The effect of shade species on soil macrofauna diversity and coffee yield in the coffee-based agroforestry system along an elevation gradient in South-eastern, Ethiopia

The effect of shade species on soil macrofauna diversity and coffee yield in the coffee-based agroforestry system along an elevation gradient in South-eastern, Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The effect of shade species on soil macrofauna diversity and coffee yield in the coffee-based agroforestry system along an elevation gradient in South-eastern, Ethiopia Tariku Olana Jawo, Mesele Negash, Kassahun Takele, Nikola Teutscherová, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7110432/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 11 You are reading this latest preprint version Abstract Native shade tree species protect crops from extreme weather conditions and improve their growth through enhanced soil fertility. Soil macrofauna are critical indicators for the sustainable management of agricultural ecosystems through soil fertility management and maintenance. The overall objective of this study was to evaluate the effect of shade species on soil macrofauna diversity and coffee yield in a coffee-based agroforestry system (CAFS) along an elevation gradient (1600–2000 masl) of Southeastern Sidama National Regional State, Ethiopia. The soil macrofauna diversity was evaluated using the Shannon diversity index. The harvested coffee yield bean was sundried, grinded and weighed using a digital measuring balance. Analysis of the results showed that a higher amount of soil macrofauna was recorded during the rainy season in both shade and full-sun coffee systems. Soil macrofauna diversity was high in CAFS and significantly differed ( p < 0.001) among the studied elevations in the rainy season. The soil macrofauna diversity was highest for mid-elevation and the least for high elevation. The mean coffee yield was slightly higher for coffee grown in full-sun than in shade coffee systems. Our result indicated a strong relationship (r = 0.90; p < 0.001) between the Shannon diversity of shade trees and soil macrofauna. The present study indicated that the shade coffee system fosters the abundance and diversity of soil macrofauna but not coffee yield. However, the abundance and diversity of soil macrofauna will help to improve soil fertility and the resilience of coffee to the impact of climate change. agroforestry climate-resilient coffee yield elevation gradient Ethiopia soil macrofauna Figures Figure 1 Figure 2 1. Introduction Soil biota plays an essential role in ecosystem functioning (Korboulewsky et al. 2016 ) and productivity (Chapman et al. 1988 ). They are critical indicators for the sustainable management of agricultural ecosystems through soil fertility management and maintenance (Okigbo, 2020 ). Soil macrofauna are animals visible to the naked eye and inhabit different soil layers (Masebo et al. 2024 ). Zulu et al. ( 2022 ) explained that soil macrofauna has an average body width greater than 2 mm and consists of many different organisms. Soil macrofauna is a significant biological component in soil processing and formation (Brussaard, 1998 ). Soil macrofauna is an 'ecosystem engineer' for their role in decomposing and distributing organic matter and affecting soil structure (Jones et al. 1994 ; Lavelle et al. 1997 ; Asfaw and Zewudie, 2021 ). A growing number of studies from various regions confirm that soil macrofauna is a good indicator of soil quality and land productivity, e.g. in Ethiopia (Asfaw and Zewudie 2021 ), Kenya (Murage et al. 2000 ), West Africa (Black and Okwakol, 1997 ) and Nicaragua (Rousseaua et al. 2013 ). Soil macrofauna influences soil's chemical and physical properties, for instance, through borrowing, casting and mixing plant litter (Edwards and Bohlen, 1996 ; Decaens et al. 2004 ). Such a role has an essential effect on plant productivity and community structure (Eisenhauer et al. 2009 ). Studies indicated that soil macrofauna directly interacts with vegetation through root herbivory (Wardle et al. 2004 ) or indirectly by transporting fungal propagules that affect the availability of soil nutrients (Lussenhop, 1992 ) and releasing nutrients (Filser, 2002 ) through organic decomposition. Studies documented the effect of vegetation on soil macrofauna abundance and diversity (Pauli et al. 2011 ; Kamau et al. 2017 ). The diversity of canopy trees can shelter different species by promoting the emergence of different microhabitats (Cavard et al. 2011 ). For instance, integrating different legume trees in AFS enhances the diversity and abundance of soil macrofauna (Kamau et al. 2017 ). Moreover, studies documented the effect of native shade tree species on soil macrofauna and thus on soil properties and quality (eg. Joshi et al. 2004 ; Oberthür et al. 2004 ; WinklerPrins and Barrios, 2007 ; Asfaw and Zewudie, 2021 ). Shade trees, in so called agroforestry systems (AFS), influence soil macrofauna by providing energy and matter via living and dead plant products such as leaf and root litter, dead wood, and rhizodeposition (Ganault et al. 2021 ). Several studies credited AFS for increasing soil macrofauna abundance and diversity (e.g. Kamau et al. 2017 ; Asfaw and Zewudie, 2021 ). AFS influences the activity, abundance, and diversity of soil microfauna through the availability of plant litter and modification of the microclimate of the system (Singh et al. 2012 ; Martin-Chave et al. 2019 ). Moreover, trees in AFS modify the local microclimate, reducing day air temperature (Moat et al. 2017 ) and light intensity (Muschler, 1998 ), intercepting the rainfall (Vaast et al. 2015 ) and increasing crop productivity. Trees and soil macrofauna play a critical role in improving the resilience and adaptation of the coffee farming system to climate change and variability (Coltri et al. 2019 ). However, soil macrofauna activities are influenced by elevation, soil characteristics, plant litter fall (Castro-Huerta et al. 2015 ), and land use practices (Asfaw and Zewudie, 2021 ). In general, several studies have shown the importance of shade trees in protecting crops from extreme weather conditions and improving their growth. For instance, shade trees protect coffee plants from rising temperatures (Hirons et al. 2018 ), increase nutrient cycling and soil organic matter and increase coffee quality (Perfecto et al. 2007 ; Lunz et al. 2005 ). Campanha et al. (2004) indicated that shade trees also impact coffee quality because of more uniform maturation under shade. DaMatta ( 2004 ) further explained that as a consequence of fewer flowers under the shade tree, enlarged coffee bean size, and fewer fruits exist per plant. Ethiopia is the home of shade-demanding arabica coffee with high genetic diversity (Daba et al. 2023 ), mainly produced by smallholder farmers in the home garden or agroforestry systems (AFS) (Jawo et al. 2023 ). In 2019/2020, the country's coffee production covered 538,000 ha of land with a total production of 447,000 tons (USDA, 2020 ), with an estimated average yield of 0.64 ton per ha (Daba et al. 2023 ), which is lower compared to the coffee yield 1.3 ton per ha in Brazil (Gomes et al. 2020 ). The low production in Ethiopia is attributed to a lack of infrastructure, an absence of improved coffee variety, and a lack of extension services (Daba et al. 2023 ). Previous empirical studies documented the role of AFS in tree species diversity, soil fertility improvement and contributions to household livelihood improvement in Ethiopia (e.g. Birhane et al. 2020 ; Tadesse et al. 2021 ; Jawo et al. 2024 ) and elsewhere in the tropics (De Beenhouwer et al. 2016 ; Asigbaase et al. 2021 ) but limited scientific evidence is available on the significant effect of coffee-based agroforestry systems (CAFS) on soil macrofauna diversity and coffee yield. Asfaw and Zewudie ( 2021 ) also pointed out that few scientific studies investigated the effect of AFS on soil macrofauna diversity and abundance in East Africa. The species diversity and abundance of soil macrofauna in CAFS compared to fun-sun coffee systems have been less studied. Hence, this study's main objective was to evaluate the shade tree species' effect on soil macrofauna diversity and coffee yield in the CAFS of Sidama, southeastern Ethiopia. Specifically, our objectives were to (i) assess soil macrofauna abundance and diversity along an elevation gradient, (ii) evaluate coffee yield and growth in different coffee production systems (with shade trees and full sun), (iii) study the relationship between shade tree species and soil macrofauna diversity. We hypothesized that shaded coffee could enhance the abundance and diversity of soil macrofauna. Moreover, season affects the diversity of soil macrofauna in the study region. This finding contributes to the knowledge and literature on the effect of shade species on soil macrofauna diversity in CAFS, which might help develop strategies for managing soil macrofauna and improving the resilience of coffee production to climate change and variability in the study region and elsewhere in East Africa. 2. Materials and methods 2.1. Site description and experimental design This study was conducted in Dale and Wensho districts in the Sidama National Region, State, Ethiopia (Fig. 1 ). The region is one of the main coffee-producing regions in the country. In our previous study, Jawo et al. ( 2023 ) described Dale district as found between 6°50′30′′N and 38°32′0′′E, with altitude ranges from 1,500–1,850 masl. Wensho is found between 06°45′11′′N and 38°30′16′′E, and the altitude ranging from 1,850–2,149 masl. In both districts, the main rainy season is from June to October, and it receives a short rain from February to April. Dale and Wensho districts receive an average annual rainfall ranging from 858–1,600 mm and 1,200–1,600 mm, respectively. Jawo et al. ( 2023 ) further described that the average annual temperature for Dale and Wensho ranges from 11 0 C – 28.4 0 C and 18 0 C – 21 0 C, respectively. The study districts, Dale and Wensho, were selected in consultation with key informants (coffee cooperatives, regional and district coffee experts), who possess knowledge of coffee production and productivity in the study region. The selection process was supported and complemented by intensive field observations. The data were collected from three elevations: low (1,600 to 1,750 masl), mid (1,750 to 1,850 masl), and high (1,850 to 2,000 masl) elevations. The data were collected from both CAFS and fun-sun coffee production systems. Accordingly, a total of 54 coffee farms (18 farms from each elevation gradient) aged 10 to 12 years were selected randomly for the study, constituting 36 and 18 farms with shade and full sun coffee production systems, respectively. At each farm (20 m x 20 m), two plots (total 108 plots = 72 with shade and 36 full-sun) were randomly laid down to conduct soil macrofauna inventory and coffee yield measurements. 2.2. Data collection At each sample plot, all woody perennials were identified and measured (total height, diameter at breast height, DBH). For soil macro-fauna data collection, a metal frame (25 x 25 cm x 10 cm depth) was placed in the soil, and the soil monolith was extracted and immediately placed in the bag for each sample plot. The data were collected from three samples per plot. The soil was hand-sorted, and macro-invertebrates (> 2mm) were extracted by tweezers, placed in a flask with ethanol (70%) and labelled, where they can be stored until laboratory analysis. After sorting, the soil was returned to the sampling sites to minimize site degradation. Most of the tree species identified on the site using species keys, such as Bekele Tesemma et al. (1993) and Ethiopia and Eritrea Flora (Edwards et al.1995; Hedberg et al. 2004 ). For those shade trees that could not be identified on the sites were taken to the herbarium of Wondo Genet College of Forestry and Natural Resources, Hawassa University, Ethiopia, for species identification. Additionally, soil samples were taken for taxonomic identification of soil microfauna at the family level in the pathology laboratory of the same institution. Coffee yield and growth parameters were measured in three 5x5 m subplots across the diagonal of the tree/shrub plot for a period of two years, 2021 and 2022. From each subplot, three coffee trees were randomly selected. From these, three branches (upper, middle, and lower part of the bush) were marked to measure different parameters, such as the branch per coffee plant, the number of leaves per coffee branch, the number of nodes per coffee branch, the coffee fruit per node, and an increment of the branch, stem length, and height Alemayehu et al. 2017 ). The sampled branches were counted, and the number of fruits per node per branch was identified. Bright red color (fully ripened) coffee bean was harvested and sun-dried until a constant weight (12%) was reached. Accordingly, the sundried coffee beans were weighed and grinded. Finally, the number of coffee yields harvested per plot (20 m x 20 m) was converted to ha to estimate coffee yield per ha. 2.3. Data analysis Diversity indices (Shannon, Simpson and Fisher's alpha) were computed with EstimateS software version 9 (Colwell, 2013) to evaluate soil macrofauna species diversity. Relative abundance (R.A) and relative frequency (R.F) of soil macrofauna were calculated. The mean of two years was used to analyse coffee yield and growth under both coffee production systems (CAFS and full sun). All statistical analyses were done using the Statistical Package for Social Science (SPSS) software version 21. A one-way analysis of variance (ANOVA) was used to test for significant sources of variation in shade and soil macrofauna species richness estimate and diversity along an elevation gradient. A General Linear Model (GLM) was run to evaluate the interaction effects between shade species and soil macrofauna Shannon diversity, and coffee yield, and stem density and coffee yield. The Pearson correlation coefficient was also used to measure the relationship between the Shannon diversity of shade trees and soil macrofauna. Least Significant Difference (LSD) post hoc was used to test the significant difference between the means across the elevations of the study regions. 