Influence of Some Land Use Types on Soil Acidity in Malga and Enemorna Ener Districts, Southern Ethiopia

Influence of Some Land Use Types on Soil Acidity in Malga and Enemorna Ener Districts, 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 Influence of Some Land Use Types on Soil Acidity in Malga and Enemorna Ener Districts, Southern Ethiopia FISSEHA Negash, Tamado Tana, Eyasu Elias, Fanuel Laekemariam This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8975238/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Purpose Soil acidity is affected differently by various land use types in Ethiopia. This research was conducted to evaluate some land use types on the surface soil fertility and acidity in the districts of Malga and Enemorna Ener. Methods Land use types such as cultivated fields (RCFs), homestead gardens (HGs), grazing lands (GLs), eucalyptus plantations (EPs) were selected, twenty-four (24) composite soil samples were collected from 0–20 cm depth from each district. Soil analysis was done and statistically analysed. Results Among the four land use types, all had low pH values and high acid levels, except for HG. The soils under HG had significantly higher pH values (p < 0.001), lower levels of acid saturation and better fertility indicators, which included exchangeable bases (Ca²⁺, Mg²⁺, K⁺ and Na²⁺), total nitrogen, organic matter, cation exchange capacity, available phosphorus, and potassium. The results revealed there were strong negative correlations between exchangeable acid cations, acid saturation and pH, whereas strong positive association were evident for some components related to fertility. Conclusion Land use type affects the soil fertility and soil acidity status. Generally, the soil of the home garden land-use type showed the best results of the four land-use types based on their high pH and low acid saturation. The remaining land-use types had low pH and high acidity, low exchangeable bases, total nitrogen, and organic matter. Exchangeable acid cations and acid saturation have negative correlations with the pH of the soil, whereas there were positive correlations between the various components of fertility. The results from this study suggest an urgent need to improve land management practices regarding the different land use types. Figures Figure 1 Figure 2 Introduction Soil acidity can result from human causes or natural causes. Some examples are land management practices, soil minerals, and climate. Soil acidity is an important part of ecological interactions that influence aspects of ecosystem stability, environmental sustainability, and agronomic productivity. Given the complicated nature of soil acidity and the different climatic ranges of agro-ecological settings, and their different land use systems in Ethiopia. Understanding how land-use practices can influence soil acidity is an important potential area of research. The southern Ethiopian districts of Malga and Enemorna Ener are an appropriate location to assess interactions within the varying forms of land use, agricultural system, and forest clearing history, and land reform due to practices evident in these relatively recently formed districts with significant agricultural activity. Soil acidity affects soil quality - how nutrients become available and how soil microbial activity occurs (Liu et al, 2020 ). The interactions involving soil acidity mean that critical nutrients such as P, Ca, and Mg are hindered from being available for uptake, while unwanted nutrients become more soluble and potentially toxic(Al, for example) to crops (Liu et al., 2020 ). As Ethiopia's economy is largely agro-based, understanding acidity and acidic behaviour drives necessary information on sustainably managing land resources and food security. Recent studies show that a shift in land use (especially shifting from a natural ecosystem to agricultural purposes) results in significant changes in the soil's properties, including both acidic and non-acidic pH (Taye et al, 2021; Abebe et al, 2022). Malga and Enemorna Ener districts are good case studies of how traditional and new agricultural practices continue to be utilized, and also various land use types such as cropping, animal grazing/pastoral lands, and forestry use. Each land use has a direct influence on soil chemistry and acidity; cropping is typically more intensive and often includes fertilizer application, which can induce soil acidification (Zheng et al., 2021 ) as opposed to natural vegetation that has a neutralizing effect on soil pH when organic matter and roots can act as a buffer against soil acidification (Kumar et al., 2022 ). Understanding the interactions of forces conflicting with each other in their opposing goal pathways must be part of developing realistic land management alternatives to aid with soil health and agricultural productivity. Similar trends were found in scientific research demonstrating that substantial changes in soil acidity could result from changes in land use. A study conducted in Ethiopia's highlands revealed that the pH of the soil dropped significantly from forest to cropland. This illustration shows how deforestation degrades soil quality (Mekonnen et al., 2021 ). Annual nutrient deposits and livestock trampling can also alter the pH values of the soil on grazing land (Sheleme et al., 2023). These results highlight the significance of assessing the precise impacts on soil acidity in the Malga and Enemorna Ener districts of the various land use practices linked to agricultural growth and environmental degradation. The socio-economic situations in these districts make the land use-soil acidity even more complex. Most of the rural residents are smallholder farmers and often are not able to improve soil fertility using modern practices (Dawit and Abera, 2023). The lack of modern farming inputs and insufficient technical knowledge leads to reduced soil quality and soil acidification, which creates food shortages and security issues. Therefore, knowing how land use types affect soil acidity entails more than soil science; it captures the growing issues around agricultural policy and rural development in southern Ethiopia. Sustainable land management practices have been part of the growing focus within the last number of years and have the potential to ameliorate soil acidification and increase soil health overall. There is evidence to suggest that agroecological practices that enhance productivity and profitability can improve pH and soil fertility, which is shown to be more sustainable, productive, and resilient to climate change (Gliessman, 2021 ). In general, Sustainable Land Management practices are helpful for soil health and resilience to climate change impacts that threaten farmer communities in Malga and Enemorna Ener districts. Soil acidity is very important in agricultural systems, but we do not know much about the effects of increasingly specific types of land use on soil pH in the Malga and Enemorna Ener districts. The majority of previous research was past focused on regions for the type of land use, or if it studied land use types specifically, focused on a land use practice without reference to local conditions, and did not assess soil acidity indicators to demonstrate it. This study will address this knowledge gap and produce a very useful analysis of the effects of various land use types on soil acidity indicators in the two districts through field surveys, soil sampling, and laboratory analysis. The study will yield vital data for agricultural and land management in southern Ethiopia. By identifying the main causes of soil acidity across the various land use types, the research will pave the way for the creation of interventions that will improve soil health and agricultural output. The study will also serve as a resource for information and communication for societal and policy actors involved in food security and sustainable land practices. Materials and Methods Description of the Study Area This study was conducted in two areas of Ethiopia: Enemorna Ener in the central region and Malga in the Sidama region. We selected these locations because they use land in very different ways, and the locations have soil acidity problems. Malga is approximately 26 kilometers southeast of Hawassa city; it is approximately 301 kilometers south of Addis Ababa. Malga is located between 6°84' and 7°02' North and 38°52' and 38°70' east. It is between 1,900 and 2,800 meters above sea level (MASL). The area covers approximately 32,651 hectares, and it is mostly plains (42%) and lower-lying areas (30%), with some mountains (21%) and swampy places (6%) (Unpublished report, the Malga District Agricultural Office in 2015). Enemorna Ener's capital, Gunchire, is 42 kilometers southwest of Wolkite city, and the district is approximately 192 kilometers southwest of Addis Ababa. It is at a relatively high altitude, ranging from 1,100 to 2,730 meters. Its location is between 7°45' and 8°10' North and 37°50' and 37°80' east. This area is mostly mountainous, especially in the north and northeast, which are part of the Gurage mountain range (unpublished report, the Malga District Agricultural Office in 2015, Zone Agricultural Department, 2015). Both areas have short rainy periods from March to May and then longer rainy periods from June to September. In Malga, the average temperature is approximately 17.2°C, and the rainfall amount is 1,400 mm. The farming areas are mostly tepid and slightly humid (73%), with some cooler, similar areas (27%) (MoARD, 2005, mentioned in Eyasu, 2016). Enemorna Ener receives between 801 and 1,400 mm of rain per year, and the temperature is approximately 13°C to 25°C. The farming areas here are warm and humid (36%) and tepid and humid (64%) (MoARD, 2005, mentioned in Eyasu, 2016). The soil is different in the two areas because of the parent rock and the environmental conditions. The soil of Malga is mainly Luvisols (46.3%), Alisols (42.7%), and Nitisols (11.0%) (Eyasu, 2016). In Enemorna Ener, mostly Nitisols (44.8%), Vertisols (30.5%), and Leptosols (10.2%) are present (Eyasu, 2016). The soil used for growing enset in Enemorna Ener can be red, brown, or even black (Muluneh, 2003). Farming is most important to both local economies. Most people are small farmers who grow crops and raise animals. In Malga, they use approximately 18,177 hectares for things such as woodlots, forests, grazing, and farming. They mainly grow enset, barley, faba beans, potatoes, and wheat. They also plant trees such as Eucalyptus, Juniperus, and Croton in their fields (unpublished reports, the Malga District Agricultural Office in 2015). Enemorna Ener uses 27.5% of its land for grazing, 23.9% for growing crops, and 6% for forests and shrubs. The main crops are enset, coffee, teff, wheat, maize, and potatoes. Eucalyptus trees are becoming more popular because they are good for money, but most natural plants have disappeared, except for a few small protected areas (unpublished reports, the Enemorna Ener District Agricultural Office in 2015 and the Garage Zone Agricultural Department, 2015). The amount of rainfall can range from 801 to 1,400 mm. The temperatures are slightly cooler, usually between 13°C and 25°C. The land is mostly warm and tepid subhumid. In Enemorna Ener, much of the land is used for animals to graze (27.5%), whereas approximately 23.9% is used for growing crops. The main crops are enset, coffee, teff, wheat, maize, and potatoes. Many Eucalyptus trees are found because they grow fast and are a source of money. This means that many original, native trees have been cut down, except in a few small, protected areas. The soil here is different from Malga; it is mostly Nitisols (44.8%) and Vertisols (30.5%). When farmer plants ensue, the soil colour changes from red and brown to black, deepening on the spot. Site Selection and Soil Sampling To assess the acidity levels in the area, a reconnaissance survey was conducted, supplemented by secondary information obtained from district administration offices. This information facilitated the selection of study farmer associations representing four distinct land use types: RCFs, GLs, HGs, and EPs. This preparatory process took place before the main study commenced in the two districts. Two farmer associations were purposefully chosen on the basis of specific criteria, including a high incidence of soil acidity issues reported by the district agricultural offices and the diversity of land use types present in each district. Purposive sampling was employed, systematically selecting land use types on the basis of visual observations of soil colour and similarities. The selection process also considered proximity to the slope and altitude to minimize the effects of these variables on soil acidity. A visual field survey assessed variations in slope, colour, management practices, and cropping patterns by traversing the area. The field was divided into uniform sections, each designated for individual sampling. Soil samples were collected from four distinct land use types, with three replications within each farmer association. In total, six samples were gathered from two representative farmer associations from each land use type in each district, resulting in 24 composite soil samples collected from each district. Following the same methodology. Each sample was securely sealed in plastic bags, labelled with tags for identification, and then transported to the laboratory for physicochemical soil analysis following standard procedures. Soil Laboratory Analysis The soil pH-H 2 O was measured potentiometrically via a digital pH meter in a suspension with a 1:2.5 soil-to-water ratio (Barauah et al., 1997). Soil pH-KCl was determined by using 1M KCl solution 1:2.5 soil/KCl suspension as outlined in (Van Reeuwijk, 1993 ). Exchangeable