [5].
Carbon dynamics and nutrient cycling are part of the maintenance of biogeochemical cycles and are good indicators to evaluate cropping systems’ capacity for sustainable crop production [6]. In this context, understanding the comparative dynamics of carbon concentrations and mass storage under both conventional and organic crop production is critical to unraveling the complex relationship between agricultural practices and ecosystem health, and inform land management choices.
Organic agricultural systems, guided by the principles of sustainability and ecological harmony, place a high value on soil health by capitalizing on the critical role of carbon in maintaining the soil’s structure, fertility, and microbial diversity [7]. In contrast to conventional practices, which often rely heavily on synthetic fertilizers and pesticides, organic practices prioritize the amplification of natural processes that regulate fundamental cycles such as nutrient cycling and carbon dynamics [8,9]. Adopting organic agricultural practices has the capacity to initiate a trajectory of changes in soil organic carbon (SOC). These changes in SOC have important implications for enhancing short-term soil productivity as well as long-term potential for carbon sequestration [10]. This transformation is strongly influenced by the range of agronomic practices used in organic farming systems, each of which catalyzes changing the carbon dynamics of soil ecosystems. For example, crop diversity across space and time is beneficial in enhancing SOC dynamics [11,12]. Similarly, using cover cropping to add organic residues into the soil can also increase organic carbon content while also creating a favorable environment for microbial communities and their activities [13,14]. Likewise, applying compost provides nutrients to plants and introduces a large amount of organic material that interacts with the soil, often enhancing SOC mass storage [15]. On the other hand, the effects of tillage operations in organic farming systems remain controversial regarding their mixed impacts on SOC dynamics [16,17]. This is because although disturbance by tillage can enhance the decomposition of organic matter, enhanced nutrient cycling promotes plant growth which may, in turn, result in accrual of carbon in organic farming systems [16,18]. Together, these different management options shape the trajectory of SOC and determine its response to land use systems and changes. Understanding the complex responses of SOC to various agronomic practices used in organic agricultural systems becomes critical. Uncovering how specific practices affect SOC provides valuable insights into the potential of organic agriculture to mitigate climate change through carbon sequestration and to enhance future soil health [10].
Our primary goal in conducting this comprehensive meta-analysis was to provide insight into the complex dynamics and changes in carbon concentrations and mass storage that can greatly diverge between conventional and organic agricultural managements. By carefully synthesizing and systematically analyzing various available data from the existing literature, we aimed to identify emerging trends, reveal subtle patterns, and elucidate the nuances of carbon dynamics in various agricultural systems. Through a thorough review, we aspire to present a panoramic and all-encompassing view of how organic agricultural practices impact the complex field of SOC dynamics.
The significance of this meta-analysis is crucial in an era when global challenges such as food security, the preservation of valuable natural resources, and the protection of environmental integrity are in the spotlight. The insights we gain from the findings of this study about SOC dynamics have the potential to guide policymakers, agricultural practitioners, and researchers. By revealing the intricate interactions between agricultural practices and the intricate web of carbon dynamics, we contribute substantially to the broader discourse around sustainable land management practices. Furthermore, the insights gained from our analysis will support environmental sustainability efforts, enrich our understanding of the global food system, and promote a more harmonious relationship between human activities and the delicate balance of the Earth’s ecosystems.
The global quest for resilient agricultural systems and sustainability can benefit from lessons emerging from innovative organic farming. With the aim of deciphering the underlying mechanisms that link soil health, carbon management, and sustainable food production, the specific objectives of this meta-analysis were as follows: (i) to evaluate carbon concentrations and carbon mass storage in soils by comparing organic and conventional agriculture and (ii) to examine the relationships linking carbon concentration and carbon mass storage with climate and soil attributes.