3. Results 3.1. Composition and abundance of soil macrofauna The study indicated that the soil macrofauna community in the study area was dominated by Pontoscolex, Centipede, and Millipede followed by beetle larvae and beetle adult families. We identified 8 families in the CAFS of Sidama. The families Crowsoniellidae, Glossoscolecidae, Lithobiidae, Scarabaeidae, Spirostreptidae were represented by 14.26%, while Formicidae and Platyarthridae were represented by 9.52% families each. In the study, a total of 459 and 240 individuals of soil macrofauna, respectively, were identified in shade and full-sun coffee production systems during the rainy season. On the other hand, 116 and 53 individuals, respectively, were identified in the shade and full-sun coffee production systems during the dry season (Tabel 1). Pontoscolex is highly abundant in the high (50.38%) and mid-elevation (36.90%), whereas Centipede is in the low elevation (48.10%) (Table 1 ). Table 1 Relative abundance (R.A) and relative frequency (R.F) of soil macrofauna groups sampled in rainy and dry seasons from CAFS (n = 72) and full-sun (n = 36) coffee production systems of Sidama, south-eastern Ethiopia Elevations Common name Family name CAFS Full-sun coffee systems Rainy season Dry season Rainy season Dry season R.A (%) R.F (%) R.A (%) R.F (%) R.A (%) R.F (%) R.A (%) R.F (%) High Beetle adult Scarabaeidae 1.50 4.08 14.29 17.65 0.00 0.00 0.00 0.00 Beetle larvae Crowsoniellidae 8.27 12.24 22.45 20.59 32.93 24.32 12.50 17.65 Earthworm Lumbricidae 1.50 4.08 4.08 5.88 7.32 10.81 4.17 5.88 Centipede Lithobiidae 17.29 18.37 24.49 26.47 17.07 18.92 25.00 23.53 Millipede Spirostreptidae 21.05 20.41 10.20 11.76 13.41 18.92 8.33 11.76 Pontoscolex Glossoscolecidae 50.38 40.82 24.49 17.65 29.27 27.03 33.33 23.53 Mid Ants Formicidae 2.98 6.94 2.56 2.86 4.62 7.69 6.25 6.25 Beetle adult Scarabaeidae 0.60 1.39 7.69 8.57 1.54 2.56 18.75 18.75 Beetle larvae Crowsoniellidae 10.12 13.89 15.38 14.29 18.46 20.51 12.5 12.5 Centipede Lithobiidae 27.98 25.00 10.26 11.43 13.85 15.38 18.75 18.75 Millipede Spirostreptidae 19.64 18.06 20.51 20.00 26.15 20.51 25.00 25.00 Platyarthrus Platyarthridae 1.79 2.78 23.08 22.86 0.00 0.00 0.00 0.00 Pontoscolex Glossoscolecidae 36.90 31.94 20.51 20.00 35.38 33.33 18.75 18.75 Termites Rhinotermitidae 0.78 1.83 0.00 0.00 0.00 0.00 0.00 0.00 Low Ants Formicidae 3.80 4.76 17.86 12.00 18.28 20.00 7.69 9.09 Beetle adult Scarabaeidae 3.16 4.76 14.29 16.00 11.83 11.11 15.38 18.18 Beetle larvae Crowsoniellidae 8.23 11.11 10.71 12.00 13.98 17.78 23.08 27.27 Platyarthrus Platyarthridae 1.27 3.17 10.71 12.00 6.45 8.89 15.38 18.18 Centipede Lithobiidae 48.10 36.51 10.71 12.00 31.18 24.44 15.38 9.09 Millipede Spirostreptidae 10.13 12.70 0.00 0.00 0.00 0.00 0.00 0.00 Pontoscolex Glossoscolecidae 24.05 23.81 10.71 12.00 18.28 17.78 23.08 18.18 3.2. Soil macrofauna diversity The mean (mean (± SD) of soil macrofauna Shannon diversity was higher for the rainy season than the dry season in both coffee production systems (Table 2 ). Soil Macrofauna Shannon diversity, Fisher Alpha, and Simpson (reverse) indices significantly differed among the studied elevations ( p 0.05) was observed for coffee grown in full sun in both seasons. Shannon diversity's mean (± SD) was slightly higher for the mid, followed by low and high elevations for both coffee production systems (Table 2 ). Table 2 Mean (± SD) soil macrofauna species diversity indices across an elevation gradient in shade and full sun coffee production systems of Sidama, south-eastern Ethiopia Coffee production systems Sampling seasons variables Elevation F- value P-value High Mid Low CAFS (n = 72) Rainy season Shannon diversity 1.44 ± 0.13 a 1.67 ± 0.09 b 1.61 ± 0.10 b 15.886 0.001 Fishers’ alpha 1.38 ± 0.36 a 1.63 ± 0.37 b 1.90 ± 0.41 c 76.681 0.001 Simpson (reverse) 3.27 ± 0.45 a 4.35 ± 0.37 b 4.18 ± 0.41 b 40.841 0.001 Dry season Shannon diversity 1.34 ± 0.15 a 1.57 ± 0.17 b 1.57 ± 0.19 b 1.113 0.335 Fishers’ alpha 2.49 ± 1.09 a 3.45 ± 1.43 b 4.34 ± 1.93 a 41.210 0.001 Simpson (reverse) 4.08 ± 0.55 a 4.69 ± 0.66 b 4.84 ± 0.74 c 3.120 0.050 Full-sun (n = 36) Rainy season Shannon diversity 1.36 ± 0.14 a 1.37 ± 0.15 a 1.50 ± 0.15 b 2.685 0.075 Fishers’ alpha 1.49 ± 0.46 a 1.90 ± 0.58 b 1.79 ± 0.50 c 20.693 0.001 Simpson (reverse) 3.76 ± 0.48 a 3.67 ± 0.47 a 4.17 ± 0.61 a 5.809 0.005 Dry Season Shannon diversity 1.30 ± 0.22 a 1.35 ± 0.26 a 1.30 ± 0.30 a 0.238 0.790 Fishers’ alpha 3.20 ± 1.36 a 4.65 ± 2.45 a 5.61 ± 3.37 a 1.342 0.275 Simpson (reverse) 3.56 ± 0.69 a 4.22 ± 0.77 b 3.83 ± 0.88 c 35.271 0.001 n number of sample plots. Similar letters show no significant differences, while different letters in a row show significant differences between elevations at a 5 % significance level. 3.3. Coffee yields and growth The coffee yield (ton ha − 1 ) significantly varied along an elevation gradient with shade (F = 5.83; p = 0.05) and full-sun (F = 7.94; p = 0.001) production systems. We found the highest mean coffee yield in the mid-elevation (0.43 ton/ha), followed by high (0.38 ton/ha) and low (0.37 ton/ha) elevations. The mean (± SD) of coffee yield was slightly higher for coffee grown in full sun than with shade coffee production systems (Table 3 ) but statistically not significant ( p > 0.05). The mean (± SD) of most of the parameters determining the coffee growth was higher for coffee grown in full sun (Table 3 ). A higher Dbh (cm) was observed in coffee growth in the shade, but the height was slightly high for the coffee plant growing in full sun. Table 3 Mean (± SD) of coffee yield and growth parameters in shade and full sun coffee production systems of Sidama, south-eastern Ethiopia Variables Coffee production systems P-value Shade coffee Full-sun coffee Number of leaves per coffee branch 14.03 ± 5.25 15.40 ± 12.49 0.67 Number of nodes per coffee branch 17.19 ± 6.97 19.05 ± 12.00 0.57 Number of nodes having coffee fruit 7.11 ± 3.40 7.93 ± 6.63 0.65 Number of coffee fruit per nodes 34.43 ± 20.93 27.44 ± 21.62 0.33 Number of branches per coffee plant 28.61 ± 9.19 32.78 ± 12.07 0.25 Branch length (cm) 72.51 ± 18.95 66.44 ± 26.20 0.43 Dbh of coffee plant (cm) 3.53 ± 1.69 3.11 ± 1.06 0.38 Height of coffee plant (m) 2.86 ± 0.83 2.92 ± 0.98 0.84 Coffee yields (ton/ha) 0.39 ± 0.06 0.4 ± 0.07 0.56 3.4. Interaction effect between shade species diversity, soil macrofauna and coffee yield General linear model results showed that shade tree species' Shannon diversity index significantly interacts with soil macrofauna diversity (t = 304.77; p < 0.001) and coffee yield (t = 10.35; p < 0.001) (Fig. 2 ). The GLM result also indicated that there is a good interaction effect of soil macrofauna diversity on coffee yield (t = 5.39; p = 0.02). Our result also indicated that stem density has not significantly interacted with coffee yield (t = 0.06; p = 0.81). Additionally, the Pearson correlation coefficient result confirmed a strong relationship (r = 0.90; p < 0.001) between the Shannon diversity and soil macrofauna species diversity (Table 4 ). Also, there is a good relationship (r = 0.36; p = 0.002) between Shannon shade species diversity and coffee yield (ton ha − 1 ). Our result also indicated a moderate relationship (r = 0.28; p = 0.023) between coffee yield (ton − 1 ) and Shannon soil macrofauna diversity. Stem density (ha − 1 ) had a weak relationship with coffee yield (r = -0.03; p = 0.806). Table 4 The relationship between shade species diversity, soil macrofauna species diversity and coffee yield in coffee-based agroforestry systems of Sidama, south-eastern Ethiopia Species diversity indices Soil Macrofauna Shade tree species Species diversity indices Elevation Shannon diversity Simpson (reverse Fisher Alpha Shannon diversity Simpson (reverse Fisher Alpha Coffee yield (ton − 1 ) Soil Macrofauna Shannon Diversity Pearson Correlation . 518 * * 1 P-value .000 Sum of Squares and Cross-products 6.510 3.289 Covariance .092 .046 Simpson (reverse) Pearson Correlation .576 ** .947 ** 1 P-value .000 .000 Sum of Squares and Cross-products 21.710 9.350 29.636 Covariance .306 .132 .417 Fisher Alpha Pearson Correlation .830 ** .201 .254 * 1 P-value .000 .090 .031 Sum of Squares and Cross-products 12.450 .789 2.990 4.684 Covariance .175 .011 .042 .066 Shade tree species Shannon diversity Pearson Correlation .351 ** .902 ** .958 ** .018 1 P-value .003 .000 .000 .878 Sum of Squares and Cross-products 5.480 3.687 11.760 .090 5.082 Covariance .077 .052 .166 .001 .072 Simpson (reverse) Pearson Correlation .362 ** .711 ** .882 ** .096 .925 ** 1 P-value .002 .000 .000 .423 .000 Sum of Squares and Cross-products 24.120 12.388 46.141 1.996 20.030 92.278 Covariance .340 .174 .650 .028 .282 1.300 Fisher Alpha Pearson Correlation .341 ** .866 ** .919 ** .072 .929 ** .849 ** 1 P-value .003 .000 .000 .547 .000 .000 Sum of Squares and Cross-products 9.760 6.480 20.642 .644 8.642 33.662 17.027 Covariance .137 .091 .291 .009 .122 .474 .240 Coffee yield (ton − 1 ) Pearson Correlation .008 . 267* . 313** − .009 . 359** .381** .320** P-value .946 .023 .008 .937 .002 .002 .006 Sum of Squares and Cross-products .030 .258 .907 − .011 .431 .431 ..704 .284 Covariance .000 0.004 .013 .000 .006 .006 .010 .004 4. Discussion 4.1. Composition and diversity of soil macrofauna Land management systems affect soil macrofauna composition and diversity. For instance, the studies by Farska et al. ( 2014 ) show that managing forest ecosystems influences the composition and diversity of soil macrofauna through litter fall. Similar studies by Manhaes et al. ( 2013 ) support the argument that the distribution and composition of soil macrofauna are influenced by small farmers' land management systems and input resources such as litter and dead roots (Mutema et al. 2013 ; Asfaw and Zewudie, 2021 ; Masebo et al. 2024 ), environmental condition and soil type (Lavelle et al. 2022 ). Our results indicated a higher diversity and abundance of soil macrofauna in shaded coffee than in full sun, similar to the studies of Masebo et al. ( 2024 ) and Asfaw and Zewudie ( 2021 ). Kamau et al. ( 2017 ) reported that the availability of legume trees and other tree species in AFS increases soil macrofauna abundance and diversity. Studies also documented that AFS promotes the abundance and diversity of soil macrofauna more than mono-culture agriculture (Pauli et al. 2011 ; Asfaw and Zewudie, 2021 ), which could be attributed to the quality of litter on the surface (Manhaes et al. 2013 ) and the availability of organic fertilizers (Eyasu, 2016 ). Our result of soil macrofauna abundance and diversity study is consistent with several studies in the tropics (e.g. Zhou et al. 2022 ; Ayuke et al. 2011; Masebo et al. 2024 ; De Valenca et al. 2017 ), who reported that land practices with better vegetable cover associated with litter production influence composition and distribution of soil macrofauna. Several studies also credited that AFS influences the abundance and diversity of soil macrofauna (e.g. Singh et al. 2012 ; Martin-Chave et al. 2019 ). Our study indicated that seasons and local climate influence soil macrofauna abundance and diversity. This is attributed to the availability of food, climatic factors, and soil physicochemical properties (Asfaw and Zewudie, 2021 ). Studies documented that high soil moisture favors the diversity and composition of soil invertebrates (Pauli et al. 2011 ). The wet season is characterized by high soil moisture, which may have contributed positively to increasing the abundance and biomass of either earthworms and/or litter-dwelling microarthropods (Asfaw and Zewudie, 2021 ). Fernandes et al. ( 2013 ) highlight that the mobility of soil fauna is high in coffee farms during the rainy season. Wiwatwitaya and Takeda ( 2005 ) described that changes in season, temperature, rainfall amount, and elevation affect the population of soil invertebrates. 