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Poor soil fertility is a major challenge affecting banana production in Uganda [1]. Inherent low soil fertility with Oxisols covering about 70% of Uganda's land area, coupled with continuous cultivation with little or no efforts to replenish the lost macro nutrients has resulted into accelerated nutrient depletion [2, 3]. Consequently, highland banana yields especially on smallholder farms are low 5-30 Mg h[a.sup.-1] y[r.sup.-1] and productivity continues to decline [4, 5]. Yet the banana potential yield is estimated at 100 Mg h[a.sup.-1] [6] and attainable yields of 70 Mg h[a.sup.-1] y[r.sup.-1] have been reported on a farm in south western Uganda [7], indicating a large yield gap. But food demand is predicted to increase given an annual population growth rate of 3%, with the youth constituting about 80% of the population [8, 9]. Banana is a staple food for over 10 million Ugandans in a total population of 38 million and number continues to grow with increasing urbanization. This implies that banana productivity has to be increased in order to keep pace with the increasing demand.

Research efforts to increase productivity of the banana systems have traditionally focused on a number of strategies such as intercropping, mulching, use of cover crops, application of mineral fertilizers and /or organic manures [2, 10]. Few farmers in Uganda use mineral fertilizers [11], with rates applied among the lowest in the world (<10 kg of nutrients h[a.sup.-1] y[r.sup.-1]). The quantities of organic materials available from common pool areas have reduced over the years due to population pressure [12], hence these materials cannot be exclusively used to improve soil fertility. Under the plan for modernization agriculture, a government of Uganda supported agricultural productivity enhancement program, government fully recognizes low soil fertility as a national concern, with a focus on locally manufacturing fertilizers such as triple super phosphate (TSP) from the available rock phosphate deposits in eastern Uganda. But the soil fertility--crop yield nexus continues to be complicated by emergent issues such as climate change, hence a need for a more integrated and feasible approach to productivity enhancement.

In trying to address soil fertility challenges, small scale farmers use various methods one of which is the integration of trees and shrubs (agroforestry) into farming systems as a plausible option for improving or sustaining soil productivity [13, 14]. Trees as components of an agroforestry system improve soil fertility through leaf litter fall, which is broken down to release nutrients. The improvement in soil fertility is often associated with leaf litter quality, the nutrient concentrations in leaf tissues and the decomposition rates [15]. However, agroforestry may not always provide a feasible solution as negative interactions may occur due to light competition with the below growing crops [16, 17], hence reducing the adoption rates for the agroforestry approaches. Some tree species such as Senna spectablis, Senna siamea, Spathodea campanulata, Milicia excelsa and Markhamia lutea were reported to have negative effects on soils [18]. This may be attributed to poor litter quality and exudates, which may affect the activities of the soil invertebrate population.

On smallholder farms in Uganda, different tree and shrub species exist in infinite numbers and compositions with no proper spacing, sequencing and canopy management recommendations resulting into limited benefits and thus low crop yields [19]. This study was conducted to: (i), quantify and compare leaf fall for the dominant tree species at different pruning regimes and during different seasons (ii), quantify and compare leaf litter biomass for the dominant tree species under different pruning regimes and during different seasons and (iii), determine nutrient concentrations and organic carbon in leaf litter materials in order to assess their potential for soil fertility improvement.


Study area

This study was conducted in Kiboga District, Uganda (1[degrees]30'N and 32[degrees]14'E) which is located in the banana-coffee agro-ecological zone. The soils are predominantly reddish-brown Ferralsols that exhibit a fine, porous and granular structure. They have low water holding capacity, low nutrient content and base saturation [20]. Rainfall is bimodal (1000-1200mm per annum) with the first season occurring in mid-March to June, and the second from August to November. Daily maximum temperatures are 18-35[degrees]C, and the minimum temperatures 8-25[degrees]C. Crop farming and animal rearing are the most important economic activities. The dominant crops and fruit trees include: bananas, coffee, beans, cassava, maize, sweet potatoes, cabbages, pineapples, tomatoes, mangoes, jack fruits, pawpaw, passion fruits and onions [21].

