{"paper_id":"4cde1034-4482-483d-90ec-5a69b81bfcea","body_text":"Influence of grazing practices and land cover types on CH 4 , CO 2 and N 2 O fluxes in semi- arid rangelands of Laikipia County, Kenya | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Influence of grazing practices and land cover types on CH 4 , CO 2 and N 2 O fluxes in semi- arid rangelands of Laikipia County, Kenya Janeth Chepkemoi, Angela Gitau, Richard Onwonga, Stephen Mureithi, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5998779/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Greenhouse gas (GHG) emissions from soils, influenced by grazing management practices and land cover types, are critical in understanding the dynamics of semi-arid rangeland ecosystems. This study was conducted in the Ilmotiok community ranch, Laikipia County, Kenya, to investigate the effects of grazing management practices (continuous and controlled grazing) and land cover types (bare ground, grass patches, and tree mosaics) on soil emissions of carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O). A completely randomized block design was used, with 36 sampling points established across three topographical positions under both grazing systems. Gas samples were collected for five weeks using static chambers, and GHG fluxes were analyzed using a gas chromatograph (GC). Results showed that controlled grazing significantly improved soil quality, with higher total organic carbon (TOC), total nitrogen (TN), and water-filled pore space (WFPS) compared to continuous grazing. Tree mosaics exhibited the highest TOC and TN levels, followed by grass patches, while bare ground had the lowest. Controlled grazing emitted higher CO₂-C flux (83.2 mg. m⁻².h⁻¹) than continuous grazing (21.5 mg. m⁻².h⁻¹), with tree mosaics showing the highest flux among land cover types. Nitrous oxide emissions were also higher under controlled grazing (19.4 µg. m⁻².h⁻¹) than continuous grazing (3.4 µg. m⁻².h⁻¹), with grass patches exhibiting the highest N₂O flux among land cover types. Methane fluxes varied, with continuous grazing acting as a methane sink (-0.037 mg. m⁻².h⁻¹) and controlled grazing as a slight source (0.016 mg. m⁻².h⁻¹). Grass patches showed the strongest methane sink potential (-0.034 mg. m⁻².h⁻¹). Regression analysis revealed that WFPS and TOC were strong positive drivers of CO₂ flux, while TN, soil texture, and C:N ratio showed negative relationships. Methane and nitrous oxide fluxes were positively associated with CO₂ emissions, indicating interconnected soil GHG dynamics. These findings highlight the critical role of sustainable grazing practices and diverse land cover in improving soil quality and managing GHG emissions in semi-arid rangelands. Integrating controlled grazing and vegetation restoration can enhance soil fertility, mitigate GHG emissions, and promote ecosystem resilience. Greenhouse gas emissions Grazing management practices Land cover types Semi-arid rangelands Figures Figure 1 Figure 2 Figure 3 Introduction Semi-arid rangelands are predominantly utilized for livestock production and wildlife tourism due to factors that limit crop production, such as low soil fertility, erratic rainfall patterns, and challenging landscape positions (Kibet et al., 2016 ). However, overgrazing in continuously grazed rangelands is a persistent problem, reducing pasture availability and negatively impacting the livelihoods of pastoral communities (Ribeiro et al., 2016 ). The Ilmotiok region in Laikipia County, Kenya, exemplifies these challenges, as it faces increasing pressure from overgrazing, which has degraded rangelands and led to reduced vegetation cover, soil compaction, and loss of productivity (Pelster et al., 2017 ). While studies have investigated the effects of grazing on vegetation and soil properties in semi-arid rangelands, there remains insufficient information on soil greenhouse gas (GHG) emissions from these systems (Oduor et al., 2018 ). Research on GHG emissions has predominantly focused on temperate regions, leaving a significant knowledge gap in Sub-Saharan Africa (Rosenstock et al., 2016 ). In Kenya, most studies on GHGs emphasize livestock enteric emissions, while the contributions of soil processes under different grazing management practices and land cover types remain poorly understood (Pelster et al., 2017 ; Oduor et al., 2018 ). Furthermore, semi-arid rangelands such as Ilmotiok, which are characterized by low rainfall and minimal nutrient inputs, are underrepresented in GHG studies despite their potential contribution to global emissions (Wachiye et al., 2019 ). This gap is problematic as soil greenhouse gases; methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O) play a crucial role in climate change, yet their dynamics in these landscapes remain unclear. Addressing this gap is essential for designing sustainable land management strategies that mitigate emissions while improving rangeland productivity (Ribeiro et al., 2016 ; Yan et al., 2016 ). Grazing patterns are known to affect CH₄, CO₂, and N₂O fluxes in soils. Grazing management directly influences CH₄ emissions through animal urine and droppings and indirectly through its effect on soil moisture (Wang et al., 2012 ; Oduor et al., 2018 ). However, the role of vegetation in methane flux has often been overlooked due to its perceived insignificance (Ciais et al., 2013 ). In pasturelands, N₂O emissions primarily result from animal droppings, urine, and denitrification processes in the soil, while in semi-arid rangelands, N₂O is emitted during the denitrification of soil nitrates (Yan et al., 2016 ). On the other hand, CO₂ emissions in natural environments are driven predominantly by soil respiration, which is associated with microbial activity and root respiration (Graham et al., 2012; Saggar et al., 2013 ). These gases interact in soil pores and influence each other, depending on soil carbon and nitrogen dynamics. For instance, research in Kenya's semi-arid rangelands demonstrated a positive correlation between CO₂ and CH₄ emissions (Oduor et al., 2018 ). Furthermore, during prolonged droughts, CO₂ emissions are reduced, but upon rewetting (a phenomenon known as the Birch effect), root respiration can cause emissions to triple (Borken et al., 1999 ; Xiao et al., 2007 ). The Ilmotiok area in Laikipia County has experienced significant degradation due to continuous grazing, yet it remains a critical resource for pastoralists and wildlife. Understanding the impacts of grazing management practices and land cover types on GHG emissions is crucial for developing strategies to mitigate emissions and restore degraded rangelands (Rosenstock et al., 2016 ). This study is critical as it provides much-needed data on soil GHG fluxes in semi-arid rangelands, a region that is underrepresented in global climate research. By focusing on Ilmotiok, Laikipia County, this research addresses local environmental challenges while contributing to the global understanding of GHG dynamics in semi-arid ecosystems. The findings will inform sustainable grazing management practices and land restoration efforts, ensuring both climate mitigation and improved livelihoods for pastoral communities dependent on these rangelands. Materials and method Study site The study was conducted at the Ilmotiok community ranch and Mpala research centre in Laikipia County, Kenya. It is located between latitudes 00°17' S and 00°45' N, and longitudes 36°15' E and 37°20' E, covering approximately 9,500 km² (Fig. 1 ). Laikipia County is part of the larger Ewaso Ng'iro Ecosystem, which stretches from the slopes of Mt. Kenya (5,199 m) in the south to the margin of the Great Rift Valley in the west (Ojwang et al., 2010; Lalampa et al., 2016). Experimental design A completely randomized block design was used to set up the experiment. The treatments were grazing management practices, and the land cover types. Land cover types were selected based on the most dominant vegetation as shown in (Fig. 2 ). The grazing management practices that were assessed included continuous grazed zones in Ilmotiok community group ranch and the controlled grazed zones in Mpala Research Centre. Topographical positions classified were used as a blocking factor. In each topographical position, mid-slope, foot slope and bottomland there was a 200m transect. The 200m transect was further blocked into 50m long 4 times (Fig. 2 ). This was to distinguish the distinct land cover type after every 50m stake and set up the static chambers. Three land cover types were assessed under each grazing management practice and topographical position, namely, bare ground, patches of grass and mosaic of trees. Three cylindrical opaque static chambers measuring 29.2cm in diameter and 15cm in height were installed 10cm deep in each land cover type making a total of 36 sampling points (3 chambers x 3 land cover types x 4 replicates) in each grazing management practice and topographical position. The chambers were installed three weeks prior to the first gas sampling. Data on surface soil properties (0-10cm) was used to establish the relationship with the GHG emission rates. GHG Gas sampling and laboratory analysis Soil emissions were sampled as from 24th January 2018 up to 28th February 2018 for each subplot. Gas samples were collected for 5 weeks consecutively from both sites one day per week, generally between 0800hrs and 1200hr local time. To avoid the influence of time, the last sub-plot to sample was the first to be sampled in the subsequent sampling event, and vice versa. Sampling was done immediately after fitting the lid (d = 29.2cm) with an aluminium tape, rubber sealing, fan, 50cm non-forced vent, a Einstich TFA thermometer model and a sampling port was fitted to the base frame using metal clamps for 30 min. Gases were collected at 4 time intervals i.e. at time zero (T0), after 10 minutes (T1), 20 minutes (T2) and lastly after 30 minutes (T3). Once the systems were operational and set i.e., thermometers and chamber leads, gases were collected using 60ml syringe with a luer lock and stored in 20ml evacuated vials. The samples were transported to the lab to be measured for CO 2 , CH 4 and N 2 O. Other measurements taken included; surface compaction, soil moisture, temperature of soil, air and chamber, air pressure and chamber height. A total of 1440 samples were collected (5 weeks x 4-time intervals x 3 vegetation types x 4 reps x 2 grazing practices x 3 topographical positions). CH 4 , CO 2 and N 2 O were analyzed at Mazingira Centre (ILRI) using a gas chromatograph (GC) which was equipped with 63 Ni electron capture detector for N 2 O while a flame ionization detector was used to detect CH 4 and CO 2 . CH4, CO2 and N 2 O fluxes were calculated depending on the peak areas detected by the GC relative to peak areas determined from the calibrated gas standards. Linear regression of standard concentrations as described by Qui et al. , (2006) was used to calculate CH 4 , CO 2 and N 2 O fluxes versus chamber closure time and corrected for soil moisture and temperature using Eq. 1 below. F= \\(\\:(\\text{P}/\\text{P}\\text{o})\\:\\text{x}\\:(\\text{M}/\\text{V}\\text{o})\\:\\text{x}\\:(\\text{d}\\text{c}/\\text{d}\\text{t})\\:\\text{x}\\:(\\text{T}\\text{o}/\\text{T})\\:\\text{x}\\:\\text{H}\\) (Eq. 1) Whereby: F = for CO 2 - C Linear flux (mg.m-2.h-1), CH 4 -C Linear flux (mg.m-2.h-1) and N 2 O- N Linear flux (µg.m-2.h-1) P = atmospheric pressure of study site (Pa) P o = atmospheric pressure (Pa) M = gas mass (g/mol) V o = molar volume (ml) dc/dt = rate of change in concentrate T o = absolute chamber temperature (°C) T = absolute chamber temperature at time of sampling (°C) H = height of static chamber at the time of sampling Above ground air temperatures at 1.5 m and inside the base chamber were measured concurrently in each gas sampling event using a Einstich—TFA digital probe thermometer. Soil temperature (°C) and soil moisture content (SM, %v/v) were measured at 5 cm surface soil depth using a probe sensor model 5MT, Decagon Devices Inc which measured both soil moisture and temperature. Water filled pore space (WFPS) was determined as described by Zhang et al. , (2012) using extra parameters measured in the field like soil moisture and bulk density. It was calculated using Eq. 8 below. \\(\\:WFPS\\:\\%=\\frac{Soil\\:moisture\\:\\left(\\%\\right)}{[1-\\left\\{\\frac{Bulk\\:density\\:\\left(\\frac{g}{{cm}^{3}}\\right)}{2.65}\\right\\}]}\\) (Eq. 2) Statistical analysis R software version 3.5.3 was used to derive ANOVA tables and separate means using Agricolae package for CH4, CO2 and N2O fluxes to test the effect of grazing practices and land cover types on soil emissions. Linear regression model was used to determine the relationship between grazing practices, land cover, WFPS, total organic carbon, total nitrogen and CN ratio to CH4, CO2 and N2O. Results Soil temperature, TOC, TN, CN ratio and WFPS Under grazing management practice and varying land cover types of the soil had varying responses. The study found that controlled grazing significantly improved soil quality compared to continuous grazing, with higher total organic carbon (TOC, 9.36–16.49 g/kg), total nitrogen (TN, 1.10–1.71 g/kg), and water-filled pore space (WFPS, 8.47–15.35%), while continuous grazing had lower TOC (9.04–12.98 g/kg), TN (1.00–1.22 g/kg), and WFPS (7.53–9.67%). Among land cover types, tree mosaics recorded the highest TOC and TN, followed by grass patches, while bare ground consistently showed the poorest soil conditions (Table 1 ). Table 1 Soil chemical and physical characteristics under both continuous grazing and controlled grazing in different land cover types Grazing system Land cover TOC TN CN Ratio WFPS Soil temperature Textural class g/kg g/kg % °C Continuous grazing BG 9.04 1.00 9.08*** 8.47 31.4 LS GR 11.69 ** 1.15 10.15 9.67 34.8 SCL TR 12.98 ** 1.22 10.72 7.53 26.5 SL Controlled grazing BG 9.36 1.1 8.67*** 13.31 32.7 SL GR 13.36 ** 1.32 ** 10.17 15.35 33 SCL TR 16.49 ** 1.71 ** 9.71 8.47 35.3 SCL Significance level: '**' 0.001, '*' 0.05, 'LS' Loamy sand, 'SCL' Sandy clay loam, 'SL' sandy loam BG-Bare ground, GR-patches of grass, TR-patches of trees Carbon dioxide (CO) soil flux The results indicate that grazing practices and land cover types significantly affect soil CO₂-C flux. Controlled grazing emitted the highest CO₂-C flux (83.2 mg. m⁻².h⁻¹) compared to continuous grazing (21.5 mg. m⁻².h⁻¹). Among land cover types, tree mosaics exhibited the highest CO₂-C flux (66.57 mg. m⁻².h⁻¹), followed by patches of grass (61.88 mg. m⁻².h⁻¹), while bare ground emitted the least (27.86 mg. m⁻².h⁻¹) (Table 2 ). Table 2 Effect of grazing practices and land cover types on CO 2 -C flux Factor Cumulative CO 2 - C Linear flux (mg.m-2.h-1) Grazing practice Continuous grazing 21.5 *** Controlled grazing 83.2 Land cover types Bare ground 27.86* Patches of Grass 61.88 Mosaic of Tree 66.57 Signifiant codes : ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 