2. Methodology

2.1. Description of Data Collection Techniques and Tools

We crafted a comprehensive set of inclusion criteria to ensure the highest quality and relevance of the studies included in our meta-analysis. These criteria spanned a broad timeframe from 1950 to 2023 and specifically targeted studies that examined changes in SOC content resulting from the process of conversion between conventional and organic farming systems. To maintain the scientific integrity of our analysis, we strictly limited our selection to peer-reviewed articles, guaranteeing that the findings we synthesized had undergone rigorous academic scrutiny.
Our inclusion criteria were carefully selected to reflect key aspects of the response of SOC content to organic and conventional agricultural practices, focusing on the following key components:
  • Soil depth: Our analysis considered the significant variation in the effects of farming practices on SOC content with depth. We focused on studies that sampled soils at depths equal or greater than 15 cm, as this range typically captures both the recent changes caused by agricultural practices and the long-term effects on SOC storage [19]. Moreover, this soil depth ensured that our findings reflect the direct and lasting impacts of conventional and organic agricultural practices on soil health, providing a thorough understanding of how these methods affect carbon dynamics across the soil profile.
  • Carbon concentration and carbon mass storage measurements: To directly assess the impact of farming practices on soil health, we included studies that measured carbon concentration or carbon mass storage. These metrics serve as direct indicators of soil organic carbon. Our analysis aimed to obtain the most relevant data to assess the carbon sequestration capacity of soils under different farming systems.
  • A comparative analysis between organic farming and conventional farming: A key aspect of our meta-analysis was the inclusion of studies that offered a comparison between conventional and organic farming systems. We specifically sought out studies that provided carbon mass storage for both systems, as these comparisons can unveil insights about the sustainability of agricultural practices. This approach is crucial for understanding how changes in SOC content relate to agricultural management, and it enables a more nuanced understanding of the benefits and drawbacks of each farming system.

2.2. Search Strategy

Our search strategy included the use of specific keywords such as “organic agriculture”, “conventional agriculture”, “soil change”, “soil properties”, and “carbon content”. A systematic literature review of the PubMed and Google Scholar databases was conducted to gather a comprehensive collection of relevant studies, yielding 257 journal publications up to 1 February 2024. Furthermore, the reference lists of significant publications served as a supplementary basis for this literature search. In particular, a comprehensive review focusing on agronomic effects by Alvarez (2022) [20] provided a number of relevant references.

2.3. Standardized Data Extraction

In order to improve the consistency and reliability of our data collection, we crafted a standardized data extraction framework according to the unique needs of our meta-analysis. The framework was specifically designed to systematically capture a wide range of critical details about each study included in our analysis. The critical information encapsulated the author, year of publication, country, and the specific location, with the aim of understanding the geographic context and potential regional soil characteristics. This allowed us to tabulate details on the agricultural practice used (e.g., compost, fertilizer) and its effect on soil properties. Soil depth, annual precipitation, and annual air temperature were also included in the table (Supplementary Table S1). This method provided depth-specific insights into SOC dynamics and the climatic influences on SOC content and mass storage. Soil texture classifications were also considered. Texture classes and texture class factors were included to assess how the soil’s composition affects carbon concentration and mass storage. Finally, carbon concentration and mass storage metrics were recorded as reference (conventional farming) and comparative (organic farming) values that enabled estimations of the response ratios (RRs) to quantitatively measure the impact of organic and conventional farming practices on SOC dynamics.

2.4. Statistical Analysis

Response ratios are a crucial metric for quantifying relative changes in SOC caused by different management systems. For our analyses, RR calculations included SOC concentrations and carbon mass storage, comparing conventional farming (reference) to organic farming (comparison) on the basis of natural logarithm transformation. Hence, the natural logarithms of RRs (LnRRs) were used as the primary response parameter in our meta-analysis to quantify the relative changes in SOC concentrations and storage. Calculations were based on the following equations:
For the SOC concentration:
L n R R c o n c e n t r a t i o n = l n C o n c e n t r a t i o n   o f   c a r b o n   i n   o r g a n i c   f a r m i n g C o n c e n t r a t i o n   o f   c a r b o n   i n   c o n v e n t i o n a l   f a r m i n g
For SOC mass storage:
L n R R m a s s   s t o r a g e = l n M a s s   s t o r a g e   o f   c a r b o n   i n   o r g a n i c   f a r m i n g M a s s   s t o r a g e   o f   c a r b o n   i n   c o n v e n t i o n a l   f a r m i n g
All statistical analyses were performed using SigmaStat 4.0 software (Grafiti LLC, Palo Alto, CA, USA), which provides various statistical functions that are suitable for scientific research.
The initial data exploration included plotting descriptive statistics and visualizations to assess the distribution and potential trends in the data. Scatterplots were created to investigate the relationship between SOC concentrations and the LnRR of carbon mass storage and environmental factors such as soil pH and annual air temperature. It is noted that although 64 study comparisons were compiled in this meta-analysis (Supplementary Table S1), only 9 of them concurrently had data for both soil pH and soil carbon mass storage. When available, soil bulk density data were also compiled.
To investigate the effect of categorical variables, such as the soil texture class, on the soil mass ratio, a one-way analysis of variance (ANOVA) was used. This test was chosen to determine if there were any statistically significant differences in the mean LnRR between the predefined groups. Before the ANOVA, the Shapiro–Wilk test was used to assess the normality of the data’s distribution. The Brown–Forsythe test was used to assess the equality of variances between groups and to ensure that the data met the assumptions required for ANOVA. Following the ANOVA, Tukey’s post hoc comparison test was applied for a multiple comparison between all pairs of soil texture categories. This step was critical for identifying specific group differences while adjusting for ongoing multiple comparisons. Moreover, when specific subsets of the data failed to meet the assumption of normality, we used a nonparametric method, the Kruskal–Wallis one-way ranked ANOVA, to validate the results of the ANOVA. An alpha level of 0.05 was used to determine statistical significance.

