Effects of Management Practices and Socio-physical Factors on Perennial Plant Diversity of Agroforestry Systems of Gedeo landscapes, Southern Ethiopia

Effects of Management Practices and Socio-physical Factors on Perennial Plant Diversity of Agroforestry Systems of Gedeo landscapes, Southern Ethiopia | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Effects of Management Practices and Socio-physical Factors on Perennial Plant Diversity of Agroforestry Systems of Gedeo landscapes, Southern Ethiopia Sileshi Lemma, Zebene Asfaw, Motuma Tolera, Akalu Teshome This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4893436/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Dec, 2024 Read the published version in Agroforestry Systems → Version 1 posted 10 You are reading this latest preprint version Abstract This study investigates the influence of different agroforestry systems, management practices, and socio-physical factors on plant diversity and richness in Gedeo indigenous agroforestry systems in southern Ethiopia. It analyzed 286 sample plots (10m x 10m), collecting data on both woody and non-woody perennial species. Insights into management practices and socio-physical conditions were gathered through surveys, focus groups, and interviews. A total of 78 plant species were identified in the study area. The Coffee-Fruit-tree system showed the highest species richness (10 species per plot) and the highest Shannon (1.482) and Simpson (0.731) diversity indices. In contrast, the Coffee-Enset-tree and Enset-Tree systems had lower species richness (6 species per plot) and fewer stems (20 to 23 per plot). The study found significant differences in species diversity and abundance across elevations, with highland farms having the lowest values (p < 0.001). Plots tilled once a year showed the highest diversity, richness, and abundance, while those ploughed three times a year had the lowest. Weeding once or twice a year did not significantly affect diversity indices, but weeding three times a year reduced them. Wealthier households had lower perennial plant species richness compared to middle-class and poor households. The prevalence of economically focused plants had a detrimental effect on species diversity and richness (p < 0.001), whereas selective tree removal had a positive impact on both. Additionally, the age of the household head and higher altitudes were associated with lower species diversity and richness (p < 0.001). Increased frequency of tillage and weeding by slashing also led to reductions in species diversity and richness. The distance from home to the main market negatively influenced species diversity and richness (p = 0.004), and altitude had a negative effect on both species richness and diversity. Agroforestry Management Diversity richness Multiple Linear regression Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Agroforestry is a dynamic, ecological based, natural resource management system through integration of trees on farms and agricultural landscapes that diversifies and sustains production for the purpose of increasing social, economic, and environmental benefits for land users at all levels (ICRAF 1997 ). Agroforestry systems (AFSs), are suggested as one of promising approach for biodiversity conservation complementing strict forest protection, while simultaneously enhancing livelihoods of poor farmers without compromising agricultural productivity (Udawatta et al.2019) It deliver key provisioning and regulating ecosystem services at both farm and landscape scale (Kuyah et al. 2016 ). The system integrates crops, timbers, and medicinal plants in semi-domesticated ecosystems (Legesse 2013 ). Furthermore, numerous valuable species are safeguarded within agroforestry systems, encompassing a variety of woody and non-woody plants. Particularly in Gedeo agroforestry, there has been a surge in recognition of its significance as a land use system that provides diverse ecosystem services due to its rich biodiversity (Tadesse 2002 ; Tesfay et al. 2024 ). These systems play a crucial role in preserving indigenous woody species diversity, climate regulation, local livelihoods, raw materials, genetic resources, and soil erosion control (Alambo 2020 ; Negash 2013 ). The quality and extent of those ecosystem services depend on the structural complexity, species richness, and management intensity of the AFS (Kennedy et al. 2016 ; Laurance et al. 2014) Furthermore Agroforestry systems are frequently praised for their ability to protect biodiversity, but their main purpose is to enhance the well-being of farmers by boosting productivity, profitability, and sustainability (Atangana et al. 2014 ). However, a key challenge lies in balancing the conservation of biodiversity with the need for agricultural productivity within traditional agroforestry practices. Research indicates that the intensity of management practices significantly influences woody species diversity in these systems, primarily by altering plant population structures (Cerda et al. 2017; Miles et al. 2006) For instance, studies have shown that the intensification of coffee management in Ethiopia has led to structural degradation, specifically impacting shade tree diversity and density (Hundera et al. 2013 ). Studies have shown that agroforestry systems that prioritize market-demanded crops tend to have lower diversity indices compared to those that maintain a mix of native and economically valuable species (Reshad et al. 2023 ). Management techniques, such as intensive weeding, cutting of indigenous tree species, and focusing on market-demanded shrub and herb species, along with increasing exotic tree species, can lead to a decrease in woody species degradation, increased soil erosion, low adaptation to climate change, and unsustainable production systems (Hundera et al. 2013 ).Additionally, practices like pruning, thinning, mulching, fertilization, irrigation, and establishing certain crop-shade tree associations can significantly impact plant productivity, crop yields, and economic performance (Jezeer et al. 2018 ; Meylan et al. 2017). These management practices play a crucial role in enhancing agricultural sustainability and productivity. Theoretical frameworks, such as the Intermediate For instance, the composition, structure, and regeneration of moist evergreen Afromontane forests was affected by coffee management intensity (Rusdi et al. 2018), and the demand of food crops by rapidly growing and impoverished human populations (Franks et al. 2017 ). The study conducted in Bale Eco-Region, Ethiopia reveals that Traditional forest coffee management refers to all practices carried out by local people to minimize the effect of woody and herbaceous plant species on existence and productivity of coffee plants i.e., carried out slashing of undergrowth (herbs, seedlings and saplings), hoeing under coffee plants, decreasing density of shade trees through thinning, planting of seedlings of improved coffee varieties, cutting of woody plants and girdling of trees (Nigatu et al. 2017 ). The Disturbance Hypothesis underscores the importance of maintaining a balance in disturbance regimes to promote biodiversity. Excessive management practices, by disrupting this balance, can simplify ecosystems, reduce the diversity of available niches, and lead to homogenization and species loss. In contrast, moderate disturbances can help maintain a dynamic and diverse ecosystem (Pickett and White 1985 ). Traditional agroforestry management practices, including intensified weeding and tillage, planting valuable perennial crops, and selectively removing or thinning indigenous tree species, impact woody diversity in various ways. Intensified weeding and tillage can reduce woody species diversity by favoring specific crops and limiting habitats for diverse tree species, often resulting in the dominance of a few pioneer species and a decline in overall biodiversity (Worku et al. 2015 ). The introduction of crops such as coffee, enset, and cacao has mixed effects on woody diversity. While these crops can coexist with various tree species, their management often involves selectively removing less economically valuable trees, which can decrease native woody species diversity but also promote the growth of species compatible with these crops (Tadesse et al. 2021 ; Uwera et al. 2022 ). Selective removal or thinning of indigenous tree species can significantly alter species composition and reduce woody diversity. This practice often targets specific species, leading to a less diverse tree population, but it can also create opportunities for the growth of other species, depending on management intensity and objectives (Makhubele et al. 2023 ; Chalite 2022 ). Socioeconomic factors also influence species diversity. Wealthier households typically have better management practices and resources, supporting higher species richness and diversity. In contrast, poorer