Nitrous oxide (NO) soil flux The study revealed significant differences in nitrous oxide (N₂O) emissions across grazing practices and land cover types. Continuous grazing, with an N₂O flux of 3.4 µg. m⁻².h⁻¹, emitted significantly less N₂O compared to controlled grazing, which recorded a flux of 19.4 µg. m⁻².h⁻¹. (Fig. 3 ). Among the land cover types, bare ground emitted the least N₂O (3.71 µg. m⁻².h⁻¹), patches of grass exhibited the highest emissions (18.81 µg. m⁻².h⁻¹), whereas mosaic of trees recorded intermediate N₂O emissions (11.43 µg. m⁻².h⁻¹). Methane (CH 4 ) soil flux The results show that grazing practices and land cover types significantly influence methane (CH₄-C) flux. Continuous grazing exhibited a negative CH₄-C flux (-0.037 mg. m⁻².h⁻¹), whereas controlled grazing showed a positive flux (0.016 mg. m⁻².h⁻¹). Among land cover types, grass patches had the strongest methane sink potential (-0.034 mg. m⁻².h⁻¹), while tree mosaics acted as a weaker sink (-0.005 mg. m⁻².h⁻¹), and bare ground emitted small amounts of methane (0.007 mg. m⁻².h⁻¹) (Table 3 ). Table 3 Effect of grazing practices and land cover types on CH 4 -C flux Factor Cumulative CH 4 -C Linear flux (mg.m-2.h-1 Grazing practice Continuous grazing -0.037 Controlled grazing 0.016 Land cover types Bare ground 0.007 Patches of Grass -0.034 Mosaic of Tree -0.005 Significant codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 Regression analysis of grazing management practices, land cover types and soil chemical and physical properties on CO 2 flux The regression analysis reveals factors influencing soil CO₂ flux, highlighting the roles of soil properties, grazing practices, and greenhouse gas interactions. Grazing management practices (Estimate = 29.61, p = 0.08) showed a near-significant effect, while land cover types (Estimate = 4.60, p = 0.81) were not significant predictors. Water-filled pore space (WFPS; Estimate = 1.49, p < 0.001) and total organic carbon (TOC; Estimate = 702.01, p < 0.001) were strong positive drivers of CO₂ emissions. Conversely, total nitrogen (TN; Estimate = -6500.98, p = 0.01) and soil texture (Estimate = -48.30, p < 0.001) showed negative effects. The carbon-to-nitrogen ratio (C: N; Estimate = -87.88, p = 0.01) was also negatively related. Methane (CH₄; Estimate = 35.37, p = 0.01) and nitrous oxide (N₂O; Estimate = 1.98, p < 0.001) fluxes were positively associated with CO₂ flux (Table 4 ). Table 4 Regression analysis of precipitation, grazing management practices, land cover types and soil chemical and physical properties for CO 2 flux Coefficients: CO 2 Flux Estimate Std. Error t value Pr(>|t|) (Intercept) 835.3664 279.0897 2.993 0 ** Grazing management practices 29.6058 10.3544 -1.79 0.08 ** Land cover type 4.6035 18.7839 0.245 0.81 WFPS 1.4944 0.4222 3.54 0 *** TOC 702.0136 200.737 3.497 0 *** TN -6500.98 2066.192 -3.146 0 ** Soil texture -48.2962 11.5338 -4.187 0 *** Soil temperature -0.4005 0.5329 -0.752 0.45 CN ratio -87.875 27.7759 -3.164 0 ** CH 4 flux 35.3684 13.1076 2.698 0.01 ** N 2 O flux 1.9792 0.1178 16.808 0 *** Significant codes: ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 Discussion Soil Properties The study highlights the significant impact of grazing management practices and land cover types on critical soil properties, including total organic carbon (TOC), total nitrogen (TN), carbon-to-nitrogen (C: N) ratio, and water-filled pore space (WFPS). Controlled grazing consistently emerged as a superior management strategy for maintaining and enhancing soil health compared to continuous grazing. By reducing grazing pressure, controlled grazing allows vegetation to recover, leading to increased organic matter inputs, improved microbial activity, and enhanced nutrient cycling and soil structure. Oduor et al. ( 2018 ) reported that controlled grazing minimizes overgrazing, enhancing soil organic matter and facilitating microbial processes, while Saggar et al. ( 2013 ) observed that higher soil moisture and TOC under controlled grazing promote nitrogen cycling and microbial activity. These improvements align with the findings of Rosenstock et al. ( 2016 ), who emphasized the role of sustainable grazing practices in improving soil fertility and maintaining productivity. In contrast, continuous grazing degrades soil quality through overgrazing, compaction, and depletion of organic matter. Excessive livestock pressure under continuous grazing reduces vegetation cover, limits organic inputs, and disrupts microbial processes. Soil compaction from continuous grazing impairs water infiltration and aeration, further reducing nutrient cycling efficiency. Ribeiro et al. ( 2016 ) noted that overgrazing causes soil compaction and nutrient depletion, diminishing the soil's ability to support plant growth. Similarly, Pelster et al. ( 2017 ) found that continuous grazing reduces vegetation cover and nitrogen cycling, exacerbating soil degradation. Yan et al. ( 2016 ) highlighted that continuous grazing negatively affects soil organic carbon and microbial activity, further emphasizing its detrimental impact on soil health. Land cover types also significantly influence soil properties. Tree mosaics consistently demonstrated the highest TOC and TN levels among land cover types, attributable to organic inputs from leaf litter and root biomass. Boutton and Liau ( 2010 ) observed that woody vegetation enhances carbon and nitrogen storage through slower decomposition rates and stabilization in soil aggregates, while Wachiye et al. ( 2019 ) found that tree cover improves soil quality by moderating soil temperature, retaining moisture, and supporting microbial activity. Grass patches also contribute to soil fertility through root turnover and litter deposition, though their effects are slightly lower than tree mosaics due to slower decomposition of grass litter (Yan et al., 2016 ). In contrast, bare ground consistently exhibited the poorest soil conditions, with minimal TOC and TN levels and low water retention. Ribeiro et al. ( 2016 ) attributed these conditions to the absence of vegetation, which limits organic matter inputs and microbial activity. Grazing management and land cover also influence specific parameters such as the C:N ratio and WFPS. Controlled grazing resulted in higher TOC, TN, and WFPS levels (16%, 22%, and 66%, respectively, compared to continuous grazing), while bulk density and C:N ratio were lower by 2% and 5%, respectively. The lower bulk density under controlled grazing reduces soil compaction, facilitating water infiltration and enhancing microbial activity. High TOC and TN levels under controlled grazing provide essential substrates for decomposition and nutrient cycling, driving greenhouse gas emissions. This phenomenon aligns with the observations of Kassa et al. ( 2010 ), who reported that woody vegetation in rangelands enhances soil carbon content, explaining the higher carbon and nitrogen levels under tree mosaics in both grazing systems. In contrast, bare ground had the lowest TN levels, attributable to its high sand content, which limits the storage of mineralized nitrogen. Boutton and Liau ( 2010 ) found that soils under woody and herbaceous vegetation tend to have higher silt and clay content, promoting slow decomposition and accumulation of TN. The C:N ratio was generally low across all systems due to the low-quality substrate. Grass-dominated areas exhibited slightly higher C:N ratios than tree mosaics, reflecting the recalcitrant lignin and suberin content of grass litter, which takes longer to decompose (Boutton & Liau, 2010 ). The higher WFPS under controlled grazing enhances microbial processes, facilitating nitrogen cycling and denitrification, which likely explains the higher N₂O flux observed in this system. Elevated TN levels under controlled grazing inhibit atmospheric methane oxidation, further influencing GHG flux dynamics. Increased WFPS and TN under controlled grazing create favorable conditions for microbial activity, driving higher CO₂, CH₄, and N₂O emissions. The findings are consistent with those of Rosenstock et al. ( 2016 ) and Acharya et al. ( 2017 ), who emphasized the interconnected roles of grazing practices and land cover in influencing soil greenhouse gas fluxes and nutrient cycling. Tree mosaics recorded the highest TN levels among land cover types, followed by grass patches, while bare ground consistently had the lowest. These trends highlight the critical role of vegetation in enhancing soil nutrient content and structure. Under continuous grazing, higher stocking density increased species diversity and tree density, indirectly influencing TOC and TN levels through increased organic inputs. These findings align with studies by Kassa et al. ( 2010 ), which demonstrated the significant contribution of woody vegetation to soil carbon content, and Boutton and Liau ( 2010 ), who emphasized the stabilizing effect of woody and herbaceous vegetation on soil nutrient cycling. Carbon dioxide (CO 2 ) The study highlights the significant influence of grazing practices and land cover types on soil carbon dioxide (CO₂-C) flux, demonstrating how these factors drive soil respiration and carbon cycling. Controlled grazing emitted substantially higher CO₂-C flux compared to continuous grazing, reflecting the positive impact of improved soil conditions under controlled grazing systems. Among land cover types, tree mosaics and grass patches showed the highest CO₂ emissions, while bare ground emitted the least, emphasizing the role of vegetation in enhancing soil carbon cycling. Controlled grazing contributed to higher CO₂-C flux due to enhanced total organic carbon (TOC) and water-filled pore space (WFPS), which create favorable conditions for microbial activity and root respiration. The accumulation of organic matter under controlled grazing provides a steady substrate for microbial decomposition, driving carbon release into the atmosphere. These findings align with Oduor et al. ( 2018 ), who reported that controlled grazing enhances soil organic matter and moisture, promoting microbial processes that increase CO₂ emissions. Furthermore, high WFPS levels improve soil respiration by facilitating the diffusion of gases, while TOC contributes to root respiration and microbial activity, as noted by Xiao et al. ( 2007 ). In contrast, continuous grazing exhibited much lower CO₂-C flux due to soil compaction and reduced vegetation cover. Compacted soils inhibit water infiltration and aeration, limiting microbial decomposition and carbon cycling. Yan et al. ( 2016 ) similarly observed that continuous grazing negatively affects soil respiration by degrading soil structure and reducing carbon inputs, while Pelster et al. ( 2017 ) found that overgrazed soils have a reduced capacity for carbon mineralization, leading to lower CO₂ emissions. Land cover types also played a critical role in regulating CO₂-C flux. Tree mosaics emitted the highest CO₂-C flux among the land cover types, driven by organic inputs from leaf litter and root activity, which enhance microbial decomposition and belowground respiration. Trees provide shade, moderating soil temperatures and maintaining moisture levels conducive to microbial processes. Kong et al. ( 2013 ) reported that soils under tree cover have higher microbial carbon and nitrogen content, which increases soil respiration and carbon flux. Grass patches also demonstrated high CO₂-C flux due to their dense vegetation cover, which contributes organic matter and supports root respiration. The rhizosphere in grass patches fosters active microbial interactions, enhancing decomposition processes and carbon cycling. Wachiye et al. ( 2019 ) highlighted that grass-covered areas contribute significantly to CO₂ emissions through root turnover and litter decomposition, although their contributions are slightly lower than those of tree-dominated areas. In contrast, bare ground emitted the lowest CO₂-C flux due to the absence of vegetation and organic inputs. The compacted nature of bare ground, combined with low TOC and minimal microbial activity, limits soil respiration. Ribeiro et al. ( 2016 ) noted that bare soils in degraded rangelands exhibit poor nutrient retention and low microbial biomass, which reduce carbon cycling. Although localized patches of animal droppings or urine on bare ground may contribute to CO₂ emissions, as observed by Yan et al. ( 2016 ), these contributions are minimal and insufficient to offset the overall lack of vegetation and organic matter. These findings underscore the critical role of grazing practices and land cover types in regulating soil carbon cycling. Controlled grazing enhances soil conditions that promote microbial decomposition and CO₂ flux, while continuous grazing suppresses these processes through soil degradation. Similarly, tree mosaics and grass patches play a vital role in enhancing soil respiration and carbon flux due to their contributions to organic matter and microbial activity. In contrast, bare ground reflects the negative impacts of degraded soil conditions on carbon cycling. These results align with Oduor et al. ( 2018 ) and Wachiye et al. ( 2019 ), who emphasized the importance of sustainable land management practices in optimizing soil respiration and maintaining soil productivity. Nitrous oxide (N 2 O) soil flux The study highlights significant differences in nitrous oxide (N₂O) emissions across grazing practices and land cover types, illustrating how soil management and vegetation influence greenhouse gas fluxes. Controlled grazing was associated with higher N₂O emissions compared to continuous grazing, which can be attributed to improved soil conditions under controlled systems. Enhanced water-filled pore space (WFPS), organic matter, and nitrogen availability create optimal conditions for microbial activity, particularly denitrification, which is a key process driving N₂O emissions (Saggar et al., 2013 ; Acharya et al., 2017 ). Moderate grazing intensities, as found in controlled grazing, improve soil moisture retention and organic matter accumulation, promoting microbial processes that enhance N₂O fluxes. Conversely, continuous grazing limits N₂O emissions due to soil compaction, reduced vegetation cover, and depleted nitrogen pools. Compacted soils restrict the formation of oxygen-free microsites necessary for denitrification, while the absence of sufficient organic inputs and microbial activity further suppresses emissions (Yan et al., 2016 ). Degraded soils under continuous grazing are characterized by poor nitrogen cycling, which constrains the production of N₂O and other greenhouse gases (Pelster et al., 2017 ). Among land cover types, grass patches were identified as hotspots for N₂O emissions due to their ability to retain moisture and organic matter, which supports active microbial nitrogen cycling. Grass roots and litter contribute organic inputs that enhance microbial activity and promote denitrification (Wachiye et al., 2019 ). Tree