# Role ofAcacia seyal on Selected Soil Properties and Sorghum Growth and Yield: A Case Study of Guba Lafto District, North Wollo, Ethiopia

Authors: Alayu Haile Belayneh; Kidane Giday Gebremedhin; Yemane G. Egziabher
Journal: International Journal of Agronomy
 (2021)

---

## Abstract

Acacia seyal is one of the multipurpose parkland agroforestry tree species in eastern and southern Africa. It is a common on-farm tree in the Rift Valley of Ethiopia, but information is limited on its effect on soil properties and sorghum growth and yield. The study was conducted to evaluate its effect on selected soil properties and sorghum growth and yield in Guba Lafto district of northern Ethiopia. Six isolated and closely comparable Acacia seyal trees growing on sorghum farms were purposely selected, and plots were marked under the canopy of trees with three radial distances (0–2 m, 2–4 m, and 4–6 m) and one outside of the tree canopy (10 m away from any tree). Soil samples from each distance zone were taken between 0–20 cm and 20–40 cm soil depths for soil property analysis. Four quadrates with 1 m2 at each distance zone in four directions were laid for sorghum growth and yield attribute valuation. The results showed that only total nitrogen (TN) was significantly higher (P<0.05) at the subsoil layer under the canopy compared to an open area, while other selected soil parameters were not affected by the tree species. Sorghum biomass yield (P=0.006) and grain yield (P=0.025) were significantly lower under the canopy of the trees than in the open area. Generally, Acacia seyal had little effect in improving soil properties and showed a negative effect on sorghum yield and growth. Further research on its effect under wide area coverage of parkland system should be performed to bring a radical shift on the intercropping farming system.


---

## Body

## 1. Introduction

The human population growth rate in sub-Saharan Africa is increasing rapidly despite the lower rate of food production which needs alternative solutions for parallel growth [1]. On the other hand, climate change is a major constraint for declining agricultural productivity which created a food crisis particularly for smallholder farmers. This needs climate mitigation action and climate-resilient agricultural strategy [2]. High chemical fertilizer input and fast-tracked soil fertility loss are also becoming another challenging factor for sustainable agricultural land productivity and wide environmental problems [1, 3, 4]. Inorganic or artificial fertilizer applications are also risky for the environment and found inhibiting the naturally important nodulation and nitrogen fixation and symbiotic associations which again affect nutrient recycling [5, 6].Thus, agricultural productivity reduction and food crisis challenges call for creating a multifunctional agricultural system that fulfills both intensified crop productivity improvement and socioeconomically valuable farming systems [7, 8]. Improving the current farming system such as climate-smart agriculture and sustainable productivity is important for sustainable development and food security via increasing agricultural productivity and enhancing the resilience of livelihoods and ecosystems to climate change [9, 10].Among the different existing options, agroforestry is one of the environmentally friendly approaches that help farmland soil improve and checked for smallholder farmers [9, 11]. It has many provisional, regulating, and cultural services, considered as climate-resilient and adaptation mechanisms [12–15]. Maintenance and improving existing practices and incorporation of multipurpose trees (MPTs) on farms are recognized as critical interventions to increase agricultural productivity in a sustainable way [9, 11]. Trees are advantageous for protecting environmental degradation, reducing external input costs, generally considered as a means for organic farming which assures agricultural sustainability [16]. Moreover, nitrogen-fixing legume trees get attention for nitrogen fertilizer enhancement, thereby enhancing crop productivity and creating an opportunity for sustainability [4, 6, 17].Acacia seyal is one of the drought-tolerant agroforestry tree species found in eastern and southern Africa and native trees to the Sahelian zone of Africa [18, 19]. It has a typical drought avoidance strategy and is adaptable to water stress conditions [20]. Unlike most other legume tree species, it nodules with both fast-growing (Rhizobium) and slow-growing (Bradyrhizobium) bacteria strains which strengthen its role in nitrogen fixation and soil improvement ability [21]. Limited research investigations were carried out so far on Acacia seyal effect and its social contribution in various countries at different levels [22–24]. In the study area, different thickets of Acacia spp. are growing in undulating hills and farmlands. The farming communities allow Acacia seyal to grow in their farmlands. The species seems well synchronized with the farming communities. However, its contribution to soil fertility and sorghum productivity has been the least studied. Therefore, this study was designed to investigate the effect of Acacia seyal on selected soil properties and sorghum growth and yield attributes in farmlands during the main rainy season in Guba Lafto district, North Wollo Zone.


## 2. Materials and Methods

### 2.1. Study Area Description

The study area is located in Guba Lafto district, North Wollo Zone of Amhara National Regional State, Ethiopia (Figures1 and 2), which is between 39°6′9″ to 39°45′58″ East and 11°34′54″ to 11°58′59″ North, and altitude ranges are between 1379 and 3200 m.a.s.l. It is situated 521 km North from Addis Ababa. The soil texture of the area is dominated by sandy clay (46%), clay-loam-brown (39%), and small percentages of sandy soil (8%) and silt soil (7%) [25]. Lithic Leptosols (92.2%) is dominated by soil type in the district followed by Eutric Cambisols (3.9%) and Eutric Leptosols (3.5%) [26]. The district is majorly described with mid (Woina Dega) agroecological conditions (46%). Rainfall is poor in distribution in the area, and it receives a bimodal rainy season in summer and short winter seasons. The mean annual and monthly rainfall and temperature range from 800 to 1050 mm and 21°C to 25°C, respectively [25]. Subsistence agriculture is the main livelihood system of the rural population with commonly mixed crop and livestock farming systems. Agricultural land is limited, which is 36% of the total land, and landholding size per household ranges from 0.4 to 1.93 ha [27]. Among various cereals and pulses, sorghum (Sorghum bicolor) and teff (Eragrostis tef) were commonly cultivated crops, especially during the summer rainy season.Figure 1
Geographical map showing the location of the study site.Figure 2
Jarsa kebele research site.