households may rely more on intensive practices that reduce diversity. Thus, local management intensification, which simplifies and reduces canopy cover, negatively impacts species richness, diversity and density in a given agroforestry systems. Therefore, Balancing agricultural production goals with the preservation of biodiversity is crucial for ensuring the continued benefits that agroforestry systems provide to both people and the environment (Matthias et al. 2016). The integration of socio-ecological frameworks in recent studies emphasizes the need to consider both management practices and socio-physical factors to promote sustainable agroforestry systems that balance economic needs with biodiversity conservation (Reshad et al. 2023 ) Because of their extremely rich biodiversity, Gedeo agroforestry are important on both local and global level in terms of provided ecosystem services (Tadesse 2002 ). The study area features three main Agroforestry Practices (AFPs): Enset-tree-based, Coffee-enset-tre- based, and Coffee-fruit-tree-based ((Negash et al. 2011 )). The Enset-based system, found at elevations of 2300–2550 meters, involves Ensete ventricosum, a crucial crop for food security and livelihoods in Ethiopia (Borrell et al. 2019 ). The Coffee-Enset-tree-based system, at 1650–1990 meters, includes coffee and enset intercropped with annual crops³. The Coffee-Fruit-tree-based system is located in the lowlands at 1500–1750 meters (Mebrate 2007 ). They play a very important role in, for example, in conservation indigenous woody species diversity (Negash 2013 ), climate regulation, local provisioning of livelihoods, raw materials and genetic resources, and local control of soil erosion (Alambo 2020 ). The practice of agroforestry in the Gedeo community is considered exemplary and has been practiced for centuries. The Gedeo community primarily practices a home-garden type of agroforestry, where subsistence crops are grown alongside trees Tadese 2002). This practice is believed to be one of the oldest agricultural systems in the region, dating back to Neolithic times (Mesele and Achalu 2008 ) suggest that the Gedeo community developed these agroforestry practices through the domestication of natural forests and the intensification of agriculture, resulting in the presence of mostly indigenous woody species in the area. But, Gedeo indigenous agroforestry systems have faced non-stop challenges. Small-scale farmers in Gedeo, are constantly faced with the difficult decision of balancing numerous social, economic, and environmental objectives as well as the pressure to increase their farming practices sustainably and produce more efficiently (IR3S/UTIAS 2016 ). To navigate this challenge, farmers must make tough decisions, prioritizing goals based on their household situations and considering the trade-offs between short-term gains and long-term objectives. Due to increased land fragmentation and landscape homogenization, agricultural expansion is also indirectly affecting the remaining woody species, with negative effects on the remaining biodiversity and the functioning of the ecosystem of the Gedeo agroforestry systems (Erenso, F., & Andemo 2022 ) Therefore One possible strategy to supply and sustain agricultural productivity while preserving biodiversity and ecosystem service sustainability, is safeguarding and perpetuating of management schemes in agricultural landscapes in agro-ecosystems had to be designed (Getahun et al. 2017). The task at hand is to enhance agroforestry output to meet the growing needs of humans for food and non-food goods, like wood, rubber, and fibers, while still staying within the limits of what agroforestry systems can sustainably support. This requires a paradigm shift from maximizing production at the expense of nature, to farming with biodiversity; where nature drives agriculture rather than suffering from it. Small farmers in Gedeo, Ethiopia must juggle multiple social, economic, and environmental goals while also trying to improve their farming methods and increase efficiency. Thus, the negative impacts on local and regional biodiversity, as well as on the overall environmental quality, continue when management on existing agricultural lands becomes more intensified (Bastin et al. 2019 ). Agroforestry systems, which integrate trees and shrubs with crops and livestock, have been recognized for their potential to enhance ecosystem services, increase agricultural productivity, and promote environmental sustainability. Understanding the diversity, richness, and composition of plant species within these systems is essential for effective management and conservation efforts. Compared to what's popular regarding the plant parts of Gedeo agroforestry systems and practices, little or no is understood regarding existing ground management practices in meeting their wants and their production area unit notable goals and regarding the challenges farmers face that limit their capability to develop ground tree resources in their agroforestry systems. For instance, the reduction in shade tree diversity and its concomitant decline in leaf-litter diversity also results in structural simplification. Such change can affect soil microbiota (Wang et al. 2017 ) as well as microclimate (Makkonen et al. 2013 ) with interacting effects on nutrient cycling processes. Thus, the study aims to assess and compare the variability in species diversity, richness, and abundance within agroforestry systems across management practices (such as weeding and tillage frequency), socioeconomic factors (like wealth and age), physical factors (such as elevation and proximity to major markets), the increase in abundance of marketable perennial species, and the removal of indigenous canopy trees affect species diversity and richness. This research will enhance our understanding of how management practices impact plant species diversity in agroforestry, aiding in the development of strategies to maintain high biodiversity. Ultimately, the findings could influence policy decisions, guide agroforestry management practices, and support biodiversity conservation in agroforestry landscapes. 2. Materials and Methods Study area description The study was conducted in indigenous agroforestry systems of Gedeo zone, Dilla Zuria District, Southern Ethiopia, lies approximately 396 km south of the Addis Ababa (Fig. 1 ). The study district, Dilla Zuria, is situated between 6º15’05’’ N- 6º26’35’’N latitude and 38º 15’55’’E and 38º 24’02’’E longitudes (Fig. 1 ), covering an area of 120 km 2 , with different land-use types such as agricultural land and AF accounting for 95%, while grassland, wetland, plantations, and others covered the remaining 5% (Mebrate 2007 ). The altitudinal range of the district ranges between 1350 m to 2550 m with a slope of 39.4–51.5% (Yirefu and Wendawek 2016). The district is characterized by three agro-ecological zones (AEZ), namely highland (Dega) constitutes 23%, Midland (Woyna Dega) cover 70%, and Lowland (Qolla) embraced 7% of the total area. The total population of Dilla Zuria was 137,715 (M = 71262, F = 66453). Study design Sampling method and sample size determination A multi-stage sampling approach was used to gather data on vegetation diversity and management practices. The Dilla Zuria District in Gedeo, Southern Ethiopia, known for its agroforestry systems (Kassa et al. 2015 ; Tadesse 2002 ), was chosen first. The area was divided into three elevation categories: lower (1500-1750m), middle (1650-1990m), and upper (2300-2550m). Kebeles within each elevation were stratified by agroforestry types. Three Kebeles—Michile Girisa (higher elevation), Michile Sisota (middle elevation), and Chichu (lower elevation)—were randomly selected. Finally, 286 households were randomly chosen from a master list of 1006 households using proportional allocation (Yamane T 1967 ) across three agroforestry practices (Table-1). Table 1 Name of Study Sites, agroforestry practices, populations from which sample were drawn and Sample Households of the study area in the Southern Ethiopia, Gedeo Site name (Kebeles) Agroforestry practices Total Household sample Elevation gradients Michile-Sitota Coffee-Enset-tree based 266 76 1650–1990 Michile-Girisa Enset-based 308 87 2300–2550 Chichu Fruit-coffee-tree-based 432 123 1500–1750 Total 1006 286 Methods of data collection Sample Layout and plant inventory A vegetation inventory was conducted on 286 randomly selected farms, with one plot per farm. Farms were divided into 10 by 10 plots using ocular estimation, and one plot was randomly chosen (Negash et al., 2011 ). If a household had multiple farms of the same type, only one was selected 1 . Quadrat sizes and sampling methods followed recommended practices (Hafte et al., 2024). Due to the small size of farms and considerations of cost and time, the quadrat size was limited to 100 m². Ethiopia’s agro-ecology is divided