Profiling tree species in the banana agroforestry system

Lwamata sub-county was purposively selected for this study because of the traditional agroforestry practice. Five villages (Kisweeka, Kiryamuddo, Nabuzaana, Buyira and Nabyoto) were randomly selected. Thirty (30) banana agroforestry farmers (6 per villagex 5) were randomly selected from the village registers and used for tree profiling. At each farm in the plantation, a 10m x 10m quadrat was demarcated using a sisal string from the farm boundary to cover the whole farm, which was a modification of 5m x 5m quadrat used by Nantale et al. [22]. This was because of the bigger size and larger spacing of the banana plants (3m x 3m) as compared with the spacing of other crops such as maize (0.75m x0.25m). Using transect walk and observation techniques [23, 24] with the guidance of the host farmers, all trees and shrubs taller than three meters within the quadrats were recorded. The four (4) most common tree species in the banana-agroforestry systems were determined and used for experimentation.

Canopy management experimentation and leaf biomass determination

Five (5) farmers (one per village) were randomly selected from the 30 farmers to host the on-farm experiments for tree pruning and leaf litter fall. At each experimental site or farm, 24 trees (3 trees x 4 most dominant species x2seasons - wet and dry) were randomly selected and marked. Each tree species per site was subjected to three different pruning regimes: 0%, 25% and 50% pruning regimes. A total of 60 trees were used in each of the two seasons' experiments (60 x 2) conducted during the rainy season (April to June) and during the dry season (January to mid- March). In order to retain canopy diameter (size) and crown shape but reduced canopy density, crown thinning was done when pruning trees [25, 26]. To attain the 25% and 50% canopy pruning regimes, all secondary branches on a tree under investigation were counted and divided by four and two, respectively. Thereafter branches to be pruned were randomly selected, labeled by making slit cuts and later pruned using three cut procedure adopted from Bedker et al. [25] to avoid splitting and damage during pruning. Pruning was done using hand saws, cutting as close to the stem as possible. Leaves from all the pruned branches were stripped off, dried under shade for 14 days and weighed to determine the leaf biomass.

Leaf litter fall, collection and measurement

Leaf litter fall was collected from the three pruning regimes (0%, 25% and 50%) using the litter trap technique [14, 27, 28]. To collect the falling leaves, a 1m x 1 m trap was constructed from a strong nylon mesh suspended on a frame of four wooden pole stands erected, 0.90m above the ground (Figure 1). One litter trap was randomly placed two meters from the tree trunk. The litter fall traps for all the 60 trees in a season were set on the same date to avoid the effect of temporal variations on the collected leaf litter materials.

Collection of fallen leaves from the litter traps was done at 14, 28, 42 and 56 days from the date of establishing the litter traps, a modification of the monthly collection interval used by Muzoora [14], Triadiati et al. [28] and Gupta et al. [29]. The shorter interval was to ensure that all the leaves falling on the trap are collected, thus minimizing effects of wind (leaves blown off the trap). At each collection, only the leaf litter of the tree species under consideration were sorted and put in labeled polythene bags indicating the tree species and pruning regime. All leaves from the non-target trees were discarded. Collected leaves were dried under shade for 14 days to a constant mass. The dried leaves were weighed using a spring hanging scale pesola (measuring range 0-30g and accuracy [+ or -] 0.3%) band the weights reported in g [m.sup.-2].