### 2.2. Experimental Design and Treatment

Sorghum (varJamyo) farmlands with similar crop history and management were systematically selected from farm communities at household level. Then, six Acacia seyal (var Fistula) trees were selected randomly from systematically selected trees along with sorghum farms. Tree height, diameter at breast height (DBH), and canopy diameter were measured (Table 1). The diameter at breast height was measured over the bark in cm via a caliper, the canopy radius was obtained by the measurement of crown width using a meter tape in both directions and the average value (m) is taken, and the height measurement was taken via a Suunto clinometer (m). Soil sampling and sorghum growth and yield measurements were obtained under three concentric radial distances (subplots) of the selected Acacia seyal and one from the open area near the base (0–2 m), under the canopy (2–4 m), the edge of the canopy (4–6 m), and control plot with 10 m away from the tree base in four directions.Table 1
DBH, canopy size, and height of selectedAcacia seyal trees for the study.
Plot no.DBH (cm)Canopy size (m)Total height (m)CoordinationXYZ1576.439.65559404130785617972494.607.32559747130819617963484.557.50559275130847918184626.209.40558211130797718365567.008.00558201130812018336495.707.8055798513081401866Min484.557.32Max6279.65Mean53.505.758.28St. error ±2.3200.410.41Plot geographical coordination was taken with GPS gadget (Garmin, USA).


### 2.3. Soil Sampling and Analysis

The soil sample was taken from each of the four radial distances of the tree, namely, D1, D2, D3, and D4, from the open area (Figure3), via a stainless auger [28] in four directions with two soil depths of 0–20 cm and 20–40 cm [29, 30] at the end of the long rainy cropping season. A total of 48 composite soil samples taken from 6 plots, 2 depths, and 4 concentric distance zones were sampled and analyzed at Sirinka Regional Research Center Soil Lab. Soil pH was determined by using a pH meter in a 1 : 2.5 soil; water suspension and soil electrical conductivity (EC) were measured using a conductivity meter in saturated paste extract. Total nitrogen (TN) was determined by the Kjeldahl acid digestion method [31], available phosphorus was determined via absorbance on a spectrophotometer following Olsen [32], cation-exchange capacity (CEC) was determined after extraction of soil with the ammonium acetate method at pH 7 following Chapman [33], soil organic carbon was determined via the oxidation method [34], soil bulk density (BD) was estimated via the oven-dry method [35], moisture content (MC) was determined by the gravimetric method [36], and texture was determined by Bouyoucos methods using a hydrometer [37].Figure 3
Soil sampling under tree canopy.
(a)(b)Growth and yield parameters of sorghum were collected from each subplot of square meter. Sorghum plant stock height and panicle length were recorded using a meter tape from 10 randomly selected sorghum plants of the net quadrant at harvest time. Total numbers of sorghum plants counted in each subplot were taken and used as their average value. Aboveground biomass of sampled sorghum was sun-dried to constant weight, and biomass yield (kg/m2) was estimated on kg/ha basis. The standing sorghum plant panicles were collected and manually threshed; the grains were cleaned and weighted at 12.5% moisture level, and the yield was estimated per hectare basis. 1000 kernel weights in gram (gm) were manually counted and weighted. The sorghum yield (kg/ha) was estimated as a total sum of biomass and grain yield. Harvest index of sorghum was determined as a fraction of grain yield in the unit of measurement of total yield and expressed in percentage (harvest index (%) = grain yield/biological yield (grain + straw) × 100) [38].


### 2.4. Statistical Analysis

Assumptions of normality and homogeneity of variance were first checked via the Shapiro–Wilk test and Levene test, respectively [39], for meaningful interpretation. The post hoc LSD test was used to determine the significant difference between treatments at 5% probability level. Characteristics of trees, soil physiochemical properties, and sorghum growth and yield parameters were described by using mean and standard deviation via descriptive statistics. The effects of tree on soil properties and sorghum growth and yield were tested by using the one-way analysis of variance and split file method. Two-way analysis of variance was employed to check the interaction between radial distance and soil depth treatments by using Wilks’ lambda test [40]. Selected soil properties’ mean value variation along soil depth was also tested by independent t-test along with each radial distance of tree. For data analysis, SPSS version 20 software (SPSS Corp., Chicago, USA) was used.