mosaics exhibited intermediate N₂O emissions, reflecting a balance between organic inputs from tree litter and the shading effects of trees, which can reduce soil temperature and microbial activity (Kong et al., 2013 ). Bare ground consistently emitted the least N₂O, as its poor soil quality, minimal vegetation cover, and reduced microbial biomass limit nitrogen cycling and the denitrification process (Ribeiro et al., 2016 ). The interplay between grazing practices and land cover types underscores the importance of sustainable land management in regulating N₂O emissions. Controlled grazing in vegetated areas enhances soil fertility but may increase N₂O emissions, highlighting the need for strategies to balance productivity with greenhouse gas mitigation. Mixed vegetation systems, such as combining trees and grass patches, can optimize soil health while minimizing environmental impacts. Sustainable practices like rotational grazing and the use of nitrogen inhibitors are critical for mitigating emissions and maintaining soil productivity in semi-arid ecosystems (Acharya et al., 2017 ; Rosenstock et al., 2016 ). These findings emphasize that while controlled grazing and vegetation restoration improve soil quality, complementary measures are necessary to minimize greenhouse gas emissions. Integrated management strategies that promote soil resilience and productivity while reducing environmental impacts are essential for sustainable land use in semi-arid rangelands (Pelster et al., 2017 ; Wachiye et al., 2019 ). Methane (CH 4 ) soil flux The study highlights the significant influence of grazing practices and land cover types on methane (CH₄-C) flux, demonstrating how soils can act as methane sinks or sources depending on management strategies and vegetation cover. Continuous grazing was identified as a methane sink, emitting negative CH₄-C flux. This is likely due to compacted soils under continuous grazing, which limit conditions for methane production while promoting methane oxidation by methanotrophic microbes. Methane oxidation occurs when oxygen diffuses into soil microsites, enabling methanotrophs to convert methane into CO₂. These findings align with Wang et al. ( 2014 ), who observed that degraded soils in semi-arid rangelands often act as methane sinks due to restricted methanogenesis and enhanced oxidation processes. Colmer ( 2003 ) also emphasized that methanotrophic activity is driven by oxygen diffusion into soil microsites, facilitating the conversion of methane into CO₂. In contrast, controlled grazing exhibited a small positive methane flux, suggesting that it acts as a methane source. This shift is likely due to improved soil conditions under controlled grazing, such as higher organic matter and moisture levels, which create a favorable environment for methanogenic microbes responsible for methane production. Zhu et al. (2015) reported that moderate grazing intensities enhance soil organic matter and moisture, potentially transitioning soils from methane sinks to sources. Similarly, Cardoso et al. ( 2016 ) found that methane emissions increase in soils with enhanced organic matter and water-filled pore space, as these conditions promote methanogenic microbial activity. Land cover types also play a critical role in methane flux dynamics. Grass patches demonstrated the strongest methane sink potential due to their active root systems, which promote oxygen diffusion into the soil and enhance methane oxidation by methanotrophic microbes. The aerobic microsites created within the rhizosphere of grass patches facilitate efficient methane oxidation, as noted by Colmer ( 2003 ). Additionally, grass patches contribute organic matter that supports microbial activity without significantly favoring methanogenesis, making them highly effective methane sinks. Yan et al. ( 2016 ) similarly observed that grass-dominated soils promote microbial processes that enhance methane oxidation due to improved oxygen availability and minimal methanogenesis. Tree mosaics also acted as methane sinks, though their capacity was weaker compared to grass patches. Organic matter from tree litter supports microbial activity, but shading effects and root competition for soil resources may limit oxygen diffusion, reducing the efficiency of methane oxidation. Oduor et al. ( 2018 ) noted that woody vegetation moderates soil temperature and moisture, which can create less favorable conditions for methanotrophic activity compared to grass patches. Kong et al. ( 2013 ) further emphasized that tree-covered soils support microbial processes, but canopy effects can influence methane flux dynamics by reducing oxygen availability. Bare ground, on the other hand, emitted small amounts of methane, reflecting its role as a minor methane source. The absence of vegetation on bare ground limits organic inputs and microbial activity, reducing the capacity for methane oxidation. Poor soil structure and minimal biological activity further constrain methane flux dynamics. Cardoso et al. ( 2016 ) reported that bare soils, with low organic matter and biological activity, tend to emit small amounts of methane due to constrained oxidation processes. Similarly, Ribeiro et al. ( 2016 ) observed that degraded soils in rangelands exhibit poor microbial activity and limited methane oxidation, resulting in minor emissions. Relationship between CO 2 flux and grazing management practices, land cover types, and soil chemical and physical properties Stepwise regression analysis showed that CO 2 flux was driven by TOC, TN, WFPS, precipitation, and CN ratio. The regression analysis identifies the key factors influencing soil CO₂ flux and provides insights into how soil properties, grazing management, land cover types, and greenhouse gas fluxes interact to regulate emissions. The intercept represents the baseline CO₂ flux when all other variables are held constant. Grazing management practices showed a near-significant effect on CO₂ flux, with a positive coefficient suggesting that controlled grazing contributes to higher CO₂ emissions compared to continuous grazing. This increase is likely due to improved soil organic matter and moisture under controlled grazing, which enhance microbial activity and root respiration. These findings align with Oduor et al. ( 2018 ), who reported that controlled grazing improves soil organic carbon (TOC) and water-filled pore space (WFPS), driving microbial decomposition and CO₂ emissions. However, the lack of statistical significance in this analysis indicates that the effect may vary depending on other interacting factors. Land cover types did not significantly influence CO₂ flux, suggesting that differences in emissions among bare ground, grass patches, and tree mosaics are driven by other variables, such as soil organic carbon and nitrogen content. In contrast, WFPS was a strong positive predictor of CO₂ flux, highlighting the importance of soil moisture in enhancing microbial decomposition and root respiration. Higher WFPS levels create optimal conditions for microbial activity, leading to increased carbon cycling and CO₂ emissions, as also noted by Saggar et al. ( 2013 ). Total organic carbon (TOC) significantly influenced CO₂ flux, with higher organic carbon levels providing essential substrates for microbial decomposition. This finding underscores the role of organic matter in driving soil respiration and carbon cycling, consistent with observations by Xiao et al. ( 2007 ), who linked TOC to enhanced microbial activity and root respiration. Conversely, total nitrogen (TN) exhibited a strong negative relationship with CO₂ flux, suggesting that high nitrogen availability may suppress carbon mineralization processes or indicate conditions less favorable for microbial decomposition. Pelster et al. ( 2017 ) similarly found that excessive nitrogen may reduce microbial activity by disrupting the balance between carbon and nitrogen cycling, particularly in nitrogen-rich soils. Soil texture emerged as a significant negative predictor of CO₂ flux, with coarser soils likely supporting reduced microbial activity due to limited water retention. In contrast, finer soils tend to store carbon rather than release it as CO₂. This result aligns with findings by Ribeiro et al. ( 2016 ), who noted that soil texture influences water retention and microbial habitat, affecting carbon cycling. Interestingly, soil temperature did not significantly affect CO₂ flux, suggesting that temperature variations within the observed range were not a major driver of emissions in this study. Wachiye et al. ( 2019 ) similarly reported that soil temperature in semi-arid regions often has a limited direct impact on CO₂ flux, as microbial processes are more strongly driven by moisture and organic matter availability. The carbon-to-nitrogen (C: N) ratio showed a significant negative relationship with CO₂ flux, indicating that soils with lower C:N ratios emit more CO₂ due to the higher quality and faster decomposition of organic matter. Toma and Hatano ( 2007 ) also observed that low C:N ratios are associated with rapid decomposition and elevated CO₂ emissions, as microbes readily metabolize high-quality organic matter. Methane (CH₄) flux was positively associated with CO₂ flux, reflecting an interaction between methane oxidation and soil respiration processes. This suggests that carbon and methane cycling are interconnected, with soils actively emitting one greenhouse gas often participating in the cycling of others. Zhao et al. ( 2019 ) found that microbial respiration driving CO₂ emissions can simultaneously enhance methane oxidation in soil microsites, linking the two fluxes. Similarly, nitrous oxide (N₂O) flux was a highly significant positive predictor of CO₂ flux, highlighting the link between microbial processes driving N₂O emissions, such as denitrification, and those contributing to CO₂ emissions. Kong et al. ( 2013 ) emphasized that denitrification and CO₂ production are often co-occurring processes in soils with active nitrogen cycling. Conclusion and recommendation This study highlights the significant impact of grazing management practices and land cover types on greenhouse gas emissions and soil properties in semi-arid rangelands, emphasizing the importance of sustainable land management. Controlled grazing proved to be a more effective strategy than continuous grazing, enhancing soil quality by increasing total organic carbon (TOC), total nitrogen (TN), and water-filled pore space (WFPS), which supported microbial activity and nutrient cycling. These improvements resulted in higher emissions of carbon dioxide (CO₂) and nitrous oxide (N₂O), alongside a slight reduction in methane (CH₄) sink potential. In contrast, continuous grazing degraded soil quality through compaction, loss of organic matter, and reduced microbial activity, leading to lower greenhouse gas emissions but significant declines in soil fertility and productivity. Land cover types also played a critical role, with tree mosaics and grass patches contributing to enhanced soil carbon and nitrogen cycling through organic inputs, root activity, and microclimatic regulation, while bare ground consistently exhibited poor soil quality and minimal greenhouse gas fluxes due to the lack of vegetation and organic matter inputs. These findings underscore the necessity of adopting controlled grazing practices, integrating diverse vegetation systems, and implementing climate-smart agricultural strategies, such as rotational grazing, nitrogen inhibitors, and reforestation, to optimize soil health, mitigate greenhouse gas emissions, and enhance ecosystem resilience in the face of climate change. Sustainable land-use policies and continuous monitoring of soil emissions are essential to ensuring long-term productivity and environmental sustainability in semi-arid ecosystems. Declarations Supplementary Information Not applicable Acknowledgements The study was financed by SLEEK ‘System for Land-based Emission Estimation in Kenya’ http://www.sleek .envir onmen t.go.ke/ . Enumerators are highly acknowledged for their assistance in data Collection and labelling. The authors would like to acknowledge Ilmotiok community ranch who allowed us to use their ranch for the experiment. Funding Not applicable Data availability Upon request, the corresponding author can provide the data supporting the findings of this study. Conflict of interest The authors declare that they have no conflict of interest. Ethical approval All authors have reviewed the manuscript and agree to its submission to this journal. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Informed consent to participate The sampling was conducted on farmers field and permission was obtained from the farmers to sample at their field. Compliance with Ethical Standards The protocols for this research were approved by NACOSTI Committee in accordance with the NATIONAL COMMISSION FOR SCIENCE, TECHNOLOGY AND INNOVATION ( NACOSTI ) References Acharya, R., Ghimire, R., Bista, P., & Domínguez-Faus, R. (2017). Influence of grazing on greenhouse gas flux in semi-arid regions. Environmental Management, 60(3), 387–399. Borken, W., Xu, Y. J., & Davidson, E. A. (1999). Birch effect: Rapid soil respiration following wetting events. Biogeochemistry, 48(1), 123–137. Boutton, T. W., & Liau, J. (2010). Woody plant encroachment and its impacts on nitrogen and carbon cycling. Ecological Applications, 20(2), 305–320. Cardoso, A. S., Berndt, A., Leytem, A., Alves, B. J. R., de Carvalho, I. P. C., & Soares, L. H. B. (2016). Greenhouse gas fluxes from grazed pastures. Agriculture, Ecosystems & Environment, 216, 1–11. Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., ... & Thornton, P. (2013). Carbon and other biogeochemical cycles. Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press. Colmer, T. D. (2003). Long-distance transport of gases in plants: A perspective on internal aeration and radial oxygen loss from roots. Plant, Cell & Environment, 26(1), 17–36. Graham, S. A., & Haynes, B. J. (2012). Soil respiration and its drivers in natural ecosystems. Soil Science Society of America Journal, 76(5), 1431–1441. Kassa, H., Dondeyne, S., Poesen, J., Frankl, A., & Nyssen, J. (2010). Woody vegetation and its effects on soil organic carbon in the highlands of Ethiopia. Geoderma, 159(3-4), 193–204. Kibet, S., Otor, C., & Lelon, J. (2016). 