4.2. Effect of shade species on coffee yield and growth Our result indicated that most of the parameters determining the coffee growth and yield (Table 3 ) were slightly higher for coffee grown in full-sun systems. This is attributed to the competition between shade trees and coffee plants. Studies reported that integrating shade species in coffee farms leads to some degree of competition for light, water, and nutrients (Lin et al. 2010; Sebuliba et al. 2022 ) due to variations in canopy and root architectures. Studies also reported that shade species consume the available nutrients for their growth and development (Schnabel et al. 2018 ), and trees compete with coffee plants for soil nutrients and moisture (Sileshi et al. 2020 ; Sebuliba et al. 2022 ). This reduced coffee yield and growth under shade trees compared to those without shaded/full-sun coffee systems. Studies also reported that arabica coffee grown in CAFS produces a lower yield than that grown in full-sun coffee systems (e.g. Kufa and Burkhardt, 2013 ; Cerda et al. 2017 ). High shade levels reduce fruit loads, resulting in lower yield because of longer internodes, fewer nodes, lower flower induction, and larger bean size (Jawo et al. 2022 ). Under a shaded tree, the initiation of flora depends on light conditions and fewer flowers are developed. Several studies reported significant differences in coffee bean size between coffee grown in the shade and full-sun coffee systems(e.g. Muschler, 2001 ; Vaast et al. 2006 ). With increasing shade levels, the coffee bean size consistently increases even with increasing shade levels (Muschler, 2001 ). However, shade trees in the coffee farm have potential benefits, such as reducing air, soil, and leaf surface temperature (Ricci et al. 2013 ). Shade trees significantly increase coffee production stability by protecting coffee plants from strong wind and rain (Alvarenga et al. 2004 ) and increase soil organic matter and nutrient cycling (Campanha et al. 2007 ). A review by Jawo et al. ( 2022 ) pointed out that the shade tree species have both positive and negative impacts on the growth and yield of coffee. For instance, the positive effects are managing local microclimate by reducing temperature and light intensity and increasing humidity and plant organ wetness. On the other hand, the authors found that shade species decrease coffee yield due to competition with light, water and soil nutrients. Jawo et al. ( 2022 ) concluded that while shade species compete with coffee plants, they help smallholder farmers produce climate-resilient coffee and high production stability in the face of climate change and variability. Moreover, shade trees help smallholder coffee producers diversify products and gain financial incentives from REDD+ (Rahn et al. 2014 ) during low coffee prices and yield failure due to climate change and pest and disease occurrences (Jawo et al. 2022 ). In our study area, coffee yield is affected by elevation gradients. The increasing temperature and shortage of rainfall decrease coffee yield and growth in the low elevation of our study area. Jawo et al. ( 2023 ) studies indicated that the smallholder farmers in the study area have started to reduce coffee bush density and shade trees to plant drought-resistant crops, khat. Hence, reducing coffee bush density decreased coffee yield in the low-elevation areas. Moat et al. ( 2017 ) conclude that climate change results in increasing temperature and erratic rainfall, decreasing yield and growth in low-elevation areas. 4.3. Interaction effect between shade species diversity, soil macrofauna and coffee yield In our previous study, Jawo et al. ( 2024 ), CAFS maintained higher shade species diversity and contributed to biodiversity conservation. Several studies reported the mutual relationship between floristic diversity and soil macrofauna diversity (e.g. Eyasu, 2016 ; Bufebo et al. 2021 ; Manhaes et al. 2013 ; De Valenca et al. 2017 ). Our present study indicated a strong relationship between shade species and soil macrofauna diversity. Our study is consistent with (Cavard et al. 2011 ), who reported that the diversity of tree species promotes different macrofauna that can provide shelter and food for different soil biota. Several studies documented that increasing plant diversity on agricultural landscapes increases the diversity of soil macrofauna (e.g. Scheu et al. 2003 ; Salamon et al. 2008 ) and favours the heterogeneity of soil microhabitats Korboulewsky et al. 2016 ). Rodríguez and Salazar (2021) concluded that AFS with high plant diversity, which is associated with a variety of plant litter, promoted the soil macrofauna to a greater extent, contributing to the system's resilience in the face of climate change and variability. Our results indicated that soil macrofauna diversity positively affects coffee yield and growth. Soil macrofauna maintains and enhances soil fertility in AFS. Several studies documented the importance of soil macrofauna in improving soil fertility and maintenance, increasing the resilience to climate change (e.g. Oberthür et al. 2004 ; Asfaw and Zewudie, 2021 ). Soil macrofauna provides valuable services, increases soil aeration and root plant penetration and increases water infiltration (Gilibert et al. 2022 ), improves cation exchange, mineralization, organic matter and nutrient cycling (Offenberg, 2015 ), which further increases the adaptation of different crops to the changing environment. Studies concluded that soil macrofauna, like earthworms, improve the chemical and physical conditions of plant growth and are involved in driving the process of ecosystems and enhancing the performance of the ecosystem (Lavelle et al. 2016). 5. Conclusion We focused on the effect of shade species on soil macrofauna diversity and coffee yield along an elevation gradient (1600–2000 masl). Our results show a higher diversity and abundance of soil macrofauna in shaded coffee than in full-sun coffee systems, particularly in the wet season. High soil macrofauna abundance and diversity in the wet season imply that soil moisture determines the seasonal dynamics of soil macrofauna. The coffee grown under shade species yields less than coffee grown in full sun due to the competition for resources (light water and nutrients) between shade trees and coffee plants. We found a strong relationship between shade species and soil macrofauna diversity because shade species promote macrofauna by providing shelter and food for different species. In CAFS, soil macrofauna also improves the fertility status of the soil by decomposing the organic matter, which in turn enhances the system's sustainability. Shade species in CAFS with high plant diversity promote the soil macrofauna, contributing to the system's resilience. Hence, policymakers should support smallholder farmers in maintaining native shade species in coffee farms that foster soil macrofauna, which in turn helps smallholder farmers adapt and mitigate climate change. Declarations The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution The study was conceptualized by Tariku Olana Jawo. and Bohdan Lojka, with substantial support from Mesele Negash and Nikola Teutscherová. Kasahun Takele participated in field data collection and transporting the sample to the laboratory. The first author prepared an inventory format, conducted field data collection and data analysis, drafted the manuscript and final writings. Acknowledgements This study was conducted with the financial support of the Cuomo Foundation through an IPCC scholarship; Hawassa University thematic research project; and Internal Grant Agency of CZU Prague (grant No. 20213110). References Alemayehu, R., Lisanewerk, N., Muktar, M., 2017. Evaluation of Coffee (Coffea arabica L.) Physical yield aspect under the canopy of Cordia Africana and Erythrina abyssinica shade trees effect in Arsi Golelcha District, Ethiopia. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7110432","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":506467566,"identity":"9dd61f17-72ec-4990-9cc6-f5fc6e8f2488","order_by":0,"name":"Tariku Olana Jawo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBElEQVRIiWNgGAWjYNCCAiBmbwCxLICYsfEAPsU8YNIAxAKrkwBpaSBSi0QCTAsDA14t9uxnHz74YGCXxz/z+cUHH35JyOm2HwbaUmMTjdMWnnRjwxkGycUSt3OKDWf2SRibnUkEajmWltuA02FpbNI8BsyJDbdz0qR5eyQStx0AamFsOIxbC/8z9t88BvWJ82+eAWup33b+IQEtEmlszDwGhxM33GA/Js3zQyLB7AYhW248Y5acYXA8ceOZHGbDmQ0ShttuAG1JwOMX9v40xg8fKqoT5x0/Dgy6PzbyZufTgYwaG5xakC00YGBsg7ITCCsHW/iAgeEPcUpHwSgYBaNgZAEABVJhBCzHSDMAAAAASUVORK5CYII=","orcid":"","institution":"Czech University of Life Sciences Prague","correspondingAuthor":true,"prefix":"","firstName":"Tariku","middleName":"Olana","lastName":"Jawo","suffix":""},{"id":506467567,"identity":"11d29993-8ef4-4eac-9b21-7e5761d5b84b","order_by":1,"name":"Mesele Negash","email":"","orcid":"","institution":"Hawassa University, Wondo Genet College of Forestry and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Mesele","middleName":"","lastName":"Negash","suffix":""},{"id":506467568,"identity":"675ce8c6-310c-48d5-87f9-ce7230187bb3","order_by":2,"name":"Kassahun Takele","email":"","orcid":"","institution":"Hawassa University, Wondo Genet College of Forestry and Natural Resources","correspondingAuthor":false,"prefix":"","firstName":"Kassahun","middleName":"","lastName":"Takele","suffix":""},{"id":506467569,"identity":"0a285f2d-e691-40e9-92c8-212a60498bd4","order_by":3,"name":"Nikola Teutscherová","email":"","orcid":"","institution":"Czech University of Life Sciences Prague","correspondingAuthor":false,"prefix":"","firstName":"Nikola","middleName":"","lastName":"Teutscherová","suffix":""},{"id":506467570,"identity":"4e5ef657-7804-43a4-8c87-463d68faa0c8","order_by":4,"name":"Bohdan Lojka","email":"","orcid":"","institution":"Czech University of Life Sciences Prague","correspondingAuthor":false,"prefix":"","firstName":"Bohdan","middleName":"","lastName":"Lojka","suffix":""}],"badges":[],"createdAt":"2025-07-13 00:08:06","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7110432/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7110432/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90399499,"identity":"1d998929-8377-413b-9cad-9542ae8db99a","added_by":"auto","created_at":"2025-09-02 09:58:09","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":281420,"visible":true,"origin":"","legend":"\u003cp\u003eMap of the study area\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7110432/v1/c64bdb3462cb03c1434d7e91.jpg"},{"id":90399688,"identity":"328eacf0-7e26-4fa9-a713-267c431013bf","added_by":"auto","created_at":"2025-09-02 10:06:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":158795,"visible":true,"origin":"","legend":"\u003cp\u003eThe General Liner Model results on the interaction effect between shade Shannon diversity, soil macrofauna diversity and coffee across an elevation gradient in shade coffee production systems of Sidama, South-eastern Ethiopia\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7110432/v1/9be007a56ff77cdb153dd558.png"},{"id":90401103,"identity":"69350584-9c8b-46f5-9215-15bd1354da90","added_by":"auto","created_at":"2025-09-02 10:22:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1600348,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7110432/v1/c05a6541-6634-485e-b2e1-c04e49154bda.