acidity was determined by saturating the soil samples with a 1 M KCl solution and titrating them with sodium hydroxide, following the methods outlined by Rowell ( 1994 ) and the National Soil Research Centre (Sahlemedhin and Taye, 2000). The exchangeable base cations were determined following the percolation tube procedure (Van Reeuwijk, 2002 ). The acid saturation (AS) were calculated as follows: The Olsen method, which employs a 0.5 M sodium bicarbonate solution at pH 8.5, was used to determine the soil available phosphorus content (Van Reeuwijk, 1993 ). The ammonium acetate method at pH 7 was used to measure the amount of potassium that was available in the soils (Van Reeuwijk, 2002 ). The Kjeldahl wet oxidation method was used to calculate the total nitrogen content of the soil (Bremner, 1996). The Walkley-Black method was used to analyse the organic matter in the soil (Walkley, 1947 ). Data analysis and statistical procedures To assess how different land use types affect the physical and chemical characteristics of the soil, statistical analysis was performed on the gathered soil data. The general linear model procedure was used to perform one-way analysis of variance (ANOVA) via SAS software version 9.0 (SAS, 2004). Mean separation was performed via the least significant difference (LSD) test at a 5% level of significance whenever significant differences (P < 0.05) were obtained. Results and Discussion Effect of land use types on soil pH The soil pH(H 2 O) of RCF ranged from 5.04–5.25 and forest land from 7.33–6.98 when measured with water, but for pH (KCl), Malga and Enemorna Ener was 4.23–4.31 for RCF and 5.70–5.75 for forest land. Soil pH (H 2 O) averages measured in water were higher relative to pH KCl solutions of between 0.29–1.63 units for all land use types (Table 1 ). The low soil pH measured with the KCl determination indicates that there are considerable quantities of exchangeable hydrogen and exchangeable aluminium ions. According to Rodrigues et al. ( 2021 ), high soil acidity based on KCl determination indicates high potential acidity and/or weatherable minerals. There are different indicators of soil acidity. Value of pH is indicative of active (solution) acidity. There were strong differences in soil acidity indicators between districts. There was a significant (p < 0.01) effect of the land use type on soil pH (H 2 O). Both districts exhibited the maximum soil pH classified as HG district land use type and the minimum soil pH classified as RCF land use type (Table 1 ). The higher levels of soil acidity for RCF suggest that regularly cultivating land, plus removing crop residues, and applying inorganic fertilizers, are all acidifying on an acidic soil (Table 1 ). Results compare favourably with the results of many other studies (Achalu, 2012). Hazelton and Murphy ( 2007 ) state that soil pH in the study area was acidic (4.5 − 5.0) to neutral (6.6 − 7.30). In this study, the HG soils were overall moderately acidic in both areas, whereas RCF, EP, and GL soils were strongly acidic (pH < 5.5) (Table 1 ). The strongly acidic pH would suggest that microbial activity and potential agricultural productivity could be limited in RCF, EP, and GL. Therefore, amelioration may be necessary with the inclusion of amendments such as compost, vermicompost, lime, and farmyard manure. Table 1 Values of pH (H 2 O) and KCL for different land uses Land Use Types Malga Enemorna Ener H 2 O KCL H 2 O KCL 5.04 c 4.23 b 5.25 b 4.31 c HG 7.33 a 5.70 a 6.98 a 5.75 a GL 5.53 b 4.45 b 5.33 b 5.04 b EP 5.32 bc 4.31 b 5.36 b 4.44 c LSD (0.05) 0.48 0.63 0.58 0.48 CV(%) 6.8 11.1 6.7 7.9 Mean values with different superscript letters in columns indicate significant differences at p < 0.05. Effect of land use on the exchangeable acidity and acid saturation The exchangeable acidity and acid saturation of the soils under the various land use types in both districts varied highly significantly (p < 0.001). Home garden soils had significantly (p < 0.001) the lowest exchangeable acidity (0.96 cmol (+) kg ⁻¹ ) at the Malga and 1.13 cmol (+) kg ⁻¹ ) at Enemorna Ener in HG soils (Table 2 ). The lower exchangeable acidity in HG soils may result from higher organic matter content due to minimal soil disturbance. In contrast, the maximum exchangeable acidity value was obtained from soils in EP (4.17 cmol (+) kg ⁻¹ ), followed by those in RCF (3.05 cmol (+) kg ⁻¹ ) at Malga and those in RCF (2.76 cmol (+) kg ⁻¹ ), followed by those in EP (2.57 cmol (+) kg ⁻¹ ) at Enemorna Ener. This finding is consistent with research by Achalu and Dechassa (2021), who reported the lowest exchangeable acidity in eucalyptus plantation areas and the highest under crop cultivated land. Continuous cultivation and the loss of basic cations through plant uptake are probably the causes of the greater exchangeable acidity seen in EP, GL, and RFL. The acid saturation percentage was significantly (p < 0.001) lower in the soils of the HG fields than in those of the other three land use types at both locations. The highest acid saturation percentages were recorded at 21.46% under the EP soils at Malga and 11.46% under the RCF soils at Enemorna Ener. Conversely, the lowest percentages were observed in HG soils, at 3.00% in Malga and 3.51% in Enemorna Ener (Table 2 ). The significant difference in acid saturation between HG and cultivated fields was probably caused by variations in agronomic management techniques and the application of farmyard manure and other organic materials in HG fields. As a result, the soil acidity is extremely high in grazing areas, EP fields, and RFL fields. These results are consistent with those of Feven et al. (2024), who reported that high AS percentages under EP and RCF were caused by low soil pH and high exchangeable acidity as a result of frequent inorganic fertilizer use and cation leaching. Under the soil EP, RCF, and GL, the average exchangeable acidity at the study sites is categorized as extremely high, per Hazelton and Murphy ( 2007 ). The correlations between exchangeable acidity and Ca+ (r = -0.779*** and − 0.411**) and K+ (r = -0.781*** and − 0.710***) at Malga and Enemorna Ener, respectively, are negative and significant (P ≤ 0.01) (Table 6 ). Table 2 Values of EXAC (cmol (+) kg ⁻¹ ) and AS (cmol (+) kg ⁻¹ ) for different land use types at Malga and Enemorna Ener districts Land Use Types Malga Enemorna Ener EXAC AS EXAC AS RCF 3.05 b 14.62 b 2.57 ab 11.46 a HG 0.96 c 3.00 d 1.13 c 3.51 c GL 1.61 c 6.51 c 1.97 b 6.76 b EP 4.17 a 21.46 a 2.76 a 11.16 a LSD (0.05) 0.79 3.3 0.77 2.72 CV(%) 15.2 13.1 30.1 13.7 Mean values with different superscript letters in columns indicate significant differences at p < 0.05. Effect of land use on the exchangeable base cations An analysis of variance showed that land use types had a significant (p < 0.05) effect on the variation of exchangeable base cations. The abundance of exchangeable base cations was highest under HG fields at both districts. At Malga: Ca²⁺ at 21.47 cmol (+) kg⁻¹, Mg²⁺ 6.56 cmol (+) kg⁻¹, Na⁺ at 0.28 cmol (+) kg⁻¹, and K⁺ at 2.23 cmol (+) kg⁻¹, and at Enemorna Ener: Ca²⁺ at 22.17 cmol (+) kg⁻¹, Mg²⁺ 6.86 cmol (+) kg⁻¹, Na⁺ 0.22 cmol (+) kg⁻¹, and K⁺ at 2.09 cmol (+) kg⁻¹. Calcium deficiencies should not occur as long as sufficient soil pH is maintained. In cases of deficiencies, lime (CaCO 3 ) can be applied to the soil to improve the base saturation of calcium before the soil can be used as productive soil. The range of exchangeable Mg²⁺ for all land use soils was 1.89 cmol (+) kg⁻¹ to 6.86 cmol (+) kg⁻¹ for both locations, which falls into the medium (1.0–3.0 cmol (+) kg⁻¹) and high (3–8 cmol (+) kg⁻¹) (Hazelton and Murphy, 2007 ). This concurs with the findings of Yihenew et al. (2015), who found high K + status at Yilmana densa district Nitisols and medium K + status at Farta district Luvisols in North West Ethiopia. The top soil exchangeable Ca²⁺ was higher by 9.1 cmol (+) kg⁻¹ in the HG land at Malga district and 6.3 cmol (+) kg⁻¹ in the HG land in Enemorna Ener than that of RCF lands (Table 3 ). However, the trend of exchangeable Ca²⁺ distribution was ordered as RCF, EP, GL, and HG in the studied districts (Table 3 ). Significant (p < 0.01) differences in exchangeable Mg²⁺ concentrations exist between the land use types in both districts (Table 3 ). Exchangeable Mg²⁺ concentrations were generally higher for all land use types (Table 3 ). Although exchangeable Mg²⁺, K⁺ and Ca²⁺ concentrations were high and medium, their availability may be limited due to soil acidity. It was suggested that exchangeable K⁺, Ca²⁺ and Mg²⁺ concentrations were decreasing in the RCF and GL land use types due to the leaching effect given intensive cultivation, removal of crop residues and decay of organic matter. Also, over the years, soil erosion, overgrazing and removal of crop harvest have led to depletion of K + , Ca²⁺ and Mg²⁺ in both cultivated and grazing lands. Some researchers have also reported that continuous cultivation and application of acid-forming inorganic fertilizers have led to depletion of exchangeable Ca²⁺ and Mg²⁺ (Mesifin, 2007; Achalu et al., 2012). One can conclude that continued cultivation and overgrazing have led to an increase in soil acidity and a decrease in basic cation concentration. Whereas this study showed that reforestation practices will help alleviate soil acidity and increase exchangeable base concentration in the soil. Compost, lime, organic wastes, and the application of manure are alternatives to decrease soil acidity and increase exchangeable bases. Reforestation practices consisting of planting of forests on the border of cultivated and grazing lands may also contribute to increasing exchangeable base in soil. According to Person's correlation matrix (Table 6 ), Ca²⁺, K+, Mg²⁺, and pH all exhibited a strong positive correlation with each other land use types were sufficient without the addition of external inputs in the form of fertilizer. Table 3 Values of EXBC (cmol (+).kg ⁻¹ ), for different land uses. In Malga and Enemorna Ener Land Use Types Malga Ca ²⁺ Mg ²⁺ Na + K + RCF 12.41 b 1.89 c 0.22 b 0.74 c HG 21.47 a 6.56 a 0.28 a 2.23 a GL 18.91 a 3.28 b 0.25 ab 1.27 b EP 13,97 b 2.81 bc 0.22 b 0.86 c LSD (0.05) 2.75 1.29 0.04 0.23 CV(%) 12.6 13. 4 13.6 12.1 Enemorna Ener Ca ²⁺ Mg ²⁺ Na + K + RCF 15.87c 2.76 c 0.28 a 0.76 c HG 22.17a 6.86 a 0.22 b 2.09 a GL 20.14 ab 5.35 b 0.23 b 1.30 b EP 17.67 bc 3.63 c 0.24 ab 0.86 c LSD (0.05) 2.88 1.49 0.04 0.18 CV(%) 4.9 13. 4 13.6 12.1 Mean values with different superscript letters in columns are significantly different when p < 0.05. Effects of land use on available phosphorus and potassium The available phosphorus content in the soils was highly significantly (p < 0.001) influenced by the different land use types in both districts. In HG fields, the highest concentrations of available phosphorus were recorded in Malga (22.4 ppm) and Enemorna Ener (22.37 ppm) (Table 4 ). Available P concentration decreased in the order of HG (22.40 ppm) > RCF (9.81 ppm) > EP (9.2 ppm) > GL (8.65 ppm) at Malga district and HG (22.37 ppm) > GL (14.62 ppm) > EP(11.72 ppm) > RCF(10.23 ppm) at Enemorna Ener district. The higher concentration of these nutrients in the HG land use type may be due to the deposition of organic wastes and farmyard manure and the carryover effects of continuous. Availability of phosphorus and potassium may be affected due to soil acidity in RCF and GL. At Malga and Enemorna Ener districts, the pH results were 5.04 and 5.25, respectively, which may contribute to the lower availability of phosphorus nutrient (9.81 and 10.23 ppm, respectively) in the RFC land use type (Table 4 ). Since Av.P was positively and significantly associated with soil OM (r = 0.539**) and pH (r = 0.727***), the HG had a comparatively greater concentration of Av.P. As a result of the higher organic matter content, which released phosphorus during its mineralization. Similarly, Lachisa et al ( 2014 ) found that the cultivated and grazing land use types had lower Av. P levels than in the soils of the forest land use type. On the contrary, De et al. ( 2022 ), Tenagne et al. (2025), and Tellen and Yerima ( 2018 ) explained the higher content of Av. P in cultivated land because of the continuous application of chemical fertilizers. The GL, which was low in soil acidity, was also relatively low in available phosphorus as compared to HG land use types in both districts. The low available P concentration in the grazing land use types could be because there was no application of chemical and organic fertilizers in these land uses. The other reason for the low available P concentration in the RCF, GL and EU land use types could be due to the inherently low soil P and/or the presence of P in unavailable form. Similar trends were observed for Av. K, which was significantly (p < 0.01) affected at both locations. The highest levels of Av. K was obtained in HG fields; (228.33 ppm) at Malga and (253.50 ppm) at Enemorna Ener (Table 4 ). Relatively, the lowest Av. K was registered on cultivated fields, GL and EP at Malga and RCF at Enemorna Ener, which were below the critical level (< 150 ppm) as described by Marx et al ( 1996 ). Wondwosen and Mohammed (2019) also reported similar results from the Wanka watershed in northwestern Ethiopia. Table 4 Values of available phosphorus (ppm) and potassium (ppm) for different land uses Land Use Types Malga Enemorna Ener Av.P Av.K Av.P Av. K RCF 9.81 b 115.33 b 10.23 c 116.83 c HG 22.40 a 228.33 a 22.37 a 253.50 a GL 8.65 b 145.67 b 14.62 b 200.33 ab EP 9.20 b 144.33 b 11.72 bc 152.33 bc LSD (0.05) 3.37 55.81 3.95 72.43 CV(%) 7.5 29.2 7.9 33.2 Mean values with different superscript letters in columns indicate significant differences at p < 0.05. Effect of land use types on organic matter, total nitrogen The difference in