Soil Fertility Dynamics Across Varied Land Use Types and Cropping Systems on Mount Kilimanjaro, Tanzania: Implications for Sustainable Soil Management

Abstract

Agricultural lands on the southern slopes of Mount Kilimanjaro comprise diverse and complex, smallholder cropping systems. This study explores the variation in soil fertility across different altitudes and their influence on cropping systems to recommend appropriate soil management practices. The study site spanned three altitudinal zones: upland (1438–1698 m), midland (901–1337 m) and lowland (680–834 m). Soil samples from 50 plots along the 25-km transect were analysed for chemical properties. Complementary data were collected to understand the cropping systems through six transect walks: two for each land use. Results indicate that soil organic carbon (SOC) and total N are highest in the upland and decrease with altitude, while exchangeable bases (Ca, Mg, K and Na) increase as altitude decreases. Soil pH is acidic at higher altitudes and alkaline at lower altitudes. Available P decreases with altitude, whereas available S shows no significant relationship with elevation. Overall, soil fertility status was better in the order of upland > midland > lowland, indicating a decreasing suitability trend for supporting crop production. Elevation significantly influenced the distribution patterns of soil nutrient levels ($p<0.05$). Upland and midland zones employ farmyard manure application, crop residue retention and cultivation under shade to preserve soil moisture and enhance biomass accumulation. In contrast, the lowland relies heavily on inorganic fertilisers, with nutrient depletion evident due to biomass transfer to higher altitudes. Soil erosion is critical in all three zones, necessitating erosion control measures. Recommendations include soil nutrient amendments in the upland and midland by liming to increase soil pH and address P deficiency and in the lowland through reducing salinity, use of nitrogenous fertilisers, biomass retention and incorporating leguminous plants. Cultivation of crops adapted to the conditions along the three zones is also advocated. These findings aim to improve soil management and crop productivity, ensuring sustainable agricultural practices in the region.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

1. Introduction

The agricultural sector in sub-Saharan Africa (SSA) is dominated by smallholders who rely heavily on rain-fed production, which is characterised by low input levels and low yields [1, 2]. This type of farming is associated with excessive soil nutrient mining and less soil nutrient replenishments, which, over time, cause a decline in soil fertility and affect crop production [3]. The tropical East African mountains, renowned for their high soil fertility and moisture, face increased pressure from the growing human population density and the impacts of climate change [4]. Continuous landscape cultivation needs external inputs and adequate soil fertility management practices to maintain farm productivity and crop yields [4, 5].

Despite the realisation of declining soil fertility in most parts of the SSA, smallholders persist in applying low levels of fertiliser compared to commercial farmers [6, 7]. Several countries in SSA have adopted fertiliser subsidy programs to improve the accessibility and affordability of fertiliser by smallholders, but its effectiveness remains uncertain [8]. The lack of proper diagnostic information and inadequate information about soil fertility and nutrient requirements has contributed to poor soil management practices and insufficient replenishment [9]. Enhancing soil information systems is crucial to better understand and propose more targeted soil fertility management.

Addressing soil fertility management remains a crucial aspect of sustainable land management. Soil degradation in East African mountainous regions impacts crop productivity due to altitude-related variation in moisture and nutrient retention, necessitating targeted interventions. Information on soil conditions and management regimes is required to help farmers manage soil fertility and improve crop yields. Site-specific and broad-based surveys on soil fertility remain a precursor to updating soil information systems in the East African region [10, 11]. Refining soil information systems is crucial because soil properties within the same area vary highly due to the differences in precipitation, land use, farming strategies and biomass inputs, amongst other factors [12]. Because of this, it is essential to understand the variability of local soil properties to ensure proper soil management interventions and soil fertility amendments to enhance crop production [13].

This study focuses on altitudinal impacts, as variations in elevation alter microclimates, which directly affect soil properties and crop productivity. The study aims to examine the spatial variation in soil fertility properties, including soil organic carbon (SOC), nutrient content (N, P and K) and pH, across three distinct land types—upland, midland and lowland. This study addresses the following key research questions: (i) How do soil fertility levels and nutrient availability vary across altitudinal zones? (ii) What soil management practices are needed to improve soil fertility sustainably across these zones? Investigating these questions will allow the development of tailored soil management practices suited to distinct land types in the East African highlands.