into three zones: Dega (highland), Woina-Dega (mid-highland), and Kolla (lowland) (Bekele, 2007). The Dega Zone includes highlands over 2,300 meters, while the Woina-Dega Zone covers areas between 1,500 and 2,300 meters 4 . Locally, this classification is divided into lower midland (1,500-1,750 meters) and upper midland (1,650-1,900 meters). Thus, the study area was stratified into upper, middle, and lower midland agroforestry farms. All woody species in the plots, whether single-stemmed (Negash et al. 2011 ) or multi-stemmed (Snowdon et al. 2002 ), with a DBH of at least 2.5 cm and a height of at least 1.5 m, were measured for their height and DBH. Their uses and growth habits were identified and recorded to the species level with the assistance of local informants. To measure the coffee shrubs in each plot, the DBH was measured at a height of 40 cm (Negash et al. 2011 ). For Enset, the basal diameter of the pseudostem of all Enset found in the quadrates was measured at a height of 10 cm (Tesfay et al. 2024 ). The scientific and vernacular names of plant species, local names, their family, genus, and other relevant information were recorded on the data sheet. For identifying the selectively removed trees number and species in the plots involves a combination of direct observation, counting, identifying species from the stamps left behind, and discussions with farmers. Socio-economic data collection A mixed-methods approach was used to collect socioeconomic data through face-to-face and key informant interviews, and focus group discussions (FGDs). Structured questionnaires were administered to 286 rural households, gathering information on socioeconomic and demographic factors, management practices, species use, wealth status, and agroforestry product utilization. Key informant interviews with community leaders, development agents, and knowledgeable farmers provided insights into community wealth status (Crabtree & Miller, 1992). Snowball sampling broadened the sample size. FGDs, involving six to twelve participants each, were conducted in three agroecological settings, focusing on plant diversity, management practices, and socioeconomic factors influencing agroforestry changes. Researchers also reviewed existing research to understand the historical and future dynamics of Gedeo agroforestry. Data analysis Floristic composition and diversity All recorded data of each agroforestry practices were pooled and the total number of species and individuals were tallied. Using the pooled data, number of individual plants, number of species and their scientific names, Name of families, life form per species (trees, shrubs and herbs), identity (Native/Non-Native) and major uses of each species across agroforestry systems were recorded in the plots using supplementary field guide (Bekele-Tesemma 2007 ), and help of knowledgably key informants (Appendix-I). The mean diameter, density, and total basal area, including number of individuals per species, ecologically and economically preferred species by farming households were calculated for each species found in sample plots across three agroforestry systems (Appendix-). Perennial plant species diversity (species richness and Shannon–Wiener diversity, and Simpson Indices) and abundances of individuals. Shannon Diversity Index (H ’ ) Shannon diversity index, Simpson's index, and evenness were calculated for all sampled plots to evaluate the richness and diversity of shade trees in the two systems. The Shannon index H’ (Shannon and Weaver 1949 ) is a measure of the number of species S and their even distribution according to the proportion of species pi and can be calculated as: $$\:{\text{H}}^{{\prime\:}}=-\sum\:_{\text{i}}^{\text{S}}\text{p}\text{i}\text{*}\left(\text{L}\text{N}\text{p}\text{i}\right)$$ The Simpson index (D) The Simpson index D is measure of diversity, which takes into account the number of species S and the relative abundance of each species with values starting from one. Higher values obtained with the Simpson index indicate greater diversity. It is calculated as (Simpson 1949 ) $$\:\text{D}=\:\:\frac{1}{{\sum\:}_{\text{i}}^{\text{S}}{\text{P}\text{i}}^{2}}$$ The number of species in each plot, known as species richness, was determined by counting the different species present in the area. Floristic structure The floristic structure of all perennial plant species was examined in terms of density, frequency, basal area, important value index, and distribution by diameter class. Microsoft Excel was used to conduct the analysis. Basal area In order to compare the basal area and density between different DBH classes, we grouped the life form of trees, shrubs and herbs based on the mean DBH classes (0–15, 16–30, 31–45, 46–60, 61–70) of stems for each of agroforestry system. We then calculated the mean of the basal area and density for each of the DBH classes for trees, shrubs and herbs. $$\:\text{B}\text{a}\text{s}\text{a}\text{l}\:\text{a}\text{r}\text{e}\text{a}\:\left(\text{B}\text{A}\right)\:\left(\frac{\text{m}2}{\text{h}\text{a}}\right)=\frac{\text{s}\text{u}\text{m}\:\text{o}\text{f}\:\text{c}\text{r}\text{o}\text{s}\text{s}\:\text{s}\text{e}\text{c}\text{t}\text{i}\text{o}\text{n}\text{a}\text{l}\:\text{a}\text{r}\text{e}\text{a}\:\text{f}\text{o}\text{r}\:\text{a}\text{l}\text{l}\:\text{t}\text{r}\text{e}\text{e}\text{s}\:\text{i}\text{n}\:\text{p}\text{l}\text{o}\text{t}\text{s}}{\text{s}\text{t}\text{u}\text{d}\text{y}\:\text{p}\text{l}\text{o}\text{t}\:\text{a}\text{r}\text{e}\text{a}\:\text{i}\text{n}\:\left(\text{h}\text{a}\right)}$$ \(\:\text{C}\text{r}\text{o}\text{s}\text{s}\:\text{s}\text{e}\text{c}\text{t}\text{i}\text{o}\text{n}\text{a}\text{l}\:\text{a}\text{r}\text{e}\text{a}\:\text{o}\text{f}\:\text{a}\:\text{t}\text{r}\text{e}\text{e}={\pi\:}\:\left(\frac{\text{D}\text{B}\text{H}\:\left(\text{c}\text{m}\right)}{200}\right)2\) = 0.00007854* (DBH cm) 2 Where Ha stands for hectare; and DBH for diameter at breast height in cm Area in ha = area of plots in ha = 0.01ha (100m 2 ) Density Density was determined by adding up all the stems found in each sample plot and then converting this total into a measurement per hectare (Mengistu and Asfaw 2016 ) $$\:\text{D}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}=\frac{\text{t}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{a}\text{l}\text{l}\:\text{t}\text{r}\text{e}\text{e}\text{s}}{\text{s}\text{a}\text{m}\text{p}\text{l}\text{e}\:\text{s}\text{i}\text{z}\text{e}\:\text{i}\text{n}\:\text{h}\text{e}\text{c}\text{t}\text{a}\text{r}\text{e}\:}$$ Important Value Index (IVI) It was calculated by summing up the relative dominance, relative Frequency and relative abundance of the species (Mueller-Dombois and Ellenberg 1974 ). It shows the degree of a certain plant species' dominance, occurrence, and abundance in comparison to other nearby specie (Whittaker 1993). It is calculated as follows: $$\:\text{R}\text{D}\text{o}=\frac{\text{B}\text{a}\text{s}\text{a}\text{l}\:\text{a}\text{r}\text{e}\text{a}\:\text{o}\text{f}\:\:\text{s}\text{p}\text{e}\text{c}\text{i}\text{e}\text{s}\:\text{A}}{\text{A}\text{r}\text{e}\text{a}\:\text{o}\text{f}\:\text{s}\text{a}\text{m}\text{p}\text{l}\text{e}\text{d}\:}\text{*}100\text{%}$$ RF= \(\:\frac{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{i}\text{n}\text{d}\text{i}\text{v}\text{i}\text{d}\text{u}\text{a}\text{l}\text{s}\:\text{i}\text{n}\:\text{a}\text{l}\text{l}\:\text{q}\text{u}\text{a}\text{d}\text{r}\text{a}\text{t}\text{e}\text{s}}{\text{T}\text{o}\text{t}\text{a}\text{l}\:\text{n}\text{u}\text{m}\text{b}\text{e}\text{r}\:\text{o}\text{f}\:\text{q}\text{u}\text{a}\text{d}\text{r}\text{a}\text{t}\text{s}\:\text{i}\text{n}\:\text{w}\text{h}\text{i}\text{c}\text{h}\:\text{t}\text{h}\text{e}\:\text{s}\text{p}\text{e}\text{c}\text{i}\text{e}\text{s}\:\text{o}\text{c}\text{c}\text{u}\text{r}\text{r}\text{e}\text{d}}\:\) RA= \(\:\frac{\text{a}\text{b}\text{u}\text{n}\text{d}\text{a}\text{n}\text{c}\text{e}\:\text{f}\text{o}\:\text{o}\text{n}\text{e}\:\text{s}\text{p}\text{e}\text{c}\text{i}\text{e}\text{s}\:}{\text{t}\text{o}\text{t}\text{a}\text{l}\:\text{a}\text{b}\text{u}\text{n}\text{d}\text{a}\text{n}\text{c}\text{e}\:\text{o}\text{f}\:\text{a}\text{l}\text{l}\:\text{s}\text{p}\text{c}\text{e}\text{i}\text{s}\:}\text{x}100\) $$\:\text{I}\text{V}\text{I}=\text{R}\text{D}0+\text{R}\text{F}+\text{R}\text{A}$$ Where, IVI is importance value index, RD is relative Dominance, RF is relative frequency and RA is relative abundance of a species Data Analysis of Effects of agroforestry systems, management practices and socio-environmental factors on species Diversity and richness The research analyzed of the impacts of AFSs, management practices, abundance of plants (specifically maintained for economic purpose: dividing the number of economically important perennial plants by the total number of perennial plants in each site (Rodrigues