Dynamics of Soil Physicochemical Properties under Different Agroforestry Practices in the Dollo watershed, Gamo Zone, Southern Ethiopia

Dynamics of Soil Physicochemical Properties under Different Agroforestry Practices in the Dollo watershed, Gamo Zone, Southern Ethiopia | 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 Dynamics of Soil Physicochemical Properties under Different Agroforestry Practices in the Dollo watershed, Gamo Zone, Southern Ethiopia Yohannes Dikola, Aynalem Gochera This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8685594/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 The dynamics of soil physicochemical properties in agroforestry systems can significantly influence crop productivity, soil health, and overall ecosystem sustainability. In the context of the Dollo watershed in the Gamo Zone of Southern Ethiopia, understanding these dynamics is vital for promoting sustainable agricultural practices and enhancing land productivity. This study investigates the impact of various agroforestry practices on the physicochemical properties of soil. Using a comparative analysis, sixty soil samples were collected from multiple agroforestry sites. The results showed that agroforestry type and soil depth had significant effects on soil bulk density, organic carbon, pH, exchangeable potassium, electrical conductivity and cation exchange capacity. The results indicated significant variations in soil physicochemical properties across different agroforestry practices and soil depth, with certain agroforestry systems enhancing soil fertility more effectively than others. Home garden agroforestry practices were found to have higher soil organic matter content than the other types of agroforestry. These findings underline the importance of selecting appropriate agroforestry practices to improve soil health and agricultural productivity in the area. Based on these results, integrating agroforestry with traditional farming methods can result in synergies that benefit both people and the environment. Continued research and extension efforts are needed to promote these sustainable practices effectively. agroforestry practices physicochemical properties soil depth soil fertility Figures Figure 1 Introduction Deforestation and conversion of forest land for human use is one of the most serious challenges of the planet, resulted in shocking rise in natural resource degradation (Meijer et al., 2015). Deforestation would lead to shortage of wood and non-wood forest products, ecological degradation, and loss of biodiversity, deterioration of ecosystem services and emission of greenhouse gases coupled with minimize carbon sequestration. The analysis determined which four factors were most closely linked to the decline in tree cover worldwide between 2001 and 2015. As to Curtis et al., (2018), the deforestation caused by commodities accounted for 27% of the total deforestation, followed by shifting agriculture (24%), wildfires (23%), urbanization (0.6%), and forestry loss inside managed forests and tree plantations (26%). Lastly, permanent conversion of forest for the expansion of commodities was the cause of deforestation. Now global estimates suggest that 30% of original forest cover has been converted for various uses and other 20% has been degraded (Rizvi et al., 2015). From global forest cover 3952 million ha African forests and tropical forests accounted about 21% and 44% of the world forest respectively (FAO 2006; Liu et al., 2009; Keenan et al., 2015; MacDicken 2015). Forest offers a widespread kind of environment objects and services to the inhabitants (Gosain et al., 2015; Awasthi et al., 2022). Addressing the problems of deforestation and forest degradation can enhance ecosystem services that have knock-on effect on other sectors (Ndayambaje et al., 2012; Meijer et al., 2015). The agroforestry was discovered as a solution to solving tropical forests problems, notably the fact that rainforests had been being depleted at an alarming rate, that the tremendous and sundry capacity of bushes in the tropics was in striking contrast to the end and recommended as a land use option (Torquebiau 2000). Ethiopia has adopted agroforestry as soil management options which are important to improve soil physic-chemical quality (Wolle et al., 2021; Dori et al., 2022). Agroforestry plantation developments on farm land have often been considered as a “quick fix” solutions to the problems of over exploitation of the natural forests resources and forest degradation. Implementation of agroforestry based agriculture may ultimately improve soil quality and limit agricultural expansion into natural forests, as well as the negative impacts of agriculture on biodiversity (Khumalo et al., 2012). Research outcomes show that the agroforestry practices plays an important role in enhancing soil fertility, soil organic matter, promoting nutrient cycling and improving agricultural production (Emire and Asfaw, 2018; Awazi and Tchamba 2019; Tsufac et al., 2019; Wollo et al., 2019; Dori 2022). However, the amount of soil property in agroforestry systems is quite variable and values are affected by agroforestry types, their management practice, climate, soil characteristics, and mycorrhizal status (Munroe and Isaac 2014; Ovung et al., 2021). The dynamics of soil physicochemical properties in agroforestry systems can significantly influence crop productivity, soil health, and overall ecosystem sustainability. In the context of the Dollo watershed in the Gamo Zone of Southern Ethiopia, understanding these dynamics is vital for promoting sustainable agricultural practices and enhancing land productivity. The lack of research investigating the impact of various agroforestry techniques on the physicochemical qualities of soil in the Dollo watershed, Gamo Zone, Southern Ethiopia, presents both a challenge and an opportunity for development in sustainable land management practices. Here the purpose of this study was to investigate the impacts of main types of agroforestry practices on the physical and chemical characteristics of the soil. Additionally, it aimed to assess how soil qualities in the southern Ethiopian lowlands of the Dollo watershed were affected by the interactions between agroforestry practices and soil depth. Materials and Methods The study was conducted in the Dollo watershed Kamba woreda, Southern Ethiopia. Its exact geographical coordinates were 39 ° 37' 06" E and 9 ° 41' 07" N with an elevation ranges from 2747 to 3674 m above sea level (Fig. 1 ). The watershed located 607 km south west of the Addis Ababa. Rainfall of the area has a bi-modal pattern. The mean annual air temperature and rainfall of the study site is 19.7°C and 1470 mm respectively. The main types of soil in the study area are cambisols, vertisols and andosols. In the study area the farming system is based on rain fed. Identification and delineation of types of agroforestry practiced To determine sample size the spatial boundaries of the study area was clearly demarcated and accurately define by using Global Positioning System (GPS). The collected data were later transferred to computer and converted in to shape file Using Quantum Geographic Information System (QGIS version 3). Applicant types of agroforestry categories (Home garden, Grassland, grass land and woodlots) were identified and selected through field observation. Methods of Soil sampling In the study area, the researchers conduct a number of in-depth field surveys between October 2022 and December 2024. We choose to include four different agroforestry practice types in this study. Certain agroforestry techniques are regarded as a form of treatment. Plots measuring 20 m by 20 m were created along line transects positioned within the watershed. At each sampling plot's four corners and at its center, soil samples were taken at depth of 0-100, 100–200, and 200–300 mm. Sixty soil samples were collected from three categories of soil depth, four types of agroforestry, and replication from each agroforestry category. In order to determine the bulk density, undisturbed soil samples were taken using a core ring from the middle of each depth category. Each plot has three composite samples obtained for physicochemical examination in the lab. To determine the fresh weight, the soil samples were weighed in the field. The sample was handled using agroforestry-specific sampling bags that were appropriate for each depth. Method of Analysis Every sample of disturbed soil has been handed, ground, and allowed to air dry. In the event that the samples have been crushed similarly to by passing via a 0.5 mm mesh sieve, a two mm mesh screen was used for the chosen soil's willpower in addition to general nitrogen. The bulky density, pH, organic matter, EC, cation exchange capacity, total nitrogen, available phosphorus and potassium were all measured on the soil samples that were taken. Soil physical analyses In order to prevent compaction, bulk density has been made in triplicate in each plot using coring devices (98.125 cm 3 soil core sampler volume) that have been carefully driven into the soil. By oven-drying core samples at 105°C for 48 hours, the soil bulk density (ρb) was ascertained using the core ring method (Agus et al., 2010; Yang et al., 2016). The weight and volume of the oven-dried soil core were used to compute bulk density. Soil chemical analyses At Arba Minch University, soil samples were first air dried before being sieved through a 2 mm and 0.5 mm screen. Three duplicates of each physicochemical analysis were performed using the techniques outlined in Olsen et al., (1982). The pH of the soil was determined using a glass electrode model of HI 2211, Hanna instruments, and a 1:2.5 soil: water dilution (Jackson 1973, Van 1992). The Kjeldahl method was used to assess total nitrogen (Olsen et al., 1954). The method of the flame photometer is used to determine the exchangeable potassium. To determine the cation exchange capacity, the ammonium acetate method was employed. Olsen's modified extractant (Jackson 1967) was used to extract the available phosphorus, and Inductively Coupled Plasma (ICP) spectroscopy was used to determine the amounts. The Walkley-Black method with modifications was applied. Statistical Analysis The data had been analyzed by SPSS model 25. The generalized linear models method was employed to compare selected soil physic-chemical content based on types of agroforestry practiced and soil depth. Multiple comparisons were conducted using the Tukey honestly significant difference test. For soil bulky density, soil pH, soil organic matter content, soil organic carbon, soil EC, cation exchange capacity, total nitrogen, available phosphorus and potassium contents comparisons according to type of agroforestry practiced and soil depth two-way ANOVA was performed followed by multiple comparisons using the Tukey honestly significant difference test. The significance level was set at p < 0.05 whenever significant variation is observed in the analysis of variance. Results and Discussion Results and Discussion Effects of Agroforestry Practices