3. Result

3.1. The Response Ratio of SOC

The LnRR for SOC concentration exhibited variability throughout the dataset, with values ranging from −0.163 to 0.588 relative to the reference value. The mean LnRR was 0.119, indicating an overall increase in the SOC concentration under organic management (Table 1). The standard error of the mean LnRR was 0.015, reflecting an accurate estimate of the concentration trend. The lower limit of the 95% confidence interval was 0.089, and the upper limit was 0.149, indicating a statistically significant increase in the SOC concentration when comparing conventional with organic agriculture.

3.2. The Response Ratio of Carbon Mass Storage

Similarly, the LnRR for SOC mass storage ranged from −0.121 to 0.405. The mean LnRR and median LnRR were both 0.129, implying higher carbon storage under the organic farming systems (Table 1). The standard error (0.039) and 95% confidence interval (0.053–0.205) for mass storage were consistent with those for concentration, indicating a significant treatment effect.

3.3. Soil Texture

Different responses were observed when the data were stratified by soil texture. The fine texture category had the highest mean LnRR for carbon concentration (0.163), followed by the medium texture category (0.121), while the coarse texture category was the least responsive to both carbon concentration and mass storage (0.063 and 0.025, respectively) (Table 2). This gradient suggests that soil texture may play a crucial role in SOC concentrations and mass storage, with finer textures being more responsive to carbon dynamics.
Additionally, the LnRR of SOC mass storage also differed across soil texture types, suggesting that soil texture affects carbon storage results if we compare fine, medium, and coarse texture categories (Table 2). However, Tukey’s post hoc comparisons showed no significant pairwise differences (p > 0.05). This may indicate that the subtler effects of texture on mass storage need to be captured in more direct comparisons between individual textural categories.

3.4. Soil pH

Scatterplots of the relationship between soil pH and the LnRR of SOC mass storage showed a clear trend. The LnRR of SOC mass storage appeared to decrease with increasing soil pH, suggesting that higher soil pH may be associated with narrower relative changes in SOC mass storage within the range of this meta-analysis (Figure 1).
The regression model further showed a significant negative correlation between soil pH and the LnRR of SOC mass storage. According to the regression analysis, the following equation was derived:
L n R R = 1.281 − ( 0.174 ∗ S o i l p H )
The negative regression coefficient of soil pH indicated that an increase in one unit in soil pH reduces the LnRR of mass storage by 0.174, with a statistical significance of p = 0.029 and a coefficient of determination (R2) of 0.48. This significant linear relationship emphasizes the importance of considering soil pH in SOC sequestration strategies. These results advocate using soil pH as a critical factor in planning and implementing land management practices, particularly those aimed at enhancing carbon storage in the agricultural environment.
Th soil bulk density results (n: 9) showed an LnRR confidence interval from −0.055 to 0.005 which informed that bulk density was not consistently different between treatment and comparison across the available studies; however, there was a slight tendency for bulk density being lower in the organic farming comparison than the conventional agriculture reference (1.20 vs. 1.24 g cm−3, respectively).