2. Materials and Methods

2.1. Description of Study Area

The study was conducted on the southern slopes of Mount Kilimanjaro (2°45′–3°25′S and 37°0′–37°43′E), approximately 15 km east of Moshi town (Figure 1). Mount Kilimanjaro is a stratovolcano composed of three main centres Shira, Kibo and Mawenzi, situated along the southern part of the East African Rift valley [14]. The surrounding area comprises the Precambrian rocks of the Mozambique belt [15]. Description of the weather conditions, soil types and cropping systems along the elevation gradient is summarised in Table 1.

[figure(s) omitted; refer to PDF]

Table 1

Description of the land use zones along the altitudinal gradient.

Land-use zone	Feature	Description
Upland	Elevation	1438–1696 m above sea level
	Precipitation	1250–2000 mm per annum
	Temperature	24°C
	Topography	20°–30° gentle slope
	Major soils	Weathered volcanic ash, humic nitisol (shine surface), haplic phaeozom (rich in organic matter), humic cambisol (slight profile development)
Midland	Elevation	901–1337 m above sea level
	Precipitation	1000–1200 mm per annum
	Temperature	26°C
	Topography	20°–30° gentle slope
	Major soils	Sodic volcanic ash (mainly in slopes), humic nitisol (shine surface), haplic phaeozom (rich in organic matter)
Lowland	Elevation	680–834 m above sea level
	Precipitation	400–900 mm per annum
	Temperature	33°C
	Topography	5°–10° flat terrain
	Major soils	Sediments influenced by volcanic ash, eutric fluvisol (alluvial with little profile development), eutric vertisol (form cracks when dry)

Note: Source: [16, 17].

The upland and midland zones primarily support perennial crops like coffee and banana, integrated with leguminous plants, while lowland areas favour annual crops such as maize and sesame. These crops are intercropped in the rhizosphere with seasonal management practices aligned with rainfall patterns.

2.2. Methods

Field data collection was conducted from April to May 2013, coinciding with the primary cropping season in the region. The data collected include landscape and cropping system characteristics to determine land uses and soil samples to determine the status of soil chemical properties. While multiyear data collection is ideal for capturing seasonal variations, logistical constraints limited the study to a single year. Nonetheless, the study region has relatively stable climatic conditions and cropping systems, allowing us to reasonably infer soil fertility dynamics from single-season data. Future studies should aim to validate these findings with multiyear data collection. An additional dataset on soil texture was obtained from Kimaro et al. [18] for the upland, midland and lowland areas of the study site. Information on soil texture is crucial for interpreting soil water-holding capacity and aeration properties.

2.2.1. Participatory Characterisation of Cropping Systems and Landscapes

Transect walks were organised to collect information from the distinct land use zones divided into upland, midland and lowland. A total of six transect walks were conducted, with each land use zone represented by two. Each transect had a length of 500 m that required approximately 3 h to traverse across the cross section of the land use zone. Five local community members were selected based on their knowledge of the area and understanding of local practices to participate in the exercise for each land use zone. During the walk, several regular stops were made to discuss and analyse the observed landscape features. A checklist was used to guide probing questions for discussing landscape features. The collected information includes data on the environment (vegetation, soil conditions, water resources and topography), agriculture (practices and cropping patterns) and climate (microclimate) conditions.

2.2.2. Soil Sampling Methods

Fifty survey plots of 1 ha (1 ha) or 10,000 (sqm) were established at about 500-m intervals from 680 to 1696 m a.s.l., along a 25-km-long transect (Figure 1). To account for variability in land use and soil conditions, 50 sampling plots were randomly selected across these zones, with 15 plots in the upland, 20 in the midland and 15 in the lowland. The study employed the Land Degradation Surveillance Framework (LDSF), which enables standard soil sampling across large landscapes, enhancing the precision and comparability of soil data. This protocol follows an inverted Y-sampling design, known for reducing spatial bias [19–21]. A minimum of three subsamples per plot for topsoil (0–20 cm) and subsoil (21–50 cm) were collected to form a composite sample, ensuring robust data collection across each zone and representation of different land uses and cropping systems.