and Soil Depth on Soil Physicochemical Properties Four different types of agroforestry practices and three soil depth categories were identified and 133 their effects on physicochemical properties (soil bulk density, soil pH, soil organic matter, soil 134 organic carbon, electroconductivity and cation exchange capacity), exchangeable nutrients (K) 135 and total nutrients (nitrogen and available P) were investigated. Soil bulk density, soil pH and soil organic matter The study results showed that agroforestry practices and soil depth had a significant ( p < 0.001) influence on soil density in the Dollo catchment. Similarly, the top soil depth for each agroforestry practice in the Dollo catchment had significantly lower soil density than the bottom soil depth of each agroforestry (Table 2). Soil bulk density differed significantly (P < 0.05) between the soils of the agroforestry practices for the surface 0-100 mm to deeper layer of 200-300 mm. At lower soil depth, higher soil bulk density was observed under the grass land (1096 kg/m3) and woodlots (1058 kg/m3) agroforestry practices than in the Grassland (1044 kg/m3) and home-garden (968 kg/m3) types of agroforestry practices (Table 1). Soil pH and organic matter in soil decreased with continuous increase in soil depth from 0-100 mm, 100-200 mm and 200-300 mm and soil bulk density increase across soil depth under all agroforestry practices shown ( Table 1 ). This result is similar with the results of (Fetene and Amera 2018) who reported that soils under grass land and agricultural land had the higher bulk density values than natural forest. It also in lined with Kebebew et al., (2022) research result that reported the highest bulk density was documented under grass and cultivated land uses; while the lowest bulk density value was recorded from enset land use types. Furthermore, our results were also consistent with the study by Mganga et al., (2011) who found higher bulk densities in grass land compared to fallow land and cultivated land in the semi-arid rangelands of Kenya. Besides, this study is similarly with the study of Negasa et al., (2017) and Dori et al., (2022) reported that soil bulk density is influenced by land use type (agroforestry) and soil depth. Similarly, increasing trends of soil bulk density with increasing soil depth were also reported by Manpoong et al., (2020). Table 1 : Main effects of agroforestry practices and soil depth on Bulk density, Soil pH and Soil organic carbon Types of agroforestry practices Soil parameters Soil bulk density kg m3 soil pH Soil organic carbon % Soil depth (mm) Soil depth (mm) Soil depth (mm) 0-100 100-200 200-300 0-100 100-200 200-300 0-100 100-200 200-300 Grassland 935 1053 1096 6.67 6.61 6.71 3.43 3.50 3.36 Home-garden 799 868 913 6.70 6.97 7.14 4.64 4.36 3.92 Parkland 968 1003 1044 6.78 6.85 7.00 4.42 4.42 4.09 Woodlots 972 1016 1058 6.83 6.98 7.31 4.20 4.13 3.22 P-values * Ns Ns * Ns * Ns Ns Ns * Significant at p = 0.05; ** significant at p = 0.01; *** significant at p = 0.001, Ns not significant. The result showed that soil pH was significantly affected by both types of agroforestry practiced (p < 0.022) and soil depth (p 0.05). Since the effects of types of agroforestry practiced, the highest mean soil pH was recorded under woodlot types of agroforestry practices (7.041); whereas the lowest mean (6.66) value was in the soils of Grassland types of agroforestry practices ( Table 2 ). The relatively low mean value of soil pH under the Grassland might be due to depletion and removal of basic cations as a result of continuous soil disturbance and a relatively high mean value of soil pH under woodlot types of agroforestry practices as a result of continuous stock of leaf litter from trees and shrubs. Correspondingly, the higher mean pH value (7.31) was detected in the 200-300 mm depth detain (Table 2). The reason could be an increase in basic cation along soil depth which increases soil pH from top to down the soil profile as pH and basic cations usually show a strong and positive relationship with each other. The result agrees with studies by (Fetene and Amera 2018; Kebebew et al., 2022) who reported the highest soil pH (5.92) was recorded under khat land use type, whereas the lowest value (5.01) was from forest land use. This result is also in agreement with the study of Kebebew et al., (2022) who reported that the soil pH is affected by different land use with the highest soil pH (5.92) recorded under the khat land use type whereas the lowest mean (5.01) under forest land. On the other hand, an increase in soil pH along the soil depth indicates the accumulation of bases (Kumar et al., 2012). No statistically difference ( p > 0.05) was observed in the soil organic matter contents across the four agroforestry practices, soil depth, and their interactions (Table 2). In terms of absolute value, higher mean values of soil organic matter were found under the home garden (4.31 %) and Grassland agroforestry practices (4.30 %) while the lowest values in woodlots (3.85 %) and Grassland (3.43 %) types of agroforestry (Table 2). This is because plant species with high biomass production result in a high addition of soil organic matter and facilitate to maintaining high levels of soil organic matter. The decline in soil organic matter content in the woodlots and Grassland types of agroforestry practices might have been serious by inadequate inputs of organic matter and continuous grass of rangelands. Studies conducted by Wolle et al., (2021) on the effects of agroforestry on soil organic matter content indicate that higher organic matter content was found under a tree canopy than in open fields due to higher litter fall and root biomass turnover in agroforestry practices. Huynh et al., (2022) study report showed that the soil organic matter content was affected by vegetation types, elevation change, and slope types. Table 2 : Interaction effects of types of agroforestry and soil depth on ρb, pH and SOM Types of agroforestry practiced Soil Bulk Density kg m3 soil pH Soil organic matter % Grassland 1022 6.66 3.43 Home garden 086 6.94 4.31 Parkland 1005 6.88 4.30 Woodlots 1015 7.04 3.85 Soil depth, mm 0-100 0919 6.724 4.174 100-200 0985 6.855 4.104 200-300 1028 7.041 3.647 Agroforestry types *** ** Ns Soil depth * ** Ns agroforestry types*depth Ns Ns Ns Means within a column followed by the different letter(s) are significantly different from each other; * significant at p = 0.05; ** significant at p = 0.01; *** significant at p = 0.001, Ns not significant. ρb - soil bulk density, pH- measure of soil acidity or alkalinity, SOM-Soil organic matte Soil organic carbon, total nitrogen and available phosphorus Soil organic carbon contents were significantly ( p 0.05). At the Dollo watershed, higher mean soil organic carbon stock was detected under the Grassland agroforestry (90.88 t/ha) than the home garden agroforestry use (76.38 t/ha) (Table 4). Soil organic carbon significantly influenced by soil depth and generally decreased with increase in soil depth. Soil organic carbon was significantly higher at 0-100 mm (141.1) compared to 100-200 (90.99) and 200-300 mm (40.56) depths in the ( Table 3 ). Different studies (Girma et al., 2020; Dori et al., 2022; Kibet et al., 2022) whose result revealed that soil organic carbon stocks were affected by agroforestry types and soil depth, and also soil organic carbon stocks decrease with increasing soil depth. This is because, of an increase in the level of soil organic matters in the grass land could have been the result of accumulation of grass and shrub litter falling o residues in the upper few centimeters soil depth and their lower rate of decomposition and disturbances by grazing animals. Table 3 : Main effects of agroforestry practices and soil depth on Soil organic matter, Total Nitrogen and Available Phosphorus Types of agroforestry practices Soil Parameters Soil organic carbon % Total nitrogen % Available Phosphorus mg kg-1 Soil depth (mm) Soil depth (mm) Soil depth (mm) 0-100 100-200 200-300 0-100 100-200 200-300 0-100 100-200 200-300 Grassland 141.1 90.99 40.56 0.17 0.17 0.17 1.060 0.94 0.91 Home-garden 118.8 75.54 34.81 0.23 0.22 0.19 1.430 1.05 1.08 Parkland 135.3 86.80 41.90 0.22 0.22 0.20 1.095 1.08 1.03 Woodlots 137.1 87.90 42.10 0.21 0.20 0.16 1.110 1.10 1.10 P-values * ** * Ns Ns Ns * Ns Ns * Significant at p = 0.05; ** significant at p = 0.01; *** significant at p = 0.001, Ns not significant. Similar study results showed that the land use type and soil depth influenced soil organic carbon ( p < 0.001) (Xiang et al., 2022). Moreover, a study on the soil physical-biochemical properties under different agroforestry systems in the Terai region of the Garhwal Hiamalayas results discovered that the soil organic content was significantly higher under different agroforestry practices as compared to the agriculture field (Singh et al., 2018). Study carried out by Jaleta (2020) reported that Grassland type of agroforestry have the highest soil organic carbon as a result of a higher number of grass root growth and biomass turnover rate. No significant variation ( p > 0.05) was observed in the total soil nitrogen among the types of agroforestry practices and soil depth. But, the highest total nitrogen content in soil was recorded from the home garden (0.216) and Grassland types of agroforestry practice (0.215) which was higher than the other two types of agroforestry practice woodlots (0.193) while the lowest under grazing land (0.172) ( Table 4 ). Soil organic carbon perecentage differed significantly ( p < 0.05) between the soils of the different land cover for the surface 0-15 mm and deeper layer of 200-300 mm. In all the layers, the soil under Home-garden types of agroforestry practices has low soil organic carbon content compared to the other types of agroforestry practices. Soil organic carbon, total nitrogen, and available Phosphorus in soil decreased with a continuous increase in soil depth from 0-100 mm, 100-200 mm, and 200-300 mm under all agroforestry practices (Table 3). Our results were comparable to those reported in previous studies of soil total nitrogen status in the topsoil of agroforestry (Vieira et al., 2016; Karki et al., 2021). Moreover, another author (Jaleta, 2020) results showed that the higher total nitrogen at the upper soil depth (0-100 mm) than at the lower depth (200-400 mm) in the central highlands of Ethiopia. No substantial difference ( p > 0.05) was observed in the available phosphorus among types of agroforestry practices and soil depth. However, higher mean values of available phosphorus were found in the home garden agroforestry practice (1.186), while the lowest values were recorded in Grassland (0.972) (Table 4). This might be due to, the continuous application of household waste and other organic matter in the home garden agroforestry practice leading to higher available phosphorus than the Grassland types. Relatively lower content of available phosphorus in the soils of Grassland use might be attributed to higher clay content of the soil, natural phosphorus deficiency of the soil, and