## 2.1. Study Area Description

The study area is located in Guba Lafto district, North Wollo Zone of Amhara National Regional State, Ethiopia (Figures1 and 2), which is between 39°6′9″ to 39°45′58″ East and 11°34′54″ to 11°58′59″ North, and altitude ranges are between 1379 and 3200 m.a.s.l. It is situated 521 km North from Addis Ababa. The soil texture of the area is dominated by sandy clay (46%), clay-loam-brown (39%), and small percentages of sandy soil (8%) and silt soil (7%) [25]. Lithic Leptosols (92.2%) is dominated by soil type in the district followed by Eutric Cambisols (3.9%) and Eutric Leptosols (3.5%) [26]. The district is majorly described with mid (Woina Dega) agroecological conditions (46%). Rainfall is poor in distribution in the area, and it receives a bimodal rainy season in summer and short winter seasons. The mean annual and monthly rainfall and temperature range from 800 to 1050 mm and 21°C to 25°C, respectively [25]. Subsistence agriculture is the main livelihood system of the rural population with commonly mixed crop and livestock farming systems. Agricultural land is limited, which is 36% of the total land, and landholding size per household ranges from 0.4 to 1.93 ha [27]. Among various cereals and pulses, sorghum (Sorghum bicolor) and teff (Eragrostis tef) were commonly cultivated crops, especially during the summer rainy season.Figure 1
Geographical map showing the location of the study site.Figure 2
Jarsa kebele research site.


## 2.2. Experimental Design and Treatment

Sorghum (varJamyo) farmlands with similar crop history and management were systematically selected from farm communities at household level. Then, six Acacia seyal (var Fistula) trees were selected randomly from systematically selected trees along with sorghum farms. Tree height, diameter at breast height (DBH), and canopy diameter were measured (Table 1). The diameter at breast height was measured over the bark in cm via a caliper, the canopy radius was obtained by the measurement of crown width using a meter tape in both directions and the average value (m) is taken, and the height measurement was taken via a Suunto clinometer (m). Soil sampling and sorghum growth and yield measurements were obtained under three concentric radial distances (subplots) of the selected Acacia seyal and one from the open area near the base (0–2 m), under the canopy (2–4 m), the edge of the canopy (4–6 m), and control plot with 10 m away from the tree base in four directions.Table 1
DBH, canopy size, and height of selectedAcacia seyal trees for the study.
Plot no.DBH (cm)Canopy size (m)Total height (m)CoordinationXYZ1576.439.65559404130785617972494.607.32559747130819617963484.557.50559275130847918184626.209.40558211130797718365567.008.00558201130812018336495.707.8055798513081401866Min484.557.32Max6279.65Mean53.505.758.28St. error ±2.3200.410.41Plot geographical coordination was taken with GPS gadget (Garmin, USA).


## 2.3. Soil Sampling and Analysis

The soil sample was taken from each of the four radial distances of the tree, namely, D1, D2, D3, and D4, from the open area (Figure3), via a stainless auger [28] in four directions with two soil depths of 0–20 cm and 20–40 cm [29, 30] at the end of the long rainy cropping season. A total of 48 composite soil samples taken from 6 plots, 2 depths, and 4 concentric distance zones were sampled and analyzed at Sirinka Regional Research Center Soil Lab. Soil pH was determined by using a pH meter in a 1 : 2.5 soil; water suspension and soil electrical conductivity (EC) were measured using a conductivity meter in saturated paste extract. Total nitrogen (TN) was determined by the Kjeldahl acid digestion method [31], available phosphorus was determined via absorbance on a spectrophotometer following Olsen [32], cation-exchange capacity (CEC) was determined after extraction of soil with the ammonium acetate method at pH 7 following Chapman [33], soil organic carbon was determined via the oxidation method [34], soil bulk density (BD) was estimated via the oven-dry method [35], moisture content (MC) was determined by the gravimetric method [36], and texture was determined by Bouyoucos methods using a hydrometer [37].Figure 3
Soil sampling under tree canopy.
(a)(b)Growth and yield parameters of sorghum were collected from each subplot of square meter. Sorghum plant stock height and panicle length were recorded using a meter tape from 10 randomly selected sorghum plants of the net quadrant at harvest time. Total numbers of sorghum plants counted in each subplot were taken and used as their average value. Aboveground biomass of sampled sorghum was sun-dried to constant weight, and biomass yield (kg/m2) was estimated on kg/ha basis. The standing sorghum plant panicles were collected and manually threshed; the grains were cleaned and weighted at 12.5% moisture level, and the yield was estimated per hectare basis. 1000 kernel weights in gram (gm) were manually counted and weighted. The sorghum yield (kg/ha) was estimated as a total sum of biomass and grain yield. Harvest index of sorghum was determined as a fraction of grain yield in the unit of measurement of total yield and expressed in percentage (harvest index (%) = grain yield/biological yield (grain + straw) × 100) [38].


## 2.4. Statistical Analysis

Assumptions of normality and homogeneity of variance were first checked via the Shapiro–Wilk test and Levene test, respectively [39], for meaningful interpretation. The post hoc LSD test was used to determine the significant difference between treatments at 5% probability level. Characteristics of trees, soil physiochemical properties, and sorghum growth and yield parameters were described by using mean and standard deviation via descriptive statistics. The effects of tree on soil properties and sorghum growth and yield were tested by using the one-way analysis of variance and split file method. Two-way analysis of variance was employed to check the interaction between radial distance and soil depth treatments by using Wilks’ lambda test [40]. Selected soil properties’ mean value variation along soil depth was also tested by independent t-test along with each radial distance of tree. For data analysis, SPSS version 20 software (SPSS Corp., Chicago, USA) was used.