Leaf analysis for nutrient concentrations and organic carbon

Composite samples of leaves from both natural fall and pruning were collected from the four dominant tree species from each of the five sites, kept in labeled polythene bags and dried under shade for 14 days. Laboratory analysis was done at the National Agricultural Research Laboratories Institute soil science laboratory to determine total nitrogen (N), total phosphorus (P), total potassium (K), calcium (Ca), magnesium (Mg) and the organic carbon content. Leaf samples were oven dried at 72[degrees]C for one day, crushed to 0.06mm, sieved and digested at 330[degrees]C using a sulphuric acid/selenium digestion mixture. Total nitrogen (N) was determined by a modified Kjeldahl method because of its suitability for diverse litter, soil types, and pH [30]. The phosphorus concentration was determined using the colorimetry method and calcium (Ca) and magnesium (Mg) concentrations by the Atomic Absorption Spectrophotometer (AAS). Potassium (K) concentrations were determined using the flame photometer. Organic carbon was analyzed by combustion in a muffle furnace. Ten grams (10g) of well mixed air-dry leaf sample (< 2mm) of known moisture content was weighed into a dry Nickel crucible. This was heated slowly in a furnace raising the temperature setting in three steps (100[degrees]C, 200[degrees]C and 500[degrees]C). The final temperature setting of 550[degrees]C was maintained for eight hours. The crucible containing a grey- white ash was removed, cooled in a desiccator and weighed. The loss in weight represented the moisture and organic matter of the sample while the residue represented the ash.

Data analysis

Analysis of covariance (ANCOVA) model was used to determine the interactions between seasons, pruning regimes and sampling period (fixed factors) and leaf fall (covariate). Before data were analysed, they were tested for normality and those which were not meeting the statistical assumptions were subjected to the appropriate transformations, leaf litter fall - log(x+1) and dry weight of litter - log (x). Analysis of variance (ANOVA) was then performed with general linear model (GLM) procedure. Means were separated by Tukey's test at 5% level of significance. In addition, leaf fall across seasons and pruning regimes were compared by unequal pair-wise t-test with degrees of freedom estimated by Satterthwaite's method. All the analysis was done using SAS software [31].


Tree species in the traditional agroforestry system

A total of 1558 trees and shrubs belonging to 40 tree and shrub species and 21 families were encountered and documented from the 30 banana farms that were surveyed. Fabaceae (22.5%), Moraceae (20.0%), Myrtaceae (7.5%), Bignoniaceae and Euphorbiaceae (5.0%) were the most prevalent families observed. Further, Ficus natalensis (15.3%), Albizia coriaria (10.2%), Artocarpus heterophyllus (10.0%) and Mangifera indica (9.2%) were the most prevalent tree species recorded.

Effect of season, pruning regime and sampling period on leaf litter fall for the selected trees

Leaf litter fall varied significantly across the selected tree species in both dry and wet seasons. The highest mean leaf litter fall was recorded underneath A. heterophyllus (7.8 [+ or -] 3.4g) and M. indica (7.8 [+ or -] 2.9g) in the dry season and underneath A. heterophyllus (5.4 [+ or -] 2.8g) in the wet season. Significantly more leaf litter fall was produced in the dry than wet season irrespective of the tree species (Table 1).

Leaf fall varied significantly (P[less than or equal to]0.05) across the pruning regimes in both the dry and wet seasons. As expected, the highest leaf fall in the dry (9.7 [+ or -] 2.8g) and wet season (6.3 [+ or -] 2.1g) was recorded under the 0% pruning regime. Leaf fall was significantly higher in the dry than wet season (Table 1).

Leaf litter fall varied significantly across the sampling periods in both dry and wet seasons. The highest leaf litter falls (7.7 [+ or -] 3.4g and 4.8 [+ or -] 2.2g) were recorded in the dry and wet seasons, respectively at 56 days sampling period. Similarly, leaf litter fall was significantly higher in the dry than wet season at all the sampling periods (Table 1).

Effect of seasons and pruning regime on dry weight of pruned leaf litter

The dry weight of the pruned leaf litter varied significantly (P[less than or equal to]0.05) across the tree species in both dry and wet season. The heaviest leaf litter was obtained from M. indica (37.1 [+ or -] 4.1kg and 42.4 [+ or -] 3.4kg for dry and wet seasons respectively). On the other hand, the pruned leaf litter was heavier in wet than dry season but not significantly (P>0.05) different (Table 2).