et al. 2018 ), removal of canopy tree species (recorded the number of canopy trees removed per plot across AFSs with visual observation at farm level and interview with household heads), Altitude (m.a.s.l), Age, Land size, Educational status, and Wealth status of farm households on species diversity and abundance of woody and non-woody plant species in the study area. The ratio of economically oriented perennial plants in AFSs by categorizing associated plants into Economic (plants maintained for income and domestic needs by AF farming households) and for ecological purpose (Asigbaase et al. 2019 ) were analyzed . Statistical analysis Descriptive statistics are employed to detail the demographic and socioeconomic characteristics of the household sample, including measures like the average, percentage, standard deviation, and frequency. One-way ANOVA tests were conducted to compare the statistical differences between different types of AFPs, as well as between different life forms (Trees, shrubs, and herbs) for normally distributed data. Prior to each test all data was tested for normality by running and plotting a Shapiro-Wilk normality test with a significance level of p < 0.05, and a Levene’s test with a significance level of p < 0.05 was performed to test for homogeneity of variance. A post hoc LSD test was carried out when significant differences were found among the AFPs. Where the data did not follow a normal distribution, a Kruskal-Wallis one-way ANOVA test was used. Further, a separate multiple linear regression model was built to analysis the impacts of agroforestry systems, management practices and social and environments factors (independent variables). This model is appropriate for investigating the impact of multiple independent variables on a single dependent variable (Montgomery et al., 2012 ). The model described the effects of the types AFSs, management activities carried out in the study area, abundance of economically managed species, and other socioeconomic factors (such as land size, wealth status, educational status, and age) and environmental factors (altitude: Different altitudes offer varying climatic conditions, impacting species composition and growth on species richness, diversity indices (Shannon and Simpson) and abundance of woody and non-woody species. 3. Results of the study Species composition and vegetation structure Species composition The vegetation inventory conducted in Gedeo agroforestry systems identified a total of 78 woody and non-woody species across three different agroforestry systems (AFSs). Among these, there were 60 tree species, 11 shrub species, and 7 herb species (Appendix-1). The most dominant plant family found was Fabaceae, represented by nine species. Other notable families included Rutaceae, Moraceae, Boraginaceae, and Euphorbiaceae, each with five species. The Coffee-Fruit-Tree-based agroforestry system was richest in species from the Fabaceae, Moraceae, and Rutaceae families. In the Coffee-Enset-based system, the dominant families were Euphorbiaceae, Fabaceae, and Bignoniaceae, while the Enset-based system was dominated by Euphorbiaceae, Fabaceae, and Rosaceae families. Additionally, 20 plant families were represented by only one species each, and 10 families had 2–3 species each (Figure-2). A survey of the Coffee-Enset agroforestry system recorded 1208 individuals from 43 species and 29 families. In the Enset-Tree system, there were 1303 individuals from 40 species across 27 families. The Coffee-Fruit-Tree system had the highest number of individuals, with 2027 belonging to 58 species in 38 families. In a study of the top 12 economically preferred perennial plant species by agroforestry farmers, Coffee arabica was found to be the most widely planted, making up 39.86% of the total stems, followed by Ensete ventricosum at 32.33%. Other species, such as Persea americana Mill, Mangifera indica L., and Carica papaya L., accounted for 0.90–2.49%. Less frequently planted species, including Musa × paradisiaca L., Rhamnus prenoides L’hér, and Aningeria altissima (A.Chev.), ranged from 0.22–0.64% (Figure-3a). This shows a clear preference for economically valuable species like Coffee arabica and Enset. Additionally, Millettia ferruginea was the most commonly chosen species among the other plants, constituting 5.62% of the total, followed by Cordia africana at 2.58% and Erythrina brucei Schweinf at 2.09% (Figure-3b). Vegetation structure DBH, Height, Density and Basal area The overall mean value in DBH, height and density were significantly (P < 0.001) different among three AFPs (Table-3). The higher mean DBH were observed in the Enset-tree AF (30.220 ± 12.761), while no difference were observed between Coffee-Enset-tree and Enset-tree AF (Table-2), whereas the lowest mean DBH were observed in the Coffee-Fruit-Tree-based (15.840 ± 7.900). From the three AFSs’ the highest mean height were recorded in Enset-tree AF. The overall mean value of species density were significantly different across AFSs (p < 0.001). The highest densities were observed in Coffee-fruit-tree AF and the lowest were in Enset-tree. There were significant variations in mean basal area (m 2 ha − 1 ) across AFSs, with the highest mean basal area found in the Enset-tree-based (126.648 ± 33.579) (Table-2). The species with the lowest basal areas were Stellaria sennii Chiov and Spathodea campanulata (Appendix-2). Table 2 Mean DBH, height, basal area and density for each agroforestry in Gedeo Ethiopia, followed by SD AFS DBH(cm) Height(m) Basal area(m 2 /ha − 1 ) Density(Nha − 1 ) Enset-tree 30.220 ± 12.761 a 9.618 ± 1.272 a 126.648 ± 33.579 a 1497.70 ± 244.94 c Coffee-Enset-tree 16.334 ± 11.92 b 5.234 ± 0.914 b 51.016 ± 18.518 b 1589.47 ± 239.76 b Coffee-Fruit-tree 15.840 ± 7.900 b 5.122 ± 0.917 b 40.542 ± 14.183 c 1647.97 ± 249.37 a p-value < 0.001 < 0.001 < 0.001 < 0.001 Note: *, **, *** Significant (F test) at P < 0.05, < 0.01, < 0.001, respectively The distribution of individuals across diameter classes varied significantly among the different AFSs. The majority of perennial plant individuals in all AFS were found in the 0–15 cm DBH class (nearly 50%). In the Coffee-fruit-tree-based agroforestry system, there was a more balanced distribution of individuals across diameter classes, with a significant number of individuals in the 0–15 cm DBH class (Figure-4a). However, in the coffee-Enset-Tree-based AFS, there was a gradual decrease in the number of individuals across diameter classes, with very few individuals in the 61–70 cm DBH class (Figure-4b). In contrast, in the Enset-Tree-based AFS, there was a notable increase in the number of individuals from the 0–15 cm to the 16–30 cm DBH class, before decreasing in the larger diameter classes (Figure-4c). Mean Comparison of species diversity (Shannon and Simpson), richness and abundance across agroforestry practices, Management activities, and socio-physical variables The study compared species diversity (Shannon and Simpson indices), richness, and abundance of woody and non-woody plants across various factors (Appendix-3). The Coffee-Fruit-Tree agroforestry system (AFS) had the highest diversity (Shannon index: 1.482), richness (6.618), Simpson index (0.731), and abundance (16.48). The Coffee-Enset-Tree AFS showed intermediate values, while the Enset-Tree AFS had the lowest diversity and abundance. These differences were statistically significant (p < 0.001). Lowland farms had the highest diversity (Shannon index: 1.378), richness (6.164), Simpson index (5.161), and abundance (16.349). Midland farms had moderate values, and highland farms had the lowest diversity and abundance, with significant differences across elevations (p < 0.001) (Table-4). Plots tilled once a year had the highest diversity (Shannon index: 1.391), richness (6.240), and abundance (16.560). Those hoed twice a year had moderate values, while plots ploughed three times a year had the lowest diversity, richness, and abundance, with all differences being statistically significant. The study found that weeding once or twice a year did not significantly affect plant diversity indices (Shannon, species richness, Simpson) or abundance. However, weeding three times a year reduced these diversity indices, though abundance remained similar. Wealth status significantly influenced plant diversity, with rich households having the highest diversity and slightly higher abundance, followed by medium wealth households, and poor households with the lowest diversity and abundance. Farmland size also impacted diversity: small farmlands had the highest diversity and species richness, medium-sized farmlands had moderate diversity and richness, and larger farmlands had the lowest diversity and species richness but the highest abundance. Wealthier households’ farms had lower perennial plant species richness compared to those of middle-class and poor households (Tablel- 4 ). Table 