organic matter content between the HG and other land use types was highly significant (p < 0.01) at both districts (Table 5 ). The HG fields had the highest SOM (6.28%) in the Malga district and (7.28%) in Enemorna Ener district. In the Malga district, HG land use types had soil organic matter contents that were 37% higher than RCF land use types. In Enemorna Ener, the highest SOM content recorded was 38.7% higher than RCF land use types (Table 5 ). At both districts, RCF, GL and EP land use types did not show significant differences (Table 5 ). According to Karltun et al. ( 2013 ), soils of all land use types had medium SOM content in both locations. There was a significant positive correlation between the SOM and pH (H 2 O) (r = 0.685** and 0.659***), Mg + (r = 0.603** and 0.687**), Ca + (r = 0.412* and 0.493**), K + (r = 0.582** and 0.731***), Av. P (r = 0.539** and 0.461*) at Malga and Enemorna Ener, respectively (Table 6 ). These findings align with previous studies, which found that RCFs had significantly lower SOM than adjacent land uses as a result of continuous cropping and the total removal of agronomic residues for fencing, construction, fuel and animal feed (Abiyot et al., 2022). Eyasu (2016) discussed the detrimental effects of the complete removal of crop residues from cultivated fields as a viable means of increasing SOM. Habtamu et al. (2014) and Yihenew and Getachew (2015) found lower SOM values in soils under cultivated land use than in soils under forestland use types. Additionally, Dilnesa et al. (2023) found the lowest SOM values among RCF soils due to increasingly complete collection of crop residues and rapid mineralization. This finding would be supported by Achalu et al. (2012), who found that less biomass return resulted in less soil OM and total nitrogen content in cultivated lands and grazing lands. Likewise, two studies (Daniel, 2020 and Getahun et al., 2022) have reported that EP demonstrates significantly lower SOM than does GL and RCF, which has been attributed to the slow decomposition of eucalyptus leaves and the accumulation of debris for fuel, which reduces the accumulation of organic matter beneath trees. Total nitrogen content in soil was significantly (p < 0.05) affected by land use types. The highest TN levels were observed in HG fields, with values of 0.24% at Malga and 0.26% at Enemorna Ener. This higher TN content in HG fields can be attributed to the seasonal deposition of organic litter, which enhances organic matter levels. The diverse plant biomass in HG contributes to improved nutrient cycling and retention, leading to higher TN concentrations. Conversely, the lowest TN content was found in RCF fields, recording 0.18% at Malga and 0.19% at Enemorna Ener. The lowest TN in the RCF may be a result of high rates of microbial decomposition, complete removal of crop residue for fuel, animal feed, and temporary construction. The trend for TN content at both locations generally followed the order: HG > EP > GL > RCF land use types (Table 5 ). According to Alemu et al. (2016); and Dilnesa et al. (2023), the RCF type with the lowest level of TN content may be due to decreased external nitrogen input, high organic matter decomposition, nitrogen leaching, and mining. Multiple studies have highlighted decreases in TN following land use change, with a strong positive correlation with soil organic matter (Abiyot et al., 2022). The high concentration of organic matter and total nitrogen seen in the HG land could be due to the accumulation of organic matter over time because it has little soil disturbance compared to the RCF and GL, hence reducing soil acidity. HG will expect reduced erosion, so the poor organic matter and total nitrogen content could be attributed to poor nutrient management in cultivated land and overgrazing in grazing land. Conclusions This study analysed soil acidity across land use types in southern Ethiopia's Malga and Enemorna Ener districts, focusing on rainfed plots, homestead gardens, grazing sites and eucalyptus plantations. The soil fertility varied noticeably between these areas. Key differences appeared in pH, cation exchange capacity, organic matter content and acidity. Phosphorus levels decreased sharply at the farmed, grazed and eucalyptus sites. Gardens presented a greater cation exchange capacity, which was linked to greater organic matter, than croplands. Converting natural lands to agriculture reduced exchangeable base saturation percentages, and the effective cation capacities of the soils under eucalyptus held more acidity than did the grazing or garden areas. Gardens consistently outperform other sites in terms of fertility markers, such as pH, organic matter, base cations and saturation levels. The mix of plants in these spaces likely increases the organic content while minimizing soil disruption. Continuous farming without proper care degrades rainfed soils, making them less fertile than pastures or gardens, turning wild ecosystems into farmland significantly negatively affect soil health. This highlights the need for better management practices to rebuild degraded soils and support sustainable land use. Introducing acid-tolerant crops could help understand how to understand the changes in the soil that remain critical for agricultural sustainability. Future work should analyse plant nutrient uptake and test soil amendments through field trials. Tailored recommendations could emerge from combining tissue analysis with practical experiments. The data revealed that cultivated conversion depleted vital soil elements, such as exchangeable bases and cations. Eucalyptus sites presented greater acidity than did grazed or garden soils. Gardens maintain healthier pH, organic, and nutrient levels, indicating their value as sustainable models The plant diversity in home gardens seems to be key for maintaining soil structure and organic inputs. Moreover, compared with pastures or managed gardens, repeated dry land farming without amendments leads to poorer fertility, and essentially, the landscape changes from natural to agricultural uses, damaging soil integrity. This highlights that two better strategies for soil restoration and species adapted to tougher conditions, keeping farms productive, require understanding how soils shift over time. More studies tracking nutrient cycling paired with on-ground tests would strengthen management advice per the local context. Declarations Acknowledgements The authors thank the Southern Agricultural Research Institute (SARI) and Capacity Building for Scaling Up Evidence-Based Best Practices in Agricultural Production in Ethiopia (CASCAPE) for funding the field work and laboratory analysis in this study. The authors also express their deep gratitude to Hawassa University and the Laboratory Staff of Horticoop Ethiopia PLC, Areka, Worabe and Hawassa Agricultural Research Centers for their invaluable support and substantial logistical assistance throughout the study. Author Contributions: All the authors contributed to the study design, data interpretation, and manuscript writing and editing, and approved the final version. Fisseha Negash was responsible for data extraction and analysis. Funding: This study's field work and laboratory analysis were funded by the Southern Agricultural Research Institute (SARI) and the Capacity Building for Scaling Up Evidence-Based Best Practices in Agricultural Production in Ethiopia (CASCAPE). Data availability: The data that support the findings of this study are available from the corresponding author upon reasonable request. Conflicts of interest: There are no conflicts of interest among the authors. Clinical Trial Number: Not applicable Ethics, Consent to Participate, and Consent to Publish declarations: Not applicable . References Tesfaye AM, Degefu MA, Assen M, Asmamaw Legass. 2022. Dynamics of land use/land cover: implications on environmental resources and human livelihoods in the Middle Awash Valley of Ethiopia. Environmental Monitoring and Assessment 194, no. 11 (2022): 833. https://doi.org/10.1007/s10661-022-10498-7 Abiyot Mebrate T, Kippie N, Zeray, Hailea G. Selected physical and chemical properties of soil under different agroecological zones in Gedeo Zone, Southern Ethiopia. Hellyon Dec. 2022;1(12). 10.1016/j.heliyon.2022.e12011 . Achalu Chimdi and Dechassa Yadeta. Spatial Variability and Status of Selected Physico-Chemical Properties of Soil in Different Land Use Types: The Case of Kiramu District, East Wollega Zone, Oromia District east Wollega Zone. Oromia J Sci sustainable Dev. 2021;9(2):70–91. https://www.researchgate.net/publication/22210345 Alemu Lelago T, Mamo W, Haile HS. Assessment and Mapping of Status and Spatial Distribution of Soil Macronutrients in Kambata Tembaro Zone, Southern Ethiopia. Adv Plants Agric Res. 2016;4(4):00144. https://doi.org/10.15406/apar.2016.04.00144 . Barauah TC, Barthakulh HP. A Text Book of Soil Analyses. New Delhi: Vikas Publishing House Pvt. Ltd.; 1997. p. 334. Beyene S, Kibret K, Teklu Erkossa. Future/emerging soil issues. The Soils of Ethiopia. Cham: Springer International Publishing; 2023. pp. 299–312. Daniel JN. 2020. Effects of Land Use Types on Selected Soil Properties in Central Highlands of Ethiopia, Appl. Environ. Soil Sci., vol. 2020. https://doi.org/10.1155/2020/7026929 Dawit Mesfin and Abera Habte. 2023. Yield and profitability of sweet potato (Ipomoea batatas (L.) Lam) as a function of increasing levels of phosphorus and varieties in southern Ethiopia. Applied and Environmental Soil Science 2023, no. 1 (2023): 2716227. https://doi.org/10.1155/2023/2716227 De P, Deb S, Deb D, Chakraborty S, Santra P, Dutta P, Hoque A, Choudhury A. Soil quality under different land uses in eastern India: Evaluation by using soil indicators and quality index. PLoS ONE. 2022;17(9):e0275062. https://doi.org/10.1371/journal . Dilnesa Bayle S, Feyissa, Tamiru S. 2023. Effects of land use and slope position on selected soil physicochemical properties in Tekorsh Sub-Watershed, East Gojjam Zone, Ethiopia. Open Agriculture. 2023;8(1): 20220147. https://doi.org/10.1515/opag-2022-0147 Eyasu Elias. 2016. Soils of the Ethiopian Highlands: Geomorphology and Properties. CASCAPE Project, ALTERA, Wageningen University and Research Centre (Wageningen UR). The Netherlands. 385pp. Tebekew FA, Tadesse TB, Alemu DA. Amsalu Awoke Ayalew. 2024. Soil Physicochemical Properties Variation under Annual Crop and Coffee Landuse in the Chentale Watershed, Upper Blue Nile Basin, Ethiopia, Appl. Environ. Soil Sci., vol. 2024, pp. 1–11, 2024. https://doi.org/10.1155/2024/2921614 Getahun Hailea F, Itannab B, Tekluc. and Getachew Agegnehu. 2022. Variation in soil properties under different land use types managed by smallholder farmers in central Ethiopia, Sustainable Environment, vol. 8, pp. 1–15, 2022. https://doi.org/10.1080/27658511.2022.2093058 Gliessman S, Maywa de Wit Montenegro. Agroecology at the UN food systems summit. Agroecology Sustainable Food Syst. 2021;45(10):1417–21. https://doi.org/10.1080/21683565.2021.1976474 . Grando DL, Deponti LP, Rodrigues MDL, Martins CG, Natale W, Schmitt DE, Siqueira GND, Palermo NM, Kaminski J, Brunetto G. Potential acidity determination for soils with high soil organic matter. Volume 49. Revista Brasileira de Ciência do Solo; 2025. p. e0240146. https://doi.org/10.36783/18069657rbcs20240146 . Habtamu Admas H, Gebrekidan B, Bedadi EA. 2014. Fertility status of soils under different land uses at Wujiraba watershed, northwestern highlands of Ethiopia. Agriculture, Forestry and Fisheries. 2014; 3(5):410–419. https://doi.org/10.11648/J.AFF.20140305.24 Hazelton P, Murphy B. 2007. Interpreting soil test results: What do all the numbers mean? CSIRO Publishing. Australia1,152. https://dio.1071/9780643094680. Karltun E, Mamo T, Bekele T, Gameda S 1. and, and Selamyihun Kidanu. 2013. Towards improved fertilizer recommendations in Ethiopia - Nutrient indices for categorization of fertilizer blends from Ethio-SIS woreda soil inventory data: A discussion paper. The Ethiopian Agricultural Transformation Agency (ATA) and the Ministry of Agriculture. Addis Ababa. Kumar A, Denre M, Agarwal BK, Shahi DK. Challenges of Stressed Soil: A Case Study of Acid Soil. Augmenting Crop Productivity in Stress Environment. Singapore: Springer Nature Singapore; 2022. pp. 391–402. Lachisa, Takele, Achalu Chimdi and Alemayehu Abebaw. Dynamics of soil fertility as influenced by different land use systems and soil depth in West Showa Zone, Gindeberet District, Ethiopia. Agric Forestry Fisheries. 2014;3(6):489–94. Liu KL, Han TF, Huang J, Asad S, Li DM, Yu XC, Huang QH, Ye HC, Hu HW, Hu ZH, Zhang HM. Links between potassium of soil aggregates and pH levels in acidic soils under long-term fertilization regimes. Soil Tillage Res. 2020;197:104480. https://doi.org/10.1016/j.still.2019.104480 . Marx E, et al. Soil test interpretation guide. Oregon State University Extension Service; 1996. Mekonnen M, Abeje T, Addisu S. 2021. Integrated watershed management on soil quality, crop productivity and climate change adaptation, dry highland of Northeast Ethiopia. Agricultural Systems 186 (2021): 102964. https://doi.org/10.1016/j.agsy.2020.102964 MoARD (Ministry of Agriculture and Rural development). 2005, and the Ethiopian Institute of Agricultural research. New Agro-ecological Zones of Ethiopia. Addis Ababa. 49 pp. Muluneh Weldetsadik. 2003. Population growth and environmental recovery: more people, more trees; lessons learned from western Gurage land Setarew Kenew, Workneh Kelbessa, Wudu Tesfa and Demesie Fantaya, editor, Ethiopian Journal of Social Sciences and Humanities. 