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E., Rufino, M. C., Rosenstock, T. S., Mango, J., Saiz, G., Diaz-Pines, E., ... & Butterbach-Bahl, K. (2017). Smallholder agriculture in sub-Saharan Africa: Greenhouse gas emissions and mitigation opportunities. Environmental Research Letters, 12(9), 095007. Ribeiro, A. A., Souza, H. A., Oliveira, F. R., & Moura, E. G. (2016). The role of grazing management in soil quality and greenhouse gas emissions. Agricultural and Forest Meteorology, 220, 39–48. Ribeiro, A. A., Souza, H. A., Oliveira, F. R., & Moura, E. G. (2016). The role of grazing management in soil quality and greenhouse gas emissions. Agricultural and Forest Meteorology, 220, 39–48. Rosenstock, T. S., Rufino, M. C., Butterbach-Bahl, K., Wollenberg, E., Richards, M., & Fraisse, C. (2016). Sustainable land management and greenhouse gas mitigation in sub-Saharan Africa. Agriculture for Development, 29, 40–45. Rosenstock, T. S., Rufino, M. C., Butterbach-Bahl, K., Wollenberg, E., Richards, M., & Fraisse, C. (2016). Sustainable land management and greenhouse gas mitigation in sub-Saharan Africa. Agriculture for Development, 29, 40–45. Saggar, S., Tate, K. R., Giltrap, D. L., & Singh, J. (2013). Soil–atmosphere exchange of nitrous oxide and methane. Plant and Soil, 309(1), 19–31. Saggar, S., Tate, K. R., Giltrap, D. L., & Singh, J. (2013). Soil–atmosphere exchange of nitrous oxide and methane. Plant and Soil, 309(1), 19–31. Toma, Y., & Hatano, R. (2007). Methane oxidation in paddy soils as influenced by soil properties and land-use practices. Soil Biology & Biochemistry, 39(8), 2045–2053. Wachiye, E. J., Waswa, B. S., & Okoth, P. F. (2019). The role of grazing practices and vegetation types in soil greenhouse gas fluxes. Journal of Soil and Water Conservation, 74(2), 123–133. https://doi.org/10.2489/jswc.74.2.123 Wachiye, E. J., Waswa, B. S., & Okoth, P. F. (2019). The role of grazing practices and vegetation types in soil greenhouse gas fluxes. Journal of Soil and Water Conservation, 74(2), 123–133. https://doi.org/10.2489/jswc.74.2.123 Wang, C., Liu, D., Zhang, X., & Han, W. (2012). Methane oxidation in semi-arid grassland soils. Soil Biology & Biochemistry, 77, 287–295. Wang, C., Liu, D., Zhang, X., & Han, W. (2014). Methane oxidation in semi-arid grassland soils. Soil Biology & Biochemistry, 77, 287–295. Xiao, C., Janssens, I. A., Liu, P., Zhou, Z., & Luo, Y. (2007). The role of soil organic matter in determining ecosystem carbon fluxes. Biogeochemistry, 84(1), 1–13. Xiao, C., Janssens, I. A., Liu, P., Zhou, Z., & Luo, Y. (2007). The role of soil organic matter in determining ecosystem carbon fluxes. Biogeochemistry, 84(1), 1–13. Yan, X., Cai, Z., Wang, S., & Smith, P. (2016). Methane emissions from global grazing lands. Global Change Biology, 22(1), 213–227. Yan, X., Cai, Z., Wang, S., & Smith, P. (2016). Methane emissions from global grazing lands. Global Change Biology, 22(1), 213–227. Zhao, X., Tang, H., & Zhu, X. (2019). Interaction between methane oxidation and soil respiration in arid soils. Soil Science and Plant Nutrition, 65(2), 149–157. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {\"props\":{\"pageProps\":{\"initialData\":{\"identity\":\"rs-5998779\",\"acceptedTermsAndConditions\":true,\"allowDirectSubmit\":true,\"archivedVersions\":[],\"articleType\":\"Research Article\",\"associatedPublications\":[],\"authors\":[{\"id\":414812587,\"identity\":\"f2206cb8-fbc3-472a-8082-aca8eff67a3c\",\"order_by\":0,\"name\":\"Janeth 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1\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":842039,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eMap of Kenya showing position of Laikipia County and the study site Ilmotiok community ranch Source Ojwang et al., 2010\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage1.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5998779/v1/a1816976f41d87b2afe18de8.png\"},{\"id\":76288677,\"identity\":\"267d7986-b362-49dc-9139-6e4a5b644b88\",\"added_by\":\"auto\",\"created_at\":\"2025-02-14 11:51:22\",\"extension\":\"jpeg\",\"order_by\":2,\"title\":\"Figure 2\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":651555,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eSketch of dominant land cover types selected in each demarcated plot in all the topographical positions under both grazing management practices\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"floatimage2.jpeg\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5998779/v1/85a9bf59ad85945567f98df1.jpeg\"},{\"id\":76288704,\"identity\":\"4aab0618-5154-418d-b904-6fc478097bda\",\"added_by\":\"auto\",\"created_at\":\"2025-02-14 11:51:24\",\"extension\":\"png\",\"order_by\":3,\"title\":\"Figure 3\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":19925,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003e\\u003cstrong\\u003eGrazing practices and land cover types on N\\u003c/strong\\u003e\\u003csub\\u003e\\u003cstrong\\u003e2\\u003c/strong\\u003e\\u003c/sub\\u003e\\u003cstrong\\u003eO fluxes\\u003c/strong\\u003e\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"3.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5998779/v1/3675dabbef6eb58b31f46d9a.png\"},{\"id\":76754424,\"identity\":\"42bad7e9-4c4a-4f25-846f-ac17ebcb3361\",\"added_by\":\"auto\",\"created_at\":\"2025-02-20 10:38:54\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":2543043,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-5998779/v1/75912d15-fc4f-4368-af48-9e25f51c70b0.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Influence of grazing practices and land cover types on CH 4 , CO 2 and N 2 O fluxes in semi- arid rangelands of Laikipia County, Kenya\",\"fulltext\":[{\"header\":\"Introduction\",\"content\":\"\\u003cp\\u003eSemi-arid rangelands are predominantly utilized for livestock production and wildlife tourism due to factors that limit crop production, such as low soil fertility, erratic rainfall patterns, and challenging landscape positions (Kibet et al., \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). However, overgrazing in continuously grazed rangelands is a persistent problem, reducing pasture availability and negatively impacting the livelihoods of pastoral communities (Ribeiro et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). The Ilmotiok region in Laikipia County, Kenya, exemplifies these challenges, as it faces increasing pressure from overgrazing, which has degraded rangelands and led to reduced vegetation cover, soil compaction, and loss of productivity (Pelster et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). While studies have investigated the effects of grazing on vegetation and soil properties in semi-arid rangelands, there remains insufficient information on soil greenhouse gas (GHG) emissions from these systems (Oduor et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eResearch on GHG emissions has predominantly focused on temperate regions, leaving a significant knowledge gap in Sub-Saharan Africa (Rosenstock et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). In Kenya, most studies on GHGs emphasize livestock enteric emissions, while the contributions of soil processes under different grazing management practices and land cover types remain poorly understood (Pelster et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Oduor et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Furthermore, semi-arid rangelands such as Ilmotiok, which are characterized by low rainfall and minimal nutrient inputs, are underrepresented in GHG studies despite their potential contribution to global emissions (Wachiye et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). This gap is problematic as soil greenhouse gases; methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O) play a crucial role in climate change, yet their dynamics in these landscapes remain unclear. Addressing this gap is essential for designing sustainable land management strategies that mitigate emissions while improving rangeland productivity (Ribeiro et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Yan et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eGrazing patterns are known to affect CH₄, CO₂, and N₂O fluxes in soils. Grazing management directly influences CH₄ emissions through animal urine and droppings and indirectly through its effect on soil moisture (Wang et al., \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Oduor et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). However, the role of vegetation in methane flux has often been overlooked due to its perceived insignificance (Ciais et al., \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). In pasturelands, N₂O emissions primarily result from animal droppings, urine, and denitrification processes in the soil, while in semi-arid rangelands, N₂O is emitted during the denitrification of soil nitrates (Yan et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). On the other hand, CO₂ emissions in natural environments are driven predominantly by soil respiration, which is associated with microbial activity and root respiration (Graham et al., 2012; Saggar et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). These gases interact in soil pores and influence each other, depending on soil carbon and nitrogen dynamics. For instance, research in Kenya's semi-arid rangelands demonstrated a positive correlation between CO₂ and CH₄ emissions (Oduor et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). Furthermore, during prolonged droughts, CO₂ emissions are reduced, but upon rewetting (a phenomenon known as the Birch effect), root respiration can cause emissions to triple (Borken et al., \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e; Xiao et al., \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe Ilmotiok area in Laikipia County has experienced significant degradation due to continuous grazing, yet it remains a critical resource for pastoralists and wildlife. Understanding the impacts of grazing management practices and land cover types on GHG emissions is crucial for developing strategies to mitigate emissions and restore degraded rangelands (Rosenstock et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThis study is critical as it provides much-needed data on soil GHG fluxes in semi-arid rangelands, a region that is underrepresented in global climate research. By focusing on Ilmotiok, Laikipia County, this research addresses local environmental challenges while contributing to the global understanding of GHG dynamics in semi-arid ecosystems. The findings will inform sustainable grazing management practices and land restoration efforts, ensuring both climate mitigation and improved livelihoods for pastoral communities dependent on these rangelands.\\u003c/p\\u003e\"},{\"header\":\"Materials and method\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStudy site\\u003c/h2\\u003e \\u003cp\\u003eThe study was conducted at the Ilmotiok community ranch and Mpala research centre in Laikipia County, Kenya. It is located between latitudes 00\\u0026deg;17' S and 00\\u0026deg;45' N, and longitudes 36\\u0026deg;15' E and 37\\u0026deg;20' E, covering approximately 9,500 km\\u0026sup2; (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e). Laikipia County is part of the larger Ewaso Ng'iro Ecosystem, which stretches from the slopes of Mt. Kenya (5,199 m) in the south to the margin of the Great Rift Valley in the west (Ojwang et al., 2010; Lalampa et al., 2016).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eExperimental design\\u003c/h3\\u003e\\n\\u003cp\\u003eA completely randomized block design was used to set up the experiment. The treatments were grazing management practices, and the land cover types. Land cover types were selected based on the most dominant vegetation as shown in (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eThe grazing management practices that were assessed included continuous grazed zones in Ilmotiok community group ranch and the controlled grazed zones in Mpala Research Centre. Topographical positions classified were used as a blocking factor. In each topographical position, mid-slope, foot slope and bottomland there was a 200m transect. The 200m transect was further blocked into 50m long 4 times (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). This was to distinguish the distinct land cover type after every 50m stake and set up the static chambers. Three land cover types were assessed under each grazing management practice and topographical position, namely, bare ground, patches of grass and mosaic of trees. Three cylindrical opaque static chambers measuring 29.2cm in diameter and 15cm in height were installed 10cm deep in each land cover type making a total of 36 sampling points (3 chambers x 3 land cover types x 4 replicates) in each grazing management practice and topographical position. The chambers were installed three weeks prior to the first gas sampling. Data on surface soil properties (0-10cm) was used to establish the relationship with the GHG emission rates.\\u003c/p\\u003e\\n\\u003ch3\\u003eGHG Gas sampling and laboratory analysis\\u003c/h3\\u003e\\n\\u003cp\\u003eSoil emissions were sampled as from 24th January 2018 up to 28th February 2018 for each subplot. Gas samples were collected for 5 weeks consecutively from both sites one day per week, generally between 0800hrs and 1200hr local time. To avoid the influence of time, the last sub-plot to sample was the first to be sampled in the subsequent sampling event, and vice versa. Sampling was done immediately after fitting the lid (d\\u0026thinsp;=\\u0026thinsp;29.2cm) with an aluminium tape, rubber sealing, fan, 50cm non-forced vent, a Einstich TFA thermometer model and a sampling port was fitted to the base frame using metal clamps for 30 min. Gases were collected at 4 time intervals i.e. at time zero (T0), after 10 minutes (T1), 20 minutes (T2) and lastly after 30 minutes (T3). Once the systems were operational and set i.e., thermometers and chamber leads, gases were collected using 60ml syringe with a luer lock and stored in 20ml evacuated vials. The samples were transported to the lab to be measured for CO\\u003csub\\u003e2\\u003c/sub\\u003e, CH\\u003csub\\u003e4\\u003c/sub\\u003e and N\\u003csub\\u003e2\\u003c/sub\\u003eO. Other measurements taken included; surface compaction, soil moisture, temperature of soil, air and chamber, air pressure and chamber height. A total of 1440 samples were collected (5 weeks x 4-time intervals x 3 vegetation types x 4 reps x 2 grazing practices x 3 topographical positions).\\u003c/p\\u003e \\u003cp\\u003eCH\\u003csub\\u003e4\\u003c/sub\\u003e, CO\\u003csub\\u003e2\\u003c/sub\\u003e and N\\u003csub\\u003e2\\u003c/sub\\u003eO were analyzed at Mazingira Centre (ILRI) using a gas chromatograph (GC) which was equipped with \\u003csup\\u003e63\\u003c/sup\\u003eNi electron capture detector for N\\u003csub\\u003e2\\u003c/sub\\u003eO while a flame ionization detector was used to detect CH\\u003csub\\u003e4\\u003c/sub\\u003e and CO\\u003csub\\u003e2\\u003c/sub\\u003e. CH4, CO2 and N\\u003csub\\u003e2\\u003c/sub\\u003eO fluxes were calculated depending on the peak areas detected by the GC relative to peak areas determined from the calibrated gas standards. Linear regression of standard concentrations as described by Qui \\u003cem\\u003eet al.\\u003c/em\\u003e, (2006) was used to calculate CH\\u003csub\\u003e4\\u003c/sub\\u003e, CO\\u003csub\\u003e2\\u003c/sub\\u003e and N\\u003csub\\u003e2\\u003c/sub\\u003eO fluxes versus chamber closure time and corrected for soil moisture and temperature using Eq.