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The effect of shade species on soil macrofauna diversity and coffee yield in the coffee-based agroforestry system along an elevation gradient in South-eastern, Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSoil biota plays an essential role in ecosystem functioning (Korboulewsky et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e) and productivity (Chapman et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e1988\u003c/span\u003e). They are critical indicators for the sustainable management of agricultural ecosystems through soil fertility management and maintenance (Okigbo, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Soil macrofauna are animals visible to the naked eye and inhabit different soil layers (Masebo et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Zulu et al. (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) explained that soil macrofauna has an average body width greater than 2 mm and consists of many different organisms. Soil macrofauna is a significant biological component in soil processing and formation (Brussaard, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). Soil macrofauna is an 'ecosystem engineer' for their role in decomposing and distributing organic matter and affecting soil structure (Jones et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e1994\u003c/span\u003e; Lavelle et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Asfaw and Zewudie, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). A growing number of studies from various regions confirm that soil macrofauna is a good indicator of soil quality and land productivity, e.g. in Ethiopia (Asfaw and Zewudie \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), Kenya (Murage et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2000\u003c/span\u003e), West Africa (Black and Okwakol, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) and Nicaragua (Rousseaua et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Soil macrofauna influences soil's chemical and physical properties, for instance, through borrowing, casting and mixing plant litter (Edwards and Bohlen, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Decaens et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Such a role has an essential effect on plant productivity and community structure (Eisenhauer et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). Studies indicated that soil macrofauna directly interacts with vegetation through root herbivory (Wardle et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) or indirectly by transporting fungal propagules that affect the availability of soil nutrients (Lussenhop, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e1992\u003c/span\u003e) and releasing nutrients (Filser, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2002\u003c/span\u003e) through organic decomposition.\u003c/p\u003e\u003cp\u003eStudies documented the effect of vegetation on soil macrofauna abundance and diversity (Pauli et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Kamau et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The diversity of canopy trees can shelter different species by promoting the emergence of different microhabitats (Cavard et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). For instance, integrating different legume trees in AFS enhances the diversity and abundance of soil macrofauna (Kamau et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Moreover, studies documented the effect of native shade tree species on soil macrofauna and thus on soil properties and quality (eg. Joshi et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Oberth\u0026uuml;r et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; WinklerPrins and Barrios, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Asfaw and Zewudie, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Shade trees, in so called agroforestry systems (AFS), influence soil macrofauna by providing energy and matter via living and dead plant products such as leaf and root litter, dead wood, and rhizodeposition (Ganault et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSeveral studies credited AFS for increasing soil macrofauna abundance and diversity (e.g. Kamau et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Asfaw and Zewudie, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). AFS influences the activity, abundance, and diversity of soil microfauna through the availability of plant litter and modification of the microclimate of the system (Singh et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Martin-Chave et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Moreover, trees in AFS modify the local microclimate, reducing day air temperature (Moat et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) and light intensity (Muschler, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), intercepting the rainfall (Vaast et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and increasing crop productivity. Trees and soil macrofauna play a critical role in improving the resilience and adaptation of the coffee farming system to climate change and variability (Coltri et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, soil macrofauna activities are influenced by elevation, soil characteristics, plant litter fall (Castro-Huerta et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), and land use practices (Asfaw and Zewudie, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn general, several studies have shown the importance of shade trees in protecting crops from extreme weather conditions and improving their growth. For instance, shade trees protect coffee plants from rising temperatures (Hirons et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), increase nutrient cycling and soil organic matter and increase coffee quality (Perfecto et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; Lunz et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). Campanha et al. (2004) indicated that shade trees also impact coffee quality because of more uniform maturation under shade. DaMatta (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) further explained that as a consequence of fewer flowers under the shade tree, enlarged coffee bean size, and fewer fruits exist per plant.\u003c/p\u003e\u003cp\u003eEthiopia is the home of shade-demanding arabica coffee with high genetic diversity (Daba et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), mainly produced by smallholder farmers in the home garden or agroforestry systems (AFS) (Jawo et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In 2019/2020, the country's coffee production covered 538,000 ha of land with a total production of 447,000 tons (USDA, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), with an estimated average yield of 0.64 ton per ha (Daba et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), which is lower compared to the coffee yield 1.3 ton per ha in Brazil (Gomes et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The low production in Ethiopia is attributed to a lack of infrastructure, an absence of improved coffee variety, and a lack of extension services (Daba et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious empirical studies documented the role of AFS in tree species diversity, soil fertility improvement and contributions to household livelihood improvement in Ethiopia (e.g. Birhane et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Tadesse et al. \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jawo et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and elsewhere in the tropics (De Beenhouwer et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Asigbaase et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) but limited scientific evidence is available on the significant effect of coffee-based agroforestry systems (CAFS) on soil macrofauna diversity and coffee yield. Asfaw and Zewudie (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also pointed out that few scientific studies investigated the effect of AFS on soil macrofauna diversity and abundance in East Africa. The species diversity and abundance of soil macrofauna in CAFS compared to fun-sun coffee systems have been less studied. Hence, this study's main objective was to evaluate the shade tree species' effect on soil macrofauna diversity and coffee yield in the CAFS of Sidama, southeastern Ethiopia. Specifically, our objectives were to (i) assess soil macrofauna abundance and diversity along an elevation gradient, (ii) evaluate coffee yield and growth in different coffee production systems (with shade trees and full sun), (iii) study the relationship between shade tree species and soil macrofauna diversity. We hypothesized that shaded coffee could enhance the abundance and diversity of soil macrofauna. Moreover, season affects the diversity of soil macrofauna in the study region. This finding contributes to the knowledge and literature on the effect of shade species on soil macrofauna diversity in CAFS, which might help develop strategies for managing soil macrofauna and improving the resilience of coffee production to climate change and variability in the study region and elsewhere in East Africa.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1. Site description and experimental design\u003c/h2\u003e\u003cp\u003eThis study was conducted in Dale and Wensho districts in the Sidama National Region, State, Ethiopia (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The region is one of the main coffee-producing regions in the country. In our previous study, Jawo et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) described Dale district as found between 6\u0026deg;50\u0026prime;30\u0026prime;\u0026prime;N and 38\u0026deg;32\u0026prime;0\u0026prime;\u0026prime;E, with altitude ranges from 1,500\u0026ndash;1,850 masl. Wensho is found between 06\u0026deg;45\u0026prime;11\u0026prime;\u0026prime;N and 38\u0026deg;30\u0026prime;16\u0026prime;\u0026prime;E, and the altitude ranging from 1,850\u0026ndash;2,149 masl. In both districts, the main rainy season is from June to October, and it receives a short rain from February to April. Dale and Wensho districts receive an average annual rainfall ranging from 858\u0026ndash;1,600 mm and 1,200\u0026ndash;1,600 mm, respectively. Jawo et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) further described that the average annual temperature for Dale and Wensho ranges from 11\u003csup\u003e0\u003c/sup\u003eC \u0026ndash; 28.4\u003csup\u003e0\u003c/sup\u003eC and 18\u003csup\u003e0\u003c/sup\u003eC \u0026ndash; 21\u003csup\u003e0\u003c/sup\u003eC, respectively.\u003c/p\u003e\u003cp\u003eThe study districts, Dale and Wensho, were selected in consultation with key informants (coffee cooperatives, regional and district coffee experts), who possess knowledge of coffee production and productivity in the study region. The selection process was supported and complemented by intensive field observations. The data were collected from three elevations: low (1,600 to 1,750 masl), mid (1,750 to 1,850 masl), and high (1,850 to 2,000 masl) elevations. The data were collected from both CAFS and fun-sun coffee production systems. Accordingly, a total of 54 coffee farms (18 farms from each elevation gradient) aged 10 to 12 years were selected randomly for the study, constituting 36 and 18 farms with shade and full sun coffee production systems, respectively. At each farm (20 m x 20 m), two plots (total 108 plots\u0026thinsp;=\u0026thinsp;72 with shade and 36 full-sun) were randomly laid down to conduct soil macrofauna inventory and coffee yield measurements.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2. Data collection\u003c/h2\u003e\u003cp\u003eAt each sample plot, all woody perennials were identified and measured (total height, diameter at breast height, DBH). For soil macro-fauna data collection, a metal frame (25 x 25 cm x 10 cm depth) was placed in the soil, and the soil monolith was extracted and immediately placed in the bag for each sample plot. The data were collected from three samples per plot. The soil was hand-sorted, and macro-invertebrates (\u0026gt;\u0026thinsp;2mm) were extracted by tweezers, placed in a flask with ethanol (70%) and labelled, where they can be stored until laboratory analysis. After sorting, the soil was returned to the sampling sites to minimize site degradation. Most of the tree species identified on the site using species keys, such as Bekele Tesemma et al. (1993) and Ethiopia and Eritrea Flora (Edwards et al.1995; Hedberg et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). For those shade trees that could not be identified on the sites were taken to the herbarium of Wondo Genet College of Forestry and Natural Resources, Hawassa University, Ethiopia, for species identification. Additionally, soil samples were taken for taxonomic identification of soil microfauna at the family level in the pathology laboratory of the same institution.