1(1):1–33. Rodrigues M, Withers PJA, Soltangheisi A, Vargas V, Holzschuh M, Pavinato PS. Tillage Systems and Cover Crops Affecting Soil Phosphorus Bioavailability in Brazilian Cerrado Oxisols. Soil Tillage Res. 2021;205:104770. https://doi.org/10.1016/j.still.2020.104770 . Rowell DL. 1994. Soil Science: Methods & Applications. Addison Wesley Longman Singapore Publishers (Pte) Ltd., England, UK. 350 p. https://doi.org/10.4324/9781315844855 Sahlemedhin Sertsu and Taye Bekele. Procedures for soil and plant analysis. National Soil Research Centre, Ethiopian Agricultural Research Organization, Addis Ababa, Ethiopia; 2000. p. 110. SAS (Statistical Analysis System) Institute. SAS/STAT User's Guide, Proprietary Software Version 9.00. Cary, NC, USA: SAS Institute; 2004. Tellen VA, Yerima BP. Effects of land use change on soil physicochemical properties in selected areas in the North West region of Cameroon. Environ Syst Res. 2018;7(1):1–29. https://doi.org/10.1186/s40068-018-0106-0 Tenagne Ewunetu YG, Selassie E, Molla, Habtamu Admase and Ashenafei Gezahegn. Soil properties under different land uses and slope gradients: Implications for sustainable land management in the Tach Karnuary watershed, Northwestern Ethiopia. Front Environ Sci. 2025;13. https://doi.org/10.3389/fenvs.2025.1518068 . Van Reeuwijk LP. 1993. Procedures for Soil Analysis. 4th Edition, International Soil Reference and Information Center, Wageningen. Van Reeuwijk LP. 2002. Procedures for soil analysis, sixth Ed. International Soil Reference and Information Center (ISRIC), Wageningen, the Netherlands. ISSN 0923–3792: no. 9. Walkley A. 1947. A critical examination of a rapid method for determining organic carbon in soils: Effect of variations in digestion conditions and of organic soil constituents. Soil Science 63(4):p 251–264, April 1947. Abera W, Mohammed Assen. Dynamics of selected soil quality indicators in response to land use/cover and elevation variations in Wanka watershed, northwestern Ethiopian highlands. Ekológia (Bratislava). 2019;38(2):126–39. https://doi.org/10.2478/eko-2019-0010 . Yihenew Gebreselassie and Getachew Ayanna. 2015. Effects of different land use systems on selected physicochemical properties of soils in northwestern Ethiopia. Journal of Agricultural Science (Toronto). 2015; 5(4):112–120. https://doi.org/10.5539/jas.v5n4p112 Zheng S, Xia Y, Hu Y, Chen X, Rui Y, Gunina A, He X, Ge T, Wu J, Su Y, Kuzyakov Y. Stoichiometry of carbon, nitrogen, and phosphorus in soil: Effects of agricultural land use and climate at a continental scale. Soil Tillage Res. 2021;209:104903. https://doi.org/10.1016/j.still.2020.104903 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 May, 2026 Reviews received at journal 03 May, 2026 Reviewers agreed at journal 20 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers invited by journal 08 Apr, 2026 Editor invited by journal 27 Mar, 2026 Editor assigned by journal 09 Mar, 2026 Submission checks completed at journal 09 Mar, 2026 First submitted to journal 26 Feb, 2026 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. 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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-8975238","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":622822839,"identity":"299c8be2-80a6-4029-a3ae-69395f6d4e5c","order_by":0,"name":"FISSEHA Negash","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABC0lEQVRIiWNgGAWjYBAC9mYgwdgA4TAzVNiAuI0H8GnhOYyi5UwamItfywFkLYwth8EM/FrY2R9/+LjDLo+ff/Hjz4UN5+3Wth8G2lJjE41TCzOPmeTMM8nFkjOemUnP3HE7eduZRKCWY2m5DTi02DPzsDHztjEnbrhxwIyZ98ztZLMDQC2MDYdxauFhZn/8+W9bfeL+G8c/f+ZtO5dsdv4hIS0MBtKMbYcTN/D3GEjzth2wM7tB0BagX3rbjifOuMFTJs1zJjnB7AbQlgQ8fuHhP/74w8+26sT+/uObP/NU2NmbnU9/+OBDjQ1OLQggkQCmEsEqEwgqBwH+A2DKnijFo2AUjIJRMKIAAMH6Zh7L/d5hAAAAAElFTkSuQmCC","orcid":"","institution":"Haramaya University","correspondingAuthor":true,"prefix":"","firstName":"FISSEHA","middleName":"","lastName":"Negash","suffix":""},{"id":622822840,"identity":"20c1ee64-1784-4deb-af75-e28c1c10982c","order_by":1,"name":"Tamado Tana","email":"","orcid":"","institution":"University of Eswatini","correspondingAuthor":false,"prefix":"","firstName":"Tamado","middleName":"","lastName":"Tana","suffix":""},{"id":622822841,"identity":"2597b912-6512-4b96-a3ff-3cbe2f157937","order_by":2,"name":"Eyasu Elias","email":"","orcid":"","institution":"Addis Ababa University","correspondingAuthor":false,"prefix":"","firstName":"Eyasu","middleName":"","lastName":"Elias","suffix":""},{"id":622822842,"identity":"0fbd0223-c240-49ab-a3b2-930b7fb82e73","order_by":3,"name":"Fanuel Laekemariam","email":"","orcid":"","institution":"Wolaita Sodo University","correspondingAuthor":false,"prefix":"","firstName":"Fanuel","middleName":"","lastName":"Laekemariam","suffix":""}],"badges":[],"createdAt":"2026-02-26 08:24:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8975238/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8975238/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107078618,"identity":"c99d7477-046c-493d-bf49-1685c8526418","added_by":"auto","created_at":"2026-04-16 13:44:57","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":401076,"visible":true,"origin":"","legend":"\u003cp\u003eMaps of Malga district\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8975238/v1/83d0ec8e8ea1c3a605d14b97.png"},{"id":109067434,"identity":"d949fed8-960e-4068-b846-1f28fc83de7d","added_by":"auto","created_at":"2026-05-12 09:50:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":398482,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the Enemorna Ener district\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8975238/v1/a74744e73dde686a060bceec.png"},{"id":109081093,"identity":"e29f5ab9-c253-4cbc-a86d-eaa89fc10bca","added_by":"auto","created_at":"2026-05-12 11:57:23","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1148150,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8975238/v1/cf99fd76-3b20-4018-8851-d056b23789c4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eInfluence of Some Land Use Types on Soil Acidity in Malga and Enemorna Ener Districts, Southern Ethiopia\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eSoil acidity can result from human causes or natural causes. Some examples are land management practices, soil minerals, and climate. Soil acidity is an important part of ecological interactions that influence aspects of ecosystem stability, environmental sustainability, and agronomic productivity. Given the complicated nature of soil acidity and the different climatic ranges of agro-ecological settings, and their different land use systems in Ethiopia. Understanding how land-use practices can influence soil acidity is an important potential area of research. The southern Ethiopian districts of Malga and Enemorna Ener are an appropriate location to assess interactions within the varying forms of land use, agricultural system, and forest clearing history, and land reform due to practices evident in these relatively recently formed districts with significant agricultural activity.\u003c/p\u003e \u003cp\u003eSoil acidity affects soil quality - how nutrients become available and how soil microbial activity occurs (Liu et al, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The interactions involving soil acidity mean that critical nutrients such as P, Ca, and Mg are hindered from being available for uptake, while unwanted nutrients become more soluble and potentially toxic(Al, for example) to crops (Liu et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As Ethiopia's economy is largely agro-based, understanding acidity and acidic behaviour drives necessary information on sustainably managing land resources and food security. Recent studies show that a shift in land use (especially shifting from a natural ecosystem to agricultural purposes) results in significant changes in the soil's properties, including both acidic and non-acidic pH (Taye et al, 2021; Abebe et al, 2022).\u003c/p\u003e \u003cp\u003eMalga and Enemorna Ener districts are good case studies of how traditional and new agricultural practices continue to be utilized, and also various land use types such as cropping, animal grazing/pastoral lands, and forestry use. Each land use has a direct influence on soil chemistry and acidity; cropping is typically more intensive and often includes fertilizer application, which can induce soil acidification (Zheng et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) as opposed to natural vegetation that has a neutralizing effect on soil pH when organic matter and roots can act as a buffer against soil acidification (Kumar et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Understanding the interactions of forces conflicting with each other in their opposing goal pathways must be part of developing realistic land management alternatives to aid with soil health and agricultural productivity.\u003c/p\u003e \u003cp\u003eSimilar trends were found in scientific research demonstrating that substantial changes in soil acidity could result from changes in land use. A study conducted in Ethiopia's highlands revealed that the pH of the soil dropped significantly from forest to cropland. This illustration shows how deforestation degrades soil quality (Mekonnen et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Annual nutrient deposits and livestock trampling can also alter the pH values of the soil on grazing land (Sheleme et al., 2023). These results highlight the significance of assessing the precise impacts on soil acidity in the Malga and Enemorna Ener districts of the various land use practices linked to agricultural growth and environmental degradation.\u003c/p\u003e \u003cp\u003eThe socio-economic situations in these districts make the land use-soil acidity even more complex. Most of the rural residents are smallholder farmers and often are not able to improve soil fertility using modern practices (Dawit and Abera, 2023). The lack of modern farming inputs and insufficient technical knowledge leads to reduced soil quality and soil acidification, which creates food shortages and security issues. Therefore, knowing how land use types affect soil acidity entails more than soil science; it captures the growing issues around agricultural policy and rural development in southern Ethiopia.\u003c/p\u003e \u003cp\u003eSustainable land management practices have been part of the growing focus within the last number of years and have the potential to ameliorate soil acidification and increase soil health overall. There is evidence to suggest that agroecological practices that enhance productivity and profitability can improve pH and soil fertility, which is shown to be more sustainable, productive, and resilient to climate change (Gliessman, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). In general, Sustainable Land Management practices are helpful for soil health and resilience to climate change impacts that threaten farmer communities in Malga and Enemorna Ener districts.\u003c/p\u003e \u003cp\u003eSoil acidity is very important in agricultural systems, but we do not know much about the effects of increasingly specific types of land use on soil pH in the Malga and Enemorna Ener districts. The majority of previous research was past focused on regions for the type of land use, or if it studied land use types specifically, focused on a land use practice without reference to local conditions, and did not assess soil acidity indicators to demonstrate it. This study will address this knowledge gap and produce a very useful analysis of the effects of various land use types on soil acidity indicators in the two districts through field surveys, soil sampling, and laboratory analysis.\u003c/p\u003e \u003cp\u003eThe study will yield vital data for agricultural and land management in southern Ethiopia. By identifying the main causes of soil acidity across the various land use types, the research will pave the way for the creation of interventions that will improve soil health and agricultural output. The study will also serve as a resource for information and communication for societal and policy actors involved in food security and sustainable land practices.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eDescription of the Study Area\u003c/h2\u003e \u003cp\u003eThis study was conducted in two areas of Ethiopia: Enemorna Ener in the central region and Malga in the Sidama region. We selected these locations because they use land in very different ways, and the locations have soil acidity problems.\u003c/p\u003e \u003cp\u003eMalga is approximately 26 kilometers southeast of Hawassa city; it is approximately 301 kilometers south of Addis Ababa. Malga is located between 6\u0026deg;84' and 7\u0026deg;02' North and 38\u0026deg;52' and 38\u0026deg;70' east. It is between 1,900 and 2,800 meters above sea level (MASL). The area covers approximately 32,651 hectares, and it is mostly plains (42%) and lower-lying areas (30%), with some mountains (21%) and swampy places (6%) (Unpublished report, the Malga District Agricultural Office in 2015).\u003c/p\u003e \u003cp\u003eEnemorna Ener's capital, Gunchire, is 42 kilometers southwest of Wolkite city, and the district is approximately 192 kilometers southwest of Addis Ababa. It is at a relatively high altitude, ranging from 1,100 to 2,730 meters. Its location is between 7\u0026deg;45' and 8\u0026deg;10' North and 37\u0026deg;50' and 37\u0026deg;80' east. This area is mostly mountainous, especially in the north and northeast, which are part of the Gurage mountain range (unpublished report, the Malga District Agricultural Office in 2015, Zone Agricultural Department, 2015).\u003c/p\u003e \u003cp\u003eBoth areas have short rainy periods from March to May and then longer rainy periods from June to September. In Malga, the average temperature is approximately 17.2\u0026deg;C, and the rainfall amount is 1,400 mm. The farming areas are mostly tepid and slightly humid (73%), with some cooler, similar areas (27%) (MoARD, 2005, mentioned in Eyasu, 2016). Enemorna Ener receives between 801 and 1,400 mm of rain per year, and the temperature is approximately 13\u0026deg;C to 25\u0026deg;C. The farming areas here are warm and humid (36%) and tepid and humid (64%) (MoARD, 2005, mentioned in Eyasu, 2016).