2.2.3. Laboratory Analysis

Soil samples were air-dried and sieved through a 2-mm mesh. About 20 g of soil from each sample was scanned using midinfrared (MIR) spectral scanning (Bruker AXS Microanalysis GmbH, Germany) at the World Agroforestry Soil Diagnostic Laboratory in Nairobi to obtain a quick analysis of the soil’s chemical properties. A subset of 30 representative soil samples was selected for wet chemistry analysis. Soil pH was analysed using the potentiometric method at a 1:2 ratio of soil to water. Available P and S and the exchangeable bases (Ca, Mg, K and Na) were extracted using diluted ammonium fluoride and nitrate [22]. Soil phosphorus sorption index (PSI) was analysed using ICP extraction with potassium dihydrogen orthophosphate [23]. SOC (%) and total nitrogen concentrations were analysed via dry combustion using the Nelson and Sommers method [24].

Chemometric analysis was applied, utilising multivariate techniques such as nonmetric multidimensional scaling (NMDS) and partial least squares regression [25]. These methods allowed for more accurate predictions of soil fertility indicators and ensured cross-validation with laboratory measurements. Information from Table 2 indicates the closeness of predicted and measured (wet chemistry) values.

Table 2

RMSEC and R-squared of the optimal first five principal components used in prediction of soil properties on the southern slopes of Mount Kilimanjaro.

Soil property (n = 30)	Number of principal components	Predictions
		RMSEC	R-squared
ExCa (mg/kg)	5	0.41	0.89
ExK (mg/kg)	5	0.61	0.9
ExMg (mg/kg)	5	0.06	0.93
P (mg/kg)	5	1.13	0.21
S (mg/kg)	5	0.15	0.92
SOC (%)	5	0.13	0.96
Total N (%)	5	0.12	0.96
pH (1:2 soil:water)	5	0.06	0.93
Al (mg/kg)	5	0.16	0.86
ExNa (mg/kg)	5	0.42	0.82
ESP (meq/100 g)	5	0.43	0.65
PSI (meq/100 g)	5	0.16	0.87

Abbreviations: ESP (%), exchangeable sodium percentage; ExBas, exchangeable base; PSI, phosphorus sorption index.

2.2.4. Statistical Analysis

Statistical analyses, including ANOVA and correlation tests, were applied to discern trends and significance across the gradient. A one-way ANOVA test was conducted to analyse three soil physical properties: sand, silt and clay. The data were divided into three elevation zones: lowland, midland and upland. The purpose of the test was to determine if there were statistically significant differences in mean percentages of each soil texture component across the elevation zones.

Descriptive statistics were calculated for the measured soil properties datasets’ central tendency, dispersion and distribution shape (see Supporting Information). The nonparametric Mann–Whitney U (MWU) test was used to compare soil properties along the elevation and with depth. Pearson’s product–moment correlation was used to determine the relationship amongst soil chemical properties. We first used ordination analyses, using NMDS to test whether soil properties were related to elevation [26]. All statistical analyses were undertaken using the R-Statistics software [27].

3. Results

3.1. Variation in Soil Physical and Chemical Properties Along the Elevational Gradient

The ANOVA tests revealed that the soil texture varies with altitude (Figure 2). The trend suggests that the soil tends to become sandier and less silty and clayey as the elevation increases. The sand content varies significantly across elevation zones ($p<0.05$). Silt content showed a near-significant difference ($p=0.055$), indicating a possible elevation effect. In contrast, clay content did not vary significantly by elevation ($p=0.44$).

[figure(s) omitted; refer to PDF]

The NMDS analysis results showed that soil chemical parameters were significantly ($p<0.05$) correlated with Elevation 1 (Figure 3). This implies that elevation played a significant role in the concentration of soil nutrients along the slopes of Mount Kilimanjaro (Figure 3(a)). The NMDS further indicates that soil properties were significantly different among the land use zones ($p<0.05$) (Figure 3(b)).

[figure(s) omitted; refer to PDF]

The findings show that SOC concentrations were high in higher elevations and decreased with lower altitudes but did not vary significantly with soil depth ($p>0.05$) (Figure 4(a)) following a pattern: upland > midland > lowland. However, topsoil had a higher concentration than subsoil in absolute terms. Overall, SOC exhibited a 30% decrease from upland to lowland, highlighting significant nutrient loss at lower altitudes. Total N increased with elevation (Figure 4(b)), showing significant variation by altitude but not with soil depth ($p>0.05$). The lowland had total N levels three times lower compared to upland.