\\u0026nbsp;1 below.\\u003c/p\\u003e \\u003cp\\u003eF= \\u003cspan class=\\\"InlineEquation\\\"\\u003e\\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:(\\\\text{P}/\\\\text{P}\\\\text{o})\\\\:\\\\text{x}\\\\:(\\\\text{M}/\\\\text{V}\\\\text{o})\\\\:\\\\text{x}\\\\:(\\\\text{d}\\\\text{c}/\\\\text{d}\\\\text{t})\\\\:\\\\text{x}\\\\:(\\\\text{T}\\\\text{o}/\\\\text{T})\\\\:\\\\text{x}\\\\:\\\\text{H}\\\\)\\u003c/span\\u003e\\u003c/span\\u003e (Eq.\\u0026nbsp;1)\\u003c/p\\u003e \\u003cp\\u003eWhereby: F\\u0026thinsp;=\\u0026thinsp;for CO\\u003csub\\u003e2\\u003c/sub\\u003e- C Linear flux (mg.m-2.h-1), CH\\u003csub\\u003e4\\u003c/sub\\u003e-C Linear flux (mg.m-2.h-1) and N\\u003csub\\u003e2\\u003c/sub\\u003eO- N Linear flux (\\u0026micro;g.m-2.h-1)\\u003c/p\\u003e \\u003cp\\u003eP\\u0026thinsp;=\\u0026thinsp;atmospheric pressure of study site (Pa)\\u003c/p\\u003e \\u003cp\\u003eP\\u003csub\\u003eo\\u003c/sub\\u003e= atmospheric pressure (Pa)\\u003c/p\\u003e \\u003cp\\u003eM\\u0026thinsp;=\\u0026thinsp;gas mass (g/mol)\\u003c/p\\u003e \\u003cp\\u003eV\\u003csub\\u003eo\\u003c/sub\\u003e= molar volume (ml)\\u003c/p\\u003e \\u003cp\\u003edc/dt\\u0026thinsp;=\\u0026thinsp;rate of change in concentrate\\u003c/p\\u003e \\u003cp\\u003eT\\u003csub\\u003eo\\u003c/sub\\u003e = absolute chamber temperature (\\u0026deg;C)\\u003c/p\\u003e \\u003cp\\u003eT\\u0026thinsp;=\\u0026thinsp;absolute chamber temperature at time of sampling (\\u0026deg;C)\\u003c/p\\u003e \\u003cp\\u003eH\\u0026thinsp;=\\u0026thinsp;height of static chamber at the time of sampling\\u003c/p\\u003e \\u003cp\\u003eAbove ground air temperatures at 1.5 m and inside the base chamber were measured concurrently in each gas sampling event using a Einstich\\u0026mdash;TFA digital probe thermometer. Soil temperature (\\u0026deg;C) and soil moisture content (SM, %v/v) were measured at 5 cm surface soil depth using a probe sensor model 5MT, Decagon Devices Inc which measured both soil moisture and temperature. Water filled pore space (WFPS) was determined as described by Zhang \\u003cem\\u003eet al.\\u003c/em\\u003e, (2012) using extra parameters measured in the field like soil moisture and bulk density. It was calculated using Eq.\\u0026nbsp;8 below.\\u003c/p\\u003e \\u003cp\\u003e \\u003cspan class=\\\"InlineEquation\\\"\\u003e \\u003cspan class=\\\"mathinline\\\"\\u003e\\\\(\\\\:WFPS\\\\:\\\\%=\\\\frac{Soil\\\\:moisture\\\\:\\\\left(\\\\%\\\\right)}{[1-\\\\left\\\\{\\\\frac{Bulk\\\\:density\\\\:\\\\left(\\\\frac{g}{{cm}^{3}}\\\\right)}{2.65}\\\\right\\\\}]}\\\\)\\u003c/span\\u003e \\u003c/span\\u003e (Eq.\\u0026nbsp;2)\\u003c/p\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eStatistical analysis\\u003c/h2\\u003e \\u003cp\\u003eR software version 3.5.3 was used to derive ANOVA tables and separate means using Agricolae package for CH4, CO2 and N2O fluxes to test the effect of grazing practices and land cover types on soil emissions. Linear regression model was used to determine the relationship between grazing practices, land cover, WFPS, total organic carbon, total nitrogen and CN ratio to CH4, CO2 and N2O.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Results\",\"content\":\"\\u003cdiv id=\\\"Sec8\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSoil temperature, TOC, TN, CN ratio and WFPS\\u003c/h2\\u003e \\u003cp\\u003eUnder grazing management practice and varying land cover types of the soil had varying responses. The study found that controlled grazing significantly improved soil quality compared to continuous grazing, with higher total organic carbon (TOC, 9.36\\u0026ndash;16.49 g/kg), total nitrogen (TN, 1.10\\u0026ndash;1.71 g/kg), and water-filled pore space (WFPS, 8.47\\u0026ndash;15.35%), while continuous grazing had lower TOC (9.04\\u0026ndash;12.98 g/kg), TN (1.00\\u0026ndash;1.22 g/kg), and WFPS (7.53\\u0026ndash;9.67%). Among land cover types, tree mosaics recorded the highest TOC and TN, followed by grass patches, while bare ground consistently showed the poorest soil conditions (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab1\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 1\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eSoil chemical and physical characteristics under both continuous grazing and controlled grazing in different land cover types\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"11\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c6\\\" colnum=\\\"6\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c7\\\" colnum=\\\"7\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c8\\\" colnum=\\\"8\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c9\\\" colnum=\\\"9\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c10\\\" colnum=\\\"10\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c11\\\" colnum=\\\"11\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eGrazing system\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eLand cover\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eTOC\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eTN\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c7\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCN Ratio\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003eWFPS\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003eSoil temperature\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eTextural class\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eg/kg\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003eg/kg\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e%\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e\\u0026deg;C\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c11\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eContinuous grazing\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.04\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.00\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e9.08***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e8.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e31.4\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eLS\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.69\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e10.15\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e9.67\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e34.8\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eSCL\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e12.98\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.22\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e10.72\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e7.53\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e26.5\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eSL\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eControlled grazing\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBG\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e9.36\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.1\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e8.67***\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e13.31\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e32.7\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eSL\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eGR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.36\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.32\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e10.17\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e15.35\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e33\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eSCL\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eTR\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e16.49\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e1.71\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c6\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c7\\\"\\u003e \\u003cp\\u003e9.71\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c8\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c9\\\"\\u003e \\u003cp\\u003e8.47\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c10\\\"\\u003e \\u003cp\\u003e35.3\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c11\\\"\\u003e \\u003cp\\u003eSCL\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colspan=\\\"11\\\" nameend=\\\"c11\\\" namest=\\\"c1\\\"\\u003e \\u003cp\\u003eSignificance level: '**' 0.001, '*' 0.05, 'LS' Loamy sand, 'SCL' Sandy clay loam, 'SL' sandy loam\\u003c/p\\u003e \\u003cp\\u003eBG-Bare ground, GR-patches of grass, TR-patches of trees\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\\n\\u003ch3\\u003eCarbon dioxide (CO) soil flux\\u003c/h3\\u003e\\n\\u003cp\\u003eThe results indicate that grazing practices and land cover types significantly affect soil CO₂-C flux. Controlled grazing emitted the highest CO₂-C flux (83.2 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;) compared to continuous grazing (21.5 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;). Among land cover types, tree mosaics exhibited the highest CO₂-C flux (66.57 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;), followed by patches of grass (61.88 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;), while bare ground emitted the least (27.86 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab2\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 2\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eEffect of grazing practices and land cover types on CO\\u003csub\\u003e2\\u003c/sub\\u003e-C flux\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFactor\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCumulative CO\\u003csub\\u003e2\\u003c/sub\\u003e- C Linear flux (mg.m-2.h-1)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGrazing practice\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eContinuous grazing\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e21.5\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eControlled grazing\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e83.2\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLand cover types\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBare ground\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.86*\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePatches of Grass\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e61.88\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e\\u0026nbsp;\\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMosaic of Tree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e66.57\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eSignifiant codes : \\u0026lsquo;***\\u0026rsquo; 0.001 \\u0026lsquo;**\\u0026rsquo; 0.01 \\u0026lsquo;*\\u0026rsquo; 0.05\\u003c/p\\u003e\\n\\u003ch3\\u003eNitrous oxide (NO) soil flux\\u003c/h3\\u003e\\n\\u003cp\\u003eThe study revealed significant differences in nitrous oxide (N₂O) emissions across grazing practices and land cover types. Continuous grazing, with an N₂O flux of 3.4 \\u0026micro;g. m⁻\\u0026sup2;.h⁻\\u0026sup1;, emitted significantly less N₂O compared to controlled grazing, which recorded a flux of 19.4 \\u0026micro;g. m⁻\\u0026sup2;.h⁻\\u0026sup1;. (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e). Among the land cover types, bare ground emitted the least N₂O (3.71 \\u0026micro;g. m⁻\\u0026sup2;.h⁻\\u0026sup1;), patches of grass exhibited the highest emissions (18.81 \\u0026micro;g. m⁻\\u0026sup2;.h⁻\\u0026sup1;), whereas mosaic of trees recorded intermediate N₂O emissions (11.43 \\u0026micro;g. m⁻\\u0026sup2;.h⁻\\u0026sup1;).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMethane (CH\\u003csub\\u003e4\\u003c/sub\\u003e) soil flux\\u003c/h2\\u003e \\u003cp\\u003eThe results show that grazing practices and land cover types significantly influence methane (CH₄-C) flux. Continuous grazing exhibited a negative CH₄-C flux (-0.037 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;), whereas controlled grazing showed a positive flux (0.016 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;). Among land cover types, grass patches had the strongest methane sink potential (-0.034 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;), while tree mosaics acted as a weaker sink (-0.005 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;), and bare ground emitted small amounts of methane (0.007 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;) (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab3\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 3\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eEffect of grazing practices and land cover types on CH\\u003csub\\u003e4\\u003c/sub\\u003e-C flux\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"3\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eFactor\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e\\u0026nbsp;\\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eCumulative CH\\u003csub\\u003e4\\u003c/sub\\u003e-C Linear flux (mg.m-2.h-1\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eGrazing practice\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eContinuous grazing\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.037\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eControlled grazing\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.016\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"2\\\" rowspan=\\\"3\\\"\\u003e \\u003cp\\u003eLand cover types\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eBare ground\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.007\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003ePatches of Grass\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.034\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eMosaic of Tree\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e-0.005\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eSignificant codes: \\u0026lsquo;***\\u0026rsquo; 0.001 \\u0026lsquo;**\\u0026rsquo; 0.01 \\u0026lsquo;*\\u0026rsquo; 0.05\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eRegression analysis of grazing management practices, land cover types and soil chemical and physical properties on CO\\u003c/b\\u003e \\u003csub\\u003e \\u003cb\\u003e2\\u003c/b\\u003e \\u003c/sub\\u003e \\u003cb\\u003eflux\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eThe regression analysis reveals factors influencing soil CO₂ flux, highlighting the roles of soil properties, grazing practices, and greenhouse gas interactions. Grazing management practices (Estimate\\u0026thinsp;=\\u0026thinsp;29.61, p\\u0026thinsp;=\\u0026thinsp;0.08) showed a near-significant effect, while land cover types (Estimate\\u0026thinsp;=\\u0026thinsp;4.60, p\\u0026thinsp;=\\u0026thinsp;0.81) were not significant predictors. Water-filled pore space (WFPS; Estimate\\u0026thinsp;=\\u0026thinsp;1.49, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and total organic carbon (TOC; Estimate\\u0026thinsp;=\\u0026thinsp;702.01, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) were strong positive drivers of CO₂ emissions. Conversely, total nitrogen (TN; Estimate = -6500.98, p\\u0026thinsp;=\\u0026thinsp;0.01) and soil texture (Estimate = -48.30, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) showed negative effects. The carbon-to-nitrogen ratio (C: N; Estimate = -87.88, p\\u0026thinsp;=\\u0026thinsp;0.01) was also negatively related. Methane (CH₄; Estimate\\u0026thinsp;=\\u0026thinsp;35.37, p\\u0026thinsp;=\\u0026thinsp;0.01) and nitrous oxide (N₂O; Estimate\\u0026thinsp;=\\u0026thinsp;1.98, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) fluxes were positively associated with CO₂ flux (Table\\u0026nbsp;\\u003cspan refid=\\\"Tab4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003cdiv class=\\\"gridtable\\\"\\u003e\\u003ctable float=\\\"Yes\\\" id=\\\"Tab4\\\" border=\\\"1\\\"\\u003e \\u003ccaption language=\\\"En\\\"\\u003e \\u003cdiv class=\\\"CaptionNumber\\\"\\u003eTable 4\\u003c/div\\u003e \\u003cdiv class=\\\"CaptionContent\\\"\\u003e \\u003cp\\u003eRegression analysis of precipitation, grazing management practices, land cover types and soil chemical and physical properties for CO\\u003csub\\u003e2\\u003c/sub\\u003e flux\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/caption\\u003e \\u003ccolgroup cols=\\\"5\\\"\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c1\\\" colnum=\\\"1\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c2\\\" colnum=\\\"2\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c3\\\" colnum=\\\"3\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c4\\\" colnum=\\\"4\\\"\\u003e\\u003c/div\\u003e \\u003cdiv align=\\\"left\\\" class=\\\"colspec\\\" colname=\\\"c5\\\" colnum=\\\"5\\\"\\u003e\\u003c/div\\u003e \\u003cthead\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c1\\\" morerows=\\\"1\\\" rowspan=\\\"2\\\"\\u003e \\u003cp\\u003eCoefficients:\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colspan=\\\"4\\\" nameend=\\\"c5\\\" namest=\\\"c2\\\"\\u003e \\u003cp\\u003eCO\\u003csub\\u003e2\\u003c/sub\\u003e Flux\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003eEstimate\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003eStd. Error\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003et value\\u003c/p\\u003e \\u003c/th\\u003e \\u003cth align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003ePr(\\u0026gt;|t|)\\u003c/p\\u003e \\u003c/th\\u003e \\u003c/tr\\u003e \\u003c/thead\\u003e \\u003ctbody\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003e(Intercept)\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e835.3664\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e279.0897\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.993\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eGrazing management practices\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e29.6058\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e10.3544\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-1.79\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.08 \\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eLand cover type\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e4.6035\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e18.7839\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e0.245\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.81\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eWFPS\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.4944\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.4222\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.54\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTOC\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e702.0136\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e200.737\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e3.497\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eTN\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-6500.98\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e2066.192\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.146\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoil texture\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-48.2962\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e11.5338\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-4.187\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0 \\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eSoil temperature\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-0.4005\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.5329\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-0.752\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.45\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCN ratio\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e-87.875\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e27.7759\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e-3.164\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0 \\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eCH\\u003csub\\u003e4\\u003c/sub\\u003e flux\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e35.3684\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e13.1076\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e2.698\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0.01\\u003csup\\u003e**\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003ctr\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c1\\\"\\u003e \\u003cp\\u003eN\\u003csub\\u003e2\\u003c/sub\\u003eO flux\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c2\\\"\\u003e \\u003cp\\u003e1.9792\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c3\\\"\\u003e \\u003cp\\u003e0.1178\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c4\\\"\\u003e \\u003cp\\u003e16.808\\u003c/p\\u003e \\u003c/td\\u003e \\u003ctd align=\\\"left\\\" colname=\\\"c5\\\"\\u003e \\u003cp\\u003e0\\u003csup\\u003e***\\u003c/sup\\u003e\\u003c/p\\u003e \\u003c/td\\u003e \\u003c/tr\\u003e \\u003c/tbody\\u003e \\u003c/colgroup\\u003e \\u003c/table\\u003e\\u003c/div\\u003e \\u003c/p\\u003e \\u003cp\\u003eSignificant codes: \\u0026lsquo;***\\u0026rsquo; 0.001 \\u0026lsquo;**\\u0026rsquo; 0.01 \\u0026lsquo;*\\u0026rsquo; 0.05\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Discussion\",\"content\":\"\\u003cdiv id=\\\"Sec13\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eSoil Properties\\u003c/h2\\u003e \\u003cp\\u003eThe study highlights the significant impact of grazing management practices and land cover types on critical soil properties, including total organic carbon (TOC), total nitrogen (TN), carbon-to-nitrogen (C: N) ratio, and water-filled pore space (WFPS). Controlled grazing consistently emerged as a superior management strategy for maintaining and enhancing soil health compared to continuous grazing. By reducing grazing pressure, controlled grazing allows vegetation to recover, leading to increased organic matter inputs, improved microbial activity, and enhanced nutrient cycling and soil structure. Oduor et al. (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) reported that controlled grazing minimizes overgrazing, enhancing soil organic matter and facilitating microbial processes, while Saggar et al. (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) observed that higher soil moisture and TOC under controlled grazing promote nitrogen cycling and microbial activity. These improvements align with the findings of Rosenstock et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), who emphasized the role of sustainable grazing practices in improving soil fertility and maintaining productivity.\\u003c/p\\u003e \\u003cp\\u003eIn contrast, continuous grazing degrades soil quality through overgrazing, compaction, and depletion of organic matter. Excessive livestock pressure under continuous grazing reduces vegetation cover, limits organic inputs, and disrupts microbial processes. Soil compaction from continuous grazing impairs water infiltration and aeration, further reducing nutrient cycling efficiency. Ribeiro et al. (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) noted that overgrazing causes soil compaction and nutrient depletion, diminishing the soil's ability to support plant growth. Similarly, Pelster et al. (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) found that continuous grazing reduces vegetation cover and nitrogen cycling, exacerbating soil degradation. Yan et al. (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) highlighted that continuous grazing negatively affects soil organic carbon and microbial activity, further emphasizing its detrimental impact on soil health.\\u003c/p\\u003e \\u003cp\\u003eLand cover types also significantly influence soil properties. Tree mosaics consistently demonstrated the highest TOC and TN levels among land cover types, attributable to organic inputs from leaf litter and root biomass. Boutton and Liau (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e) observed that woody vegetation enhances carbon and nitrogen storage through slower decomposition rates and stabilization in soil aggregates, while Wachiye et al. (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) found that tree cover improves soil quality by moderating soil temperature, retaining moisture, and supporting microbial activity. Grass patches also contribute to soil fertility through root turnover and litter deposition, though their effects are slightly lower than tree mosaics due to slower decomposition of grass litter (Yan et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). In contrast, bare ground consistently exhibited the poorest soil conditions, with minimal TOC and TN levels and low water retention. Ribeiro et al. (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) attributed these conditions to the absence of vegetation, which limits organic matter inputs and microbial activity.\\u003c/p\\u003e \\u003cp\\u003eGrazing management and land cover also influence specific parameters such as the C:N ratio and WFPS. Controlled grazing resulted in higher TOC, TN, and WFPS levels (16%, 22%, and 66%, respectively, compared to continuous grazing), while bulk density and C:N ratio were lower by 2% and 5%, respectively. The lower bulk density under controlled grazing reduces soil compaction, facilitating water infiltration and enhancing microbial activity. High TOC and TN levels under controlled grazing provide essential substrates for decomposition and nutrient cycling, driving greenhouse gas emissions. This phenomenon aligns with the observations of Kassa et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e), who reported that woody vegetation in rangelands enhances soil carbon content, explaining the higher carbon and nitrogen levels under tree mosaics in both grazing systems.\\u003c/p\\u003e \\u003cp\\u003eIn contrast, bare ground had the lowest TN levels, attributable to its high sand content, which limits the storage of mineralized nitrogen. Boutton and Liau (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e) found that soils under woody and herbaceous vegetation tend to have higher silt and clay content, promoting slow decomposition and accumulation of TN. The C:N ratio was generally low across all systems due to the low-quality substrate. Grass-dominated areas exhibited slightly higher C:N ratios than tree mosaics, reflecting the recalcitrant lignin and suberin content of grass litter, which takes longer to decompose (Boutton \\u0026amp; Liau, \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe higher WFPS under controlled grazing enhances microbial processes, facilitating nitrogen cycling and denitrification, which likely explains the higher N₂O flux observed in this system. Elevated TN levels under controlled grazing inhibit atmospheric methane oxidation, further influencing GHG flux dynamics. Increased WFPS and TN under controlled grazing create favorable conditions for microbial activity, driving higher CO₂, CH₄, and N₂O emissions. The findings are consistent with those of Rosenstock et al. (\\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) and Acharya et al. (\\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), who emphasized the interconnected roles of grazing practices and land cover in influencing soil greenhouse gas fluxes and nutrient cycling.\\u003c/p\\u003e \\u003cp\\u003eTree mosaics recorded the highest TN levels among land cover types, followed by grass patches, while bare ground consistently had the lowest. These trends highlight the critical role of vegetation in enhancing soil nutrient content and structure. Under continuous grazing, higher stocking density increased species diversity and tree density, indirectly influencing TOC and TN levels through increased organic inputs. These findings align with studies by Kassa et al. (\\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e), which demonstrated the significant contribution of woody vegetation to soil carbon content, and Boutton and Liau (\\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2010\\u003c/span\\u003e), who emphasized the stabilizing effect of woody and herbaceous vegetation on soil nutrient cycling.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eCarbon dioxide (CO\\u003csub\\u003e2\\u003c/sub\\u003e)\\u003c/h2\\u003e \\u003cp\\u003eThe study highlights the significant influence of grazing practices and land cover types on soil carbon dioxide (CO₂-C) flux, demonstrating how these factors drive soil respiration and carbon cycling. Controlled grazing emitted substantially higher CO₂-C flux compared to continuous grazing, reflecting the positive impact of improved soil conditions under controlled grazing systems. Among land cover types, tree mosaics and grass patches showed the highest CO₂ emissions, while bare ground emitted the least, emphasizing the role of vegetation in enhancing soil carbon cycling.\\u003c/p\\u003e \\u003cp\\u003eControlled grazing contributed to higher CO₂-C flux due to enhanced total organic carbon (TOC) and water-filled pore space (WFPS), which create favorable conditions for microbial activity and root respiration. The accumulation of organic matter under controlled grazing provides a steady substrate for microbial decomposition, driving carbon release into the atmosphere. These findings align with Oduor et al. (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), who reported that controlled grazing enhances soil organic matter and moisture, promoting microbial processes that increase CO₂ emissions. Furthermore, high WFPS levels improve soil respiration by facilitating the diffusion of gases, while TOC contributes to root respiration and microbial activity, as noted by Xiao et al. (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e). In contrast, continuous grazing exhibited much lower CO₂-C flux due to soil compaction and reduced vegetation cover. Compacted soils inhibit water infiltration and aeration, limiting microbial decomposition and carbon cycling. Yan et al. (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) similarly observed that continuous grazing negatively affects soil respiration by degrading soil structure and reducing carbon inputs, while Pelster et al. (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) found that overgrazed soils have a reduced capacity for carbon mineralization, leading to lower CO₂ emissions.