## 3. Results and Discussion

### 3.1. Effect ofAcacia seyal on Soil Properties

#### 3.1.1. Soil Physical Properties

Soil particle fractions did not significantly vary (P>0.05) with distance from tree, showing that the soil properties were less affected by management practices [41–43]. Similar findings were reported for the case of different parkland tree species such as Acacia seyal, Ziziphus spina-christi, and Acacia senegal [44–46]. In contrary to this, a study in Saudi showed a significant variation of texture following distance from Acacia tree species, other than Acacia seyal species, where the clay percentage was higher under the canopy of the species than outside the canopy [47]. This difference probably was due to species variation [19] and land use type where those species were used as woodlots for a bit longer time which could bring changes to soil texture compared to the open area.Moisture content was not significantly affected by the species along both soil depths (P>0.05) at the harvesting season (Table 2). However, the lowest (34.64%) and highest (35.72%) moisture contents were recorded near the tree base (D1) and the edge of the canopy (D3), respectively, for the topsoil layer (Table 2). Normally, tree species are expected to maintain soil moisture by reducing rain speed and increasing infiltration rate [48]. However, such was not the case with Acacia seyal and this might be due to competition effect of small diameter and superficial lateral roots of the species [49]. Similar results for Balanites aegyptiaca, Acacia tortilis, and Ziziphus spina-christi species, respectively, on crop farms were found by Kassa et al. [50], Akpo et al. [51], and Wolle et al. [45]. Bulk density (BD) was statistically in par across the distance from tree and soil depths (Table 2). It might be due to low organic carbon availability under the canopy of a tree which directly influences soil aggregate stability which again affects the bulk density of soil.Table 2
Mean values (±SD) of selected soil physical parameters under soils in different distances from trees and two soil depths inAcacia seyal parkland system.
Soil propertiesSoil depthDistance from treeP valueNear tree boleUnder canopyEdge of canopyFar from canopyClay (%)155.25a ± 1.7356.25a ± 4.4756.17a ± 3.2555.0a ± 7.860.958256.67a ± 5.7956.83a ± 6.1358.58a ± 3.5655.42a ± 9.440.871Silt (%)122.83a ± 3.0223.58a ± 1.3621.42a ± 2.1323.75a ± 3.450.418223.17a ± 4.8624.75a ± 2.2120.83a ± 2.6223.75a ± 3.450.272Sand (%)121.92a ± 2.9420.83a ± 3.4722.42a ± 4.2321.25a ± 5.90.921220.17a ± 4.618.42a ± 5.6520.08a ± 3.9220.83a ± 7.30.891MC (%)134.64a ± 1.1635.06a ± 1.0635.72a ± 2.0234.77a ± 1.830.649235.09a ± 1.4434.90a ± 0.3235.18a ± 1.0434.86a ± 0.890.937BD (gm/cm3)11.06a ± 0.041.06a ± 0.031.03a ± 0.081.07a ± 0.080.6321.05a ± 0.061.04a ± 0.031.05a ± 0.041.06a ± 0.040.94Rows with the same superscript letters are not significantly different atP<0.05; soil depth with 1 and 2 represents 0–20 cm and 20–40 cm, respectively.


#### 3.1.2. Soil Chemical Properties

Soil pH and electrical conductivity (EC) were found not significantly influenced by the presence ofAcacia seyal under both soil layers (Table 3). However, electrical conductivity (EC) was a bit higher, ranging from 0.053 to 0.077 ds/m at the subsoil layer than surface soil which ranges from 0.048 to 0.063 ds/m (Table 3). This indicates that the surface soil looks less saline than subsurface soil [52]. Even though not significant, it was also relatively higher around the tree bole than an open area. This might be due to the microbial association and low leaching problems of base-forming cations around a tree [53]. The result agreed with Kassa et al. [50] and Wolle et al. [45] who reported nonsignificant effects of Balanites aegyptiaca and Ziziphus spina-christi over EC at the arid areas of Humera and Hibru districts of Ethiopia, respectively.Table 3
Mean values (±SD) of selected soil chemical parameters under soils in different distances from trees and two soil depths inAcacia seyal parkland system.
Soil propertiesSoil depthDistance from treeP valueNear tree boleUnder canopyEdge of canopyFar from canopypH16.3a ± 0.216.27a ± 0.166.25a ± 0.196.25a ± 0.290.9926.25a ± 0.196.25a ± 0.216.22a ± 0.256.27a ± 0.240.98EC (ds/m)10.063a ± 0.0180.053a ± 0.0130.048a ± 0.0110.051a ± 0.0130.1920.077a ± 0.020.053a ± 0.020.054a ± 0.010.069a ± 0.020.13P (ppm)123.81a ± 2.9224.52a ± 3.4024.16a ± 2.5624.73a ± 4.280.66226.18a ± 4.7223.71a ± 3.2624.84a ± 4.3925.91a ± 4.90.75OC (%)10.66a ± 0.230.59a ± 0.150.66a ± 0.150.63a ± 0.170.9120.59a ± 0.190.65a ± 0.120.5a ± 0.1990.66a ± 0.210.4TN (%)10.15a ± 0.040.13a ± 0.020.11a ± 0.060.10a ± 0.050.2620.18a ± 0.090.13ab ± 0.040.14ab ± 0.0290.09b ± 0.0320.03∗CEC (meq/100 g soil)139.0a ± 14.7849.0a ± 4.1742.2a ± 10.2245.27a ± 3.980.33237.0a ± 15.947.3a ± 6.9243.03a ± 4.7448.27a ± 3.880.17Rows with the same superscript letters are not significantly different atP<0.05; soil depth with 1and 2 represents 0–20 cm and 20–40 cm, respectively.Besides that soil organic carbon (SOC) was expected to be significantly higher under the tree canopy, as most perennial trees contributed to organic matter input via litterfall and fine root turnover. In this study, this was not confirmed. This might be due to the lower contribution of litter decomposition to soil carbon [54]. It was in line with the finding for an integrated system of Acacia seyal with sorghum cropping system in Sudan [55].TN was significantly affected by the presence ofAcacia seyal tree species (P=0.035) under the subsoil layer (20–40 cm) with P≤0.05 (Table 3). Acacia seyal showed a positive significant effect (P=0.035) of the available nitrogen of the subsurface soil (Table 3). Decreasing trend was observed from the tree base to the open area. The pattern was expected since most legume tree species, including Acacia seyal, have a positive effect on soil total N [56, 57]. It may also be associated with under-tree organic carbon availability via litterfall and fine root turnover from trees and droppings of animals resting under the tree [58]. Likewise, higher (42% more) nitrogen availability under the canopy of Acacia seyal compared to the open area was reported in the Rift Valley of Ethiopia [44]. The similar findings on different agroforestry tree species was also reported by Boffa et al. [59] and Hassan et al. [60]. Available phosphorus was found not affected by the species, and this might be due to the lower litter accumulation potential of the species which is related to organic phosphorus availability.Numerically, CEC had a lower mean value around the root zone for both soil depths compared to the open area but was not statistically different (Table3). In agreement with this, lower but statistically similar CEC values under Acacia seyal in the sorghum intercropping system was reported by Deng et al. [55]. Different parkland species such as Balanites aegyptiaca and Ziziphus spina-christi were reported as having similar effects but other species such as Acacia tortilis and Faidherbia albida showed a positive significant effect on CEC [44, 45, 50]. This might be due to the significant effect of those species on SOC which are directly related with CEC, and in the other ways, SOC was statistically similar in our case [58].