4 Mean (± SD) trees and other perennial plant Shannon diversity (H’), species richness (S’), Simpson index (D) and abundance across categories of agroforestry systems, elevation gradients (altitude), Management activities, and socioeconomic variables in the study area Predictors Category H’ S’ D Abundance AFSs Coffee-Fruit-Tree 1.482 ± 0.193 a 6.618 ± 1.113 a 0.731 ± 0.074 a 16.48 ± 2.45 a Coffee-Enset- Tree 0.972 ± 0.228 b 4.276 ± 0.810 b 0.531 ± 0.124 b 16.03 ± 2.33 a Enset-Tree 0.788 ± 0.160 c 3.874 ± 0.606 c 0.424 ± 0.0.0101 c 14.97 ± 2.44 b p-value P < 0.001 P < 0.001 P < 0.001 P < 0.001 Altitude Lowland 1.378 ± 0.294 a 6.164 ± 1.435 a 5.161 ± 1.563 a 16.349 ± 2.382 a Midland 0.993 ± 0.215 b 4.298 ± 0.689 b 0.686 ± 0.129 b 16.170 ± 2.632 a Highland 0.788 ± 0.160 c 3.874 ± 0.606 c 0.545 ± 0.113 c 14.966 ± 2.442 b p-value P < 0.001 P < 0.001 P < 0.001 P < 0.001 Tillage frequency once a year 1.391 ± 0.270 6.240 ± 1.469 0.692 ± 0.112 16.560 ± 2.447 a Twice a year 1.106 ± 0.371 5.030 ± 1.535 0.564 ± 0.175 15.940 ± 2.337 a trice a year 0.898 ± 0.233 4.180 ± 0.789 0.495 ± 0.129 14.950 ± 2.812 b p-value P < 0.001 P < 0.001 P < 0.001 P < 0.05 Weeding frequency once a year 1.176 ± 0.391 a 5.194 ± 1.559 a 0.606 ± 0.171 a 15.582 ± 2.714 a Twice a year 1.187 ± 0.369 a 5.440 ± 1.643 a 0.601 ± 0.165 a 16.209 ± 2.474 a trice a year 1.022 ± 0.308 b 4.694 ± 1.328 b 0.533 ± 0.160 b 15.659 ± 2.373 a p-value P < 0.05 P < 0.05 P 0.05 Wealth Poor 0.870 ± 0.288 b 4.127 ± 1.123 a 0.462 ± 0.153 a 14.982 ± 2.313 b Medium 1.173 ± 0.364 a 5.292 ± 1.548 b 0.597 ± 0.167 b 16.105 ± 2.626 a Rich 1.271 ± 0.305 a 5.733 ± 1.539 c 0.650 ± 0.124 c 16.150 ± 2.177 a p-value P < 0.001 P < 0.001 P < 0.001 P < 0.05 Land size Small 1.254 ± 0.354 b 5.651 ± 1.562 a 0.635 ± 0.157 b 16.009 ± 2.591 a Medium 1.069 ± 0.353 a 4.890 ± 1.470 a 0.553 ± 0.168 a 15.779 ± 2.522 a Large 1.035 ± 0.358 a 4.706 ± 1.759 a 0.533 ± 0.153 a 16.353 ± 1.869 a p-value p < 0.001 p < 0.001 p 0.05 Note: *, **, *** Significant at P < 0.05, < 0.01, < 0.001, respectively The Influences of management practices and socio-physical conditions on species diversity and Richness A multiple linear regression model was used to assess how different factors influence species diversity and richness. The study found significant interactions between management practices and socio-economic factors affecting species diversity and richness. The model was statistically significant (F = 51.975, p < 0.001) and explained 65.4% of the variance in species diversity (R² = 0.654) (Tabel-5). It showed that a higher ratio of economically oriented woody and non-woody plants negatively impacted species diversity (p < 0.001). In contrast, selective tree removal at the plot level positively influenced species diversity (β = 0.125, t = 3.106, p = 0.002), suggesting that plots where trees are removed tend to have higher species diversity. A total of 623 trees were removed for different reasons by agroforestry farm households. The highest mean number of trees removal was observed in Enset-based AF (2.41), followed by Coffee-Enset-tree and the least was observed in Coffee-fruit-tree-based. Additionally, the age of the household head had a negative impact on species diversity (p < 0.001), implying that older household heads are associated with lower species diversity. Altitude had a strong negative effect (p < 0.001), with higher altitudes correlating with lower species diversity. The frequency of tillage by hand tools per year negatively affected species diversity (p < 0.05), as did the frequency of weeding by slashing per year (p < 0.001). Farmers in the focus group discussions indicated that frequent tillage with hand tools and weeding by slashing often result in the unintentional cutting of seedlings and saplings. This unselective clearing reduces the number of young plants that could mature into trees, thereby leading to a decline in species diversity in the Gedeo agroforestry systems. The distance from home to the main market had a significant negative effect (p = 0.004), suggesting that greater distances might reduce species diversity (Table − 5). An analysis also found that various factors significantly impacted species richness, explaining 57.5% of the variance (F = 37.216, p < 0.001, R² = 0.575). The study revealed that a higher proportion of economically maintained woody and non-woody perennial plants negatively affected species richness (p < 0.001), indicating that for every unit increase in these plants, species richness decreases by 0.148 units. Conversely, tree removal at the plot level positively impacted species richness (p < 0.001), with species richness increasing by 0.202 units per unit increase in tree removal. Wealth status and educational attainment were not significant predictors of species richness. The age of the household head had a significant negative effect on species richness (p < 0.001), suggesting that older household heads manage plots with lower species richness. Lastly, land size did not significantly influence species richness (p = 0.271) (Tabel-5). The study found that the distance from home to the main market did not significantly impact species richness (p = 0.806). Altitude, however, had a significant negative effect on species richness (p < 0.001), with species richness decreasing by 0.526 units for every unit increase in altitude. More frequent tillage negatively affected species richness (p = 0.013), with species richness decreasing by 0.115 units for each additional tillage frequency per year. Similarly, increased weeding frequency also reduced species richness (p = 0.019), with a 0.094 unit decrease in species richness for each additional weeding instance per year (Table-5). Table 5 The Multiple linear regression coefficients describing species richness and diversity (H’) and Simpson indices with Management and socio-environment factors Dependent variables Predictors β R 2 F t p-value Species diversity (Shannon) Abundance of plants maintained for Economic purpose -0.197 0.654 51.975 -5.354 0.000*** Selective Removal of tree 0.125 3.106 0.002** Tillage frequency per year -0.104 -2.52 0.012** Weeding Frequency per Year -0.133 -3.712 0.000*** Wealth Status -0.025 -0.586 0.558(NS) Age -0.125 -3.344 0.001** Education status 0.007 0.194 0.847 (NS) Land Size in Ha -0.041 -1.077 0.282 (NS) Distance from home to main market -0.097 -1.664 0.004** Altitude -0.597 -8.9 0.000*** Species richness Economic goal plants ratio -0.148 0.575 37.216 -3.632 0.000*** Removal of tree at plot level 0.202 4.526 0.000*** Tillage frequency -0.115 -2.508 0.013** Weeding Frequency -0.094 -2.368 0.019** Wealth Status 0.003 0.071 0.943(NS) Age of HHH -0.185 -4.449 0.000*** Educational Status -0.024 -0.566 0.572 (NS) Land Size in Ha -0.047 -1.102 0.271(NS) Distance from home to main market -0.016 -0.246 0.806 (NS) Altitude -0.526 -7.078 0.000*** Note: *, **, *** Significant (F test) at P < 0.05, < 0.01, < 0.001, respectively 4. Discussion Species composition, structure, and determinant of diversity and abundance of woody and non-woody plant species in Gedeo AFSs Efforts to conserve biodiversity in many countries have traditionally focused on natural forests, but there is growing recognition of the potential of Agroforestry Systems (AFS) to contribute to biodiversity conservation outside of traditional forest areas (Fahad et al. 2022 ). The ecosystem services provided by AFSs at both the farm and landscape scale depend on the structural complexity, species richness, and management intensity of the system (Kuyah et al. 2014). Research conducted by Udawatta et al. ( 2019 ) has shown that AFS can help combat tree cover loss and support biodiversity. While biodiversity in AFS may be lower than in natural forests, it is generally higher than in Monoculture Systems (MCS) (Haggar et al. 2019 ). The list of 78 plant species in the study area shows the AFSs are rich in diversity and can be considered to be among the highly diverse agricultural land in Ethiopia (Tadesse 2002 ). The result was comparable with other study results outside the study area. The highest plant species richness was reported in Southern Ethiopia, with a range of 50–198 plant species (Abebe et al. 2006 ;Asfaw and Woldu 1997 ; Negash and Achalu 2008 ; Tamrat 2011 ). This was followed by South-west Ethiopia, Central Ethiopia with a range of 27–114 plant species (Mengesha 2010 ; Tolera et al. 2008 ) reported 90 woody species in south-eastern Ethiopia, including native tree species such as Juniperus procera Hochst. ex, Podocarpus falcatus, and Hagenia abyssinica. Moreover, research has documented a diverse array of plant species in tropical home gardens, such as 475 species in north-eastern India (Ulman et al. 2021 ) and 419 species in south-western Bangladesh (Kabir and Webb 2007 ). In Uganda, research by Boffa et al. (2005) revealed that farmers preserved around 25% of the woody species found in a protected natural area, with