\u003c/p\u003e\u003cp\u003eCoffee yield and growth parameters were measured in three 5x5 m subplots across the diagonal of the tree/shrub plot for a period of two years, 2021 and 2022. From each subplot, three coffee trees were randomly selected. From these, three branches (upper, middle, and lower part of the bush) were marked to measure different parameters, such as the branch per coffee plant, the number of leaves per coffee branch, the number of nodes per coffee branch, the coffee fruit per node, and an increment of the branch, stem length, and height Alemayehu et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The sampled branches were counted, and the number of fruits per node per branch was identified. Bright red color (fully ripened) coffee bean was harvested and sun-dried until a constant weight (12%) was reached. Accordingly, the sundried coffee beans were weighed and grinded. Finally, the number of coffee yields harvested per plot (20 m x 20 m) was converted to ha to estimate coffee yield per ha.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3. Data analysis\u003c/h2\u003e\u003cp\u003eDiversity indices (Shannon, Simpson and Fisher's alpha) were computed with EstimateS software version 9 (Colwell, 2013) to evaluate soil macrofauna species diversity. Relative abundance (R.A) and relative frequency (R.F) of soil macrofauna were calculated. The mean of two years was used to analyse coffee yield and growth under both coffee production systems (CAFS and full sun).\u003c/p\u003e\u003cp\u003eAll statistical analyses were done using the Statistical Package for Social Science (SPSS) software version 21. A one-way analysis of variance (ANOVA) was used to test for significant sources of variation in shade and soil macrofauna species richness estimate and diversity along an elevation gradient. A General Linear Model (GLM) was run to evaluate the interaction effects between shade species and soil macrofauna Shannon diversity, and coffee yield, and stem density and coffee yield. The Pearson correlation coefficient was also used to measure the relationship between the Shannon diversity of shade trees and soil macrofauna. Least Significant Difference (LSD) post hoc was used to test the significant difference between the means across the elevations of the study regions.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Composition and abundance of soil macrofauna\u003c/h2\u003e\u003cp\u003eThe study indicated that the soil macrofauna community in the study area was dominated by Pontoscolex, Centipede, and Millipede followed by beetle larvae and beetle adult families. We identified 8 families in the CAFS of Sidama. The families Crowsoniellidae, Glossoscolecidae, Lithobiidae, Scarabaeidae, Spirostreptidae were represented by 14.26%, while Formicidae and Platyarthridae were represented by 9.52% families each. In the study, a total of 459 and 240 individuals of soil macrofauna, respectively, were identified in shade and full-sun coffee production systems during the rainy season. On the other hand, 116 and 53 individuals, respectively, were identified in the shade and full-sun coffee production systems during the dry season (Tabel 1). Pontoscolex is highly abundant in the high (50.38%) and mid-elevation (36.90%), whereas Centipede is in the low elevation (48.10%) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eRelative abundance (R.A) and relative frequency (R.F) of soil macrofauna groups sampled in rainy and dry seasons from CAFS (n\u0026thinsp;=\u0026thinsp;72) and full-sun (n\u0026thinsp;=\u0026thinsp;36) coffee production systems of Sidama, south-eastern Ethiopia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eElevations\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eCommon name\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eFamily name\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e\u003cp\u003eCAFS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"4\" nameend=\"c11\" namest=\"c8\"\u003e\u003cp\u003eFull-sun coffee systems\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u003cp\u003eRainy season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e\u003cp\u003eDry season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e\u003cp\u003eRainy season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e\u003cp\u003eDry season\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eR.A\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eR.F\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eR.A\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eR.F\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eR.A\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eR.F\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eR.A\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eR.F\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeetle adult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScarabaeidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeetle larvae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCrowsoniellidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e32.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e24.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e12.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e17.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEarthworm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLumbricidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e10.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e4.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e5.88\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCentipede\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLithobiidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e17.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e17.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e18.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e25.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e23.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMillipede\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpirostreptidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e21.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e20.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e18.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e8.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e11.76\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePontoscolex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlossoscolecidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e50.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e40.82\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e24.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e29.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e27.03\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e23.53\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003eMid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFormicidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e6.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e6.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeetle adult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScarabaeidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.39\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e18.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeetle larvae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCrowsoniellidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e13.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e12.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCentipede\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLithobiidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e27.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e25.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e18.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMillipede\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpirostreptidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e19.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e18.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e26.15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e25.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e25.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlatyarthrus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlatyarthridae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.79\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePontoscolex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlossoscolecidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e36.90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e31.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e20.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e35.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e33.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e18.75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e18.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTermites\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRhinotermitidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAnts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFormicidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e17.86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e7.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeetle adult\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eScarabaeidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.76\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e14.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e16.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e11.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e18.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBeetle larvae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCrowsoniellidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e8.23\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e13.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e27.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePlatyarthrus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePlatyarthridae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e6.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.89\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e18.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCentipede\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLithobiidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48.10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e36.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e31.18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e24.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e15.38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e9.09\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMillipede\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSpirostreptidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e10.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e0.00\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePontoscolex\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlossoscolecidae\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e24.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e23.81\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e10.71\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e18.28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e17.78\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e23.