### 3.2. Effect ofAcacia seyal on Sorghum Growth and Yield

The presence ofAcacia seyal had a significant negative effect on aboveground biomass (P=0.006) and grain (P=0.025) yields of sorghum (Figure 4). However, other growth and yield attributes were not statistically affected (P>0.05). This might be due to the shading effect of the trees on seed emergence and seedling survival and could also be due to allopathic and superficial root competition effects of the species [49, 61]. It could also be due to the shade intolerance character of sorghum [62] which highly affected its under-canopy performance [61].Figure 4
Effect ofAcacia seyal on sorghum grain and biomass yield (kg/ha). Graphs with the same superscript letters are not statistically different at P>0.05.
(a)(b)Sorghum aboveground biomass (AGB) and grain yield were found affected by the presence ofAcacia seyal (P≤0.05). AGB was lower at D1 (2368.33 kg/ha) compared to D4 (6558.33 kg/ha) with more than fold amount (177%) (Figure 4). It might be correlated with the number of sorghum stands counted and its growth performance [62]. Grain yield was also observed to be higher (142% more) at D4 compared to D1 (Figure 4). The result was unexpected since the symbiotic and nitrogen-fixing ability of Acacia seyal is believed to boost yield by enhancing soil fertility [21]. However, this might be due to the shading effect of Acacia seyal and its allopathic effect on sorghum survival and growth performance which is directly related to yield [49, 61]. Superficial small diameter roots at the surface layer and underground root competition of the species also contribute to the yield reduction effect [63]. The negative effect of Acacia seyal intercropping on sorghum yield and growth performance in South Sudan was also reported by Deng et al. [55]. However, other species such as Faidherbia albida were found to have a positive contribution to sorghum yield in the case of Tahtay Maychew district of northern Ethiopia [60].


## 3.1. Effect ofAcacia seyal on Soil Properties

### 3.1.1. Soil Physical Properties

Soil particle fractions did not significantly vary (P>0.05) with distance from tree, showing that the soil properties were less affected by management practices [41–43]. Similar findings were reported for the case of different parkland tree species such as Acacia seyal, Ziziphus spina-christi, and Acacia senegal [44–46]. In contrary to this, a study in Saudi showed a significant variation of texture following distance from Acacia tree species, other than Acacia seyal species, where the clay percentage was higher under the canopy of the species than outside the canopy [47]. This difference probably was due to species variation [19] and land use type where those species were used as woodlots for a bit longer time which could bring changes to soil texture compared to the open area.Moisture content was not significantly affected by the species along both soil depths (P>0.05) at the harvesting season (Table 2). However, the lowest (34.64%) and highest (35.72%) moisture contents were recorded near the tree base (D1) and the edge of the canopy (D3), respectively, for the topsoil layer (Table 2). Normally, tree species are expected to maintain soil moisture by reducing rain speed and increasing infiltration rate [48]. However, such was not the case with Acacia seyal and this might be due to competition effect of small diameter and superficial lateral roots of the species [49]. Similar results for Balanites aegyptiaca, Acacia tortilis, and Ziziphus spina-christi species, respectively, on crop farms were found by Kassa et al. [50], Akpo et al. [51], and Wolle et al. [45]. Bulk density (BD) was statistically in par across the distance from tree and soil depths (Table 2). It might be due to low organic carbon availability under the canopy of a tree which directly influences soil aggregate stability which again affects the bulk density of soil.Table 2
Mean values (±SD) of selected soil physical parameters under soils in different distances from trees and two soil depths inAcacia seyal parkland system.
Soil propertiesSoil depthDistance from treeP valueNear tree boleUnder canopyEdge of canopyFar from canopyClay (%)155.25a ± 1.7356.25a ± 4.4756.17a ± 3.2555.0a ± 7.860.958256.67a ± 5.7956.83a ± 6.1358.58a ± 3.5655.42a ± 9.440.871Silt (%)122.83a ± 3.0223.58a ± 1.3621.42a ± 2.1323.75a ± 3.450.418223.17a ± 4.8624.75a ± 2.2120.83a ± 2.6223.75a ± 3.450.272Sand (%)121.92a ± 2.9420.83a ± 3.4722.42a ± 4.2321.25a ± 5.90.921220.17a ± 4.618.42a ± 5.6520.08a ± 3.9220.83a ± 7.30.891MC (%)134.64a ± 1.1635.06a ± 1.0635.72a ± 2.0234.77a ± 1.830.649235.09a ± 1.4434.90a ± 0.3235.18a ± 1.0434.86a ± 0.890.937BD (gm/cm3)11.06a ± 0.041.06a ± 0.031.03a ± 0.081.07a ± 0.080.6321.05a ± 0.061.04a ± 0.031.05a ± 0.041.06a ± 0.040.94Rows with the same superscript letters are not significantly different atP<0.05; soil depth with 1 and 2 represents 0–20 cm and 20–40 cm, respectively.