continuous use as Grassland use without application of phosphorus fertilizers in agricultural lands. In addition to this, higher available phosphorus was observed at the upper depth (0-100 mm) than both at the lower depth (100-200 mm) and (200-300 mm) (Table 3). The result of the present study is consistent with a study by Kebebew et al., (2022), who reported higher phosphorus content in cultivated soil than in other land use. This statement is in keeping with that of Wolle et al., (2021) and Dori et al., (2022) reported that higher available phosphorus was found in the home garden, while the lowest values were recorded in the Grassland and forest garden. Kiflu and Beyene (2013) also reported that higher available phosphorus is found in the surface soil. Table 4 : Interaction effects of types of Agroforestry and soil depth on SOC, TN and Aval P Types of agroforestry practiced Soil organic carbon % Total nitrogen % Available phosphorus mg kg-1 Grassland 90.88 0.17 0.97 Home garden 76.38 0.21 1.19 Parkland 88.00 0.21 1.07 Woodlots 89.03 0.19 1.10 Soil depth (mm) 0-100 2.304 0.209 1.208 100-200 2.133 0.205 1.042 20-300 1.967 0.182 1.037 Agroforestry types *** Ns Ns Soil depth Ns Ns Ns agroforestry types*depth Ns Ns Ns Means within a column followed by the different letter(s) are significantly different from each other at p < 0.05; * significant at p = 0.05; ** significant at p = 0.01; *** significant at p = 0.001, Ns not significant. SOC-Soil organic carbon, TN-total nitrogen, Available Phosphorus Exchangeable potassium, cation exchange capacity and electro conductivity The two-way ANOVA result showed that there was a significant difference ( p ≤ 0.006) in mean exchangeable potassium between agroforestry types while soil depth and their interactions had no significant effect ( p > 0.05). Higher exchangeable potassium was found under the home garden agroforestry (0.378) and Grassland agroforestry practice (0.347) while the lowest exchangeable potassium was found in grass land (0.280). This is probably because of the large availability of microbial life under agroforestry trees and the fast decomposition of organic matter added to the soil from leaf and litter falls. Exchangeable potassium in soil decreased with an increase in soil depth from 0-100 mm, (0.330) 100-200 mm (0.322), and 200-300 mm (0.319) under all agroforestry practices (Table 6). This is probably an effect of higher organic matter accumulation in the home garden agroforestry practice which released phosphorus during its mineralization. This is in agreement with the results of several studies (Kebede et al., 2021; Ovung et al., 2021; Wolle et al., 2021; Dori et al., 2022; Etafa 2022) in their research exchangeable potassium significantly ( p ≤ 0.05) varied among agroforestry practices in Ethiopia. This result is also in line with Subba and Dhara (2017) reported that higher exchangeable potassium was found in fruit-based agroforestry practices in west Bengal, India. Additionally, our result was in agreed with Dori et al., (2022) that higher exchangeable potassium was found in the home garden agroforestry, while the lowest values were recorded in Grassland agroforestry. The statistical analysis result indicated that there was a highly significant difference achieved ( p 0.05). Higher mean cation exchange capacity was found under Grassland agroforestry type (24.60) and home garden agroforestry practice (22.714) however, the lowest cation exchange capacity was found in Grassland (13.728) and woodlots type of agroforestry (13.470) (Table 6). A comparable increase in organic matter and soil cation exchange capacity was detected under Grassland agroforestry practice. This is due to low surface water movement and leaching of cations in Grassland, high organic matter, and lower consumption of these cations by grasses. The mean cation exchange capacity in soil decreased with increasing in soil depth from 0-100 mm, (21.835) 100-200 mm (17.120), and 200-300 mm (16.929) under all agroforestry practices (Table 5). The higher cation exchange capacity in Grassland compared to the home garden and Grassland could be explained by the alteration in organic matter content among the agroforestry types. The cation exchange capacity value in the Grassland use decreased mainly due to the reduction in organic matter residual content through continual cultivation. The reasonable amount of cation exchange capacity in the soil is determined by the amount of humus substance in the soil; therefore, soils containing high organic matter residual contents have high cation exchange capacity. The result was in line with the study result of Abate and Kibret (2016) stated that there is interaction effect of land use and soil depths in mean cation exchange capacity was significant. Also, the study carried out by Fetene and Amera (2018) similarly reported the highest cation exchange capacity under the forest (23.27cmolckg-1) and the lowest value under cultivated lands (21.44 cmolc kg-1). Similarly, this result is in line with the study of Dori et al., (2022) who reported that the cation exchange capacities of the soils were significantly affected by different agroforestry practices. Moreover, other studies agreed with our study results which reported that the cation exchange capacities of the soils were meaningfully affected by agroforestry systems. This is because, of the differences in soil organic matter and carbon due to dense growth of vegetation increase, thereby increasing cation exchange capacities (Bajracharya et al., 2015). Also, earlier studies tell that the cation exchange capacities of home gardens were higher than in mono cropping system Ethiopia Wolka et al., (2021), and higher cation exchange capacities in the soil under Cordia Africana and Ficus sur based agroforestry compared to outside the canopy area (Emire and Asfaw 2018). Table 5 : Main effects of agroforestry practices and soil depth on Exc. Potassium, CEC and Electro conductivity Types of agroforestry practices Soil parameters Exchangeable potassium (mmolc kg-1) Cation exchange capacity (mmolc kg-1) Electro conductivity (mS m-1) Soil depth ( mm) Soil depth (mm) Soil depth (mm) 0-100 100-200 200-300 0-100 100-200- 200-300 0-100 100-200 200-300 Grassland 0.28 0.27 0.27 29.79 24.39 19.62 110.4 87.60 91.3 Home garden 0.38 0.37 0.36 22.94 19.65 25.56 205.7 175.9 144.5 Parkland 0.35 0.34 0.35 17.78 12.02 11.38 121.2 106.8 91.2 Woodlots 0.29 0.28 0.28 16.83 12.42 11.16 177.3 150.6 122.7 P-values Ns Ns Ns Ns Ns Ns * * * * Significant at p = 0.05; ** significant at p = 0.01; *** significant at p = 0.001, Ns not significant. Soil electro conductivity was highly significantly ( p < 0.000) influenced by type of agroforestry practices and soil depth ( p 0.05). The maximum and the minimum electro-conductivity of the soils were achieved from the home garden (175.387) and Grassland (96.43) respectively ( Table 6 ). Soil electro conductivity significantly ( P < 0.05) differed between the soils of the agroforestry practices for the surface 0-15 mm to a deeper layer of 21-30 mm, however insignificant difference was identified between exchangeable potassium and cation exchange capacity. Table 6 : Interaction effects of types of agroforestry and soil depth on Exc Potassium, CEC and EC Types of agroforestry practiced Exchangeable potassium cmol c kg-1 Cation exchange Capacity cmol c kg-1 Electro conductivity ds m-1 Grassland 0.27 24.6 96.43 Home garden 0.37 22.72 175.37 Parkland 0.35 13.73 106.4 Woodlots 0.28 13.43 150.2 Soil depth, mm 0-100 0.330 21.835 153.843 100-200 0.322 17.120 130.212 200-300 0.319 16.929 108.61l Agroforestry types * *** *** Soil depth Ns * * agroforestry types*depth Ns Ns Ns Means within a column followed by the different letter(s) are significantly different from each other at p < 0.05; * significant at p = 0.05; ** significant at p = 0.01; *** significant at p = 0.001, Ns not significant. Exc. K- exchangeable Potassium, CEC- Cation exchange capacity and EC-Electro conductivity The soil electro conductivity showed a decrease with continual soil depth 0-100 mm, (153.843) 100-200 mm (130.212), and 200-300 mm (108.617) under all agroforestry practices. Soil exchangeable potassium and cation exchange capacity in soil varied within soil depth across soil depth under all agroforestry practices (Table 5). Larger electroconductivity values were obtained for the home garden agroforestry, since the application of ammonium base fertilizers, cation loss as a result of leaching, and water erosion due to continuous farming near the resident's home. The result in line with a study by Singh et al., (2018) who reported that the soil electro-conductivity decreased significantly with a continual decrease of soil depth and relatively higher under agroforestry as compared to the agriculture field. Additionally, Nega and Heluf (2013) and Jaleta (2020) also reported that the electrical conductivity of soil was significantly influenced by land use dissimilarity. Consistently, this result is in agreement with the result of Mulugeta et al., (2019) who found the lowest electrical conductivity values under Grassland use compared with agricultural land use. Conclusions In this study, researchers examined the effects of different types of agroforestry on the physicochemical properties of soils across three different soil depths. The results showed that both the type of agroforestry and soil depth significantly impacted soil physicochemical quality in the study area. Home garden agroforestry practices maintained higher soil quality compared to Grassland, Grassland, and woodlot agroforestry soils, indicating that home garden practices are a healthier way to maintain soil fertility. Grassland agroforestry practices led to higher organic carbon content in the upper soil layer due to more grass root growth, zero grazing methods, lower surface movement and leaching, and faster biomass turnover rate. However, the type of agroforestry did not significantly affect organic matter content, available phosphorus, and total nitrogen. Physicochemical properties were higher in the upper soil layer and decreased towards the lower layer, except for soil bulk density and PH. Overall, the study shows that agroforestry systems, especially home garden and grassland practices, simultaneously improve soil health, bolster food security, support climate mitigation and adaptation, encourage resource-efficient production, and support sustainable land use, all of which contribute to SDGs 2, 12, 13, and 15. Generally, the researchers recommend continued research and extension efforts are needed to promote these sustainable practices effectively. Declarations Competing Interest The authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Consent to Publish Each author has read the finished manuscript, given their approval, and agreed to its publication. The manuscript is not currently being considered for publication anywhere and has never been published before. Use of artificial intelligence tools The authors stated that none of the concepts, procedures, texts, information, findings, or portions of this submission originated from, utilised, or profited from artificial intelligence (AI) software. Funding This research was financially supported by the Arba Minch University Ethiopia through University thematic research funding program. Author Contribution Yohannes Dikola: Conceived and designed the experiments performed the experiment; analyzed and interpreted the data; contributed material, reagents, analysis tools; wrote the paper.Aynalem Gochera: conceived and designed the experiments; analyzed and interpreted the data Acknowledgement The authors are thankful to school of graduate studies, Arba Minch University; Ethiopia for providing support. Data Availability All of the data generated by the study and reported in the paper are included in the article. Additional data sets are available from the relevant author upon request. Geolocation 60° 5' 43'' N to 37° 12' 42'' E Clinical trial number not applicable’ Ethics and Consent to Participate declarations ‘Ethics and Consent to Participate declarations not applicable’ References Abate N, Kibret K, (2016) Effects of land use, soil depth and topography on soil physicochemical properties along the toposequence at the Wadla Delanta Massif, Northcentral Highlands of Ethiopia. Environment and Pollution 5:57–71. Agus F, Hairiah K, Mulyani A, (2010) Measuring carbon stock in peat soils: practical guidelines. Bogor, Indonesia: World Agroforestry Centre. Awasthi P, Bargali K, Bargali S, Jhariya, MK, (2022) Structure and functioning of Coriaria nepalensis dominated shrublands in degraded hills of Kumaun Himalaya. I. Dry matter dynamics. Land Degradation & Development 33:1474–1494. 