3.5. Temperature

We examined the effect of annual air temperature on the LnRR of SOC concentration (Figure 2A) and the LnRR of mass storage (Figure 2B). Each point in the scatterplot represents an observation from the dataset collected in this meta-analysis. A wide dispersion of data points was noted for the LnRR of SOC concentration over the observed temperature range, with no discernible pattern (Figure 2A). This variability suggests that the effect of temperature on SOC concentrations may be moderated by other factors that were not captured by this specific analysis. By contrast, a slight positive correlation was found between temperature and the LnRR of SOC mass storage (Figure 2B). Notably, data points at higher temperatures exhibited higher LnRR values, suggesting a possible enhancing effect of temperature on SOC mass storage. However, given the dispersed and limited range of the data, this trend needs to be interpreted with caution.
For carbon concentrations, the regression analyses did not produce statistically significant models, indicating the lack of a clear relationship between air temperature and the LnRR of SOC concentration over the temperature range studied. In contrast, the regression model for carbon mass storage showed a significant positive correlation with air temperature (Figure 2B) (p = 0.023, albeit with a low R2 value of 0.0713). The model was formulated as follows:
L n R R = 0.0135 + ( 0.00705   ∗   T e m p e r a t u r e )  
The results show that for each 1 °C increase in temperature, the LnRR of carbon mass storage increased by 0.00705.

4. Discussion

Our comprehensive meta-analysis showed that the LnRR of SOC concentrations and carbon mass storage changed significantly when we compared conventional with organic farming practices. Specifically, upon normalization of the comparative data using a logarithmic transformation of the response ratios, significant differences in SOC concentrations and mass storage were revealed. The results show that organic farming treatments resulted in an overall increase in SOC concentrations and carbon mass storage. These differences, evidenced by the LnRRs, indicate significant changes in SOC dynamics through the implementation of organic farming. Conventional and organic systems differ in many aspects such as their tillage practices, crop types, inclusion of cover crops, organic amendments, and other management choices, which can all interactively contribute to the results. These results agree with earlier studies [21,22]; however, it also differs from other studies [23].
Regarding the effect of soil texture on SOC concentrations and carbon mass storage, the results showed that significant differences were observed with certain soil textures and that the texture class had a significant effect on SOC concentrations and carbon mass storage. The enhanced carbon storage capacity of medium- and fine-textured soils was particularly compelling, suggesting that these soil texture types have the potential to be stronger carbon sinks under organic management practices. The mechanistic basis for this observation may stem from the different structural stability and pore size distribution of such soils, which can affect the turnover of organic matter and the subsequent carbon sequestration [24,25]. This finding is also in line with Telles et al. (2003) [26], who reported strong differences in SOC accrual with contrasting soil textures in forest soils. However, larger sample sizes in future meta-analyses or more refined soil properties may be needed to better elucidate the specific nature of the effects of soil texture on SOC concentrations and carbon mass storage.
Our meta-analysis found a significant inverse relationship between soil pH and the LnRR of SOC mass storage. This is a compelling picture of SOC dynamics in conventional compared with organic agriculture. An increase in soil pH was associated with a decrease in the LnRR of carbon mass storage, suggesting that with increasing soil pH (e.g., higher than 7.0), organic agriculture sequesters relatively less carbon than conventional methods, and vice versa. This finding is consistent with the hypothesis that soil pH determines SOC dynamics implicitly through microbial-mediated carbon stabilization processes [27]. Soil pH is a significant master variable in soil science and has implications for many biological, chemical, and physical processes. Soil pH affects the composition and activity of soil microbial communities, which plays a crucial role in the decomposition of organic matter and stabilization processes [28]. Our results support the general view in the scientific community that low soil pH inhibits microbial activity [29] and that low soil pH is often a characteristic of organic farming systems resulting from the application of certain organic amendments [30]. In the context of our meta-analysis, soil pH was inversely related to the RR of carbon storage, contributing to this ongoing discussion. Understanding how soil pH affects carbon storage is critical to developing effective land management strategies. Because agricultural practices can significantly alter the soil pH [29], our findings suggest that optimizing soil pH management may be a beneficial strategy for carbon sequestration. However, the sample size in this meta-analysis may need to be increased to draw definitive conclusions. In addition, the specific range of soil pH values available in our meta-analysis may limit the generalizability of our findings. Future studies should include a wider pH range and larger sample sizes to validate and extend our results.
The analysis showed that the effect of annual air temperature on SOC concentrations and carbon mass storage is subtle and may be influenced by several factors. The observed lack of a clear linear relationship between temperature and the LnRR of SOC concentration emphasizes the complexity of SOC dynamics. This result is consistent with the understanding that SOC mass storage is influenced by factors other than temperature, such as soil texture, moisture, organic matter inputs, and microbial activity [20]. In contrast, the slight positive correlation between temperature and the LnRR of SOC mass storage is consistent with the findings of Thornley et al. (2001) [31], who suggested that in temperate regions, increased carbon sequestration in organic farming practices is temperature-dependent. However, the differences in the magnitude and significance of this relationship reported in the literature may be attributed to differences in the study design, regional climate, and soil type. The variability and lack of significant models of LnRRs of SOC concentrations at different temperatures were unexpected. Our study identified certain data limitations: although a positive trend was observed between temperature and the LnRR of SOC mass storage, temperature may explain only a small portion of the variability in the dataset (as R2 < 10%). This suggests that other factors that were not considered in our analysis, such as erosive processes associated with the terrain’s attributes or intrinsic soil properties such as the water-holding capacity may play essential roles in the overall carbon dynamics. The limited temperature range and data points in some geographic regions also restrict the generalization of the findings.
Future research should focus on long-term field trials comparing carbon and nitrogen dynamics under various soil types and climatic conditions. There is also a need to study the interactions between organic agricultural practices and their combined effects on both carbon and nitrogen cycling. In addition, exploring the socioeconomic factors that influence the adoption of organic agriculture could also provide insights into how best to promote practices that are conducive to soil health and are accomplished through carbon sequestration efforts.