\\u003c/p\\u003e \\u003cp\\u003eLand cover types also played a critical role in regulating CO₂-C flux. Tree mosaics emitted the highest CO₂-C flux among the land cover types, driven by organic inputs from leaf litter and root activity, which enhance microbial decomposition and belowground respiration. Trees provide shade, moderating soil temperatures and maintaining moisture levels conducive to microbial processes. Kong et al. (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) reported that soils under tree cover have higher microbial carbon and nitrogen content, which increases soil respiration and carbon flux. Grass patches also demonstrated high CO₂-C flux due to their dense vegetation cover, which contributes organic matter and supports root respiration. The rhizosphere in grass patches fosters active microbial interactions, enhancing decomposition processes and carbon cycling. Wachiye et al. (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) highlighted that grass-covered areas contribute significantly to CO₂ emissions through root turnover and litter decomposition, although their contributions are slightly lower than those of tree-dominated areas.\\u003c/p\\u003e \\u003cp\\u003eIn contrast, bare ground emitted the lowest CO₂-C flux due to the absence of vegetation and organic inputs. The compacted nature of bare ground, combined with low TOC and minimal microbial activity, limits soil respiration. Ribeiro et al. (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) noted that bare soils in degraded rangelands exhibit poor nutrient retention and low microbial biomass, which reduce carbon cycling. Although localized patches of animal droppings or urine on bare ground may contribute to CO₂ emissions, as observed by Yan et al. (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), these contributions are minimal and insufficient to offset the overall lack of vegetation and organic matter.\\u003c/p\\u003e \\u003cp\\u003eThese findings underscore the critical role of grazing practices and land cover types in regulating soil carbon cycling. Controlled grazing enhances soil conditions that promote microbial decomposition and CO₂ flux, while continuous grazing suppresses these processes through soil degradation. Similarly, tree mosaics and grass patches play a vital role in enhancing soil respiration and carbon flux due to their contributions to organic matter and microbial activity. In contrast, bare ground reflects the negative impacts of degraded soil conditions on carbon cycling. These results align with Oduor et al. (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) and Wachiye et al. (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e), who emphasized the importance of sustainable land management practices in optimizing soil respiration and maintaining soil productivity.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eNitrous oxide (N\\u003csub\\u003e2\\u003c/sub\\u003eO) soil flux\\u003c/h2\\u003e \\u003cp\\u003eThe study highlights significant differences in nitrous oxide (N₂O) emissions across grazing practices and land cover types, illustrating how soil management and vegetation influence greenhouse gas fluxes. Controlled grazing was associated with higher N₂O emissions compared to continuous grazing, which can be attributed to improved soil conditions under controlled systems. Enhanced water-filled pore space (WFPS), organic matter, and nitrogen availability create optimal conditions for microbial activity, particularly denitrification, which is a key process driving N₂O emissions (Saggar et al., \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Acharya et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Moderate grazing intensities, as found in controlled grazing, improve soil moisture retention and organic matter accumulation, promoting microbial processes that enhance N₂O fluxes.\\u003c/p\\u003e \\u003cp\\u003eConversely, continuous grazing limits N₂O emissions due to soil compaction, reduced vegetation cover, and depleted nitrogen pools. Compacted soils restrict the formation of oxygen-free microsites necessary for denitrification, while the absence of sufficient organic inputs and microbial activity further suppresses emissions (Yan et al., \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). Degraded soils under continuous grazing are characterized by poor nitrogen cycling, which constrains the production of N₂O and other greenhouse gases (Pelster et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eAmong land cover types, grass patches were identified as hotspots for N₂O emissions due to their ability to retain moisture and organic matter, which supports active microbial nitrogen cycling. Grass roots and litter contribute organic inputs that enhance microbial activity and promote denitrification (Wachiye et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Tree mosaics exhibited intermediate N₂O emissions, reflecting a balance between organic inputs from tree litter and the shading effects of trees, which can reduce soil temperature and microbial activity (Kong et al., \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Bare ground consistently emitted the least N₂O, as its poor soil quality, minimal vegetation cover, and reduced microbial biomass limit nitrogen cycling and the denitrification process (Ribeiro et al., \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe interplay between grazing practices and land cover types underscores the importance of sustainable land management in regulating N₂O emissions. Controlled grazing in vegetated areas enhances soil fertility but may increase N₂O emissions, highlighting the need for strategies to balance productivity with greenhouse gas mitigation. Mixed vegetation systems, such as combining trees and grass patches, can optimize soil health while minimizing environmental impacts. Sustainable practices like rotational grazing and the use of nitrogen inhibitors are critical for mitigating emissions and maintaining soil productivity in semi-arid ecosystems (Acharya et al., \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Rosenstock et al., \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThese findings emphasize that while controlled grazing and vegetation restoration improve soil quality, complementary measures are necessary to minimize greenhouse gas emissions. Integrated management strategies that promote soil resilience and productivity while reducing environmental impacts are essential for sustainable land use in semi-arid rangelands (Pelster et al., \\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Wachiye et al., \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eMethane (CH\\u003csub\\u003e4\\u003c/sub\\u003e) soil flux\\u003c/h2\\u003e \\u003cp\\u003eThe study highlights the significant influence of grazing practices and land cover types on methane (CH₄-C) flux, demonstrating how soils can act as methane sinks or sources depending on management strategies and vegetation cover. Continuous grazing was identified as a methane sink, emitting negative CH₄-C flux. This is likely due to compacted soils under continuous grazing, which limit conditions for methane production while promoting methane oxidation by methanotrophic microbes. Methane oxidation occurs when oxygen diffuses into soil microsites, enabling methanotrophs to convert methane into CO₂. These findings align with Wang et al. (\\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e), who observed that degraded soils in semi-arid rangelands often act as methane sinks due to restricted methanogenesis and enhanced oxidation processes. Colmer (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e) also emphasized that methanotrophic activity is driven by oxygen diffusion into soil microsites, facilitating the conversion of methane into CO₂.\\u003c/p\\u003e \\u003cp\\u003eIn contrast, controlled grazing exhibited a small positive methane flux, suggesting that it acts as a methane source. This shift is likely due to improved soil conditions under controlled grazing, such as higher organic matter and moisture levels, which create a favorable environment for methanogenic microbes responsible for methane production. Zhu et al. (2015) reported that moderate grazing intensities enhance soil organic matter and moisture, potentially transitioning soils from methane sinks to sources. Similarly, Cardoso et al. (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) found that methane emissions increase in soils with enhanced organic matter and water-filled pore space, as these conditions promote methanogenic microbial activity.\\u003c/p\\u003e \\u003cp\\u003eLand cover types also play a critical role in methane flux dynamics. Grass patches demonstrated the strongest methane sink potential due to their active root systems, which promote oxygen diffusion into the soil and enhance methane oxidation by methanotrophic microbes. The aerobic microsites created within the rhizosphere of grass patches facilitate efficient methane oxidation, as noted by Colmer (\\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e). Additionally, grass patches contribute organic matter that supports microbial activity without significantly favoring methanogenesis, making them highly effective methane sinks. Yan et al. (\\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) similarly observed that grass-dominated soils promote microbial processes that enhance methane oxidation due to improved oxygen availability and minimal methanogenesis.\\u003c/p\\u003e \\u003cp\\u003eTree mosaics also acted as methane sinks, though their capacity was weaker compared to grass patches. Organic matter from tree litter supports microbial activity, but shading effects and root competition for soil resources may limit oxygen diffusion, reducing the efficiency of methane oxidation. Oduor et al. (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e) noted that woody vegetation moderates soil temperature and moisture, which can create less favorable conditions for methanotrophic activity compared to grass patches. Kong et al. (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) further emphasized that tree-covered soils support microbial processes, but canopy effects can influence methane flux dynamics by reducing oxygen availability.\\u003c/p\\u003e \\u003cp\\u003eBare ground, on the other hand, emitted small amounts of methane, reflecting its role as a minor methane source. The absence of vegetation on bare ground limits organic inputs and microbial activity, reducing the capacity for methane oxidation. Poor soil structure and minimal biological activity further constrain methane flux dynamics. Cardoso et al. (\\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) reported that bare soils, with low organic matter and biological activity, tend to emit small amounts of methane due to constrained oxidation processes. Similarly, Ribeiro et al. (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e) observed that degraded soils in rangelands exhibit poor microbial activity and limited methane oxidation, resulting in minor emissions.\\u003c/p\\u003e \\u003cp\\u003e \\u003cb\\u003eRelationship between CO\\u003c/b\\u003e \\u003csub\\u003e \\u003cb\\u003e2\\u003c/b\\u003e \\u003c/sub\\u003e \\u003cb\\u003eflux and grazing management practices, land cover types, and soil chemical and physical properties\\u003c/b\\u003e\\u003c/p\\u003e \\u003cp\\u003eStepwise regression analysis showed that CO\\u003csub\\u003e2\\u003c/sub\\u003e flux was driven by TOC, TN, WFPS, precipitation, and CN ratio. The regression analysis identifies the key factors influencing soil CO₂ flux and provides insights into how soil properties, grazing management, land cover types, and greenhouse gas fluxes interact to regulate emissions. The intercept represents the baseline CO₂ flux when all other variables are held constant. Grazing management practices showed a near-significant effect on CO₂ flux, with a positive coefficient suggesting that controlled grazing contributes to higher CO₂ emissions compared to continuous grazing. This increase is likely due to improved soil organic matter and moisture under controlled grazing, which enhance microbial activity and root respiration. These findings align with Oduor et al. (\\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e), who reported that controlled grazing improves soil organic carbon (TOC) and water-filled pore space (WFPS), driving microbial decomposition and CO₂ emissions. However, the lack of statistical significance in this analysis indicates that the effect may vary depending on other interacting factors.\\u003c/p\\u003e \\u003cp\\u003eLand cover types did not significantly influence CO₂ flux, suggesting that differences in emissions among bare ground, grass patches, and tree mosaics are driven by other variables, such as soil organic carbon and nitrogen content. In contrast, WFPS was a strong positive predictor of CO₂ flux, highlighting the importance of soil moisture in enhancing microbial decomposition and root respiration. Higher WFPS levels create optimal conditions for microbial activity, leading to increased carbon cycling and CO₂ emissions, as also noted by Saggar et al. (\\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eTotal organic carbon (TOC) significantly influenced CO₂ flux, with higher organic carbon levels providing essential substrates for microbial decomposition. This finding underscores the role of organic matter in driving soil respiration and carbon cycling, consistent with observations by Xiao et al. (\\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e), who linked TOC to enhanced microbial activity and root respiration. Conversely, total nitrogen (TN) exhibited a strong negative relationship with CO₂ flux, suggesting that high nitrogen availability may suppress carbon mineralization processes or indicate conditions less favorable for microbial decomposition. Pelster et al. (\\u003cspan citationid=\\\"CR13\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) similarly found that excessive nitrogen may reduce microbial activity by disrupting the balance between carbon and nitrogen cycling, particularly in nitrogen-rich soils.