The dry weight of the pruned leaf litter varied significantly across the tree species under both pruning regimes. The highest dry weight of pruned leaf litter was recorded from M. indica (39.9 [+ or -] 4.1 and 45.7 [+ or -] 2.1kg) for 25% and 50% pruning regimes, respectively. Similarly, the pruned leaf litter was significantly higher under 50% than 25% pruning regimes in the dry (29.8 [+ or -] 9.7 kg) and wet (31.8 [+ or -] 10.6 kg) seasons for all the dominant tree species. Significantly higher leaf litter was obtained in the wet (23.9 [+ or -] 8.8 kg) than in the dry (18.8 [+ or -] 7.8 kg) season for the 25% pruning regime but not for the 50% pruning regime (Table 2).

Nutrient concentrations in the litter of the dominant tree species

The Carbon to Nitrogen (C:N) ratios were in the order: M. indica (4.31)>A. heterophyllus (4.11)>A. coriaria (3.80) >F. natalensis (3.29). The Nitrogen concentrations were in order: A. heterophyllus (3.7%), F. natalensis (2.4%), A. coriaria (2.2%), and M. indica (1.6%). The highest concentration of the other nutrients was recorded in: A. coriaria leaf litter, that is, P (0.2%), K (1.3%), Ca (1.2%) and Mg (0.8%)- (Table 3).


Tree species in the banana agroforestry system

Results showed that an average of 51 trees and shrubs per farm were intercropped with banana. These findings are similar to those of other studies in Uganda such as Muzoora [14] who observed that growing of trees with other crops is common on smallholder farms in Uganda due to provision of a range of benefits to households. Sebukyu and Mosango [32] also noted that several tree species are intercropped with different banana cultivars in the traditional banana cropping systems of central Uganda. Fabaceae, Moraceae, Myrtaceae, Bignoniaceae and Euphorbiaceae were the dominant families whereas; Ficus natalensis, Albizia coriaria, Artocarpus heterophyllus and Mangifera indica were the commonest tree species. These tree species were reported to be the dominant in agroforestry systems in central Uganda [32, 33, 34]. The high prevalence of these tree species in the agroforestry systems was attributed to factors such as drought tolerance, the ability to regenerate naturally, being easy to manage, maturing fast, and easiness of their propagation [34]. However, the occurrence of Artocarpus heterophyllus (Jack fruit) and Mangifera indica (Mango tree) on farms is also attributed to their contribution as sources of food and income. Ficus natalensis and Albizia coriaria have small easily decomposable leaves, which are also used as fodder for animals. In this regard, the occurrence of particular trees on farms based on decisions made by farmers should be used as a basis for promotion of tree planting in the banana agroforestry system.

Leaf fall and pruned leaf biomass

Leaf litter fall was found to be influenced by season and was highest in the dry season (Table 1). This concurs with the findings of Barlow et al. [35]. In this study, the higher leaf fall in the dry season as compared to the wet season could be related to leaf senescence, low precipitation/drought, low atmospheric humidity, high wind speed and temperature [c.f. 28, 29, 36, 37, 38]. Leaf litter fall is a key process in the dynamics of agroforestry ecosystems, being a linkage between the tree and soil nutrient pool and, therefore, the starting point for nutrient cycling [39]. Therefore, knowledge of these processes is essential for sustainable management of agroforestry systems and would be very important for soil fertility management in the banana agroforestry systems.

Weight of leaf litter fall varied across the dominant four tree species with A. heterophyllus and M. indica having the highest weight registered in the dry season (Table 2). Differences in leaf biomass that is size and number [29, 40], tree crown architecture and phenology [38, 41] amongst tree species are some of the factors found to influence amounts of leaf fall. The higher amounts of leaf fall registered in the dry season as compared to the wet season could be attributed to harsh climatic conditions of high temperatures, wind speed, low precipitation, low soil moisture and atmospheric humidity that increase natural leaf senescence [27, 28, 41, 42]. Leaf litter-fall was noted to have decreased with increasing pruning intensity for all the selected tree species (Table 2). Pruning reduces the above ground biomass and thus the amount of leaf litter produced [38]. Pruning of branches also reduces the number of buds from which new branches and leaves are formed therefore diminishing overall photosynthesis of pruned trees [36]. Litter production and weight of pruned leaf weights has been found to vary because of tree phenology [38]; tree species' architecture, tree age and local environmental conditions [43]. Differences in litter quality, varying amounts of water soluble phenolic compounds, flavonoids, tannins, physicochemical properties of litter and the presence of thick cuticle among tree species have been cited to influence litter production and weight of pruned leaves [44].