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Yang, Q, Luo W, Jiang Z, Li W, Yuan D, (2016) Improve the prediction of soil bulk density by cokriging with predicted soil water content as auxiliary variable. Journal of Soils and Sediments 16:77–84. Young A, (1991) Soil fertility. In: Biophysical Research for Asian Agroforestry (M.E. Avery, M.G.R. Cannel, and C. K. Ong Eds). Winrock International USA and South Asia Books, USA, 187 − 20 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. 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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-8685594","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591029934,"identity":"410b2f26-60f5-4951-ab43-c0353b2657a2","order_by":0,"name":"Yohannes Dikola","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA4ElEQVRIiWNgGAWjYDCCw2wMDA8YauTs2xuAPAMLIrUkMBwzNuA5ANIiQYSWA2AtzIkbJBJAXCK08B1nS3yQUMOWuF3y+dUNPwokGPjbuxPwapE8zHbYIOGYjPHO2TllN3uADpM4c3YDXi0Gh9nbJBLY2GQbbuek3eABajGQyCVGyz9mxoabZ9Ju/iFOC9sxicQ2ZsUNN9iP3SbKFqBfkg0S+44ZS/bksN2WMZDgIegXvvPHDB98+FYjx89+/NnNN39s5Pjbe/FrQQI8BmCSWOUgwP6AFNWjYBSMglEwggAAAxdKMZUaBiEAAAAASUVORK5CYII=","orcid":"","institution":"Arba Minch University","correspondingAuthor":true,"prefix":"","firstName":"Yohannes","middleName":"","lastName":"Dikola","suffix":""},{"id":591029936,"identity":"9cfa10f2-897b-492f-84ac-9e3fab10336d","order_by":1,"name":"Aynalem Gochera","email":"","orcid":"","institution":"Arba Minch University","correspondingAuthor":false,"prefix":"","firstName":"Aynalem","middleName":"","lastName":"Gochera","suffix":""}],"badges":[],"createdAt":"2026-01-24 09:53:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8685594/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8685594/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102792050,"identity":"c0b953dc-d1a0-4c10-a86c-02af25c0bf0c","added_by":"auto","created_at":"2026-02-16 17:28:11","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":316315,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study watershed location\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8685594/v1/2d1b870e847e23a847e48d0c.png"},{"id":108007349,"identity":"9c5ca10a-cd1a-4d00-b60c-2202b198d40e","added_by":"auto","created_at":"2026-04-28 12:59:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":711147,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8685594/v1/98da203f-4c87-420f-aa5f-787a4591b4a8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Dynamics of Soil Physicochemical Properties under Different Agroforestry Practices in the Dollo watershed, Gamo Zone, Southern Ethiopia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDeforestation and conversion of forest land for human use is one of the most serious challenges of the planet, resulted in shocking rise in natural resource degradation (Meijer et al., 2015). Deforestation would lead to shortage of wood and non-wood forest products, ecological degradation, and loss of biodiversity, deterioration of ecosystem services and emission of greenhouse gases coupled with minimize carbon sequestration. The analysis determined which four factors were most closely linked to the decline in tree cover worldwide between 2001 and 2015. As to Curtis et al., (2018), the deforestation caused by commodities accounted for 27% of the total deforestation, followed by shifting agriculture (24%), wildfires (23%), urbanization (0.6%), and forestry loss inside managed forests and tree plantations (26%). Lastly, permanent conversion of forest for the expansion of commodities was the cause of deforestation. Now global estimates suggest that 30% of original forest cover has been converted for various uses and other 20% has been degraded (Rizvi et al., 2015).\u003c/p\u003e \u003cp\u003eFrom global forest cover 3952\u0026nbsp;million ha African forests and tropical forests accounted about 21% and 44% of the world forest respectively (FAO 2006; Liu et al., 2009; Keenan et al., 2015; MacDicken 2015). Forest offers a widespread kind of environment objects and services to the inhabitants (Gosain et al., 2015; Awasthi et al., 2022). Addressing the problems of deforestation and forest degradation can enhance ecosystem services that have knock-on effect on other sectors (Ndayambaje et al., 2012; Meijer et al., 2015). The agroforestry was discovered as a solution to solving tropical forests problems, notably the fact that rainforests had been being depleted at an alarming rate, that the tremendous and sundry capacity of bushes in the tropics was in striking contrast to the end and recommended as a land use option (Torquebiau 2000). Ethiopia has adopted agroforestry as soil management options which are important to improve soil physic-chemical quality (Wolle et al., 2021; Dori et al., 2022).\u003c/p\u003e \u003cp\u003eAgroforestry plantation developments on farm land have often been considered as a \u0026ldquo;quick fix\u0026rdquo; solutions to the problems of over exploitation of the natural forests resources and forest degradation. Implementation of agroforestry based agriculture may ultimately improve soil quality and limit agricultural expansion into natural forests, as well as the negative impacts of agriculture on biodiversity (Khumalo et al., 2012). Research outcomes show that the agroforestry practices plays an important role in enhancing soil fertility, soil organic matter, promoting nutrient cycling and improving agricultural production (Emire and Asfaw, 2018; Awazi and Tchamba 2019; Tsufac et al., 2019; Wollo et al., 2019; Dori 2022). However, the amount of soil property in agroforestry systems is quite variable and values are affected by agroforestry types, their management practice, climate, soil characteristics, and mycorrhizal status (Munroe and Isaac 2014; Ovung et al., 2021).\u003c/p\u003e \u003cp\u003eThe dynamics of soil physicochemical properties in agroforestry systems can significantly influence crop productivity, soil health, and overall ecosystem sustainability. In the context of the Dollo watershed in the Gamo Zone of Southern Ethiopia, understanding these dynamics is vital for promoting sustainable agricultural practices and enhancing land productivity. The lack of research investigating the impact of various agroforestry techniques on the physicochemical qualities of soil in the Dollo watershed, Gamo Zone, Southern Ethiopia, presents both a challenge and an opportunity for development in sustainable land management practices. Here the purpose of this study was to investigate the impacts of main types of agroforestry practices on the physical and chemical characteristics of the soil. Additionally, it aimed to assess how soil qualities in the southern Ethiopian lowlands of the Dollo watershed were affected by the interactions between agroforestry practices and soil depth.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003eThe study was conducted in the Dollo watershed Kamba woreda, Southern Ethiopia. Its exact geographical coordinates were 39\u003csup\u003e\u0026deg;\u003c/sup\u003e37' 06\" E and 9\u003csup\u003e\u0026deg;\u003c/sup\u003e41' 07\" N with an elevation ranges from 2747 to 3674 m above sea level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The watershed located 607 km south west of the Addis Ababa. Rainfall of the area has a bi-modal pattern. The mean annual air temperature and rainfall of the study site is 19.7\u0026deg;C and 1470 mm respectively. The main types of soil in the study area are cambisols, vertisols and andosols. In the study area the farming system is based on rain fed.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eIdentification and delineation of types of agroforestry practiced\u003c/h2\u003e \u003cp\u003eTo determine sample size the spatial boundaries of the study area was clearly demarcated and accurately define by using Global Positioning System (GPS). The collected data were later transferred to computer and converted in to shape file Using Quantum Geographic Information System (QGIS version 3). Applicant types of agroforestry categories (Home garden, Grassland, grass land and woodlots) were identified and selected through field observation.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMethods of Soil sampling\u003c/h3\u003e\n\u003cp\u003eIn the study area, the researchers conduct a number of in-depth field surveys between October 2022 and December 2024. We choose to include four different agroforestry practice types in this study. Certain agroforestry techniques are regarded as a form of treatment. Plots measuring 20 m by 20 m were created along line transects positioned within the watershed. At each sampling plot's four corners and at its center, soil samples were taken at depth of 0-100, 100\u0026ndash;200, and 200\u0026ndash;300 mm. Sixty soil samples were collected from three categories of soil depth, four types of agroforestry, and replication from each agroforestry category. In order to determine the bulk density, undisturbed soil samples were taken using a core ring from the middle of each depth category. Each plot has three composite samples obtained for physicochemical examination in the lab. To determine the fresh weight, the soil samples were weighed in the field. The sample was handled using agroforestry-specific sampling bags that were appropriate for each depth.