\\u003c/p\\u003e \\u003cp\\u003eSoil texture emerged as a significant negative predictor of CO₂ flux, with coarser soils likely supporting reduced microbial activity due to limited water retention. In contrast, finer soils tend to store carbon rather than release it as CO₂. This result aligns with findings by Ribeiro et al. (\\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e), who noted that soil texture influences water retention and microbial habitat, affecting carbon cycling. Interestingly, soil temperature did not significantly affect CO₂ flux, suggesting that temperature variations within the observed range were not a major driver of emissions in this study. Wachiye et al. (\\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) similarly reported that soil temperature in semi-arid regions often has a limited direct impact on CO₂ flux, as microbial processes are more strongly driven by moisture and organic matter availability.\\u003c/p\\u003e \\u003cp\\u003eThe carbon-to-nitrogen (C: N) ratio showed a significant negative relationship with CO₂ flux, indicating that soils with lower C:N ratios emit more CO₂ due to the higher quality and faster decomposition of organic matter. Toma and Hatano (\\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e) also observed that low C:N ratios are associated with rapid decomposition and elevated CO₂ emissions, as microbes readily metabolize high-quality organic matter.\\u003c/p\\u003e \\u003cp\\u003eMethane (CH₄) flux was positively associated with CO₂ flux, reflecting an interaction between methane oxidation and soil respiration processes. This suggests that carbon and methane cycling are interconnected, with soils actively emitting one greenhouse gas often participating in the cycling of others. Zhao et al. (\\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e) found that microbial respiration driving CO₂ emissions can simultaneously enhance methane oxidation in soil microsites, linking the two fluxes. Similarly, nitrous oxide (N₂O) flux was a highly significant positive predictor of CO₂ flux, highlighting the link between microbial processes driving N₂O emissions, such as denitrification, and those contributing to CO₂ emissions. Kong et al. (\\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e) emphasized that denitrification and CO₂ production are often co-occurring processes in soils with active nitrogen cycling.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003eConclusion and recommendation\\u003c/h2\\u003e \\u003cp\\u003eThis study highlights the significant impact of grazing management practices and land cover types on greenhouse gas emissions and soil properties in semi-arid rangelands, emphasizing the importance of sustainable land management. Controlled grazing proved to be a more effective strategy than continuous grazing, enhancing soil quality by increasing total organic carbon (TOC), total nitrogen (TN), and water-filled pore space (WFPS), which supported microbial activity and nutrient cycling. These improvements resulted in higher emissions of carbon dioxide (CO₂) and nitrous oxide (N₂O), alongside a slight reduction in methane (CH₄) sink potential. In contrast, continuous grazing degraded soil quality through compaction, loss of organic matter, and reduced microbial activity, leading to lower greenhouse gas emissions but significant declines in soil fertility and productivity. Land cover types also played a critical role, with tree mosaics and grass patches contributing to enhanced soil carbon and nitrogen cycling through organic inputs, root activity, and microclimatic regulation, while bare ground consistently exhibited poor soil quality and minimal greenhouse gas fluxes due to the lack of vegetation and organic matter inputs. These findings underscore the necessity of adopting controlled grazing practices, integrating diverse vegetation systems, and implementing climate-smart agricultural strategies, such as rotational grazing, nitrogen inhibitors, and reforestation, to optimize soil health, mitigate greenhouse gas emissions, and enhance ecosystem resilience in the face of climate change. Sustainable land-use policies and continuous monitoring of soil emissions are essential to ensuring long-term productivity and environmental sustainability in semi-arid ecosystems.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e\\u003cstrong\\u003eSupplementary Information\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eAcknowledgements\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe study was financed by SLEEK \\u0026lsquo;System for Land-based Emission Estimation in Kenya\\u0026rsquo; http://www.sleek .envir onmen t.go.ke/ . Enumerators are highly acknowledged for their assistance in data Collection and labelling. The authors would like to acknowledge Ilmotiok community ranch who allowed us to use their ranch for the experiment.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eFunding\\u0026nbsp;\\u003c/strong\\u003eNot applicable\\u003cstrong\\u003e\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eData availability\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eUpon request, the corresponding author can provide the data supporting the findings of this study.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConflict of interest\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare that they have no conflict of interest.\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eEthical approval\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eAll authors have reviewed the manuscript and agree to its submission to this journal.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eConsent for publication\\u003c/strong\\u003e\\u0026nbsp;\\u003c/p\\u003e\\n\\u003cp\\u003eNot applicable.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompeting interests\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe authors declare no competing interests.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eInformed consent to participate\\u0026nbsp;\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe sampling was conducted on farmers field and permission was obtained from the farmers to sample at their field.\\u003c/p\\u003e\\n\\u003cp\\u003e\\u003cstrong\\u003eCompliance with Ethical Standards\\u003c/strong\\u003e\\u003c/p\\u003e\\n\\u003cp\\u003eThe protocols for this research were approved by NACOSTI Committee in accordance with the\\u0026nbsp;NATIONAL COMMISSION FOR SCIENCE, TECHNOLOGY AND INNOVATION (\\u003cstrong\\u003eNACOSTI\\u003c/strong\\u003e)\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eAcharya, R., Ghimire, R., Bista, P., \\u0026amp; Dom\\u0026iacute;nguez-Faus, R. 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Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press.\\u003c/li\\u003e\\n\\u003cli\\u003eColmer, T. D. (2003). Long-distance transport of gases in plants: A perspective on internal aeration and radial oxygen loss from roots. Plant, Cell \\u0026amp; Environment, 26(1), 17\\u0026ndash;36. \\u003c/li\\u003e\\n\\u003cli\\u003eGraham, S. A., \\u0026amp; Haynes, B. J. (2012). Soil respiration and its drivers in natural ecosystems. Soil Science Society of America Journal, 76(5), 1431\\u0026ndash;1441. \\u003c/li\\u003e\\n\\u003cli\\u003eKassa, H., Dondeyne, S., Poesen, J., Frankl, A., \\u0026amp; Nyssen, J. (2010). Woody vegetation and its effects on soil organic carbon in the highlands of Ethiopia. Geoderma, 159(3-4), 193\\u0026ndash;204. \\u003c/li\\u003e\\n\\u003cli\\u003eKibet, S., Otor, C., \\u0026amp; Lelon, J. (2016). 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Agricultural and Forest Meteorology, 220, 39\\u0026ndash;48. \\u003c/li\\u003e\\n\\u003cli\\u003eRosenstock, T. S., Rufino, M. C., Butterbach-Bahl, K., Wollenberg, E., Richards, M., \\u0026amp; Fraisse, C. (2016). Sustainable land management and greenhouse gas mitigation in sub-Saharan Africa. Agriculture for Development, 29, 40\\u0026ndash;45. \\u003c/li\\u003e\\n\\u003cli\\u003eRosenstock, T. S., Rufino, M. C., Butterbach-Bahl, K., Wollenberg, E., Richards, M., \\u0026amp; Fraisse, C. (2016). Sustainable land management and greenhouse gas mitigation in sub-Saharan Africa. Agriculture for Development, 29, 40\\u0026ndash;45. \\u003c/li\\u003e\\n\\u003cli\\u003eSaggar, S., Tate, K. R., Giltrap, D. L., \\u0026amp; Singh, J. (2013). Soil\\u0026ndash;atmosphere exchange of nitrous oxide and methane. Plant and Soil, 309(1), 19\\u0026ndash;31. \\u003c/li\\u003e\\n\\u003cli\\u003eSaggar, S., Tate, K. R., Giltrap, D. L., \\u0026amp; Singh, J. (2013). Soil\\u0026ndash;atmosphere exchange of nitrous oxide and methane. Plant and Soil, 309(1), 19\\u0026ndash;31. \\u003c/li\\u003e\\n\\u003cli\\u003eToma, Y., \\u0026amp; Hatano, R. (2007). Methane oxidation in paddy soils as influenced by soil properties and land-use practices. Soil Biology \\u0026amp; Biochemistry, 39(8), 2045\\u0026ndash;2053. \\u003c/li\\u003e\\n\\u003cli\\u003eWachiye, E. J., Waswa, B. S., \\u0026amp; Okoth, P. F. (2019). The role of grazing practices and vegetation types in soil greenhouse gas fluxes. Journal of Soil and Water Conservation, 74(2), 123\\u0026ndash;133. https://doi.org/10.2489/jswc.74.2.123\\u003c/li\\u003e\\n\\u003cli\\u003eWachiye, E. J., Waswa, B. S., \\u0026amp; Okoth, P. F. (2019). The role of grazing practices and vegetation types in soil greenhouse gas fluxes. Journal of Soil and Water Conservation, 74(2), 123\\u0026ndash;133. https://doi.org/10.2489/jswc.74.2.123\\u003c/li\\u003e\\n\\u003cli\\u003eWang, C., Liu, D., Zhang, X., \\u0026amp; Han, W. (2012). Methane oxidation in semi-arid grassland soils. Soil Biology \\u0026amp; Biochemistry, 77, 287\\u0026ndash;295. \\u003c/li\\u003e\\n\\u003cli\\u003eWang, C., Liu, D., Zhang, X., \\u0026amp; Han, W. (2014). Methane oxidation in semi-arid grassland soils. Soil Biology \\u0026amp; Biochemistry, 77, 287\\u0026ndash;295. \\u003c/li\\u003e\\n\\u003cli\\u003eXiao, C., Janssens, I. A., Liu, P., Zhou, Z., \\u0026amp; Luo, Y. (2007). The role of soil organic matter in determining ecosystem carbon fluxes. Biogeochemistry, 84(1), 1\\u0026ndash;13. \\u003c/li\\u003e\\n\\u003cli\\u003eXiao, C., Janssens, I. A., Liu, P., Zhou, Z., \\u0026amp; Luo, Y. (2007). The role of soil organic matter in determining ecosystem carbon fluxes. Biogeochemistry, 84(1), 1\\u0026ndash;13. \\u003c/li\\u003e\\n\\u003cli\\u003eYan, X., Cai, Z., Wang, S., \\u0026amp; Smith, P. (2016). Methane emissions from global grazing lands. Global Change Biology, 22(1), 213\\u0026ndash;227.\\u003c/li\\u003e\\n\\u003cli\\u003eYan, X., Cai, Z., Wang, S., \\u0026amp; Smith, P. (2016). Methane emissions from global grazing lands. Global Change Biology, 22(1), 213\\u0026ndash;227. \\u003c/li\\u003e\\n\\u003cli\\u003eZhao, X., Tang, H., \\u0026amp; Zhu, X. (2019). Interaction between methane oxidation and soil respiration in arid soils. Soil Science and Plant Nutrition, 65(2), 149\\u0026ndash;157. \\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":true,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true},\"keywords\":\"Greenhouse gas emissions, Grazing management practices, Land cover types, Semi-arid rangelands\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-5998779/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-5998779/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eGreenhouse gas (GHG) emissions from soils, influenced by grazing management practices and land cover types, are critical in understanding the dynamics of semi-arid rangeland ecosystems. This study was conducted in the Ilmotiok community ranch, Laikipia County, Kenya, to investigate the effects of grazing management practices (continuous and controlled grazing) and land cover types (bare ground, grass patches, and tree mosaics) on soil emissions of carbon dioxide (CO₂), methane (CH₄), and nitrous oxide (N₂O). A completely randomized block design was used, with 36 sampling points established across three topographical positions under both grazing systems. Gas samples were collected for five weeks using static chambers, and GHG fluxes were analyzed using a gas chromatograph (GC). Results showed that controlled grazing significantly improved soil quality, with higher total organic carbon (TOC), total nitrogen (TN), and water-filled pore space (WFPS) compared to continuous grazing. Tree mosaics exhibited the highest TOC and TN levels, followed by grass patches, while bare ground had the lowest. Controlled grazing emitted higher CO₂-C flux (83.2 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;) than continuous grazing (21.5 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;), with tree mosaics showing the highest flux among land cover types. Nitrous oxide emissions were also higher under controlled grazing (19.4 \\u0026micro;g. m⁻\\u0026sup2;.h⁻\\u0026sup1;) than continuous grazing (3.4 \\u0026micro;g. m⁻\\u0026sup2;.h⁻\\u0026sup1;), with grass patches exhibiting the highest N₂O flux among land cover types. Methane fluxes varied, with continuous grazing acting as a methane sink (-0.037 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;) and controlled grazing as a slight source (0.016 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;). Grass patches showed the strongest methane sink potential (-0.034 mg. m⁻\\u0026sup2;.h⁻\\u0026sup1;). Regression analysis revealed that WFPS and TOC were strong positive drivers of CO₂ flux, while TN, soil texture, and C:N ratio showed negative relationships. Methane and nitrous oxide fluxes were positively associated with CO₂ emissions, indicating interconnected soil GHG dynamics. These findings highlight the critical role of sustainable grazing practices and diverse land cover in improving soil quality and managing GHG emissions in semi-arid rangelands. Integrating controlled grazing and vegetation restoration can enhance soil fertility, mitigate GHG emissions, and promote ecosystem resilience.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Influence of grazing practices and land cover types on CH 4 , CO 2 and N 2 O fluxes in semi- arid rangelands of Laikipia County, Kenya\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2025-02-14 11:51:17\",\"doi\":\"10.21203/rs.3.rs-5998779/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"researchsquare\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":true,\"externalIdentity\":\"\",\"sideBox\":\"\",\"snPcode\":\"\",\"submissionUrl\":\"/submission\",\"title\":\"Research Square\",\"twitterHandle\":\"researchsquare\",\"acdcEnabled\":true,\"dfaEnabled\":false,\"editorialSystem\":\"\",\"reportingPortfolio\":\"\",\"inReviewEnabled\":false,\"inReviewRevisionsEnabled\":true}}],\"origin\":\"\",\"ownerIdentity\":\"c5073490-3b35-4e29-913e-f28468c40fa6\",\"owner\":[],\"postedDate\":\"February 14th, 2025\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"posted\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2025-02-20T10:38:38+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2025-02-14 11:51:17\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-5998779\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-5998779\",\"identity\":\"rs-5998779\",\"version\":[\"v1\"]},\"buildId\":\"8U1c8b4HqxoKbykW_rLl7\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}