In all the selected tree species, the 50% pruning regime registered the highest dry weight of pruned leaf biomass in both wet and dry seasons, with M.indica registering the highest weight while A.coriaria had the lowest. The 50% pruning regime had the highest number of pruned branches hence the highest volumes of leaf mass obtained. However, the dry weight of leaves varied with the dry season having lower weight as compared to the wet season. This reduction in weight in the dry season as compared to the wet season could be attributed to the increased natural leaf fall that is induced by increased natural leaf senescence [27, 28, 42].

Nutrient concentrations in the leaf litter materials

Nutrient concentrations of leaf litter differed significantly across the selected tree species (Table 3). Variations in foliar organic carbon and nutrient concentrations, amount of leaf litter produced and canopy structure [29], and soil nutrient status [43, 45] have been reported to influence the overall potential of tree species to improve soil fertility. In this study, leaf litter from A. coriaria and F. natalensis had the lowest C:N ratios and higher concentrations of nitrogen, potassium, calcium, magnesium and phosphorus as compared to the other tree species. For C:N ratios of above 30, net nitrogen immobilization occurs while net nitrogen mineralization begins when C:N ratios fall below 15 [14]. Higher nitrogen and phosphorus concentrations in litter usually lead to faster decomposition [36]. Hence A. coriaria and F. natalensis leaf litter have a higher potential to release nutrients to improve soil fertility in the banana agroforestry system. Despite the better total litter nutrient status for A. coriaria and F. natalensis, the amount of litter produced from the pruned braches was lower due to the small leaf size. From this study, A. coriaria generated 20 kg of leaf litter after pruning. Assuming 50 trees per hectare (at a spacing of 9 m x 9 m), 1 Mg of litter will be generated adding 22 kg N, 2 kg P, 13 kg K, 1.2 kg Ca and 8 kg Mg, in addition to nutrients added through litter fall and nutrients from other sources. Farmers should be encouraged to plant more of A. coriaria and F. natalensis in order to generate more litter on-farm.


Forty tree and shrub species belonging to 21 families were found in the banana agroforestry cropping system of Kiboga district in Uganda with F. natalensis, A. coriaria, A. heterophyllus and M. indica being the most prominent species. However, there is need to determine the best spacing patterns for these tree species based on canopy structure. This will reduce over shading as tree canopy pruning is still a rare practice in the banana agroforestry cropping system.

Leaf litter fall was influenced by season and was highest in the dry season. Weight of leaf litter fall varied amongst the dominant four tree species with A. heterophyllus and M.indica having the highest weight registered in the dry season. As expected, leaf litter fall decreased with increasing pruning intensity for all the dominant tree species. In all the tree species, the 50% pruning regime registered the highest dry weight of pruned leaf biomass in both wet and dry seasons, with M. indica registering the highest weight while A.coriaria had the lowest. Therefore, canopy pruning should be done in the wet season and at 50% pruning regime where higher leaf biomass is produced for organic matter and soil fertility enrichment. However, since canopy pruning is gender sensitive, women are unable to carry out this practice. Under such circumstances, there is a need to quantify the labor costs and possible damage to crops by the pruned branches. A complete benefit-cost analysis study will increase the adoption rates for this practice.