half of these species deliberately planted due to their perceived benefits. This highlights the role of farmers in actively managing and conserving biodiversity in AFS. In fact, in Ethiopia, Enset ventricosum is arguably the most important crop for food security and rural livelihoods for more than one fourth of the country’s population has been depend on it (Borrell et al. 2019 ). Persea americana Mill and Mangifera indica (two major fruit crops mainly maintained for income sources) and Millettia Ferringson and Cordia africana (maintained for mainly for soil fertility, shade and livestock feed) also common species observed. This finding is consistent with previous research by (Ereso 2023 ), who reported almost similar numbers of woody species in agroforestry systems. Regarding species diversity, the study revealed that the coffee-fruit-tree AF had the highest diversity of species, which aligns with the results of (Negash et al. 2011 ) and (Tesfay et al. 2024 ) in the same study area. Coffee-Fruit-tree based AF were more diverse in terms of shade tree species than the coffee and enset based AF farms. The results of the present study are comparable to studies that investigated coffee-Enset agroforestry (Tesfay et al. 2024 ) Enset-based agroforestry (Negash et al. 2011 ) and Coffee-fruit –tree-based agroforestry (Asfaw et al. 2015 ) found significantly higher flora diversity compared to the other two (Negash et al. 2011 ). The study highlights a strong emphasis on economically valuable perennial plants, particularly Coffee-fruit-tree-based agroforestry systems, in the research area. Coffee arabica and Enset ventricosum were the dominant species, making up over 72% of the total individual plants in the three agroforestry systems. About 222 (77.62%) sample households have removed old indigenous trees to make room for more marketable perennial crops through practices like selective canopy thinning. This aligns with the research suggesting that Farmers actively cultivated both annual and perennial herbs, as well as various woody species, in order to fulfill their subsistence and economic needs. The result is aligned with several studies. For instance, according (Talemos et al. 2013), 159 plant species were identified in the homegarden, with 70.44 percent of them considered beneficial to the local community. The other finding suggested that shift towards intensifying the cultivation of marketable and consumption crops at the expense of biodiversity, despite the well-documented advantages of biodiverse ecosystems in terms of resilience and resource efficiency (Tilman et al. 2014), climate regulation, agricultural support, and cultural significance (Karp et al. 2013 ). The Gedeo agroforestry system has been recognized for its unique characteristics, combining sustainable practices with the conservation of endangered woody species and wildlife (IR3S/UTIAS 2016 ). The traditional agroforestry practiced of the Gedeo people consists of a diverse mix of trees, shrubs, and annuals closely planted to form a continuous vegetation cover, making it a model land-use system in the region (Negash et al. 2011 ). Currently, most members of the Gedeo community practice a home-garden type of agroforestry, where subsistence crops are grown alongside trees (Legesse 2013 ). Despite its potential, the Gedeo agroforestry system is facing challenges due to increasing population pressure, leading to the deterioration of agroforestry practices and the degradation of forest species essential to the system (Bishaw et al. 2013 ). Cash crops like khat have encroached on the cultivation of staple crops like enset and coffee in the region, as farmers strive to increase coffee production to meet global demands and accommodate the growing human population (Abebe et al. 2009 ). The main component crops, enset ( Ensete ventricosum an herbaceous monocarpic banana-like plant) and coffee ( Coffea arabica L.), are the pillars of food security (Negash et al. 2011 ). The result of study revealed that, economically maintained perennial crops abundance was covered about 80% of their farmland. To achieve higher yields, farmers are clearing competing understorey vegetation and selectively cutting trees, often favoring shade-providing species while sacrificing overall vegetation structure and diversity (Aerts et al. 2011 ). Furthermore, The Gedeo youth, engaged in cutting trees for firewood, charcoal, and construction materials, and the truck drivers, loading the resulting lumber to sell in nearby towns (Regassa et al. 2017). Besides, the proportion of land use and land cover change of agroforestry area account for 72.8% during 1988, which was reduced to 49.5% and 45.1% during 2002 and 2018, respectively (Erenso and Andemo 2022 ). This all indicating that intensive management practices can decrease woody plant biodiversity and hinder tree regeneration (Hundera et al. 2013 ). Tree composition of high-intensity plantations is dominated by species that fulfill landowners’ economic and dietary needs, which reduce the number of species (Erenso and Andemo 2022 ). Competing understorey vegetation is removed and trees are selectively cut, systematically taking out the tallest individuals or unwanted species and selecting for preferred shade providing species. This gradual development of monocropping (high-intensity management crops) plots within the integrated multistory systems of Gedeo AF involves a negative overall trend in terms of crop diversity (Linger 2014 ), can lead to microclimatic changes, such as increased light radiation, temperatures, and wind speeds, which can further The impacts of agroforestry types, management practices, Socioeconomic and environmental factors on species diversity and richness The findings of this study underscore the intricate relationships between agroforestry management practices, socio-economic factors, and environmental conditions on species diversity within the Gedeo agroforestry systems. The significant negative impact of the economically motivated intensification of marketable perennial plants on species diversity (β = -0.197, t = -5.354, p < 0.001) aligns with findings from other studies that highlight the trade-offs between economic plant cultivation and biodiversity. For instance, a systematic review by Apriyani et al. ( 2024 ) found that the increasing demand for high-value tropical commodities often leads to reduced biodiversity in rainforest areas. The result also depicted that selective tree removal at the plot level positively influence species diversity (β = 0.125, t = 3.106, p = 0.002). This result suggests that certain management practices, such as selective tree removal, can enhance biodiversity. This finding is supported by research indicating that managed tree removal can create a more heterogeneous environment, promoting species diversity (National Academy of Sciences 2021 ). The negative impact of the household head’s age on species diversity (β = -0.125, t = -3.344, p = 0.001) suggests that older household heads may be less likely to adopt biodiversity-friendly practices. This is consistent with studies that show younger farmers are often more open to innovative and sustainable agricultural practices (FAO 2019 ). The strong negative effect of altitude on species diversity (β = -0.597, t = -8.9, p < 0.001) highlights the challenges of maintaining diversity at higher elevations. Higher altitudes often have harsher environmental conditions, which can limit species diversity (Allen et al. 2014 ). A study on rocky intertidal elevation gradients found that environmental stress affects species richness and diversity, although the relationship can be complex and context-dependent (Scrosati et al. 2011). The results also indicated that a significant negative effect of the distance from home to the main market on species diversity. This suggests that as the distance increases, species diversity decrease, and the effect is statistically significant at p < 0.05 level. For instance, a study on plant species found that island area and distance to the mainland significantly affected species richness and composition (Aggemyr et al. 2018 ). By understanding the complex interactions between agricultural practices, socio-economic factors, and environmental conditions, stakeholders can develop strategies that promote sustainable agriculture and biodiversity conservation. This might be because the intrinsic ecological characteristics and specific management practices have a more direct impact on biodiversity (Tscharntke et al. 2012 ). The findings our study also revealed the impact of agroforestry farm management, socioeconomic, and environmental factors on species richness in the Gedeo agroforestry landscapes provide valuable insights into the complex dynamics influencing biodiversity in these systems. The negative impact of the economically derived motives suggests that prioritizing economically valuable plants