### 3.1.2. Soil Chemical Properties

Soil pH and electrical conductivity (EC) were found not significantly influenced by the presence ofAcacia seyal under both soil layers (Table 3). However, electrical conductivity (EC) was a bit higher, ranging from 0.053 to 0.077 ds/m at the subsoil layer than surface soil which ranges from 0.048 to 0.063 ds/m (Table 3). This indicates that the surface soil looks less saline than subsurface soil [52]. Even though not significant, it was also relatively higher around the tree bole than an open area. This might be due to the microbial association and low leaching problems of base-forming cations around a tree [53]. The result agreed with Kassa et al. [50] and Wolle et al. [45] who reported nonsignificant effects of Balanites aegyptiaca and Ziziphus spina-christi over EC at the arid areas of Humera and Hibru districts of Ethiopia, respectively.Table 3
Mean values (±SD) of selected soil chemical parameters under soils in different distances from trees and two soil depths inAcacia seyal parkland system.
Soil propertiesSoil depthDistance from treeP valueNear tree boleUnder canopyEdge of canopyFar from canopypH16.3a ± 0.216.27a ± 0.166.25a ± 0.196.25a ± 0.290.9926.25a ± 0.196.25a ± 0.216.22a ± 0.256.27a ± 0.240.98EC (ds/m)10.063a ± 0.0180.053a ± 0.0130.048a ± 0.0110.051a ± 0.0130.1920.077a ± 0.020.053a ± 0.020.054a ± 0.010.069a ± 0.020.13P (ppm)123.81a ± 2.9224.52a ± 3.4024.16a ± 2.5624.73a ± 4.280.66226.18a ± 4.7223.71a ± 3.2624.84a ± 4.3925.91a ± 4.90.75OC (%)10.66a ± 0.230.59a ± 0.150.66a ± 0.150.63a ± 0.170.9120.59a ± 0.190.65a ± 0.120.5a ± 0.1990.66a ± 0.210.4TN (%)10.15a ± 0.040.13a ± 0.020.11a ± 0.060.10a ± 0.050.2620.18a ± 0.090.13ab ± 0.040.14ab ± 0.0290.09b ± 0.0320.03∗CEC (meq/100 g soil)139.0a ± 14.7849.0a ± 4.1742.2a ± 10.2245.27a ± 3.980.33237.0a ± 15.947.3a ± 6.9243.03a ± 4.7448.27a ± 3.880.17Rows with the same superscript letters are not significantly different atP<0.05; soil depth with 1and 2 represents 0–20 cm and 20–40 cm, respectively.Besides that soil organic carbon (SOC) was expected to be significantly higher under the tree canopy, as most perennial trees contributed to organic matter input via litterfall and fine root turnover. In this study, this was not confirmed. This might be due to the lower contribution of litter decomposition to soil carbon [54]. It was in line with the finding for an integrated system of Acacia seyal with sorghum cropping system in Sudan [55].TN was significantly affected by the presence ofAcacia seyal tree species (P=0.035) under the subsoil layer (20–40 cm) with P≤0.05 (Table 3). Acacia seyal showed a positive significant effect (P=0.035) of the available nitrogen of the subsurface soil (Table 3). Decreasing trend was observed from the tree base to the open area. The pattern was expected since most legume tree species, including Acacia seyal, have a positive effect on soil total N [56, 57]. It may also be associated with under-tree organic carbon availability via litterfall and fine root turnover from trees and droppings of animals resting under the tree [58]. Likewise, higher (42% more) nitrogen availability under the canopy of Acacia seyal compared to the open area was reported in the Rift Valley of Ethiopia [44]. The similar findings on different agroforestry tree species was also reported by Boffa et al. [59] and Hassan et al. [60]. Available phosphorus was found not affected by the species, and this might be due to the lower litter accumulation potential of the species which is related to organic phosphorus availability.Numerically, CEC had a lower mean value around the root zone for both soil depths compared to the open area but was not statistically different (Table3). In agreement with this, lower but statistically similar CEC values under Acacia seyal in the sorghum intercropping system was reported by Deng et al. [55]. Different parkland species such as Balanites aegyptiaca and Ziziphus spina-christi were reported as having similar effects but other species such as Acacia tortilis and Faidherbia albida showed a positive significant effect on CEC [44, 45, 50]. This might be due to the significant effect of those species on SOC which are directly related with CEC, and in the other ways, SOC was statistically similar in our case [58].