Nutrient concentrations of leaf litter differed significantly across the selected tree species. Leaf litter from A. coriaria and F. natalensis had the lowest C:N ratios and higher concentrations of nitrogen, potassium, calcium, magnesium and phosphorus as compared to the other tree species. F. natalensis and A. coriaria should be integrated more in banana plantations. A. heterophyllus and M. indica should be integrated in a spatial pattern or on boundaries of banana gardens or farmlands to provide leaf litter (the cut and carry system) as a source of mulch or animal feed. The manure produced by the animals can then be used to improve soil fertility.


We acknowledge the Austrian Development Agency for providing financial support under the project awarded to Bioversity International and other research partners. Extra assistance from the Banana program, National Agriculture Research Organization and College of Agriculture and Environmental Sciences, Makerere University is appreciated. We also thank the farmers of Kiboga district who allowed us to conduct this research on their farms.


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(1) Ssebulime G (1), Nyombi K (2*), Kagezi GH (2), Mpiira S (3), Byabagambi S (2), Tushemereirwe WK (2), Kubiriba J (2), Karamura EB (4) and C Staver (4)

(1) Makerere University, College of Agricultural and Environmental Sciences P.O. Box 7062, Kampala, Uganda

(2) National Agricultural Research Organization, Coffee Research Institute, Kituuza, P.O. Box 185 Mukono, Uganda

(3) Bioversity International, ParcScientifiqueAgropolis II, 34397 Montpellier, France

(4) United States Agency for international Development, Kampala, Uganda

(*) Corresponding author email:

DOI: 10.18697/ajfand.81.16700
Table 1: Mean [+ or -] SD leaf fall (g [m.sup.-2]) from the dominant
four tree species subjected to three pruning regimes at different
sampling periods during different seasons in Kiboga district, Central

Tree species/regime/period

Tree species
Albizia coriaria             4.4 [+ or -] 1.7 bA
Artocarpus heterophyllus     7.8 [+ or -] 3.4aA
Ficus natalensis             7.1 [+ or -] 2.8aA
Mangifera indica             7.8 [+ or -] 2.9aA
CV                          17.88
Pruning regime
0%                           9.7 [+ or -] 2.8aA
25%                          6.2 [+ or -] 2.1bA
50%                          4.4 [+ or -] 1.6 cA
CV                          14.51
F value                    116.75***
Sampling period (days)
14                           5.8 [+ or -] 2.6bA
28                           6.5 [+ or -] 2.8bA
42                           7.0 [+ or -] 3.2abA
56                           7.7 [+ or -] 3.4aA
CV                          19.98
F value                      4.06**

Tree species/regime/period           Season        P value

Tree species
Albizia coriaria             2.5 [+ or -] 1.7 Bb   <0.0001
Artocarpus heterophyllus     5.4 [+ or -] 2.8 Ab   <0.0001
Ficus natalensis             4.5 [+ or -] 1.9 Ab   <0.0001
Mangifera indica             4.5 [+ or -] 2.0aB    <0.0001
CV                          27.15
Pruning regime
0%                           6.3 [+ or -] 2.1aB    <0.0001
25%                          3.6 [+ or -] 1.8bB    <0.0001
50%                          2.7 [+ or -] 1.4cB    <0.0001
CV                          26.12
F value                     70.26***
Sampling period (days)
14                           3.7 [+ or -] 2.5bB    <0.0001
28                           4.0 [+ or -] 2.4 abB  <0.0001
42                           4.4 [+ or -] 2.3abB   <0.0001
56                           4.8 [+ or -] 2.2aB    <0.0001
CV                          32.50
F value                      2.63*

Same letters within a column (small letters) and row (capital
letters) indicate means are not significantly different across
tree species and season respectively by Tukey's test. SD is the
standard deviation. CV--coefficient of variation

Table 2: Mean dry weight (kg) of leaf litter from the pruned branches
of the dominant tree species in the banana agroforestry system in
Uganda collected during different seasons for trees subjected to
different pruning regimes

Tree species              Season/pruning regime
                          Dry                     Wet