may reduce diversity. This aligns with findings from other studies indicating that monoculture or low-diversity systems focused on economic gain can lead to a decline in species richness (Perfecto and Vandermeer 2010 ).Older household heads may be more inclined to traditional practices that do not prioritize diversity. Similar trends have been noted in other regions where older farmers are less likely to adopt biodiversity-friendly practices (Kassie et al. 2013 ). The strong negative impact of altitude on species richness is consistent with ecological principles that higher altitudes often have harsher conditions, leading to lower biodiversity. This is supported by studies showing that species richness generally decreases with increasing altitude due to factors like temperature and oxygen availability (Rahbek 1995 ). Both tillage frequency (β = -0.115, t = -2.508, p = 0.013) and weeding frequency (β = -0.094, t = -2.368, p = 0.019) negatively affected species richness. Tillage is a common agricultural practice used to prepare the soil for planting and manage weeds. However, frequent tillage can have negative impacts on species richness. The result of our study found that for every unit increase in tillage frequency, species richness decreases by 0.115 units. Frequent tillage can damage the root systems of woody plants. For instance, in agroforestry systems where trees like Cordia africana and Acacia tortilis are integrated with crops, tillage can sever roots, reducing the trees' ability to absorb water and nutrients (Gebirehiwot et al. 2021 ). This can lead to stunted growth and even tree mortality. Tillage increases soil erosion, which can expose the roots of woody plants and make them more susceptible to environmental stress. In areas like the Gurawa District in Oromia, Ethiopia, soil erosion due to tillage has been shown to negatively impact the distribution and composition of woody plant species (Abdurezak 2023 ). Repeated tillage can lead to soil compaction, which reduces the soil's ability to retain water and nutrients. This can be particularly detrimental to young woody plants that have less developed root systems. For example, in parkland agroforestry systems, soil compaction has been observed to hinder the growth of trees like Croton macrostachyus (Fahad et al. 2022 ). Tillage disrupts the soil's microbial community, which plays a crucial role in nutrient cycling and soil health. This disruption can negatively affect the growth of woody plants that rely on symbiotic relationships with soil microbes. Studies have shown that reduced microbial activity due to tillage can lead to poorer growth outcomes for trees in agroforestry systems (Kun et al. 2023 ). In agroforestry systems, tillage is often used to prepare the soil for annual crops. This can create competition for resources between the crops and woody plants. For example, in systems where maize is grown alongside trees, tillage can favor the crops at the expense of the trees, reducing the overall species richness (Ong et al. 1999). By understanding these impacts, farmers can adopt practices that minimize tillage and promote the health and diversity of woody plant species in agroforestry systems. Techniques like reduced tillage or no-till farming can help maintain soil structure and health, benefiting both crops and trees. Weeding is an essential practice in traditional agroforestry systems of Gedeo to manage unwanted plants, favoring economically valuable perennial plants for income and household consumption. The study indicates that increased weeding frequency, specifically through slashing with hand tools, is associated with a reduction in species richness, with a decrease of 0.094 units for every unit increase in weeding frequency. Research on the dynamics of weeds in agroforestry systems indicates that the presence of diverse woody species can influence weed cover and biomass (Tzuc-Martínez et al., 2017 ). Reduced species richness can lead to a loss of biodiversity, which is crucial for ecosystem stability and resilience (Sharma et al. 2022). Diverse woody species provide various ecosystem services, such as soil fertility, microclimate regulation, and erosion control. A decrease in species richness can diminish these benefits (Maria et al. 2024 ). For smallholder farmers, the loss of diverse woody species can affect the availability of timber, firewood, and other forest products, impacting their livelihoods (Tzuc-Martínez et al., 2017 ). Balancing weeding practices to manage unwanted plants while preserving species richness is essential for the sustainability of traditional agroforestry systems. Encouraging practices that maintain or enhance woody species diversity can help achieve both ecological and economic goals (Sharma et al. 2022). Declarations Author Contribution Subject: Authorship Contributions StatementThe contributions of the authors are as follows:• Sileshi Lemma conceived the research idea and developed the original draft of the manuscript.• Sileshi Lemma and Dr. Zebene Asfaw Developed The research design and methodology • Sileshi Lemma conducted the formal analysis, prepared the original draft, and carried out the investigation.• All authors participated in reviewing and finalizing the manuscript.• Supervision was provided by Dr. Zebene Asfaw.Sileshi Lemma With Best Regards Data Availability Data is provided within the manuscript or supplementary information files (Appendix-1;Appendix-2 and Appendix-3) References Abdurezak, I (2023) Soil and land use management practices and its effects on woody plant species composition and distribution in Gurawa District, East Hararghe Zone, Oromia, Ethiopia (Master’s thesis, Haramaya University). Abebe, T., Wiersum, K. F., & Bongers, F (2009) Spatial and Temporal Variation in Crop Diversity in Agroforestry Homegardens of Southern Ethiopia. Agroforestry Systems, 78(3):309–322. https://doi.org/10.1007/s10457-009-9246-6. Abebe, T., Wiersum, K. F., Bongers, F., & Sterck, F (2006) Diversity and Dynamics in Homegardens of Southern Ethiopia. Advances in Agroforestry. 123–142. https://doi.org/10.1007/978-1-4020-4948-4_8. Aerts, R., Hundera, K., Berecha, G., Gijbels, P., Baeten, M., Van Mechelen, M., & Honnay, O (2011) Semi-Forest Coffee Cultivation and the Conservation of Ethiopian Afromontane Rainforest Fragments. Forest Ecology and Management. 261(6):1034-1041. DOI:10.1016/j.foreco.2010.12.025. Aggemyr, E., Auffret, A.G., Jädergård, L. et al (2018) Species richness and composition differ in response to landscape and biogeography. Landscape Ecol 33, 2273–2284. https://doi.org/10.1007/s10980-018-0742-9 Alambo, F. I (2020) Agroforestry-Based Livelihoods in the Face of Cultural and Socio-Economic Dynamics in Rural Gedeo, Southern Ethiopia. The Journal of Rural and Community Development. 15(3):113–132. Allen, T., Prosperi, P., Cogill, B., & Flichman, G (2014) Agricultural biodiversity, social–ecological systems and sustainable dietsProceedings of the Nutrition Society. Apriyani, V., Holle, M. J. M., & Mumbunan, S (2024) A systematic map of evidence on the relationship between agricultural production and biodiversity in tropical rainforest areas Environmental Evidence. Asfaw, Z. Mulata, Y. Assefa, B. Abebe, T. Duna, S. Mulugeta, G. Mebrahten, H. and Kassa, H (2015) Enhancing the Role of Forestry in Building Climate Resilient Green Economy in Ethiopia: Strategy for Scalling up Effective Forest Management Practices in S. Asfaw, Z., and Woldu, Z (1997) Crop Associations of Home Gardens in Welayta and Gurage in Southern Ethiopia.” SINET: Ethiopian Journal of Science 20(1). https://doi.org/10.4314/sinet.v20i1.18092. Asigbaase, M., Sjogersten, S., Lomax, B. H., & Dawoe, E (2019) Tree Diversity and Its Ecological Importance Value in Organic and Conventional Cocoa Agroforests in Ghana. PLOS ONE 14(1):0210557. https://doi.org/10.1371/journal.pone.0210557. Atangana, A., Khasa, D., Chang, S., & Degrande, A (2014) Agroforestry and Biodiversity Conservation in Tropical Landscapes, In Tropical Agroforestry (pp. 227-232). Dordrecht: Springer. https://doi.org/10.1007/978-94-007-7723-1_11 Atangana, A., Khasa, D., Chang, S., Degrande A (2014) Tropical Agroforestry, in: Tropical Agroforestry. Netherlands, Dordrecht. Springer. Bastin, J. F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh, D et al. (2019) The Global Tree Restoration Potential.Science 365(6448):76–79. https://doi.org/10.1126/science.aax0848. Bekele-Tesemma (2007) Useful Trees of Ethiopia: Identification, Propagation and Management in 17 Agroecological Zones. Edited by S. D. and P. M. Bo Tengnäs, Ensermu Kelbesa. Nirobi: Nairobi: RELMA in ICRAF Project. Bishaw, Badege, Henry Neufeldt, Jeremias Mowo, Abdu Abdelkadir, Jonathan Muriuki et al. (2013) Farmers’ Strategies for Adapting to and Mitigating Climate Variability and Change through Agroforestry in Ethiopia and Kenya. Forestry Communications Group, Oregon State University, Corvallis, edited by and A. D. Caryn M. Davis, Bryan Bernart. Oregon. Borrell, J. S., Biswas, M. K., Goodwin, M., Blomme, G., Schwarzacher, T., Heslop-Harrison, Wilkin et.al. (2019) Enset in Ethiopia: A Poorly Characterized but Resilient Starch Staple. Annals of Botany.” 