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e18.18\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Soil macrofauna diversity\u003c/h2\u003e\u003cp\u003eThe mean (mean (\u0026plusmn;\u0026thinsp;SD) of soil macrofauna Shannon diversity was higher for the rainy season than the dry season in both coffee production systems (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Soil Macrofauna Shannon diversity, Fisher Alpha, and Simpson (reverse) indices significantly differed among the studied elevations (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in the rainy season for the shade coffee production systems. No statistical significance (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) was observed for coffee grown in full sun in both seasons. Shannon diversity's mean (\u0026plusmn;\u0026thinsp;SD) was slightly higher for the mid, followed by low and high elevations for both coffee production systems (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD) soil macrofauna species diversity indices across an elevation gradient in shade and full sun coffee production systems of Sidama, south-eastern Ethiopia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCoffee production systems\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eSampling seasons\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003evariables\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c6\" namest=\"c4\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eF- value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eCAFS (n\u0026thinsp;=\u0026thinsp;72)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRainy\u003c/p\u003e\u003cp\u003eseason\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShannon diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.13\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.09\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.10\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e15.886\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFishers\u0026rsquo; alpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e76.681\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSimpson (reverse)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.27\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.18\u0026thinsp;\u0026plusmn;\u0026thinsp;0.41\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e40.841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDry season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShannon diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.17 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.57\u0026thinsp;\u0026plusmn;\u0026thinsp;0.19 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.113\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.335\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFishers\u0026rsquo; alpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.09 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.45\u0026thinsp;\u0026plusmn;\u0026thinsp;1.43 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.34\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e41.210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSimpson (reverse)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.08\u0026thinsp;\u0026plusmn;\u0026thinsp;0.55 \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.69\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66 \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74 \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e3.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.050\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003eFull-sun (n\u0026thinsp;=\u0026thinsp;36)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eRainy season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShannon diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.36\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.37\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.15\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2.685\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.075\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFishers\u0026rsquo; alpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.49\u0026thinsp;\u0026plusmn;\u0026thinsp;0.46\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.90\u0026thinsp;\u0026plusmn;\u0026thinsp;0.58\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.79\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e20.693\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSimpson (reverse)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.48\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3.67\u0026thinsp;\u0026plusmn;\u0026thinsp;0.47\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.61\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e5.809\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.005\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003eDry Season\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eShannon diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.22\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.26\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.30\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.238\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.790\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFishers\u0026rsquo; alpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.20\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.65\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5.61\u0026thinsp;\u0026plusmn;\u0026thinsp;3.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e1.342\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.275\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSimpson (reverse)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.56\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4.22\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.83\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e35.271\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.001\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003en number of sample plots. Similar letters show no significant differences, while different letters in a row show significant differences between elevations at a \u003cem\u003e5\u003c/em\u003e% significance level.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Coffee yields and growth\u003c/h2\u003e\u003cp\u003eThe coffee yield (ton ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) significantly varied along an elevation gradient with shade (F\u0026thinsp;=\u0026thinsp;5.83; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.05) and full-sun (F\u0026thinsp;=\u0026thinsp;7.94; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) production systems. We found the highest mean coffee yield in the mid-elevation (0.43 ton/ha), followed by high (0.38 ton/ha) and low (0.37 ton/ha) elevations. The mean (\u0026plusmn;\u0026thinsp;SD) of coffee yield was slightly higher for coffee grown in full sun than with shade coffee production systems (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) but statistically not significant (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The mean (\u0026plusmn;\u0026thinsp;SD) of most of the parameters determining the coffee growth was higher for coffee grown in full sun (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). A higher Dbh (cm) was observed in coffee growth in the shade, but the height was slightly high for the coffee plant growing in full sun.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD) of coffee yield and growth parameters in shade and full sun coffee production systems of Sidama, south-eastern Ethiopia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCoffee production systems\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cem\u003eP-value\u003c/em\u003e\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShade coffee\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFull-sun coffee\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of leaves per coffee branch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e14.03\u0026thinsp;\u0026plusmn;\u0026thinsp;5.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e15.40\u0026thinsp;\u0026plusmn;\u0026thinsp;12.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.67\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of nodes per coffee branch\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e17.19\u0026thinsp;\u0026plusmn;\u0026thinsp;6.97\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e19.05\u0026thinsp;\u0026plusmn;\u0026thinsp;12.00\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of nodes having coffee fruit\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e7.11\u0026thinsp;\u0026plusmn;\u0026thinsp;3.40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e7.93\u0026thinsp;\u0026plusmn;\u0026thinsp;6.63\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of coffee fruit per nodes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e34.43\u0026thinsp;\u0026plusmn;\u0026thinsp;20.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e27.44\u0026thinsp;\u0026plusmn;\u0026thinsp;21.62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNumber of branches per coffee plant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e28.61\u0026thinsp;\u0026plusmn;\u0026thinsp;9.19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e32.78\u0026thinsp;\u0026plusmn;\u0026thinsp;12.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBranch length (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e72.51\u0026thinsp;\u0026plusmn;\u0026thinsp;18.95\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e66.44\u0026thinsp;\u0026plusmn;\u0026thinsp;26.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDbh of coffee plant (cm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e3.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.38\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight of coffee plant (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e2.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e2.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.84\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCoffee yields (ton/ha)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e\u003cp\u003e0.39\u0026thinsp;\u0026plusmn;\u0026thinsp;0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e\u003cp\u003e0.4\u0026thinsp;\u0026plusmn;\u0026thinsp;0.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.56\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Interaction effect between shade species diversity, soil macrofauna and coffee yield\u003c/h2\u003e\u003cp\u003eGeneral linear model results showed that shade tree species' Shannon diversity index significantly interacts with soil macrofauna diversity (t\u0026thinsp;=\u0026thinsp;304.77; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and coffee yield (t\u0026thinsp;=\u0026thinsp;10.35; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The GLM result also indicated that there is a good interaction effect of soil macrofauna diversity on coffee yield (t\u0026thinsp;=\u0026thinsp;5.39; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02). Our result also indicated that stem density has not significantly interacted with coffee yield (t\u0026thinsp;=\u0026thinsp;0.06; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.81). Additionally, the Pearson correlation coefficient result confirmed a strong relationship (r\u0026thinsp;=\u0026thinsp;0.90; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the Shannon diversity and soil macrofauna species diversity (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Also, there is a good relationship (r\u0026thinsp;=\u0026thinsp;0.36; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) between Shannon shade species diversity and coffee yield (ton ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e). Our result also indicated a moderate relationship (r\u0026thinsp;=\u0026thinsp;0.28; p\u0026thinsp;=\u0026thinsp;0.023) between coffee yield (ton\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) and Shannon soil macrofauna diversity. Stem density (ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) had a weak relationship with coffee yield (r = -0.03; p\u0026thinsp;=\u0026thinsp;0.806).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe relationship between shade species diversity, soil macrofauna species diversity and coffee yield in coffee-based agroforestry systems of Sidama, south-eastern Ethiopia\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"12\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003eSpecies diversity indices\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c8\" namest=\"c6\"\u003e\u003cp\u003eSoil Macrofauna\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"3\" nameend=\"c11\" namest=\"c9\"\u003e\u003cp\u003eShade tree species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eSpecies diversity indices\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eElevation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eShannon diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eSimpson (reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eFisher Alpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eShannon diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eSimpson (reverse\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eFisher Alpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003eCoffee yield (ton \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eSoil Macrofauna\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c3\" namest=\"c2\" rowspan=\"4\"\u003e\u003cp\u003eShannon Diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.