[figure(s) omitted; refer to PDF]

An increase in soil pH was observed with decreasing elevation (Figure 4(c)), indicating a strong alkalinity in the lowland and acidity in the upland. Altitude significantly influenced soil pH differences ($p<0.05$), while no significant differences were observed by the soil depth ($p>0.05$). The subsoil exhibited a higher absolute pH value compared to the topsoil. Available P decreased with elevation (Figure 4(d)), indicating higher concentration in lowland > midland > and upland sequential order. The available P indicated no statistically significant variation by soil depth ($p>0.05$) but did show significance based on elevation between upland and lowland ($p<0.05$). Available S increased with elevation (Figure 4(e)), but there was no statistical significance observed either by altitude or by soil depth ($p>0.05$). Exchangeable bases (Ca, Mg, K and Na) (Figures 4(f), 4(g), 4(h), 4(i)) were observed to be significantly higher in the lowland compared to the upland ($p<0.05$). However, no significant variation was observed by soil depth ($p>0.05$). The concentration levels of Ca and Mg decreased with elevation, with the order being lowland > midland > upland. Meanwhile, no significant difference was observed between K and Na for upland and midland.

3.2. Correlation of Soil Properties Along the Elevation Gradient

Soil chemical properties (Table 3) did not differ significantly with depth along the altitudinal gradient (MWU test, df = 1, $p>0.05$). The findings revealed that correlations amongst soil elements varied. Soil pH showed strong correlations with exchangeable bases (Ca, Mg, K and Na) and available P (r > 0, $p<0.01$) but strong negative correlations with SOC and total N (r < 0, $p<0.01$). The soil pH and S correlation were positive but not statistically significant (r > 0, $p>0.05$).

Table 3

Pearson’s product-moment correlation amongst soil properties (top soil) on the southern slopes of Mount Kilimanjaro, Tanzania.

Soil property	pH	ExCa	ExK	ExMg	ExNa	Al	$p$	S	SOC (%)	Total N
Soil pH (1:2 soil:water)	1
ExCa (mg/kg)	0.833${}^{\mathrm{\ast }\mathrm{\ast }}$	1
ExK (mg/kg)	0.68${}^{\mathrm{\ast }\mathrm{\ast }}$	0.589${}^{\mathrm{\ast }\mathrm{\ast }}$	1
ExMg (mg/kg)	0.563${}^{\mathrm{\ast }\mathrm{\ast }}$	0.674${}^{\mathrm{\ast }\mathrm{\ast }}$	0.164	1
ExNa (mg/kg)	0.851${}^{\mathrm{\ast }\mathrm{\ast }}$	0.738${}^{\mathrm{\ast }\mathrm{\ast }}$	0.757${}^{\mathrm{\ast }\mathrm{\ast }}$	0.312${}^{\mathrm{\ast }}$	1
Al (mg/kg)	−0.811${}^{\mathrm{\ast }\mathrm{\ast }}$	−0.802${}^{\mathrm{\ast }\mathrm{\ast }}$	−0.399${}^{\mathrm{\ast }}$	−0.738${}^{\mathrm{\ast }\mathrm{\ast }}$	−0.568${}^{\mathrm{\ast }\mathrm{\ast }}$	1
P (mg/kg)	0.583${}^{\mathrm{\ast }\mathrm{\ast }}$	0.532${}^{\mathrm{\ast }\mathrm{\ast }}$	0.754${}^{\mathrm{\ast }\mathrm{\ast }}$	0.09	0.487${}^{\mathrm{\ast }}$	−0.403${}^{\mathrm{\ast }}$	1
S (mg/kg)	0.207	0.229	0.599${}^{\mathrm{\ast }\mathrm{\ast }}$	−0.396${}^{\mathrm{\ast }}$	0.54${}^{\mathrm{\ast }\mathrm{\ast }}$	0.228	0.406${}^{\mathrm{\ast }}$	1
SOC (%)	−0.628${}^{\mathrm{\ast }\mathrm{\ast }}$	−0.416${}^{\mathrm{\ast }}$	−0.248	−0.55${}^{\mathrm{\ast }\mathrm{\ast }}$	−0.334${}^{\mathrm{\ast }}$	0.693${}^{\mathrm{\ast }}$	−0.156	0.426${}^{\mathrm{\ast }}$	1
Total N (%)	−0.643${}^{\mathrm{\ast }\mathrm{\ast }}$	−0.477${}^{\mathrm{\ast }}$	−0.277	−0.605${}^{\mathrm{\ast }\mathrm{\ast }}$	−0.351${}^{\mathrm{\ast }}$	0.727${}^{\mathrm{\ast }\mathrm{\ast }}$	−0.172	0.41${}^{\mathrm{\ast }}$	0.989${}^{\mathrm{\ast }\mathrm{\ast }}$	1

Note: 95% confidence interval.