\u003c/p\u003e\n\u003ch3\u003eMethod of Analysis\u003c/h3\u003e\n\u003cp\u003eEvery sample of disturbed soil has been handed, ground, and allowed to air dry. In the event that the samples have been crushed similarly to by passing via a 0.5 mm mesh sieve, a two mm mesh screen was used for the chosen soil's willpower in addition to general nitrogen. The bulky density, pH, organic matter, EC, cation exchange capacity, total nitrogen, available phosphorus and potassium were all measured on the soil samples that were taken.\u003c/p\u003e\n\u003ch3\u003eSoil physical analyses\u003c/h3\u003e\n\u003cp\u003eIn order to prevent compaction, bulk density has been made in triplicate in each plot using coring devices (98.125 cm\u003csup\u003e3\u003c/sup\u003e soil core sampler volume) that have been carefully driven into the soil. By oven-drying core samples at 105\u0026deg;C for 48 hours, the soil bulk density (ρb) was ascertained using the core ring method (Agus et al., 2010; Yang et al., 2016). The weight and volume of the oven-dried soil core were used to compute bulk density.\u003c/p\u003e\n\u003ch3\u003eSoil chemical analyses\u003c/h3\u003e\n\u003cp\u003eAt Arba Minch University, soil samples were first air dried before being sieved through a 2 mm and 0.5 mm screen. Three duplicates of each physicochemical analysis were performed using the techniques outlined in Olsen et al., (1982). The pH of the soil was determined using a glass electrode model of HI 2211, Hanna instruments, and a 1:2.5 soil: water dilution (Jackson 1973, Van 1992). The Kjeldahl method was used to assess total nitrogen (Olsen et al., 1954). The method of the flame photometer is used to determine the exchangeable potassium. To determine the cation exchange capacity, the ammonium acetate method was employed. Olsen's modified extractant (Jackson 1967) was used to extract the available phosphorus, and Inductively Coupled Plasma (ICP) spectroscopy was used to determine the amounts. The Walkley-Black method with modifications was applied.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe data had been analyzed by SPSS model 25. The generalized linear models method was employed to compare selected soil physic-chemical content based on types of agroforestry practiced and soil depth. Multiple comparisons were conducted using the Tukey honestly significant difference test. For soil bulky density, soil pH, soil organic matter content, soil organic carbon, soil EC, cation exchange capacity, total nitrogen, available phosphorus and potassium contents comparisons according to type of agroforestry practiced and soil depth two-way ANOVA was performed followed by multiple comparisons using the Tukey honestly significant difference test. The significance level was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 whenever significant variation is observed in the analysis of variance.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cp\u003e\u003cstrong\u003eResults and Discussion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEffects of Agroforestry Practices and Soil Depth on Soil Physicochemical Properties\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFour different types of agroforestry practices and three soil depth categories were identified and 133 their effects on physicochemical properties (soil bulk \u0026nbsp;density, soil pH, soil organic matter, soil 134 organic carbon, electroconductivity \u0026nbsp; and cation exchange capacity), exchangeable nutrients (K) 135 and total nutrients (nitrogen and available P) were investigated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoil bulk density, soil pH and soil organic matter\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study results showed that agroforestry practices and soil depth had a significant (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) influence on soil density in the Dollo catchment. Similarly, the top soil depth for each agroforestry practice in the Dollo catchment had significantly lower soil density than the bottom soil depth of each agroforestry\u0026nbsp;(Table 2). Soil bulk density differed significantly (P \u0026lt; 0.05)\u0026nbsp;between the soils of the agroforestry practices for the surface 0-100 mm to deeper layer of 200-300 mm.\u0026nbsp;At lower soil depth, higher soil bulk density was observed under the grass land (1096 kg/m3) and woodlots (1058 kg/m3) agroforestry practices than in the Grassland (1044 kg/m3) and home-garden (968 kg/m3) types of agroforestry practices (Table 1).\u003c/p\u003e\n\u003cp\u003eSoil pH and organic matter in soil decreased with continuous increase in soil depth from 0-100 mm, 100-200 \u0026nbsp; mm and 200-300 mm and soil bulk density increase across soil depth under all agroforestry practices shown\u0026nbsp;(\u003cstrong\u003eTable 1\u003c/strong\u003e). This result is similar with the results of (Fetene and Amera 2018) who reported that soils under grass land and agricultural land had the higher bulk density values than natural forest. It also in lined with Kebebew\u003cem\u003e\u0026nbsp;\u003c/em\u003eet al., (2022) research result that reported the highest bulk density was documented under grass and cultivated land uses; while the lowest bulk density value was recorded from enset land use types. Furthermore, our results were also consistent with the study by Mganga et al.,\u0026nbsp;(2011) who found higher bulk densities in grass land compared to fallow land and cultivated land in the semi-arid rangelands of Kenya. Besides, this study is similarly with the study of Negasa et al., (2017) and Dori et al., (2022) reported that soil bulk density is influenced by land use type (agroforestry) and soil depth. Similarly, increasing trends of soil bulk density with increasing soil depth were also reported by Manpoong et al., (2020). \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e: Main effects of agroforestry practices and soil depth on Bulk density, Soil pH and Soil organic carbon\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"606\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTypes of agroforestry practices\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 498px;\"\u003e\n \u003cp\u003eSoil parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSoil bulk density\u0026nbsp;kg m3\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003esoil pH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSoil organic carbon %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSoil depth (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSoil depth (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eSoil depth (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e935\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1053\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e6.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e3.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eHome-garden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e799\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e868\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3.92\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eParkland \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e4.09\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eWoodlots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e972\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e1016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1058\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e7.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e4.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e4.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e3.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eP-values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;* Significant at p = 0.05; ** significant at p = 0.01; *** significant at p = 0.001, Ns not significant.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe result showed that soil pH was significantly affected by both types of agroforestry practiced (p \u0026lt; 0.022) and soil depth (p \u0026lt; 0.016) while their interactions had no effect (p \u0026gt; 0.05). Since the effects of types of agroforestry practiced, the highest mean soil pH was recorded under woodlot types of agroforestry practices (7.041); whereas the lowest mean (6.66) value was in the soils of Grassland types of agroforestry practices (\u003cstrong\u003eTable 2\u003c/strong\u003e). \u0026nbsp;The relatively low mean value of soil pH under the Grassland might be due to depletion and removal of basic cations as a result of continuous soil disturbance and a relatively high mean value of soil pH under woodlot types of agroforestry practices as a result of continuous stock of leaf litter from trees and shrubs.\u003c/p\u003e\n\u003cp\u003eCorrespondingly, the higher mean pH value (7.31) was detected in the 200-300 mm depth detain (Table 2). The reason could be an increase in basic cation along soil depth which increases soil pH from top to down the soil profile as pH and basic cations usually show a strong and positive relationship with each other. The result agrees with studies by (Fetene and Amera 2018; Kebebew et al., 2022) who reported the highest soil pH (5.92) was recorded under khat land use type, whereas the lowest value (5.01) was from forest land use. This result is also in agreement with the study of Kebebew et al., (2022) who reported that the soil pH is affected by different land use with the highest soil pH (5.92) recorded under the khat land use type whereas the lowest mean (5.01) under forest land. On the other hand, an increase in soil pH along the soil depth indicates the accumulation of bases (Kumar et al., 2012).\u003c/p\u003e\n\u003cp\u003eNo statistically difference (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) was observed in the soil organic matter contents across the four agroforestry practices, soil depth, and their interactions (Table 2). In terms of absolute value, higher mean values of soil organic matter were found under the home garden (4.31 %) and Grassland agroforestry practices (4.30 %) while the lowest values in woodlots (3.85 %) and Grassland (3.43 %) types of agroforestry (Table 2). This is because plant species with high biomass production result in a high addition of soil organic matter and facilitate to maintaining high levels of soil organic matter. The decline in soil organic matter content in the woodlots and Grassland types of agroforestry practices might have been serious by inadequate inputs of organic matter and continuous grass of rangelands. Studies conducted by Wolle et al., (2021) on the effects of agroforestry on soil organic matter content indicate that higher organic matter content was found under a tree canopy than in open fields due to higher litter fall and root biomass turnover in agroforestry practices. Huynh et al., (2022) study report showed that the soil organic matter content was affected by vegetation types, elevation change, and slope types.