5. Conclusions

Our meta-analysis revealed significant increases in SOC concentration and carbon storage under organic farming systems when compared to conventional agriculture. Variations in LnRR across soil textures suggest that inherent soil properties influence the impact of organic agriculture on SOC dynamics. In addition, our analyses revealed the critical relationship of soil pH and SOC storage, with pH values above neutral being associated with declining accumulation of carbon in organic agriculture relative to conventional farming. This finding suggests that appropriately managing soil pH may be a key strategy for optimizing SOC sequestration under organic farming practices. Moreover, air temperature was positively correlated with carbon mass storage, highlighting the role of environmental factors on SOC dynamics.
Future research should focus on elucidating the mechanisms underlying the observed increase in SOC under organic agriculture, exploring the long-term stability of carbon sequestration, and assessing the broader environmental and socioeconomic impacts of organic agricultural practices. Overall, this study provides valuable insights into sustainable land management practices for underpinning soil health, food security, and climate-resilient agriculture.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/su162411260/s1. Supplementary Table S1—Soil carbon data and comparison.

Author Contributions

Conceptualization, T.Z., H.K. and G.H.-R.; methodology, T.Z. and G.H.-R.; software, G.H.-R.; validation, H.K. and G.H.-R.; formal analysis, T.Z.; investigation, T.Z.; resources, T.Z. and G.H.-R.; data curation, T.Z.; writing—original draft preparation, T.Z.; writing—review and editing, T.Z., H.K. and G.H.-R.; visualization, T.Z. and G.H.-R.; supervision, G.H.-R.; project administration, H.K. and G.H.-R.; funding acquisition, H.K. and G.H.-R. All authors have read and agreed to the published version of the manuscript.

Funding

Organic Science Cluster 4 (OSC4) Agriculture and Agri-Food Canada’s (AAFC) Sustainable Canadian Agricultural Partnership, and Results Driven Agriculture Research (RDAR).

Data Availability Statement

Data is included in Supplementary Table S1.

Conflicts of Interest

The authors declare no conflict of interest.