${}^{\mathrm{\ast }}$Correlation is significant at α = 0.05.

${}^{\mathrm{\ast }\mathrm{\ast }}$Correlation is significant at α = 0.01.

SOC exhibited a strong positive correlation with S (r > 0, $p<0.05$) but was negatively correlated with available P and exchangeable bases (Ca, Mg, K and Na) (r < 0, $p>0.05$). Total N indicated a strong positive correlation with S (r > 0, $p<0.05$) but was negatively correlated with P and exchangeable bases (Ca, Mg, K and Na) (r < 0, $p>0.05$). Strong correlations were found amongst exchangeable bases and with available P and S, respectively, except for Mg against S (r > 0, $p<0.001$). Available P correlated strongly with S (r > 0, $p<0.05$).

3.3. Characteristics of Cropping Systems Along the Altitudinal Gradient

In the upland zone, the Chagga home gardens with the size of 0.2–1.2 ha dominate the agricultural land, producing Arabica coffee (Coffea arabica) as the main cash crop, with food crops mainly comprising banana (Musa spp.), beans (Phaseolus vulgaris), round potatoes (Solanum tuberosum), vegetables of various types and coco yams (Dioscorea spp.). Livestock keeping focuses primarily on zero grazing, particularly with dairy cattle. The application of farmyard manure is a common practice due to its availability at the household level. Agroforestry is characterised by high tree cover, averaging 60 trees per hectare, planted along farm boundaries. Traditional irrigation systems are widespread, with extensive networks operating across farms. The climate in this area is relatively colder, and fog incidences are higher.

At the midland zone, there are intensive Chagga home gardens in the form of multistrata agroforestry. Cash crops cultivated include coffee, cardamom and banana (Musa spp.), and food crops mainly include maize (Zea mays) and beans (Phaseolus vulgaris). Large trees are found in the area, providing shade to the undergrowth crops and supplying woody products. Zero grazing, mainly for dairy cattle, is a widespread undertaking. The application of farmyard manure is a common practice due to its abundance. Farms are intertwined with traditional irrigation schemes. The microclimate is less cold, and the area has relatively higher agricultural productivity.

The lowland zone, few scattered settlements are found, surrounded by open fields on flat terrains, cultivating cash crops like sunflower (Helianthus annuus), sesame (Sesamum indicum) and tomatoes (Solanum lycopersicum), and food crops such as maize (Zea mays), paddy rice (Oryza sativa), sorghum (Sorghum bicolor), cassava (Manihot esculenta) and pigeon peas (Cajanus cajan). Livestock grazes freely in this area, especially locally bred Zebu cattle. The area has a minimal tree cover, with an estimated 10 stems per hectare, dominated by scattered trees and shrubs. The hot, dry and humid microclimate results in a lack of soil moisture. Soils in parts of the landscape contain high clay content, which cracks during the dry season. Several areas are found to be waterlogged during the wet season. The location is prone to excessive soil erosion, exhibited by the formation of large gullies.

4. Discussion

4.1. Soil Fertility Across the Elevation Gradient

The higher SOC content in the upland zone was adequate to support lush plant growth, while the midland and lowland exhibited low SOC levels. Higher elevations tend to have more organic matter in the soil due to cooler temperatures and slower decomposition rates. This result conforms with a previous study by Zech et al. [11], who reported an increase of SOC content on the slopes of Mount Kilimanjaro with increasing elevation. Similarly, Tsozue et al. [28] reported increased SOC content by elevation to a tropical mountain in Central Africa. The differences in SOC concentration with elevations are usually due to a combination of factors, including land management regimes, vegetation cover, temperatures and precipitation [29]. The inverse relationship between soil pH and SOC is likely due to pH’s influence on microbial activity, which accelerates organic matter decomposition at lower pH levels.

The higher total N concentration in the upland correlates with the availability of organic matter content (Figure 4(b); Tables S1, S2, S3). Gerschlauer et al. [30] and Ensslin et al. [12] also noted an increasing trend of nitrogen with elevation in Mount Kilimanjaro. The nitrogen deficiency in the midland and lowland zones is primarily attributed to more intensive agricultural activities and higher temperatures, which accelerate the decomposition of organic matter, leading to mineralisation, volatilisation and denitrification. Varje et al. [31] also noted intensive nitrogen leaching along the slopes of Mount Kilimanjaro due to excessive surface runoff and intensive microirrigation schemes.