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e: Interaction effects of types of agroforestry and soil depth on \u0026rho;b, pH and SOM\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eTypes of agroforestry practiced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSoil Bulk Density\u0026nbsp;kg m3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003esoil pH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSoil organic matter %\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e1022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eHome garden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.31\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eParkland \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e1005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e4.30\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eWoodlots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e1015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e7.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003e3.85\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"top\" style=\"width: 612px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil depth, mm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e0919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.724\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e4.174\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e0985\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e6.855\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e4.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e1028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e7.041\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e3.647\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eAgroforestry types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e** \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNs \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eSoil depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003eagroforestry types*depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 66px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNs \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 216px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 168px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 6px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMeans within a column followed by the different letter(s) are significantly different from each other; * significant at \u003cem\u003ep\u003c/em\u003e = 0.05; ** significant at \u003cem\u003ep\u003c/em\u003e = 0.01; *** significant at \u003cem\u003ep\u003c/em\u003e = 0.001, Ns not significant. \u0026nbsp;\u0026rho;b - soil bulk density, pH-\u0026nbsp;measure of soil acidity or alkalinity, SOM-Soil organic matte\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoil organic carbon, total nitrogen and available phosphorus\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSoil organic carbon contents were significantly (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.02) affected by the type of agroforestry practices and soil depth, but an insignificant difference was observed by the interaction effect of agroforestry practices and soil depth (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). At the Dollo watershed, higher mean soil organic carbon stock was detected under the Grassland agroforestry (90.88 t/ha) than the home garden agroforestry use (76.38 t/ha) (Table 4). Soil organic carbon significantly influenced by soil depth and generally decreased with increase in soil depth. Soil organic carbon was significantly higher at 0-100 mm (141.1) compared to 100-200 \u0026nbsp; (90.99) and 200-300 mm (40.56) depths in the (\u003cstrong\u003eTable 3\u003c/strong\u003e). Different studies (Girma et al., 2020; Dori et al., 2022; Kibet et al., 2022) whose result revealed that soil organic carbon stocks were affected by agroforestry types and soil depth, and also soil organic carbon stocks decrease with increasing soil depth. This is because, of an increase in the level of soil organic matters in the grass land could have been the result of accumulation of grass and shrub litter falling o residues in the upper few centimeters soil depth and their lower rate of decomposition and disturbances by grazing animals.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e: Main effects of agroforestry practices and soil depth on Soil organic matter, Total Nitrogen and Available Phosphorus \u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"666\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eTypes of agroforestry practices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 528px;\"\u003e\n \u003cp\u003eSoil Parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eSoil organic carbon %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eTotal nitrogen %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eAvailable Phosphorus \u0026nbsp; \u0026nbsp; mg kg-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 168px;\"\u003e\n \u003cp\u003eSoil depth (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eSoil depth (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 216px;\"\u003e\n \u003cp\u003eSoil depth (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e141.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e90.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e40.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eHome-garden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e118.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e75.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e34.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eParkland \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e135.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e86.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e41.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eWoodlots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e137.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e87.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e42.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.20\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e1.110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 138px;\"\u003e\n \u003cp\u003eP-values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e**\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Significant at \u003cem\u003ep\u003c/em\u003e = 0.05; ** significant at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.01; *** significant at \u003cem\u003ep\u003c/em\u003e = 0.001, Ns not significant.\u003c/p\u003e\n\u003cp\u003eSimilar study results showed that the land use type and soil depth influenced soil organic carbon (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (Xiang et al., 2022). Moreover, a study on the soil physical-biochemical properties under different agroforestry systems in the Terai region of the Garhwal Hiamalayas results discovered that the soil organic content was significantly higher under different agroforestry practices as compared to the agriculture field (Singh et al., 2018). Study carried out by Jaleta (2020) reported that Grassland type of agroforestry have the highest soil organic carbon as a result of a higher number of grass root growth and biomass turnover rate.\u003c/p\u003e\n\u003cp\u003eNo significant variation (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05) was observed in the total soil nitrogen among the types of agroforestry practices and soil depth. But, the highest total nitrogen content in soil was recorded from the home garden (0.216) and Grassland types of agroforestry practice (0.215) which was higher than the other two types of agroforestry practice woodlots (0.193) while the lowest under grazing land (0.172) (\u003cstrong\u003eTable 4\u003c/strong\u003e). Soil organic carbon perecentage differed significantly (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05) between the soils of the different land cover for the surface 0-15 mm and deeper layer of 200-300 mm. In all the layers, the soil under Home-garden types of agroforestry practices has low soil organic carbon content compared to the other types of agroforestry practices. Soil organic carbon, total nitrogen, and available Phosphorus in soil decreased with a continuous increase in soil depth from 0-100 mm, 100-200 \u0026nbsp; \u0026nbsp;mm, and 200-300 mm under all agroforestry practices (Table 3). Our results were comparable to those reported in previous studies of soil total nitrogen status in the topsoil of agroforestry (Vieira et al., 2016; Karki et al., 2021). Moreover, another author (Jaleta, 2020) results showed that the higher total nitrogen at the upper soil depth (0-100 mm) than at the lower depth (200-400 mm) in the central highlands of Ethiopia. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNo substantial difference (\u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026gt; 0.05) was observed in the available phosphorus among types of agroforestry practices and soil depth. However, higher mean values of available phosphorus were found in the home garden agroforestry practice (1.186), while the lowest values were recorded in Grassland (0.972) (Table 4). This might be due to, the continuous application of household waste and other organic matter in the home garden agroforestry practice leading to higher available phosphorus than the Grassland types. Relatively lower content of available phosphorus in the soils of Grassland use might be attributed to higher clay content of the soil, natural phosphorus deficiency of the soil, and continuous use as Grassland use without application of phosphorus fertilizers in agricultural lands. In addition to this, higher available phosphorus was observed at the upper depth (0-100 mm) than both at the lower depth (100-200 mm) and (200-300 mm) (Table 3). The result of the present study is consistent with a study by Kebebew et al., (2022), who reported higher phosphorus content in cultivated soil than in other land use. This statement is in keeping with that of\u0026nbsp;Wolle et al., (2021) and Dori et al., (2022) reported that higher available phosphorus was found in the home garden, while the lowest values were recorded in the Grassland and forest garden. Kiflu and Beyene (2013) also reported that higher available phosphorus is found in the surface soil.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e: Interaction effects of types of Agroforestry and soil depth on SOC, TN and Aval P\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"600\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTypes of agroforestry practiced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSoil organic carbon %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eTotal nitrogen %\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eAvailable phosphorus mg kg-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e90.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eHome garden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e76.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.19\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eParkland \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e88.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eWoodlots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e89.