\u003c/p\u003e \u003cp\u003eThe soil is different in the two areas because of the parent rock and the environmental conditions. The soil of Malga is mainly Luvisols (46.3%), Alisols (42.7%), and Nitisols (11.0%) (Eyasu, 2016). In Enemorna Ener, mostly Nitisols (44.8%), Vertisols (30.5%), and Leptosols (10.2%) are present (Eyasu, 2016). The soil used for growing enset in Enemorna Ener can be red, brown, or even black (Muluneh, 2003).\u003c/p\u003e \u003cp\u003eFarming is most important to both local economies. Most people are small farmers who grow crops and raise animals. In Malga, they use approximately 18,177 hectares for things such as woodlots, forests, grazing, and farming. They mainly grow enset, barley, faba beans, potatoes, and wheat. They also plant trees such as Eucalyptus, Juniperus, and Croton in their fields (unpublished reports, the Malga District Agricultural Office in 2015).\u003c/p\u003e \u003cp\u003eEnemorna Ener uses 27.5% of its land for grazing, 23.9% for growing crops, and 6% for forests and shrubs. The main crops are enset, coffee, teff, wheat, maize, and potatoes. Eucalyptus trees are becoming more popular because they are good for money, but most natural plants have disappeared, except for a few small protected areas (unpublished reports, the Enemorna Ener District Agricultural Office in 2015 and the Garage Zone Agricultural Department, 2015). The amount of rainfall can range from 801 to 1,400 mm. The temperatures are slightly cooler, usually between 13\u0026deg;C and 25\u0026deg;C. The land is mostly warm and tepid subhumid.\u003c/p\u003e \u003cp\u003eIn Enemorna Ener, much of the land is used for animals to graze (27.5%), whereas approximately 23.9% is used for growing crops. The main crops are enset, coffee, teff, wheat, maize, and potatoes. Many Eucalyptus trees are found because they grow fast and are a source of money. This means that many original, native trees have been cut down, except in a few small, protected areas. The soil here is different from Malga; it is mostly Nitisols (44.8%) and Vertisols (30.5%). When farmer plants ensue, the soil colour changes from red and brown to black, deepening on the spot.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSite Selection and Soil Sampling\u003c/h3\u003e\n\u003cp\u003eTo assess the acidity levels in the area, a reconnaissance survey was conducted, supplemented by secondary information obtained from district administration offices. This information facilitated the selection of study farmer associations representing four distinct land use types: RCFs, GLs, HGs, and EPs. This preparatory process took place before the main study commenced in the two districts. Two farmer associations were purposefully chosen on the basis of specific criteria, including a high incidence of soil acidity issues reported by the district agricultural offices and the diversity of land use types present in each district. Purposive sampling was employed, systematically selecting land use types on the basis of visual observations of soil colour and similarities. The selection process also considered proximity to the slope and altitude to minimize the effects of these variables on soil acidity. A visual field survey assessed variations in slope, colour, management practices, and cropping patterns by traversing the area. The field was divided into uniform sections, each designated for individual sampling. Soil samples were collected from four distinct land use types, with three replications within each farmer association. In total, six samples were gathered from two representative farmer associations from each land use type in each district, resulting in 24 composite soil samples collected from each district. Following the same methodology. Each sample was securely sealed in plastic bags, labelled with tags for identification, and then transported to the laboratory for physicochemical soil analysis following standard procedures.\u003c/p\u003e\n\u003ch3\u003eSoil Laboratory Analysis\u003c/h3\u003e\n\u003cp\u003eThe soil pH-H\u003csub\u003e2\u003c/sub\u003eO was measured potentiometrically via a digital pH meter in a suspension with a 1:2.5 soil-to-water ratio (Barauah et al., 1997). Soil pH-KCl was determined by using 1M KCl solution 1:2.5 soil/KCl suspension as outlined in (Van Reeuwijk, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1993\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExchangeable acidity was determined by saturating the soil samples with a 1 M KCl solution and titrating them with sodium hydroxide, following the methods outlined by Rowell (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) and the National Soil Research Centre (Sahlemedhin and Taye, 2000). The exchangeable base cations were determined following the percolation tube procedure (Van Reeuwijk, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The acid saturation (AS) were calculated as follows:\u003c/p\u003e \u003cp\u003e\u003cimg src=\"data:image/png;base64,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\"\u003e\u003c/p\u003e\n\u003cp\u003eThe Olsen method, which employs a 0.5 M sodium bicarbonate solution at pH 8.5, was used to determine the soil available phosphorus content (Van Reeuwijk, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1993\u003c/span\u003e). The ammonium acetate method at pH 7 was used to measure the amount of potassium that was available in the soils (Van Reeuwijk, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The Kjeldahl wet oxidation method was used to calculate the total nitrogen content of the soil (Bremner, 1996). The Walkley-Black method was used to analyse the organic matter in the soil (Walkley, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1947\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eData analysis and statistical procedures\u003c/h3\u003e\n\u003cp\u003eTo assess how different land use types affect the physical and chemical characteristics of the soil, statistical analysis was performed on the gathered soil data. The general linear model procedure was used to perform one-way analysis of variance (ANOVA) via SAS software version 9.0 (SAS, 2004). Mean separation was performed via the least significant difference (LSD) test at a 5% level of significance whenever significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) were obtained.\u003c/p\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eEffect of land use types on soil pH\u003c/h2\u003e \u003cp\u003eThe soil pH(H\u003csub\u003e2\u003c/sub\u003eO) of RCF ranged from 5.04\u0026ndash;5.25 and forest land from 7.33\u0026ndash;6.98 when measured with water, but for pH (KCl), Malga and Enemorna Ener was 4.23\u0026ndash;4.31 for RCF and 5.70\u0026ndash;5.75 for forest land. Soil pH (H\u003csub\u003e2\u003c/sub\u003eO) averages measured in water were higher relative to pH KCl solutions of between 0.29\u0026ndash;1.63 units for all land use types (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The low soil pH measured with the KCl determination indicates that there are considerable quantities of exchangeable hydrogen and exchangeable aluminium ions. According to Rodrigues et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), high soil acidity based on KCl determination indicates high potential acidity and/or weatherable minerals. There are different indicators of soil acidity. Value of pH is indicative of active (solution) acidity. There were strong differences in soil acidity indicators between districts.\u003c/p\u003e \u003cp\u003eThere was a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) effect of the land use type on soil pH (H\u003csub\u003e2\u003c/sub\u003eO). Both districts exhibited the maximum soil pH classified as HG district land use type and the minimum soil pH classified as RCF land use type (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The higher levels of soil acidity for RCF suggest that regularly cultivating land, plus removing crop residues, and applying inorganic fertilizers, are all acidifying on an acidic soil (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Results compare favourably with the results of many other studies (Achalu, 2012).\u003c/p\u003e \u003cp\u003eHazelton and Murphy (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) state that soil pH in the study area was acidic (4.5\u0026thinsp;\u0026minus;\u0026thinsp;5.0) to neutral (6.6\u0026thinsp;\u0026minus;\u0026thinsp;7.30). In this study, the HG soils were overall moderately acidic in both areas, whereas RCF, EP, and GL soils were strongly acidic (pH\u0026thinsp;\u0026lt;\u0026thinsp;5.5) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The strongly acidic pH would suggest that microbial activity and potential agricultural productivity could be limited in RCF, EP, and GL. Therefore, amelioration may be necessary with the inclusion of amendments such as compost, vermicompost, lime, and farmyard manure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues of pH (H\u003csub\u003e2\u003c/sub\u003eO) and KCL for different land uses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLand Use Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMalga\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eEnemorna Ener\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKCL\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eH\u003csub\u003e2\u003c/sub\u003eO\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKCL\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.04\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.23\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.25\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.31\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.33\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.70\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.98\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.75\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.53\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.45\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.04\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.32\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.31\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.36\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.44\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e0.63\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.58\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.48\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e6.8\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e11.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e6.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e7.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMean values with different superscript letters in columns indicate significant differences at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eEffect of land use on the exchangeable acidity and acid saturation\u003c/h3\u003e\n\u003cp\u003eThe exchangeable acidity and acid saturation of the soils under the various land use types in both districts varied highly significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Home garden soils had significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) the lowest exchangeable acidity (0.96 cmol (+) kg\u003csup\u003e⁻\u0026sup1;\u003c/sup\u003e) at the Malga and 1.13 cmol (+) kg\u003csup\u003e⁻\u0026sup1;\u003c/sup\u003e) at Enemorna Ener in HG soils (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The lower exchangeable acidity in HG soils may result from higher organic matter content due to minimal soil disturbance. In contrast, the maximum exchangeable acidity value was obtained from soils in EP (4.17 cmol (+) kg\u003csup\u003e⁻\u0026sup1;\u003c/sup\u003e), followed by those in RCF (3.05 cmol (+) kg\u003csup\u003e⁻\u0026sup1;\u003c/sup\u003e) at Malga and those in RCF (2.76 cmol (+) kg\u003csup\u003e⁻\u0026sup1;\u003c/sup\u003e), followed by those in EP (2.57 cmol (+) kg\u003csup\u003e⁻\u0026sup1;\u003c/sup\u003e) at Enemorna Ener. This finding is consistent with research by Achalu and Dechassa (2021), who reported the lowest exchangeable acidity in eucalyptus plantation areas and the highest under crop cultivated land. Continuous cultivation and the loss of basic cations through plant uptake are probably the causes of the greater exchangeable acidity seen in EP, GL, and RFL.\u003c/p\u003e \u003cp\u003eThe acid saturation percentage was significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) lower in the soils of the HG fields than in those of the other three land use types at both locations. The highest acid saturation percentages were recorded at 21.46% under the EP soils at Malga and 11.46% under the RCF soils at Enemorna Ener. Conversely, the lowest percentages were observed in HG soils, at 3.00% in Malga and 3.51% in Enemorna Ener (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe significant difference in acid saturation between HG and cultivated fields was probably caused by variations in agronomic management techniques and the application of farmyard manure and other organic materials in HG fields. As a result, the soil acidity is extremely high in grazing areas, EP fields, and RFL fields.