Available P observed in all three altitudinal zones ranged from high to low. The available P decreases with increasing elevation; the lower level in the upland is attributed to cooler temperatures, which slow down biological processes, including phosphorus mineralisation, and increased precipitation that enhances leaching from the soil. Phosphorus availability in soil is generally highest in soils with slightly acidic to neutral pH (pH 5.5–7.5); however, soil pH in the upland was acidic and contained a high concentration of Al (Table S1), which explains the low levels of available P. Available P is bound to aluminium and iron oxides, making it less accessible to plants as Al cause precipitation effect of hydroxyl phosphates (H₂PO₄) [32]. Low P availability in the upland also might be contributed by eroded sediment (particulate P) and dissolution in the surface runoff and the interflows. A previous study by Vaje et al. [33] indicated high soil nutrient loss of 32 t·ha⁻¹ and surface runoff (142 mm) (13 kg ha⁻¹) on the slopes of Mount Kilimanjaro. Addressing P may include the application of Minjingu rock phosphate fertiliser, which has been effective in increasing available P in Tanzanian acidic soils [34]. Additional efforts, such as the use of limes and other variants of phosphate fertilisers, can provide a further solution for increasing P [35]. Management interventions controlling soil erosion and minimising surface runoff can also form appropriate mitigation measures.

Exchangeable bases (Ca, Mg, K and Na) were in high concentration in the lowland and midland compared to upland (Figures 4(f), 4(g), 4(h), 4(i)). In the upland, the solubility and availability of exchangeable bases might have been affected by the acidity nature of the soil and the high level of Al concentration, as also noted by Edmeades et al. [36]. A combination of leaching, surface runoff and soil erosion may have contributed to low levels of exchangeable bases in the upland [33]. Similarly, high precipitation in the upland may have also contributed to the acceleration of leaching of exchangeable bases in the upland [37].

The pattern of available S distribution indicated an increased availability of S in the lowland and upland compared to the midland (Figure 4(e)). These distribution patterns can be explained by multiple factors such as leaching, precipitation effect and temperature [38, 39]. Studies have shown that a large part of S comprises organic forms (more than 98%); hence, its availability is linked to the status of organic matter in the soil [40]. The availability of organic matter may explain the increased available S in the upland zone. Similarly, another important characteristic of S includes its availability in the form of sulphate ion (SO₄^2-) for plant uptake, making it prone to leaching and precipitation by exchangeable bases such as Ca (Ca²⁺) [40, 41]. Our current study (Table S2) noted a higher concentration of exchangeable Ca in the midland zone, suggesting there might be interferences with available S. Efforts to keep the levels of available S at adequate range on the slopes of Mount Kilimanjaro should include avoidance of sulphur losses, sulphur replenishment through organic matter inputs especially on farms in the midland zone and application of S fertilisers throughout the landscape [42, 43].

4.2. Correlations Amongst Soil Properties

Soil properties have been shown to correlate with one another (Table 3; Table S4), indicating that they influence one another or are influenced by similar factors. The positive correlation of pH with exchangeable bases and P suggests that alkaline conditions are favourable for the availability of these nutrients. In contrast, acidic soils (lower pH) tend to retain more organic carbon and N, possibly due to reduced microbial activity and slower decomposition.

SOC content correlated positively with total N and available S, which implies that the increase of organic matter corresponds to an increase of N and S, a similar observation noted by other studies [44]. Furthermore, it has been explained that most soil N and S are found in organic forms, which implies the link with organic matter. The negative correlation of SOC with exchangeable bases (Ca, Mg, K and Na) and P may imply that areas rich in organic matter experience nutrient fixation or immobilisation, limiting nutrient availability to plants.

Our observation noted that a correlation between total N (%) and available S was positive and significant (Table 3; Table S4), which corresponds to a relationship noted by Jamal et al. [40] that the supply of N translated to increased utilisation of available S in plants. This may reflect their joint cycling in organic matter and their importance as components of essential biomolecules (e.g., proteins and amino acids).