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 600px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil depth\u0026nbsp;\u003c/strong\u003e(mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.209\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.208\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e2.133\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e20-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003e0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e1.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eAgroforestry types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eNs \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNs \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSoil depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eagroforestry types*depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 162px;\"\u003e\n \u003cp\u003eNs \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;Means within a column followed by the different letter(s) are significantly different from each other at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e\u0026lt; 0.05; * significant at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.05; ** significant at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.01; *** significant at\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.001, Ns not significant. \u0026nbsp;SOC-Soil organic carbon, TN-total nitrogen, Available Phosphorus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExchangeable potassium, cation exchange capacity and electro conductivity\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe two-way ANOVA result showed that there was a significant difference (\u003cem\u003ep\u003c/em\u003e \u0026le; 0.006) in mean exchangeable potassium between agroforestry types while soil depth and their interactions had no significant effect (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). Higher exchangeable potassium was found under the home garden agroforestry (0.378) and Grassland agroforestry practice (0.347) while the lowest exchangeable potassium was found in grass land (0.280). This is probably because of the large availability of microbial life under agroforestry trees and the fast decomposition of organic matter added to the soil from leaf and litter falls. Exchangeable potassium in soil decreased with an increase in soil depth from 0-100 mm, (0.330) 100-200 \u0026nbsp; mm (0.322), and 200-300 mm (0.319) under all agroforestry practices (Table 6). This is probably an effect of higher organic matter accumulation in the home garden agroforestry practice which released phosphorus during its mineralization. This is in agreement with the results of several studies (Kebede et al., 2021; Ovung et al., 2021; Wolle et al., 2021; Dori et al., 2022; Etafa 2022) in their research exchangeable potassium significantly (\u003cem\u003ep\u003c/em\u003e \u0026le; 0.05) varied among agroforestry practices in Ethiopia. This result is also in line with Subba and Dhara (2017) reported that higher exchangeable potassium was found in fruit-based agroforestry practices in west Bengal, India. Additionally, our result was in agreed with Dori et al., (2022) that higher exchangeable potassium was found in the home garden agroforestry, while the lowest values were recorded in Grassland agroforestry. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe statistical analysis result indicated that there was a highly significant difference achieved (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.000) only in mean cation exchange capacity between agroforestry types whereas; soil depth and interactions had no significant effect (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). Higher mean cation exchange capacity was found under Grassland agroforestry type (24.60) and home garden agroforestry practice (22.714) however, the lowest cation exchange capacity was found in Grassland (13.728) and woodlots type of agroforestry (13.470) (Table 6). A comparable increase in organic matter and soil cation exchange capacity was detected under Grassland agroforestry practice. This is due to low surface water movement and leaching of cations in Grassland, high organic matter, and lower consumption of these cations by grasses. The mean cation exchange capacity in soil decreased with increasing in soil depth from 0-100 mm, (21.835) 100-200 mm (17.120), and 200-300 mm (16.929) under all agroforestry practices (Table 5). The higher cation exchange capacity in Grassland compared to the home garden and Grassland could be explained by the alteration in organic matter content among the agroforestry types. The cation exchange capacity value in the Grassland use decreased mainly due to the reduction in organic matter residual content through continual cultivation. The reasonable amount of cation exchange capacity in the soil is determined by the amount of humus substance in the soil; therefore, soils containing high organic matter residual contents have high cation exchange capacity.\u003c/p\u003e\n\u003cp\u003eThe result was in line with the study result of Abate and Kibret (2016) stated that there is interaction effect of land use and soil depths in mean cation exchange capacity was significant. Also, the study carried out by Fetene and Amera (2018) similarly reported the highest cation exchange capacity under the forest (23.27cmolckg-1) and the lowest value under cultivated lands (21.44 cmolc kg-1). Similarly, this result is in line with the study of Dori et al., (2022) who reported that the cation exchange capacities of the soils were significantly affected by different agroforestry practices. Moreover, other studies agreed with our study results which reported that the cation exchange capacities of the soils were meaningfully affected by agroforestry systems. This is because, of the differences in soil organic matter and carbon due to dense growth of vegetation increase, thereby increasing cation exchange capacities (Bajracharya et al., 2015). Also, earlier studies tell that the cation exchange capacities of home gardens were higher than in mono cropping system Ethiopia Wolka et al., (2021), and higher cation exchange capacities in the soil under \u003cem\u003eCordia Africana\u003c/em\u003e and \u003cem\u003eFicus sur\u003c/em\u003e based agroforestry compared to outside the canopy area (Emire and Asfaw 2018).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5\u003c/strong\u003e: Main effects of agroforestry practices and soil depth on Exc. Potassium, CEC and Electro conductivity\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"4\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eTypes of\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eagroforestry practices\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"9\" valign=\"top\" style=\"width: 504px;\"\u003e\n \u003cp\u003eSoil parameters\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eExchangeable potassium (mmolc kg-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eCation exchange capacity (mmolc kg-1)\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eElectro conductivity\u003c/p\u003e\n \u003cp\u003e(mS m-1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 156px;\"\u003e\n \u003cp\u003eSoil depth ( mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSoil depth (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSoil depth (mm)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e100-200-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e29.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e24.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e19.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e110.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e87.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e91.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eHome garden \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 54px;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 48px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e22.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e19.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e25.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e205.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e175.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e144.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eParkland \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e17.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e12.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e11.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e121.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e106.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eWoodlots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e16.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e12.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e11.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e177.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e150.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e122.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eP-values\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 48px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 54px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e* Significant at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.05; ** significant at \u003cem\u003ep\u003c/em\u003e = 0.01; *** significant at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.001, Ns not significant.\u003c/p\u003e\n\u003cp\u003eSoil electro conductivity was highly significantly (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.000) influenced by type of agroforestry practices and soil depth (\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.003), but an insignificant difference was detected by their interaction effect of agroforestry practices and soil depth (\u003cem\u003ep\u003c/em\u003e \u0026gt; 0.05). The maximum and the minimum electro-conductivity of the soils were achieved from the home garden (175.387) and Grassland (96.43) respectively (\u003cstrong\u003eTable 6\u003c/strong\u003e). \u0026nbsp;Soil electro conductivity significantly (\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) differed between the soils of the agroforestry practices for the surface 0-15 mm to a deeper layer of 21-30 mm, however insignificant difference was identified between exchangeable potassium and cation exchange capacity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 6\u003c/strong\u003e: Interaction effects of types of agroforestry and soil depth on Exc Potassium, CEC and EC\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eTypes of agroforestry practiced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eExchangeable potassium cmol\u003csub\u003ec\u0026nbsp;\u003c/sub\u003ekg-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eCation exchange Capacity cmol\u003csub\u003ec\u0026nbsp;\u003c/sub\u003e kg-1 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eElectro conductivity ds m-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eGrassland\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e24.