\u003c/p\u003e \u003cp\u003eThese results are consistent with those of Feven et al. (2024), who reported that high AS percentages under EP and RCF were caused by low soil pH and high exchangeable acidity as a result of frequent inorganic fertilizer use and cation leaching. Under the soil EP, RCF, and GL, the average exchangeable acidity at the study sites is categorized as extremely high, per Hazelton and Murphy (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The correlations between exchangeable acidity and Ca+ (r = -0.779*** and \u0026minus;\u0026thinsp;0.411**) and K+ (r = -0.781*** and \u0026minus;\u0026thinsp;0.710***) at Malga and Enemorna Ener, respectively, are negative and significant (P\u0026thinsp;\u0026le;\u0026thinsp;0.01) (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues of EXAC (cmol (+) kg\u003csup\u003e⁻\u0026sup1;\u003c/sup\u003e) and AS (cmol (+) kg\u003csup\u003e⁻\u0026sup1;\u003c/sup\u003e) for different land use types at Malga and Enemorna Ener districts\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLand Use Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMalga\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eEnemorna Ener\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEXAC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEXAC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.05\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14.62\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.57\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.46\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.96\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.00\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.13\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.51\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.61\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.51\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.97\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.76\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.17\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.46\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.76\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e11.16\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e0.79\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e3.3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.77\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e2.72\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e15.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e13.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e30.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e13.7\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMean values with different superscript letters in columns indicate significant differences at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003ch3\u003eEffect of land use on the exchangeable base cations\u003c/h3\u003e\n\u003cp\u003eAn analysis of variance showed that land use types had a significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) effect on the variation of exchangeable base cations. The abundance of exchangeable base cations was highest under HG fields at both districts. At Malga: Ca\u0026sup2;⁺ at 21.47 cmol (+) kg⁻\u0026sup1;, Mg\u0026sup2;⁺ 6.56 cmol (+) kg⁻\u0026sup1;, Na⁺ at 0.28 cmol (+) kg⁻\u0026sup1;, and K⁺ at 2.23 cmol (+) kg⁻\u0026sup1;, and at Enemorna Ener: Ca\u0026sup2;⁺ at 22.17 cmol (+) kg⁻\u0026sup1;, Mg\u0026sup2;⁺ 6.86 cmol (+) kg⁻\u0026sup1;, Na⁺ 0.22 cmol (+) kg⁻\u0026sup1;, and K⁺ at 2.09 cmol (+) kg⁻\u0026sup1;. Calcium deficiencies should not occur as long as sufficient soil pH is maintained. In cases of deficiencies, lime (CaCO\u003csub\u003e3\u003c/sub\u003e) can be applied to the soil to improve the base saturation of calcium before the soil can be used as productive soil. The range of exchangeable Mg\u0026sup2;⁺ for all land use soils was 1.89 cmol (+) kg⁻\u0026sup1; to 6.86 cmol (+) kg⁻\u0026sup1; for both locations, which falls into the medium (1.0\u0026ndash;3.0 cmol (+) kg⁻\u0026sup1;) and high (3\u0026ndash;8 cmol (+) kg⁻\u0026sup1;) (Hazelton and Murphy, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis concurs with the findings of Yihenew et al. (2015), who found high K\u003csup\u003e+\u003c/sup\u003e status at Yilmana densa district Nitisols and medium K\u003csup\u003e+\u003c/sup\u003e status at Farta district Luvisols in North West Ethiopia. The top soil exchangeable Ca\u0026sup2;⁺ was higher by 9.1 cmol (+) kg⁻\u0026sup1; in the HG land at Malga district and 6.3 cmol (+) kg⁻\u0026sup1; in the HG land in Enemorna Ener than that of RCF lands (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). However, the trend of exchangeable Ca\u0026sup2;⁺ distribution was ordered as RCF, EP, GL, and HG in the studied districts (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) differences in exchangeable Mg\u0026sup2;⁺ concentrations exist between the land use types in both districts (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Exchangeable Mg\u0026sup2;⁺ concentrations were generally higher for all land use types (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although exchangeable Mg\u0026sup2;⁺, K⁺ and Ca\u0026sup2;⁺ concentrations were high and medium, their availability may be limited due to soil acidity. It was suggested that exchangeable K⁺, Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺ concentrations were decreasing in the RCF and GL land use types due to the leaching effect given intensive cultivation, removal of crop residues and decay of organic matter.\u003c/p\u003e \u003cp\u003eAlso, over the years, soil erosion, overgrazing and removal of crop harvest have led to depletion of K\u003csup\u003e+\u003c/sup\u003e, Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺ in both cultivated and grazing lands. Some researchers have also reported that continuous cultivation and application of acid-forming inorganic fertilizers have led to depletion of exchangeable Ca\u0026sup2;⁺ and Mg\u0026sup2;⁺ (Mesifin, 2007; Achalu et al., 2012). One can conclude that continued cultivation and overgrazing have led to an increase in soil acidity and a decrease in basic cation concentration. Whereas this study showed that reforestation practices will help alleviate soil acidity and increase exchangeable base concentration in the soil. Compost, lime, organic wastes, and the application of manure are alternatives to decrease soil acidity and increase exchangeable bases. Reforestation practices consisting of planting of forests on the border of cultivated and grazing lands may also contribute to increasing exchangeable base in soil. According to Person's correlation matrix (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), Ca\u0026sup2;⁺, K+, Mg\u0026sup2;⁺, and pH all exhibited a strong positive correlation with each other land use types were sufficient without the addition of external inputs in the form of fertilizer.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues of EXBC (cmol (+).kg\u003csup\u003e⁻\u0026sup1;\u003c/sup\u003e), for different land uses. In Malga and Enemorna Ener\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLand Use Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eMalga\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa\u003csup\u003e\u0026sup2;⁺\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMg\u003csup\u003e\u0026sup2;⁺\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12.41\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.89\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.74\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.56\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.23\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18.91\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.28\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.25\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13,97\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.81\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.75\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.29\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.23\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e12.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e13. 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e12.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eEnemorna Ener\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCa\u003csup\u003e\u0026sup2;⁺\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMg\u003csup\u003e\u0026sup2;⁺\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNa\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eK\u003csup\u003e+\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.87c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.76\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.28\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.76\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.17a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.86\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.22\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.09\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20.14\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.35\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.23\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17.67\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.63\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.24\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.86\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e2.88\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e1.49\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0.18\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e4.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e13. 4\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e13.6\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e12.1\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMean values with different superscript letters in columns are significantly different when p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEffects of land use on available phosphorus and potassium\u003c/h2\u003e \u003cp\u003eThe available phosphorus content in the soils was highly significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) influenced by the different land use types in both districts. In HG fields, the highest concentrations of available phosphorus were recorded in Malga (22.4 ppm) and Enemorna Ener (22.37 ppm) (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Available P concentration decreased in the order of HG (22.40 ppm)\u0026thinsp;\u0026gt;\u0026thinsp;RCF (9.81 ppm)\u0026thinsp;\u0026gt;\u0026thinsp;EP (9.2 ppm)\u0026thinsp;\u0026gt;\u0026thinsp;GL (8.65 ppm) at Malga district and HG (22.37 ppm)\u0026thinsp;\u0026gt;\u0026thinsp;GL (14.62 ppm)\u0026thinsp;\u0026gt;\u0026thinsp;EP(11.72 ppm)\u0026thinsp;\u0026gt;\u0026thinsp;RCF(10.23 ppm) at Enemorna Ener district. The higher concentration of these nutrients in the HG land use type may be due to the deposition of organic wastes and farmyard manure and the carryover effects of continuous. Availability of phosphorus and potassium may be affected due to soil acidity in RCF and GL. At Malga and Enemorna Ener districts, the pH results were 5.04 and 5.25, respectively, which may contribute to the lower availability of phosphorus nutrient (9.81 and 10.23 ppm, respectively) in the RFC land use type (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Since Av.P was positively and significantly associated with soil OM (r\u0026thinsp;=\u0026thinsp;0.539**) and pH (r\u0026thinsp;=\u0026thinsp;0.727***), the HG had a comparatively greater concentration of Av.P. As a result of the higher organic matter content, which released phosphorus during its mineralization. Similarly, Lachisa et al (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) found that the cultivated and grazing land use types had lower Av. P levels than in the soils of the forest land use type. On the contrary, De et al. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), Tenagne et al. (2025), and Tellen and Yerima (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) explained the higher content of Av. P in cultivated land because of the continuous application of chemical fertilizers. The GL, which was low in soil acidity, was also relatively low in available phosphorus as compared to HG land use types in both districts. The low available P concentration in the grazing land use types could be because there was no application of chemical and organic fertilizers in these land uses. The other reason for the low available P concentration in the RCF, GL and EU land use types could be due to the inherently low soil P and/or the presence of P in unavailable form.\u003c/p\u003e \u003cp\u003eSimilar trends were observed for Av. K, which was significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) affected at both locations. The highest levels of Av. K was obtained in HG fields; (228.33 ppm) at Malga and (253.50 ppm) at Enemorna Ener (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Relatively, the lowest Av. K was registered on cultivated fields, GL and EP at Malga and RCF at Enemorna Ener, which were below the critical level (\u0026lt;\u0026thinsp;150 ppm) as described by Marx et al (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1996\u003c/span\u003e). Wondwosen and Mohammed (2019) also reported similar results from the Wanka watershed in northwestern Ethiopia.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues of available phosphorus (ppm) and potassium (ppm) for different land uses\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eLand Use Types\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMalga\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eEnemorna Ener\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAv.P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAv.K\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAv.P\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAv. K\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRCF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.81\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e115.