Tree species
Albizia coriaria          14.6 [+ or -] 7.5 cA    17.8 [+ or -] 5.1 dA
Artocarpus heterophyllus  19.8 [+ or -] 5.1bcA    22.3 [+ or -] 5.5cA
Ficus natalensis          25.6 [+ or -] 7.2bA     28.8 [+ or -] 4.1bA
Mangifera indica          37.1 [+ or -] 4.1aA     42.4 [+ or -] 3.4aA
CV                         8.85                    5.30
F value                   22.14***                47.39***
                          25%                     50%
Tree species
Mangifera indica          39.9 [+ or -] 4.1aB     45.7 [+ or -] 2.1 aA
Ficus natalensis          22.3 [+ or -] 1.4bB     32.1 [+ or -] 2.6bA
Artocarpus heterophyllus  16.4 [+ or -] 3.5cB     25.7 [+ or -] 3.3cA
Albizia coriaria          12.8 [+ or -] 2.2 dB    19.7 [+ or -] 1.6dA
CV                         5.12                    2.71
F value                   77.71***               152.26***
Pruning regimes           Dry                     Wet
25%                       18.8 [+ or -] 7.8bB     23.9 [+ or -] 8.8bA
50%                       29.8 [+ or -] 9.7aA     31.8 [+ or -] 10.6aA
P value                    0.002                   0.0093

Tree species
                           P value

Tree species
Albizia coriaria
Artocarpus heterophyllus    0.0614
Ficus natalensis            0.2227
Mangifera indica            0.1888
CV                          0.0734
F value

Tree species
Mangifera indica
Ficus natalensis           <0.0001
Artocarpus heterophyllus   <0.0001
Albizia coriaria           <0.0001
CV                         <0.0001
F value
Pruning regimes
25%                        P value
50%                         0.0381
P value                     0.5415

Same letters within a column (small letters) and row (capital letters)
indicate means are not significantly different across tree species and
season respectively by Tukey's test. CV--coefficient of variation

Table 3: Nutrient concentrations in leaf litter collected from the
dominant tree species in the banana agroforestry system in Uganda

Tree species

Albizia coriaria              2.2 [+ or -] 0.0 cA
Artocarpus heterophyllus      3.7 [+ or -] 0.0 aA
Ficus natalensis              2.4 [+ or -] 0.0 bA
Mangifera indica              1.6 [+ or -] 0.0 dA
CV                            0.74
F value                   11171.0***

Tree species                Nutrient concentration (%)
                            Phosphorous            Potassium

Albizia coriaria            0.2 [+ or -] 0.0 aE    1.3 [+ or -] 0.0 aB
Artocarpus heterophyllus    0.1 [+ or -] 0.0 bE    1.1 [+ or -] 0.0 bB
Ficus natalensis            0.1 [+ or -] 0.0 bD    1.0 [+ or -] 0.0 cB
Mangifera indica            0.1 [+ or -] 0.0 bE    0.8 [+ or -] 0.0 dC
CV                          9.98                   1.49
F value                    30.00***              946.67***

Tree species
                             Calcium                 Magnesium

Albizia coriaria             1.2 [+ or -] 0.0 aC     0.8 [+ or -] 0.0 aD
Artocarpus heterophyllus     1.0 [+ or -] 0.0 bC     0.2 [+ or -] 0.0 dD
Ficus natalensis             1.0 [+ or -] 0.0 bB     0.6 [+ or -] 0.0 bC
Mangifera indica             1.0 [+ or -] 0.0 bB     0.5 [+ or -].0.0 cD
CV                           2.04                    2.26
F value                    194.48***              2220.0***

Tree species                CV     F value

Albizia coriaria           1.20   17795.1***
Artocarpus heterophyllus   1.00   75063.2***
Ficus natalensis           2.01    9055.23***
Mangifera indica           2.13    5649***
F value
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Article Details
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Author:Ssebulime, G.; Nyombi, K.; Kagezi, G.H.; Mpiira, S.; Byabagambi, S.; Tushemereirwe, W.K.; Kubiriba,
Publication:African Journal of Food, Agriculture, Nutrition and Development
Article Type:Report
Geographic Code:6UGAN
Date:Apr 1, 2018

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