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e96.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eHome garden\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e22.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e175.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eParkland \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e13.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e106.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eWoodlots\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e13.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e150.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 612px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSoil depth, mm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e0-100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 150px;\"\u003e\n \u003cp\u003e0.330\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e21.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 144px;\"\u003e\n \u003cp\u003e153.843\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e100-200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.322\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e17.120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e130.212\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003e200-300\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e16.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e108.61l\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eAgroforestry types\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e*** \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eSoil depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003e*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 174px;\"\u003e\n \u003cp\u003eagroforestry types*depth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 150px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eNs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 144px;\"\u003e\n \u003cp\u003eNs \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eMeans within a column followed by the different letter(s) are significantly different from each other at \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05; * significant at \u003cem\u003ep\u0026nbsp;\u003c/em\u003e= 0.05; ** significant at\u003cem\u003e\u0026nbsp;p\u003c/em\u003e = 0.01; *** significant at \u003cem\u003ep\u003c/em\u003e = 0.001, Ns not significant. \u0026nbsp;Exc. K- exchangeable Potassium, CEC- Cation exchange capacity and EC-Electro conductivity\u003c/p\u003e\n\u003cp\u003eThe soil electro conductivity showed a decrease with continual soil depth 0-100 mm, (153.843) 100-200 mm (130.212), and 200-300 mm (108.617) under all agroforestry practices. Soil exchangeable potassium and cation exchange capacity in soil varied within soil depth across soil depth under all agroforestry practices (Table 5). Larger electroconductivity \u0026nbsp; \u0026nbsp;values were obtained for the home garden agroforestry, since the application of ammonium base fertilizers, cation loss as a result of leaching, and water erosion due to continuous farming near the resident\u0026apos;s home. The result in line with a study by Singh et al., (2018) who reported that the soil electro-conductivity decreased significantly with a continual decrease of soil depth and relatively higher under agroforestry as compared to the agriculture field. Additionally, Nega and Heluf (2013) and Jaleta (2020) also reported that the electrical conductivity of soil was significantly influenced by land use dissimilarity. Consistently, this result is in agreement with the result of Mulugeta et al., (2019) who found the lowest electrical conductivity values under Grassland use compared with agricultural land use.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn this study, researchers examined the effects of different types of agroforestry on the physicochemical properties of soils across three different soil depths. The results showed that both the type of agroforestry and soil depth significantly impacted soil physicochemical quality in the study area. Home garden agroforestry practices maintained higher soil quality compared to Grassland, Grassland, and woodlot agroforestry soils, indicating that home garden practices are a healthier way to maintain soil fertility. Grassland agroforestry practices led to higher organic carbon content in the upper soil layer due to more grass root growth, zero grazing methods, lower surface movement and leaching, and faster biomass turnover rate. However, the type of agroforestry did not significantly affect organic matter content, available phosphorus, and total nitrogen. Physicochemical properties were higher in the upper soil layer and decreased towards the lower layer, except for soil bulk density and PH. Overall, the study shows that agroforestry systems, especially home garden and grassland practices, simultaneously improve soil health, bolster food security, support climate mitigation and adaptation, encourage resource-efficient production, and support sustainable land use, all of which contribute to SDGs 2, 12, 13, and 15. Generally, the researchers recommend continued research and extension efforts are needed to promote these sustainable practices effectively.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that they have no competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish\u003c/strong\u003e \u003cp\u003eEach author has read the finished manuscript, given their approval, and agreed to its publication. The manuscript is not currently being considered for publication anywhere and has never been published before.\u003c/p\u003e \u003ch2\u003eUse of artificial intelligence tools\u003c/h2\u003e \u003cp\u003eThe authors stated that none of the concepts, procedures, texts, information, findings, or portions of this submission originated from, utilised, or profited from artificial intelligence (AI) software.\u003c/p\u003e \u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis research was financially supported by the Arba Minch University Ethiopia through University thematic research funding program.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYohannes Dikola: Conceived and designed the experiments performed the experiment; analyzed and interpreted the data; contributed material, reagents, analysis tools; wrote the paper.Aynalem Gochera: conceived and designed the experiments; analyzed and interpreted the data\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eThe authors are thankful to school of graduate studies, Arba Minch University; Ethiopia for providing support.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll of the data generated by the study and reported in the paper are included in the article. Additional data sets are available from the relevant author upon request.\u003c/p\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eGeolocation\u003c/h2\u003e \u003cp\u003e60\u0026deg; 5' 43'' N to 37\u0026deg; 12' 42'' E\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eClinical trial number\u003c/strong\u003e \u003cp\u003enot applicable\u0026rsquo;\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthics and Consent to Participate declarations\u003c/strong\u003e \u003cp\u003e\u0026lsquo;Ethics and Consent to Participate declarations not applicable\u0026rsquo;\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e "},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAbate N, Kibret K, (2016) Effects of land use, soil depth and topography on soil physicochemical properties along the toposequence at the Wadla Delanta Massif, Northcentral Highlands of Ethiopia. \u003cem\u003eEnvironment and Pollution\u003c/em\u003e 5:57\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAgus F, Hairiah K, Mulyani A, (2010) Measuring carbon stock in peat soils: practical guidelines. Bogor, Indonesia: World Agroforestry Centre.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAwasthi P, Bargali K, Bargali S, Jhariya, MK, (2022) Structure and functioning of Coriaria nepalensis dominated shrublands in degraded hills of Kumaun Himalaya. I. 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In: Biophysical Research for Asian Agroforestry (M.E. Avery, M.G.R. Cannel, and C. K. Ong Eds). Winrock International USA and South Asia Books, USA, 187\u0026thinsp;\u0026minus;\u0026thinsp;20\u003c/span\u003e\u003c/li\u003e\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":"[email protected]","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":"agroforestry practices, physicochemical properties, soil depth, soil fertility","lastPublishedDoi":"10.21203/rs.3.rs-8685594/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8685594/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe dynamics of soil physicochemical properties in agroforestry systems can significantly influence crop productivity, soil health, and overall ecosystem sustainability. In the context of the Dollo watershed in the Gamo Zone of Southern Ethiopia, understanding these dynamics is vital for promoting sustainable agricultural practices and enhancing land productivity. This study investigates the impact of various agroforestry practices on the physicochemical properties of soil. Using a comparative analysis, sixty soil samples were collected from multiple agroforestry sites. The results showed that agroforestry type and soil depth had significant effects on soil bulk density, organic carbon, pH, exchangeable potassium, electrical conductivity and cation exchange capacity. The results indicated significant variations in soil physicochemical properties across different agroforestry practices and soil depth, with certain agroforestry systems enhancing soil fertility more effectively than others. Home garden agroforestry practices were found to have higher soil organic matter content than the other types of agroforestry. These findings underline the importance of selecting appropriate agroforestry practices to improve soil health and agricultural productivity in the area. Based on these results, integrating agroforestry with traditional farming methods can result in synergies that benefit both people and the environment. Continued research and extension efforts are needed to promote these sustainable practices effectively.\u003c/p\u003e","manuscriptTitle":"Dynamics of Soil Physicochemical Properties under Different Agroforestry Practices in the Dollo watershed, Gamo Zone, Southern Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 17:28:06","doi":"10.21203/rs.3.rs-8685594/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","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":"ee9e0a9f-fb7b-4227-a778-dda1ca520648","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-28T10:40:00+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 17:28:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8685594","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8685594","identity":"rs-8685594","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}