## 3.1.1. Soil Physical Properties

Soil particle fractions did not significantly vary (P>0.05) with distance from tree, showing that the soil properties were less affected by management practices [41–43]. Similar findings were reported for the case of different parkland tree species such as Acacia seyal, Ziziphus spina-christi, and Acacia senegal [44–46]. In contrary to this, a study in Saudi showed a significant variation of texture following distance from Acacia tree species, other than Acacia seyal species, where the clay percentage was higher under the canopy of the species than outside the canopy [47]. This difference probably was due to species variation [19] and land use type where those species were used as woodlots for a bit longer time which could bring changes to soil texture compared to the open area.Moisture content was not significantly affected by the species along both soil depths (P>0.05) at the harvesting season (Table 2). However, the lowest (34.64%) and highest (35.72%) moisture contents were recorded near the tree base (D1) and the edge of the canopy (D3), respectively, for the topsoil layer (Table 2). Normally, tree species are expected to maintain soil moisture by reducing rain speed and increasing infiltration rate [48]. However, such was not the case with Acacia seyal and this might be due to competition effect of small diameter and superficial lateral roots of the species [49]. Similar results for Balanites aegyptiaca, Acacia tortilis, and Ziziphus spina-christi species, respectively, on crop farms were found by Kassa et al. [50], Akpo et al. [51], and Wolle et al. [45]. Bulk density (BD) was statistically in par across the distance from tree and soil depths (Table 2). It might be due to low organic carbon availability under the canopy of a tree which directly influences soil aggregate stability which again affects the bulk density of soil.Table 2
Mean values (±SD) of selected soil physical parameters under soils in different distances from trees and two soil depths inAcacia seyal parkland system.
Soil propertiesSoil depthDistance from treeP valueNear tree boleUnder canopyEdge of canopyFar from canopyClay (%)155.25a ± 1.7356.25a ± 4.4756.17a ± 3.2555.0a ± 7.860.958256.67a ± 5.7956.83a ± 6.1358.58a ± 3.5655.42a ± 9.440.871Silt (%)122.83a ± 3.0223.58a ± 1.3621.42a ± 2.1323.75a ± 3.450.418223.17a ± 4.8624.75a ± 2.2120.83a ± 2.6223.75a ± 3.450.272Sand (%)121.92a ± 2.9420.83a ± 3.4722.42a ± 4.2321.25a ± 5.90.921220.17a ± 4.618.42a ± 5.6520.08a ± 3.9220.83a ± 7.30.891MC (%)134.64a ± 1.1635.06a ± 1.0635.72a ± 2.0234.77a ± 1.830.649235.09a ± 1.4434.90a ± 0.3235.18a ± 1.0434.86a ± 0.890.937BD (gm/cm3)11.06a ± 0.041.06a ± 0.031.03a ± 0.081.07a ± 0.080.6321.05a ± 0.061.04a ± 0.031.05a ± 0.041.06a ± 0.040.94Rows with the same superscript letters are not significantly different atP<0.05; soil depth with 1 and 2 represents 0–20 cm and 20–40 cm, respectively.