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10.23\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e116.83\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.40\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e228.33\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e253.50\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.65\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e145.67\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.62\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e200.33\u003csup\u003eab\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9.20\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e144.33\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.72\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e152.33\u003csup\u003ebc\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLSD (0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e3.37\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e55.81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e3.95\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e72.43\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCV(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e7.5\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e29.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e7.9\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e33.2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMean values with different superscript letters in columns indicate significant differences at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eEffect of land use types on organic matter, total nitrogen\u003c/h2\u003e \u003cp\u003eThe difference in organic matter content between the HG and other land use types was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) at both districts (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). The HG fields had the highest SOM (6.28%) in the Malga district and (7.28%) in Enemorna Ener district. In the Malga district, HG land use types had soil organic matter contents that were 37% higher than RCF land use types. In Enemorna Ener, the highest SOM content recorded was 38.7% higher than RCF land use types (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). At both districts, RCF, GL and EP land use types did not show significant differences (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). According to Karltun et al. (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), soils of all land use types had medium SOM content in both locations. There was a significant positive correlation between the SOM and pH (H\u003csub\u003e2\u003c/sub\u003eO) (r\u0026thinsp;=\u0026thinsp;0.685** and 0.659***), Mg\u003csup\u003e+\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.603** and 0.687**), Ca\u003csup\u003e+\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.412* and 0.493**), K\u003csup\u003e+\u003c/sup\u003e (r\u0026thinsp;=\u0026thinsp;0.582** and 0.731***), Av. P (r\u0026thinsp;=\u0026thinsp;0.539** and 0.461*) at Malga and Enemorna Ener, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese findings align with previous studies, which found that RCFs had significantly lower SOM than adjacent land uses as a result of continuous cropping and the total removal of agronomic residues for fencing, construction, fuel and animal feed (Abiyot et al., 2022). Eyasu (2016) discussed the detrimental effects of the complete removal of crop residues from cultivated fields as a viable means of increasing SOM. Habtamu et al. (2014) and Yihenew and Getachew (2015) found lower SOM values in soils under cultivated land use than in soils under forestland use types. Additionally, Dilnesa et al. (2023) found the lowest SOM values among RCF soils due to increasingly complete collection of crop residues and rapid mineralization. This finding would be supported by Achalu et al. (2012), who found that less biomass return resulted in less soil OM and total nitrogen content in cultivated lands and grazing lands. Likewise, two studies (Daniel, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e and Getahun et al., 2022) have reported that EP demonstrates significantly lower SOM than does GL and RCF, which has been attributed to the slow decomposition of eucalyptus leaves and the accumulation of debris for fuel, which reduces the accumulation of organic matter beneath trees.\u003c/p\u003e \u003cp\u003eTotal nitrogen content in soil was significantly (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) affected by land use types. The highest TN levels were observed in HG fields, with values of 0.24% at Malga and 0.26% at Enemorna Ener. This higher TN content in HG fields can be attributed to the seasonal deposition of organic litter, which enhances organic matter levels. The diverse plant biomass in HG contributes to improved nutrient cycling and retention, leading to higher TN concentrations.\u003c/p\u003e \u003cp\u003eConversely, the lowest TN content was found in RCF fields, recording 0.18% at Malga and 0.19% at Enemorna Ener. The lowest TN in the RCF may be a result of high rates of microbial decomposition, complete removal of crop residue for fuel, animal feed, and temporary construction. The trend for TN content at both locations generally followed the order: HG\u0026thinsp;\u0026gt;\u0026thinsp;EP\u0026thinsp;\u0026gt;\u0026thinsp;GL\u0026thinsp;\u0026gt;\u0026thinsp;RCF land use types (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). According to Alemu et al. (2016); and Dilnesa et al. (2023), the RCF type with the lowest level of TN content may be due to decreased external nitrogen input, high organic matter decomposition, nitrogen leaching, and mining. Multiple studies have highlighted decreases in TN following land use change, with a strong positive correlation with soil organic matter (Abiyot et al., 2022).\u003c/p\u003e \u003cp\u003eThe high concentration of organic matter and total nitrogen seen in the HG land could be due to the accumulation of organic matter over time because it has little soil disturbance compared to the RCF and GL, hence reducing soil acidity. HG will expect reduced erosion, so the poor organic matter and total nitrogen content could be attributed to poor nutrient management in cultivated land and overgrazing in grazing land.\u003c/p\u003e\u003cp\u003e\u003cimg 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\"\u003e\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study analysed soil acidity across land use types in southern Ethiopia's Malga and Enemorna Ener districts, focusing on rainfed plots, homestead gardens, grazing sites and eucalyptus plantations. The soil fertility varied noticeably between these areas. Key differences appeared in pH, cation exchange capacity, organic matter content and acidity. Phosphorus levels decreased sharply at the farmed, grazed and eucalyptus sites. Gardens presented a greater cation exchange capacity, which was linked to greater organic matter, than croplands. Converting natural lands to agriculture reduced exchangeable base saturation percentages, and the effective cation capacities of the soils under eucalyptus held more acidity than did the grazing or garden areas. Gardens consistently outperform other sites in terms of fertility markers, such as pH, organic matter, base cations and saturation levels. The mix of plants in these spaces likely increases the organic content while minimizing soil disruption. Continuous farming without proper care degrades rainfed soils, making them less fertile than pastures or gardens, turning wild ecosystems into farmland significantly negatively affect soil health. This highlights the need for better management practices to rebuild degraded soils and support sustainable land use. Introducing acid-tolerant crops could help understand how to understand the changes in the soil that remain critical for agricultural sustainability.\u003c/p\u003e \u003cp\u003eFuture work should analyse plant nutrient uptake and test soil amendments through field trials. Tailored recommendations could emerge from combining tissue analysis with practical experiments. The data revealed that cultivated conversion depleted vital soil elements, such as exchangeable bases and cations. Eucalyptus sites presented greater acidity than did grazed or garden soils. Gardens maintain healthier pH, organic, and nutrient levels, indicating their value as sustainable models\u003c/p\u003e \u003cp\u003eThe plant diversity in home gardens seems to be key for maintaining soil structure and organic inputs. Moreover, compared with pastures or managed gardens, repeated dry land farming without amendments leads to poorer fertility, and essentially, the landscape changes from natural to agricultural uses, damaging soil integrity. This highlights that two better strategies for soil restoration and species adapted to tougher conditions, keeping farms productive, require understanding how soils shift over time. More studies tracking nutrient cycling paired with on-ground tests would strengthen management advice per the local context.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e The authors thank the Southern Agricultural Research Institute (SARI) and Capacity Building for Scaling Up Evidence-Based Best Practices in Agricultural Production in Ethiopia (CASCAPE) for funding the field work and laboratory analysis in this study. The authors also express their deep gratitude to Hawassa University and the Laboratory Staff of Horticoop Ethiopia PLC, Areka, Worabe and Hawassa Agricultural Research Centers for their invaluable support and substantial logistical assistance throughout the study.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthor Contributions:\u0026nbsp;\u003c/strong\u003eAll the authors contributed to the study design, data interpretation, and manuscript writing and editing, and approved the final version. Fisseha Negash was responsible for data extraction and analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eThis study\u0026apos;s field work and laboratory analysis were funded by the Southern Agricultural Research Institute (SARI) and the Capacity Building for Scaling Up Evidence-Based Best Practices in Agricultural Production in Ethiopia (CASCAPE).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability:\u0026nbsp;\u003c/strong\u003eThe data that support the findings of this study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest:\u0026nbsp;\u003c/strong\u003eThere are no conflicts of interest among the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Trial Number:\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics, Consent to Participate, and Consent to Publish declarations:\u0026nbsp;\u003c/strong\u003eNot applicable\u003cstrong\u003e.\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eTesfaye AM, Degefu MA, Assen M, Asmamaw Legass. 2022. Dynamics of land use/land cover: implications on environmental resources and human livelihoods in the Middle Awash Valley of Ethiopia. Environmental Monitoring and Assessment 194, no. 11 (2022): 833. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10661-022-10498-7\u003c/span\u003e\u003cspan address=\"10.1007/s10661-022-10498-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbiyot Mebrate T, Kippie N, Zeray, Hailea G. Selected physical and chemical properties of soil under different agroecological zones in Gedeo Zone, Southern Ethiopia. Hellyon Dec. 2022;1(12). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.heliyon.2022.e12011\u003c/span\u003e\u003cspan address=\"10.1016/j.heliyon.2022.e12011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAchalu Chimdi and Dechassa Yadeta. Spatial Variability and Status of Selected Physico-Chemical Properties of Soil in Different Land Use Types: The Case of Kiramu District, East Wollega Zone, Oromia District east Wollega Zone. Oromia J Sci sustainable Dev. 2021;9(2):70\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.researchgate.net/publication/22210345\u003c/span\u003e\u003cspan address=\"https://www.researchgate.net/publication/22210345\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlemu Lelago T, Mamo W, Haile HS. Assessment and Mapping of Status and Spatial Distribution of Soil Macronutrients in Kambata Tembaro Zone, Southern Ethiopia. Adv Plants Agric Res. 2016;4(4):00144. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.15406/apar.2016.04.00144\u003c/span\u003e\u003cspan address=\"10.15406/apar.2016.04.00144\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarauah TC, Barthakulh HP. A Text Book of Soil Analyses. New Delhi: Vikas Publishing House Pvt. Ltd.; 1997. p. 334.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeyene S, Kibret K, Teklu Erkossa. Future/emerging soil issues. The Soils of Ethiopia. Cham: Springer International Publishing; 2023. pp. 299\u0026ndash;312.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaniel JN. 2020. Effects of Land Use Types on Selected Soil Properties in Central Highlands of Ethiopia, Appl. Environ. Soil Sci., vol. 2020. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2020/7026929\u003c/span\u003e\u003cspan address=\"10.1155/2020/7026929\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDawit Mesfin and Abera Habte. 2023. Yield and profitability of sweet potato (Ipomoea batatas (L.) Lam) as a function of increasing levels of phosphorus and varieties in southern Ethiopia. Applied and Environmental Soil Science 2023, no. 1 (2023): 2716227. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2023/2716227\u003c/span\u003e\u003cspan address=\"10.1155/2023/2716227\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe P, Deb S, Deb D, Chakraborty S, Santra P, Dutta P, Hoque A, Choudhury A. Soil quality under different land uses in eastern India: Evaluation by using soil indicators and quality index. PLoS ONE. 2022;17(9):e0275062. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal\u003c/span\u003e\u003cspan address=\"10.1371/journal\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDilnesa Bayle S, Feyissa, Tamiru S. 2023. Effects of land use and slope position on selected soil physicochemical properties in Tekorsh Sub-Watershed, East Gojjam Zone, Ethiopia. Open Agriculture. 2023;8(1): 20220147. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1515/opag-2022-0147\u003c/span\u003e\u003cspan address=\"10.1515/opag-2022-0147\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEyasu Elias. 2016. Soils of the Ethiopian Highlands: Geomorphology and Properties. CASCAPE Project, ALTERA, Wageningen University and Research Centre (Wageningen UR). The Netherlands. 385pp.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTebekew FA, Tadesse TB, Alemu DA. Amsalu Awoke Ayalew. 2024. Soil Physicochemical Properties Variation under Annual Crop and Coffee Landuse in the Chentale Watershed, Upper Blue Nile Basin, Ethiopia, Appl. Environ. Soil Sci., vol. 2024, pp. 1\u0026ndash;11, 2024. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2024/2921614\u003c/span\u003e\u003cspan address=\"10.1155/2024/2921614\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGetahun Hailea F, Itannab B, Tekluc. and Getachew Agegnehu. 2022. Variation in soil properties under different land use types managed by smallholder farmers in central Ethiopia, Sustainable Environment, vol. 8, pp. 1\u0026ndash;15, 2022. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/27658511.2022.2093058\u003c/span\u003e\u003cspan address=\"10.1080/27658511.2022.2093058\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGliessman S, Maywa de Wit Montenegro. Agroecology at the UN food systems summit. Agroecology Sustainable Food Syst. 2021;45(10):1417\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/21683565.2021.1976474\u003c/span\u003e\u003cspan address=\"10.1080/21683565.2021.1976474\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrando DL, Deponti LP, Rodrigues MDL, Martins CG, Natale W, Schmitt DE, Siqueira GND, Palermo NM, Kaminski J, Brunetto G. Potential acidity determination for soils with high soil organic matter. Volume 49. Revista Brasileira de Ci\u0026ecirc;ncia do Solo; 2025. p. e0240146. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.36783/18069657rbcs20240146\u003c/span\u003e\u003cspan address=\"10.36783/18069657rbcs20240146\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHabtamu Admas H, Gebrekidan B, Bedadi EA. 2014. Fertility status of soils under different land uses at Wujiraba watershed, northwestern highlands of Ethiopia. Agriculture, Forestry and Fisheries. 2014; 3(5):410\u0026ndash;419. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11648/J.AFF.20140305.24\u003c/span\u003e\u003cspan address=\"10.11648/J.AFF.20140305.24\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHazelton P, Murphy B. 2007. Interpreting soil test results: What do all the numbers mean? CSIRO Publishing. Australia1,152. https://dio.1071/9780643094680.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKarltun E, Mamo T, Bekele T, Gameda S 1. and, and Selamyihun Kidanu. 2013. Towards improved fertilizer recommendations in Ethiopia - Nutrient indices for categorization of fertilizer blends from Ethio-SIS woreda soil inventory data: A discussion paper. The Ethiopian Agricultural Transformation Agency (ATA) and the Ministry of Agriculture. Addis Ababa.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKumar A, Denre M, Agarwal BK, Shahi DK. Challenges of Stressed Soil: A Case Study of Acid Soil. Augmenting Crop Productivity in Stress Environment. Singapore: Springer Nature Singapore; 2022. pp. 391\u0026ndash;402.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLachisa, Takele, Achalu Chimdi and Alemayehu Abebaw. Dynamics of soil fertility as influenced by different land use systems and soil depth in West Showa Zone, Gindeberet District, Ethiopia. Agric Forestry Fisheries. 2014;3(6):489\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu KL, Han TF, Huang J, Asad S, Li DM, Yu XC, Huang QH, Ye HC, Hu HW, Hu ZH, Zhang HM. Links between potassium of soil aggregates and pH levels in acidic soils under long-term fertilization regimes. Soil Tillage Res. 2020;197:104480. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.still.2019.104480\u003c/span\u003e\u003cspan address=\"10.1016/j.still.2019.104480\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarx E, et al. Soil test interpretation guide. Oregon State University Extension Service; 1996.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMekonnen M, Abeje T, Addisu S. 2021. Integrated watershed management on soil quality, crop productivity and climate change adaptation, dry highland of Northeast Ethiopia. Agricultural Systems 186 (2021): 102964. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agsy.2020.102964\u003c/span\u003e\u003cspan address=\"10.1016/j.agsy.2020.102964\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoARD (Ministry of Agriculture and Rural development). 2005, and the Ethiopian Institute of Agricultural research. New Agro-ecological Zones of Ethiopia. Addis Ababa. 49 pp.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuluneh Weldetsadik. 2003. Population growth and environmental recovery: more people, more trees; lessons learned from western Gurage land Setarew Kenew, Workneh Kelbessa, Wudu Tesfa and Demesie Fantaya, editor, Ethiopian Journal of Social Sciences and Humanities. 1(1):1\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodrigues M, Withers PJA, Soltangheisi A, Vargas V, Holzschuh M, Pavinato PS. Tillage Systems and Cover Crops Affecting Soil Phosphorus Bioavailability in Brazilian Cerrado Oxisols. Soil Tillage Res. 2021;205:104770. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.still.2020.104770\u003c/span\u003e\u003cspan address=\"10.1016/j.still.2020.104770\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRowell DL. 1994. Soil Science: Methods \u0026amp; Applications. Addison Wesley Longman Singapore Publishers (Pte) Ltd., England, UK. 350 p. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.4324/9781315844855\u003c/span\u003e\u003cspan address=\"10.4324/9781315844855\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSahlemedhin Sertsu and Taye Bekele. Procedures for soil and plant analysis. National Soil Research Centre, Ethiopian Agricultural Research Organization, Addis Ababa, Ethiopia; 2000. p. 110.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSAS (Statistical Analysis System) Institute. SAS/STAT User's Guide, Proprietary Software Version 9.00. Cary, NC, USA: SAS Institute; 2004.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTellen VA, Yerima BP. Effects of land use change on soil physicochemical properties in selected areas in the North West region of Cameroon. Environ Syst Res. 2018;7(1):1\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40068-018-0106-0\u003c/span\u003e\u003cspan address=\"10.1186/s40068-018-0106-0\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTenagne Ewunetu YG, Selassie E, Molla, Habtamu Admase and Ashenafei Gezahegn. Soil properties under different land uses and slope gradients: Implications for sustainable land management in the Tach Karnuary watershed, Northwestern Ethiopia. Front Environ Sci. 2025;13. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fenvs.2025.1518068\u003c/span\u003e\u003cspan address=\"10.3389/fenvs.2025.1518068\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Reeuwijk LP. 1993. Procedures for Soil Analysis. 4th Edition, International Soil Reference and Information Center, Wageningen.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Reeuwijk LP. 2002. Procedures for soil analysis, sixth Ed. International Soil Reference and Information Center (ISRIC), Wageningen, the Netherlands. ISSN 0923\u0026ndash;3792: no. 9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWalkley A. 1947. A critical examination of a rapid method for determining organic carbon in soils: Effect of variations in digestion conditions and of organic soil constituents. Soil Science 63(4):p 251\u0026ndash;264, April 1947.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbera W, Mohammed Assen. Dynamics of selected soil quality indicators in response to land use/cover and elevation variations in Wanka watershed, northwestern Ethiopian highlands. Ekol\u0026oacute;gia (Bratislava). 2019;38(2):126\u0026ndash;39. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2478/eko-2019-0010\u003c/span\u003e\u003cspan address=\"10.2478/eko-2019-0010\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYihenew Gebreselassie and Getachew Ayanna. 2015. Effects of different land use systems on selected physicochemical properties of soils in northwestern Ethiopia. Journal of Agricultural Science (Toronto). 2015; 5(4):112\u0026ndash;120. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.5539/jas.v5n4p112\u003c/span\u003e\u003cspan address=\"10.5539/jas.v5n4p112\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng S, Xia Y, Hu Y, Chen X, Rui Y, Gunina A, He X, Ge T, Wu J, Su Y, Kuzyakov Y. Stoichiometry of carbon, nitrogen, and phosphorus in soil: Effects of agricultural land use and climate at a continental scale. Soil Tillage Res. 2021;209:104903. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.still.2020.104903\u003c/span\u003e\u003cspan address=\"10.1016/j.still.2020.104903\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Soil](https://link.springer.com/journal/44378)","snPcode":"44378","submissionUrl":"https://submission.nature.com/new-submission/44378/3","title":"Discover Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-8975238/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8975238/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eSoil acidity is affected differently by various land use types in Ethiopia. This research was conducted to evaluate some land use types on the surface soil fertility and acidity in the districts of Malga and Enemorna Ener.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eLand use types such as cultivated fields (RCFs), homestead gardens (HGs), grazing lands (GLs), eucalyptus plantations (EPs) were selected, twenty-four (24) composite soil samples were collected from 0\u0026ndash;20 cm depth from each district. Soil analysis was done and statistically analysed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the four land use types, all had low pH values and high acid levels, except for HG. The soils under HG had significantly higher pH values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), lower levels of acid saturation and better fertility indicators, which included exchangeable bases (Ca\u0026sup2;⁺, Mg\u0026sup2;⁺, K⁺ and Na\u0026sup2;⁺), total nitrogen, organic matter, cation exchange capacity, available phosphorus, and potassium. The results revealed there were strong negative correlations between exchangeable acid cations, acid saturation and pH, whereas strong positive association were evident for some components related to fertility.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eLand use type affects the soil fertility and soil acidity status. Generally, the soil of the home garden land-use type showed the best results of the four land-use types based on their high pH and low acid saturation. The remaining land-use types had low pH and high acidity, low exchangeable bases, total nitrogen, and organic matter. Exchangeable acid cations and acid saturation have negative correlations with the pH of the soil, whereas there were positive correlations between the various components of fertility. The results from this study suggest an urgent need to improve land management practices regarding the different land use types.\u003c/p\u003e","manuscriptTitle":"Influence of Some Land Use Types on Soil Acidity in Malga and Enemorna Ener Districts, Southern Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-16 13:44:48","doi":"10.21203/rs.3.rs-8975238/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-05-10T17:32:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T08:55:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246026433550582806369378314820216346284","date":"2026-04-20T09:37:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"53570426812536626556877000716235304415","date":"2026-04-12T14:36:31+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T01:29:00+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-27T17:56:48+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-09T07:06:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-09T07:05:06+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Soil","date":"2026-02-26T08:21:01+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-soil","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Soil](https://link.springer.com/journal/44378)","snPcode":"44378","submissionUrl":"https://submission.nature.com/new-submission/44378/3","title":"Discover Soil","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"920a1555-965f-4ec7-b728-cc69891dc0b6","owner":[],"postedDate":"April 16th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"","date":"2026-05-10T17:32:27+00:00","index":46,"fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-03T08:55:21+00:00","index":45,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-16T13:44:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-16 13:44:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8975238","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8975238","identity":"rs-8975238","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}