4.3. Relationship Between Soil Chemical Properties and Cropping Systems

The elevation gradient creates a distinct microenvironment that significantly changes soil chemical properties, impacting soil fertility, vegetation growth and land use sustainability. The gradient along the slopes of Mount Kilimanjaro strongly influenced the soil’s chemical properties (Figure 3). A general trend was exhibited as transcending from lower to higher elevations, whereby soil composition changes due to differences in topography, temperature, precipitation and vegetation types [45]. Several studies have indicated how elevation and land management strongly influence soil properties [28, 46].

Higher elevations with cooler, wetter conditions may support different soil chemical properties compared to the warmer and drier conditions at lower elevations. The most typical trend is that higher elevations often result in higher SOC levels due to increased organic matter and a slower rate of decomposition, which influences nutrient availability compared to lower elevations [47]. The soil’s physical properties indicated increased sand content with increasing elevation. At the upland, there appears to be a balanced ratio of sand, silt and clay that brings beneficial effects to aeration, drainage and increased microbial activities. This implies that the higher elevations are likely to be more fertile than the lower elevations. On the other hand, higher elevations retain more nutrients due to less leaching and high soil organic matter, while lower elevations might experience nutrient leaching, runoff and depletion [48]. If not sustainably managed, the agricultural interventions contribute to soil degradation and nutrient depletion along the elevation gradient. For instance, Shara et al. [49] reported a decline in soil fertility with elevation due to farming practices in Ethiopia. In the study site, the cropping systems of the Chagga home garden in the upland and midland and monocropping in the lowland reflect the adaptation by smallholders to the site conditions. While the Chagga home gardens have evolved over the centuries, crop farming in the lowland area results from a recent villagisation programme that was introduced in the 1970s [50], where grazing lands and woodlands were apportioned and converted into croplands, albeit on relatively unproductive land.

Soil fertility enhancements in the lowlands should encompass an integrated soil fertility management approach. Smallholders temporarily flood their farm fields and apply ashes to address the salinity; however, more efforts are needed as these methods have yet to solve the problem. The prevailing practice of using fire in farm-field preparation and burning crop residuals should be discouraged, as this is known to cause volatilisation for N and hydrolysis of C [49]. Restricting other means of removal of crop residuals, such as restricting grazing and halting biomass transfer, could be promoted to ensure nutrient recycling. Similarly, other interventions may include improving vegetation cover to enhance nutrient cycling through efficient interaction with crops and livestock [52].

5. Conclusions

Soil chemical properties on the southern slopes of Mount Kilimanjaro exhibited high variability along the altitudinal gradient. Differences in climate parameters, altitude, land use and soil parent material can explain the variability. This study identified significant pH-related nutrient deficiencies in the upland and salinity issues in the lowland, underscoring the need for targeted, zone-specific interventions. Overall, the upland and midland areas contained soil elements at sufficient levels to support plant growth, unlike the lowland areas where a combination of farm management practices and environmental conditions resulted in low and declining soil fertility. Addressing deficiencies of the soil elements to support plant growth is likely to be realised through improved management regimes. Priority interventions include addressing soil acidity in the upland areas to improve soil nutrient availability (i.e., exchangeable bases to sufficient levels), stabilising the available P. Sustaining nutrient levels in the midland and addressing deficiencies of total N and exchangeable K remain key. Saline conditions in the lowland and depleted SOC and total N required immediate attention, including desalination and enhancing organic matter retention and inputs. Such targeted interventions could improve productivity and enhance livelihoods across various altitudes. While our study provides important insights, the single-year data collection limits its scope, and future research should aim to validate these findings with multiyear sampling to account for seasonal variability. Future research should also explore seasonal influences on nutrient levels and investigate soil microbial populations across the altitudinal gradient for a more holistic soil fertility model.

Author Contributions

All authors listed have made a substantial, direct and intellectual contribution to the work and approved it for publication.

Funding

This work was part of the PhD program within the Climate Change Impacts on Ecosystem Services and Food Security in Eastern Africa (CHIESA) project, 2011–2015. CHIESA was funded by the Ministry for Foreign Affairs of Finland and coordinated by the International Centre of Insect Physiology and Ecology (ICIPE) in Nairobi, Kenya. World Agroforestry and CGIAR’s CRP programs on Forests, Trees and Agroforestry (FTA) and Water, Land and Ecosystems (WLE) provided support for participation of Fergus Sinclair and Ermias Aynekulu.

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