\u003cb\u003e518\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e*\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSum of Squares and Cross-products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6.510\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.289\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCovariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.092\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.046\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c3\" namest=\"c2\" rowspan=\"4\"\u003e\u003cp\u003eSimpson (reverse)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.576\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e.947\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSum of Squares and Cross-products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e21.710\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e9.350\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e29.636\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCovariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.306\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.417\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c3\" namest=\"c2\" rowspan=\"4\"\u003e\u003cp\u003eFisher Alpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.830\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.201\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e.254\u003c/b\u003e\u003csup\u003e\u003cb\u003e*\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSum of Squares and Cross-products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e12.450\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.789\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.990\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e4.684\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCovariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.175\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.042\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.066\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e\u003cp\u003eShade tree species\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c3\" namest=\"c2\" rowspan=\"4\"\u003e\u003cp\u003eShannon diversity\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.351\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e.902\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e.958\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.018\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.878\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSum of Squares and Cross-products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3.687\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.090\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCovariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.077\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.052\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.001\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c3\" namest=\"c2\" rowspan=\"4\"\u003e\u003cp\u003eSimpson (reverse)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.362\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e.711\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e.882\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.096\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e.925\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.423\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSum of Squares and Cross-products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e24.120\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e12.388\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e46.141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.996\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e92.278\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCovariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.340\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.174\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.650\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.028\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.282\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.300\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" morerows=\"3\" nameend=\"c3\" namest=\"c2\" rowspan=\"4\"\u003e\u003cp\u003eFisher Alpha\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e.341\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u003cb\u003e.866\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u003cb\u003e.919\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.072\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cb\u003e.929\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e.849\u003c/b\u003e\u003csup\u003e\u003cb\u003e**\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.003\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.547\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSum of Squares and Cross-products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e9.760\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e6.480\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.644\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8.642\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e33.662\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e17.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCovariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.137\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.091\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.291\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.122\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.474\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u003cp\u003eCoffee yield (ton \u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePearson Correlation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.\u003cb\u003e267*\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.\u003cb\u003e313**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.009\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.\u003cb\u003e359**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u003cb\u003e.381**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e\u003cb\u003e.320**\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.946\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.937\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSum of Squares and Cross-products\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.030\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e.258\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.907\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026minus;\u0026thinsp;.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e..704\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.284\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCovariance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.004\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e.013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e.000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e.006\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e.010\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e.004\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003e4.1. Composition and diversity of soil macrofauna\u003c/h2\u003e\u003cp\u003eLand management systems affect soil macrofauna composition and diversity. For instance, the studies by Farska et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) show that managing forest ecosystems influences the composition and diversity of soil macrofauna through litter fall. Similar studies by Manhaes et al. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) support the argument that the distribution and composition of soil macrofauna are influenced by small farmers' land management systems and input resources such as litter and dead roots (Mutema et al. \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Asfaw and Zewudie, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Masebo et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), environmental condition and soil type (Lavelle et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur results indicated a higher diversity and abundance of soil macrofauna in shaded coffee than in full sun, similar to the studies of Masebo et al. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) and Asfaw and Zewudie (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Kamau et al. (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) reported that the availability of legume trees and other tree species in AFS increases soil macrofauna abundance and diversity. Studies also documented that AFS promotes the abundance and diversity of soil macrofauna more than mono-culture agriculture (Pauli et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Asfaw and Zewudie, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), which could be attributed to the quality of litter on the surface (Manhaes et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) and the availability of organic fertilizers (Eyasu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Our result of soil macrofauna abundance and diversity study is consistent with several studies in the tropics (e.g. Zhou et al. \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ayuke et al. 2011; Masebo et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; De Valenca et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e), who reported that land practices with better vegetable cover associated with litter production influence composition and distribution of soil macrofauna. Several studies also credited that AFS influences the abundance and diversity of soil macrofauna (e.g. Singh et al. \u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Martin-Chave et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur study indicated that seasons and local climate influence soil macrofauna abundance and diversity. This is attributed to the availability of food, climatic factors, and soil physicochemical properties (Asfaw and Zewudie, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Studies documented that high soil moisture favors the diversity and composition of soil invertebrates (Pauli et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The wet season is characterized by high soil moisture, which may have contributed positively to increasing the abundance and biomass of either earthworms and/or litter-dwelling microarthropods (Asfaw and Zewudie, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Fernandes et al. (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) highlight that the mobility of soil fauna is high in coffee farms during the rainy season. Wiwatwitaya and Takeda (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2005\u003c/span\u003e) described that changes in season, temperature, rainfall amount, and elevation affect the population of soil invertebrates.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e4.2. Effect of shade species on coffee yield and growth\u003c/h2\u003e\u003cp\u003eOur result indicated that most of the parameters determining the coffee growth and yield (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) were slightly higher for coffee grown in full-sun systems. This is attributed to the competition between shade trees and coffee plants. Studies reported that integrating shade species in coffee farms leads to some degree of competition for light, water, and nutrients (Lin et al. 2010; Sebuliba et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) due to variations in canopy and root architectures. Studies also reported that shade species consume the available nutrients for their growth and development (Schnabel et al. \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), and trees compete with coffee plants for soil nutrients and moisture (Sileshi et al. \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sebuliba et al. \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). This reduced coffee yield and growth under shade trees compared to those without shaded/full-sun coffee systems. Studies also reported that arabica coffee grown in CAFS produces a lower yield than that grown in full-sun coffee systems (e.g. Kufa and Burkhardt, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Cerda et al. \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). High shade levels reduce fruit loads, resulting in lower yield because of longer internodes, fewer nodes, lower flower induction, and larger bean size (Jawo et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Under a shaded tree, the initiation of flora depends on light conditions and fewer flowers are developed. Several studies reported significant differences in coffee bean size between coffee grown in the shade and full-sun coffee