## 3.1.2. Soil Chemical Properties

Soil pH and electrical conductivity (EC) were found not significantly influenced by the presence ofAcacia seyal under both soil layers (Table 3). However, electrical conductivity (EC) was a bit higher, ranging from 0.053 to 0.077 ds/m at the subsoil layer than surface soil which ranges from 0.048 to 0.063 ds/m (Table 3). This indicates that the surface soil looks less saline than subsurface soil [52]. Even though not significant, it was also relatively higher around the tree bole than an open area. This might be due to the microbial association and low leaching problems of base-forming cations around a tree [53]. The result agreed with Kassa et al. [50] and Wolle et al. [45] who reported nonsignificant effects of Balanites aegyptiaca and Ziziphus spina-christi over EC at the arid areas of Humera and Hibru districts of Ethiopia, respectively.Table 3
Mean values (±SD) of selected soil chemical parameters under soils in different distances from trees and two soil depths inAcacia seyal parkland system.
Soil propertiesSoil depthDistance from treeP valueNear tree boleUnder canopyEdge of canopyFar from canopypH16.3a ± 0.216.27a ± 0.166.25a ± 0.196.25a ± 0.290.9926.25a ± 0.196.25a ± 0.216.22a ± 0.256.27a ± 0.240.98EC (ds/m)10.063a ± 0.0180.053a ± 0.0130.048a ± 0.0110.051a ± 0.0130.1920.077a ± 0.020.053a ± 0.020.054a ± 0.010.069a ± 0.020.13P (ppm)123.81a ± 2.9224.52a ± 3.4024.16a ± 2.5624.73a ± 4.280.66226.18a ± 4.7223.71a ± 3.2624.84a ± 4.3925.91a ± 4.90.75OC (%)10.66a ± 0.230.59a ± 0.150.66a ± 0.150.63a ± 0.170.9120.59a ± 0.190.65a ± 0.120.5a ± 0.1990.66a ± 0.210.4TN (%)10.15a ± 0.040.13a ± 0.020.11a ± 0.060.10a ± 0.050.2620.18a ± 0.090.13ab ± 0.040.14ab ± 0.0290.09b ± 0.0320.03∗CEC (meq/100 g soil)139.0a ± 14.7849.0a ± 4.1742.2a ± 10.2245.27a ± 3.980.33237.0a ± 15.947.3a ± 6.9243.03a ± 4.7448.27a ± 3.880.17Rows with the same superscript letters are not significantly different atP<0.05; soil depth with 1and 2 represents 0–20 cm and 20–40 cm, respectively.Besides that soil organic carbon (SOC) was expected to be significantly higher under the tree canopy, as most perennial trees contributed to organic matter input via litterfall and fine root turnover. In this study, this was not confirmed. This might be due to the lower contribution of litter decomposition to soil carbon [54]. It was in line with the finding for an integrated system of Acacia seyal with sorghum cropping system in Sudan [55].TN was significantly affected by the presence ofAcacia seyal tree species (P=0.035) under the subsoil layer (20–40 cm) with P≤0.05 (Table 3). Acacia seyal showed a positive significant effect (P=0.035) of the available nitrogen of the subsurface soil (Table 3). Decreasing trend was observed from the tree base to the open area. The pattern was expected since most legume tree species, including Acacia seyal, have a positive effect on soil total N [56, 57]. It may also be associated with under-tree organic carbon availability via litterfall and fine root turnover from trees and droppings of animals resting under the tree [58]. Likewise, higher (42% more) nitrogen availability under the canopy of Acacia seyal compared to the open area was reported in the Rift Valley of Ethiopia [44]. The similar findings on different agroforestry tree species was also reported by Boffa et al. [59] and Hassan et al. [60]. Available phosphorus was found not affected by the species, and this might be due to the lower litter accumulation potential of the species which is related to organic phosphorus availability.Numerically, CEC had a lower mean value around the root zone for both soil depths compared to the open area but was not statistically different (Table3). In agreement with this, lower but statistically similar CEC values under Acacia seyal in the sorghum intercropping system was reported by Deng et al. [55]. Different parkland species such as Balanites aegyptiaca and Ziziphus spina-christi were reported as having similar effects but other species such as Acacia tortilis and Faidherbia albida showed a positive significant effect on CEC [44, 45, 50]. This might be due to the significant effect of those species on SOC which are directly related with CEC, and in the other ways, SOC was statistically similar in our case [58].


## 3.2. Effect ofAcacia seyal on Sorghum Growth and Yield

The presence ofAcacia seyal had a significant negative effect on aboveground biomass (P=0.006) and grain (P=0.025) yields of sorghum (Figure 4). However, other growth and yield attributes were not statistically affected (P>0.05). This might be due to the shading effect of the trees on seed emergence and seedling survival and could also be due to allopathic and superficial root competition effects of the species [49, 61]. It could also be due to the shade intolerance character of sorghum [62] which highly affected its under-canopy performance [61].Figure 4
Effect ofAcacia seyal on sorghum grain and biomass yield (kg/ha). Graphs with the same superscript letters are not statistically different at P>0.05.
(a)(b)Sorghum aboveground biomass (AGB) and grain yield were found affected by the presence ofAcacia seyal (P≤0.05). AGB was lower at D1 (2368.33 kg/ha) compared to D4 (6558.33 kg/ha) with more than fold amount (177%) (Figure 4). It might be correlated with the number of sorghum stands counted and its growth performance [62]. Grain yield was also observed to be higher (142% more) at D4 compared to D1 (Figure 4). The result was unexpected since the symbiotic and nitrogen-fixing ability of Acacia seyal is believed to boost yield by enhancing soil fertility [21]. However, this might be due to the shading effect of Acacia seyal and its allopathic effect on sorghum survival and growth performance which is directly related to yield [49, 61]. Superficial small diameter roots at the surface layer and underground root competition of the species also contribute to the yield reduction effect [63]. The negative effect of Acacia seyal intercropping on sorghum yield and growth performance in South Sudan was also reported by Deng et al. [55]. However, other species such as Faidherbia albida were found to have a positive contribution to sorghum yield in the case of Tahtay Maychew district of northern Ethiopia [60].


## 4. Conclusions

The present findings showed that the widely grownAcacia seyal in the study area had small effect on selected soil physicochemical properties for both surface and subsurface soil layers. The positive effect was observed only on the subsurface total nitrogen availability. The species had negative effect on biomass and grain yield of sorghum due to the combined effect of the aboveground canopy and root competition of the species. Even though it was found less important in soil improvement and sorghum production enhancement, farmers allow growing the species on their farms and the reason might be the interest for its other production benefits such as fuelwood and cash value. Thus, it needs investigation on its off-farm socioeconomic benefits for understanding its alternative roles. The current research experiment work might not represent the whole Acacia seyal parkland system and was conducted on farmers’ cropland which may not have similar management practices. Thus, for further research, it could be recommended to investigate wide area coverage, with controlled environment to bring a radical shift on the species intercropping system. Since the tree density and its management system also affect the productivity of companion crops on farmland, study for the determination of optimal tree density and its appropriate management for the species could be more beneficial.



---

*Source: 6666674-2021-01-07.xml*