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Figure 1. Scatterplot of the logarithm of the response ratio (LnRR) for soil organic carbon mass storage against soil pH. The corresponding linear regression equation is LnRR = 1.281 − (0.174 × Soil pH) with R2 = 0.478.
Figure 1. Scatterplot of the logarithm of the response ratio (LnRR) for soil organic carbon mass storage against soil pH. The corresponding linear regression equation is LnRR = 1.281 − (0.174 × Soil pH) with R2 = 0.478.
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Figure 2. Relationship between annual air temperature and the logarithm of the response ratio (LnRR) for soil organic carbon metrics. (A) Scatterplot showing the relationship between annual air temperature (°C) and the LnRR for soil organic carbon concentration. Data are distributed across a temperature range of ~5–35 °C, with no evident linear trend. (B) Scatterplot displaying the relationship between air temperature (°C) and the LnRR for soil organic carbon mass storage. The distribution suggests a slight positive trend across a temperature range of ~6–18 °C, indicating a potential increase in the response ratios with rising temperature. In panel B, the solid line represents the best-fitting model equation LnRR = 0.0135 + (0.00705 × Temperature) with R2 = 0.0713.
Figure 2. Relationship between annual air temperature and the logarithm of the response ratio (LnRR) for soil organic carbon metrics. (A) Scatterplot showing the relationship between annual air temperature (°C) and the LnRR for soil organic carbon concentration. Data are distributed across a temperature range of ~5–35 °C, with no evident linear trend. (B) Scatterplot displaying the relationship between air temperature (°C) and the LnRR for soil organic carbon mass storage. The distribution suggests a slight positive trend across a temperature range of ~6–18 °C, indicating a potential increase in the response ratios with rising temperature. In panel B, the solid line represents the best-fitting model equation LnRR = 0.0135 + (0.00705 × Temperature) with R2 = 0.0713.
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Table 1. Descriptive statistics for soil organic carbon (SOC) concentrations and mass storage in the reference (conventional farming) and comparison (organic farming) groups.
Table 1. Descriptive statistics for soil organic carbon (SOC) concentrations and mass storage in the reference (conventional farming) and comparison (organic farming) groups.
SOC ConcentrationMass Storage
Reference
(g C kg–1)		Comparison
(g C kg–1)		LnRRReference
(Mg C ha–1)		Comparison
(Mg C ha–1)		LnRR
Sample size646464161616
Minimum3.23.8–0.16311.814.8–0.121
Maximum81.070.50.588439.0389.00.405
Mean18.219.80.119224.8248.60.129
Median14.315.30.094197.3280.80.132
Standard error1.91.70.01526.023.50.039
Lower confidence interval14.516.40.089173.9202.50.053
Upper confidence interval21.823.30.149275.7294.70.205
Table 2. Average natural logarithm of the response ratio (LnRR) for soil organic carbon (SOC) concentrations and mass storage by texture class.
Table 2. Average natural logarithm of the response ratio (LnRR) for soil organic carbon (SOC) concentrations and mass storage by texture class.
Average LnRR of SOC ConcentrationnAverage LnRR of Carbon Mass Storagen
Coarse texture 0.063150.0256
Medium texture 0.121330.1939
Fine texture 0.163140.1731
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Zhao, T.; Kubota, H.; Hernandez-Ramirez, G. Contrasting Soil Organic Carbon Concentrations and Mass Storage Between Conventional Farming and Organic Farming: A Meta-Analysis. Sustainability 2024, 16, 11260. https://doi.org/10.3390/su162411260

AMA Style

Zhao T, Kubota H, Hernandez-Ramirez G. Contrasting Soil Organic Carbon Concentrations and Mass Storage Between Conventional Farming and Organic Farming: A Meta-Analysis. Sustainability. 2024; 16(24):11260. https://doi.org/10.3390/su162411260

Chicago/Turabian Style

Zhao, Tingxuan, Hiroshi Kubota, and Guillermo Hernandez-Ramirez. 2024. "Contrasting Soil Organic Carbon Concentrations and Mass Storage Between Conventional Farming and Organic Farming: A Meta-Analysis" Sustainability 16, no. 24: 11260. https://doi.org/10.3390/su162411260

APA Style

Zhao, T., Kubota, H., & Hernandez-Ramirez, G. (2024). Contrasting Soil Organic Carbon Concentrations and Mass Storage Between Conventional Farming and Organic Farming: A Meta-Analysis. Sustainability, 16(24), 11260. https://doi.org/10.3390/su162411260

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