systems(e.g. Muschler, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Vaast et al. \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). With increasing shade levels, the coffee bean size consistently increases even with increasing shade levels (Muschler, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2001\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eHowever, shade trees in the coffee farm have potential benefits, such as reducing air, soil, and leaf surface temperature (Ricci et al. \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Shade trees significantly increase coffee production stability by protecting coffee plants from strong wind and rain (Alvarenga et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) and increase soil organic matter and nutrient cycling (Campanha et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). A review by Jawo et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) pointed out that the shade tree species have both positive and negative impacts on the growth and yield of coffee. For instance, the positive effects are managing local microclimate by reducing temperature and light intensity and increasing humidity and plant organ wetness. On the other hand, the authors found that shade species decrease coffee yield due to competition with light, water and soil nutrients. Jawo et al. (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) concluded that while shade species compete with coffee plants, they help smallholder farmers produce climate-resilient coffee and high production stability in the face of climate change and variability. Moreover, shade trees help smallholder coffee producers diversify products and gain financial incentives from REDD+ (Rahn et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) during low coffee prices and yield failure due to climate change and pest and disease occurrences (Jawo et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eIn our study area, coffee yield is affected by elevation gradients. The increasing temperature and shortage of rainfall decrease coffee yield and growth in the low elevation of our study area. Jawo et al. (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) studies indicated that the smallholder farmers in the study area have started to reduce coffee bush density and shade trees to plant drought-resistant crops, khat. Hence, reducing coffee bush density decreased coffee yield in the low-elevation areas. Moat et al. (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) conclude that climate change results in increasing temperature and erratic rainfall, decreasing yield and growth in low-elevation areas.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e4.3. Interaction effect between shade species diversity, soil macrofauna and coffee yield\u003c/h2\u003e\u003cp\u003eIn our previous study, Jawo et al. (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), CAFS maintained higher shade species diversity and contributed to biodiversity conservation. Several studies reported the mutual relationship between floristic diversity and soil macrofauna diversity (e.g. Eyasu, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Bufebo et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Manhaes et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; De Valenca et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Our present study indicated a strong relationship between shade species and soil macrofauna diversity. Our study is consistent with (Cavard et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), who reported that the diversity of tree species promotes different macrofauna that can provide shelter and food for different soil biota. Several studies documented that increasing plant diversity on agricultural landscapes increases the diversity of soil macrofauna (e.g. Scheu et al. \u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Salamon et al. \u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) and favours the heterogeneity of soil microhabitats Korboulewsky et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Rodr\u0026iacute;guez and Salazar (2021) concluded that AFS with high plant diversity, which is associated with a variety of plant litter, promoted the soil macrofauna to a greater extent, contributing to the system's resilience in the face of climate change and variability.\u003c/p\u003e\u003cp\u003eOur results indicated that soil macrofauna diversity positively affects coffee yield and growth. Soil macrofauna maintains and enhances soil fertility in AFS. Several studies documented the importance of soil macrofauna in improving soil fertility and maintenance, increasing the resilience to climate change (e.g. Oberth\u0026uuml;r et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Asfaw and Zewudie, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Soil macrofauna provides valuable services, increases soil aeration and root plant penetration and increases water infiltration (Gilibert et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), improves cation exchange, mineralization, organic matter and nutrient cycling (Offenberg, \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e), which further increases the adaptation of different crops to the changing environment. Studies concluded that soil macrofauna, like earthworms, improve the chemical and physical conditions of plant growth and are involved in driving the process of ecosystems and enhancing the performance of the ecosystem (Lavelle et al. 2016).\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eWe focused on the effect of shade species on soil macrofauna diversity and coffee yield along an elevation gradient (1600\u0026ndash;2000 masl). Our results show a higher diversity and abundance of soil macrofauna in shaded coffee than in full-sun coffee systems, particularly in the wet season. High soil macrofauna abundance and diversity in the wet season imply that soil moisture determines the seasonal dynamics of soil macrofauna. The coffee grown under shade species yields less than coffee grown in full sun due to the competition for resources (light water and nutrients) between shade trees and coffee plants. We found a strong relationship between shade species and soil macrofauna diversity because shade species promote macrofauna by providing shelter and food for different species. In CAFS, soil macrofauna also improves the fertility status of the soil by decomposing the organic matter, which in turn enhances the system's sustainability. Shade species in CAFS with high plant diversity promote the soil macrofauna, contributing to the system's resilience. Hence, policymakers should support smallholder farmers in maintaining native shade species in coffee farms that foster soil macrofauna, which in turn helps smallholder farmers adapt and mitigate climate change.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe study was conceptualized by Tariku Olana Jawo. and Bohdan Lojka, with substantial support from Mesele Negash and Nikola Teutscherov\u0026aacute;. Kasahun Takele participated in field data collection and transporting the sample to the laboratory. The first author prepared an inventory format, conducted field data collection and data analysis, drafted the manuscript and final writings.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eThis study was conducted with the financial support of the Cuomo Foundation through an IPCC scholarship; Hawassa University thematic research project; and Internal Grant Agency of CZU Prague (grant No. 20213110).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlemayehu, R., Lisanewerk, N., Muktar, M., 2017. Evaluation of Coffee (Coffea arabica L.) Physical yield aspect under the canopy of \u003cem\u003eCordia Africana\u003c/em\u003e and \u003cem\u003eErythrina abyssinica\u003c/em\u003e shade trees effect in Arsi Golelcha District, Ethiopia. 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DOI 10.1007/s11284-004-0013-x\u003c/li\u003e\n\u003cli\u003eZhou, Y., Liu, C., Ai N., Tuo, X., Zhang, Z., Gao, R., Qin, J., Yuan, C., 2022. Characteristics of soil macrofauna and its coupling relationship with environmental factors in the loess area of Northern Shaanxi. Sustainability 14:24 \u0026ndash; 84. https://doi.org/10.3390/su14052484\u003c/li\u003e\n\u003cli\u003eZulu, S.G., Motsa, N.M., Sithole, N.J., Magwaza, L.S., Ncama, K., 2022. Soil Macrofauna Abundance and Taxonomic Richness under Long-Term No-Till Conservation Agriculture in a Semi-Arid Environment of South Africa. Agronomy 12. https://doi.org/10.3390/agronomy12030722\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"agroforestry, climate-resilient, coffee yield, elevation gradient, Ethiopia, soil macrofauna","lastPublishedDoi":"10.21203/rs.3.rs-7110432/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7110432/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eNative shade tree species protect crops from extreme weather conditions and improve their growth through enhanced soil fertility. Soil macrofauna are critical indicators for the sustainable management of agricultural ecosystems through soil fertility management and maintenance. The overall objective of this study was to evaluate the effect of shade species on soil macrofauna diversity and coffee yield in a coffee-based agroforestry system (CAFS) along an elevation gradient (1600\u0026ndash;2000 masl) of Southeastern Sidama National Regional State, Ethiopia. The soil macrofauna diversity was evaluated using the Shannon diversity index. The harvested coffee yield bean was sundried, grinded and weighed using a digital measuring balance. Analysis of the results showed that a higher amount of soil macrofauna was recorded during the rainy season in both shade and full-sun coffee systems. Soil macrofauna diversity was high in CAFS and significantly differed (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) among the studied elevations in the rainy season. The soil macrofauna diversity was highest for mid-elevation and the least for high elevation. The mean coffee yield was slightly higher for coffee grown in full-sun than in shade coffee systems. Our result indicated a strong relationship (r\u0026thinsp;=\u0026thinsp;0.90; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) between the Shannon diversity of shade trees and soil macrofauna. The present study indicated that the shade coffee system fosters the abundance and diversity of soil macrofauna but not coffee yield. However, the abundance and diversity of soil macrofauna will help to improve soil fertility and the resilience of coffee to the impact of climate change.\u003c/p\u003e","manuscriptTitle":"The effect of shade species on soil macrofauna diversity and coffee yield in the coffee-based agroforestry system along an elevation gradient in South-eastern, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 09:58:04","doi":"10.21203/rs.3.rs-7110432/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-28T15:42:01+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-26T15:04:45+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-11T19:53:52+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"84106623783313278849115938589177619791","date":"2025-11-11T17:40:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246026433550582806369378314820216346284","date":"2025-11-01T06:41:48+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231195715182619348716383521402099507840","date":"2025-10-18T13:11:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"126450685194872839244926522940962300942","date":"2025-08-26T07:37:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-25T12:27:01+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-07-16T10:35:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-07-15T14:10:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2025-07-12T23:55:20+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"agroforestry-systems","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"agfo","sideBox":"Learn more about [Agroforestry Systems](http://link.springer.com/journal/10457)","snPcode":"10457","submissionUrl":"https://submission.nature.com/new-submission/10457/3","title":"Agroforestry Systems","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"ccf4489b-5de7-4e8c-8db9-89aea26ae781","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"in-revision","subjectAreas":[],"tags":[],"updatedAt":"2026-02-08T19:08:49+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 09:58:04","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7110432","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7110432","identity":"rs-7110432","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}