123(5):747–766. https://doi.org/10.1093/aob/mcy214. Cerda Clementine Allinne, Christian Gary, Philippe Tixier, et al. (2017) Effects of Shade, Altitude and Management on Multiple Ecosystem Services in Coffee Agroecosystems.” Europ. J. Agronomy. 82:308–319. doi: Doi.org/10.1016/j.eja.2016.09.019. Chalite, G. C. (2022). Management and the influence of socioeconomic factors on tree species diversity in traditional agroforestry practices in Demba Gofa District, South Ethiopia. Research Aspects in Biological Science , 2, 18-26. https://doi.org/10.9734/bpi/rabs/v2/2765C Crabtree, B.F. & MiUer, W (1992) Doing qualitative research. Newbury Park, CA: Sage. Erenso, F., & Andemo, A (2022) Vulnerability of Agrobiodiversity and Agroforestry Settings to Climate Change in Gedeo Zone, Ethiopia. International Journal of Agronomy 15(3):1–13. https://doi.org/10.1155/2022/8738482. Ereso, T (2023) Woody Species Conservation, Management, and Its Socioeconomic Importance of Agroforestry Practice in Ethiopia. 1(8). https://doi.org/https://doi.org/10.1155/2023/2623291. Fahad, Shah, Sangram Bhanudas Chavan, Akash Ravindra Chichaghare et al. (2022) Agroforestry Systems for Soil Health Improvement and Maintenance. Sustainability 14(22):1–25. https://doi.org/10.3390/su142214877. FAO (2019) The State of the World’s Biodiversity for Food and Agriculture FAO Commission on Genetic Resources for Food and Agriculture. Franks, P., Hou Jones, X., Fikreyesus, D., Sintayehu, M., Mamuye, S., Danso, E., Meshack, C., McNicol, I. and Soesbergen, A (2017) Reconciling Forest Conservation with Food Production in Sub-Saharan Africa: Case Studies from Ethiopia, Ghana and Tanzania. London. IIED. Gebirehiwot, H. T., Kedanu, A. A., & Adugna, M. T 2021) The role of woody plant functional traits for sustainable soil management in the agroforestry system of Ethiopia. In L. Hufnagel (Ed.), Biodiversity of Ecosystems. IntechOpen. https://doi.org/10.5772/intechopen.99296 Getahun Haile, Mulugeta Lemenih, Feyera Senbeta, Fisseha Itanna (2017) Plant Diversity and Determinant Factors across Smallholder Agricultural Management Units in Central Ethiopia.” Agroforest Syst. 91(4):677–695. doi: Doi: 10.1007/s10457-016-0038-5. Haggar, J., Pons, D., Saenz, L., Vides, M., 2019 (2019) Contribution of Agroforestry Systems to Sustaining Biodiversity in Fragmented Forest Landscapes.” Agric. Ecosyst. Environ. 283. doi: https://doi.org/10.1016/j.agee.2019.06.006. Hundera K., Aerts R., Fontaine A., Van Mechelen M., Gijbels P., Honnay O., Muys B (2013 )Effects of Coffee Management Intensity on Composition, Structure, and Regeneration Status of Ethiopian Moist Evergreen Afromontane Forests.” Environ. Manag. 51(3):801–809. Doi: 10.1007/s00267-012-9976-5. ICRAF (1997) International Centre for Research in Agroforestry (ICRAF) Medium Term Plan 1998-2000. IR3S/UTIAS, UNU-IAS &. 2016. Socio-Ecological Production Landscapes and Seascapes (SEPLS) in Africa. United Nations University Institute for the Advanced Study of Sustainability, Tokyo. Jezeer R. Rosalien E, Santos Maria J, Boot René G A, Junginger Martin, Verweij Pita A (2018) Effects of Shade and Input Management on Economic Performance of Small-Scale Peruvian Coffee Systems. Agricultural Systems.” Agricultural Systems 162:179–190. doi: Doi.org/10.1016/j.agsy.2018.01.014. Kabir, M. E., & Webb, E. L (2007) Can Homegardens Conserve Biodiversity in Bangladesh? Biotropica 40(1):95–103. https://doi.org/10.1111/j.1744-7429.2007.00346.x. Karp, D. S., Mendenhall, C. D., Sandí, R. F., Chaumont, N., Ehrlich, P. R., Hadly, E. A., & Daily, G. C (2013) Forest Bolsters Bird Abundance, Pest Control and Coffee Yield. Ecology Letters 16(11):1339–1347. doi: https://doi.org/10.1111/ele.12173. Kassa, Gezahegn, Tesfaye Abebe, and Zeleke Ewnetu (2015) Diversity, Density and Management of Trees in Different Agro-Forestry Practices of Yem Special District, Southern Ethiopia. SINET: Ethiop. J. Sci 38(1):1–16. Kassie, M., Jaleta, M., Shiferaw, B., Mmbando, F., & Mekuria, M 2013 Adoption of interrelated sustainable agricultural practices in smallholder systems: Evidence from rural Tanzania. Technological Forecasting and Social Change, 80(3), 525-540. https://doi.org/10.1016/j.techfore.2012.08.007 Kennedy C.M., Daniela A. Miteva, Leandro Baumgarten, Peter L. Hawthorne, Kei Sochi, Stephen Polasky, James R. Oakleaf, Elizabeth M. Uhlhorn, and Joseph Kiesecker (2016) Bigger Is Better: Improved Nature Conservation and Economic Returns from Landscape-Level Mitigation. Science Advance. 2(7):107-116. doi: Doi: 10.1126/sciadv.1501021. Kun, Á., Simon, B., Zalai, M., Kolozsvári, I., Bozán, C., Jancsó, M., Körösparti, J. T., Kovács, G. P., Gyuricza, C., & Bakti, B (2023) Effect of mulching on soil quality in an agroforestry system irrigated with reused water. Agronomy, 13(6), 1622. https://doi.org/10.3390/agronomy13061622 Kuyah, S., Öborn, I., Jonsson, M., Dahlin, A. S., Barrios, E., Muthuri, C., Sinclair (2016) Trees in Agricultural Landscapes Enhance Provision of Ecosystem Services in Sub-Saharan Africa. International Journal of Biodiversity Science, Ecosystem Services & Management 1–19. https://doi.org/10.1080/21513732.2016.1214178. Laurance, W. F., Carolina Useche, D., Rendeiro, J., Kalka, M., Bradshaw, C. J. A., Sloan, S. P., Zamzani, F (2012) Averting Biodiversity Collapse in Tropical Forest Protected Areas.” Nature 489(7415). doi: https://doi.org/10.1038/nature11318. Legesse, Abiyot (2013) The Dynamics of Indigenous Knowledge Pertaining to Agroforestry Systems of Gedeo: Implications for Sustainability. PhD Dissertation. University of South Africa. Linger, E (2014) Agro-Ecosystem and Socio-Economic Role of Homegarden Agroforestry in Jabithenan District, North-Western Ethiopia: Implication for Climate Change Adaptation. SpringerPlus 3(1). https://doi.org/10.1186/2193-1801-3-154. Makhubele, L., Araia, M.G. & Chirwa, P.W. Harvesting distance effect on tree species diversity in traditional agroforestry landscape: a case of Vhembe Biosphere Reserve in South Africa. Biodivers Conserv 32 , 3397–3421 (2023). https://doi.org/10.1007/s10531-023-02671-2 Makkonen, M., Berg, M. P., van Logtestijn, R. S. P., van Hal, J. R., & Aerts, R (2013) Do Physical Plant Litter Traits Explain Non-Additivity in Litter Mixtures? A Test of the Improved Microenvironmental Conditions Theory. Oikos, 122. doi: //doi.org/10.1111/j.1600-0706.2012.20750.x. Maria, D. M., Chagas, K. P. T., da Silva Lins, T. R., de Cademartori, P. H. G., & Vidaurre, G. B (2024) A review on the quality of wood from agroforestry systems. Agroforestry Systems, 98, 715-737. Matthias De Beenhouwer, Lore Geeraert, Jan Mertens, Maarten Van Geel, Raf Aerts, Koen Vanderhaegen, Olivier Honnay (2016) Biodiversity and Carbon Storage Co-Benefits of Coffee Agroforestry across a Gradient of Increasing Management Intensity in the SW E. Agriculture, Ecosystems and Environment 222:193–199. Doi.org/10.1016/j.agee.2016.02.017. Mebrate (2007) Agroforestry Practices in Gedeo Zone, Ethiopia: A Geographical Analysis. Ph.D. Dissertation, Panjab University, Chandigarh, India. Mengesha, B (2010) Alternative Technologies for Sustainable Agricultural Production and Agroecosystem Conservation in Arsi Highlands, Southeastern Ethiopia’, PhD Thesis. Addis Ababa University, Ethiopia. Mengistu, B., and Asfaw, Z. (2016) Woody Species Diversity and Structure of Agroforestry and Adjacent Land Uses in Dallo Mena District, South-East Ethiopia. Natural Resources 07(10):515–34. https://doi.org/10.4236/nr.2016.710044. Mesele N., M., Achalu, N (2008) History of Indigenous Agroforestry in Gedeo, Southern Ethiopia, Based Onlocal Community Interviews: Vegetation Diversity and Structure in the Land Use Systems.” Ethiop. J. Nat.Resour. 10(1):31–52. Meylan Louise, Gary Christian, Allinne Clémentine, Ortiz Jorge, Jackson Louise, Rapidel, Bruno (2017) Evaluating the Effect of Shade Trees on Provision of Ecosystem Services in Intensively Managed Coffee. 245:32–42. Doi:10.1016/j.agee.2017.05.005. Montgomery, D. C., Peck, E. A., & Vining, G. G (2012) Introduction to linear regression analysis. John Wiley & Sons. Nelder, J. A., & Wedderburn, R. W. M. (1972) Generalized Linear Models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370-384. Mueller-Dombois, D., and Ellenberg, H (1974) Aims and Methods of Vegetation Ecology. John Wiley & Sons. National Academy of Sciences (2021) Agriculture’s Impacts on Biodiversity, the Environment, and Climate The Challenge of Feeding the World Sustainably: Summary of the US-UK Scientific Forum on Sustainable Agriculture. Negash (2013) The Indigenous Agroforestry Systems of the South-Eastern Rift Valley Escarpment, Ethiopia: Their Biodiversity, Carbon Stocks, and Litterfall. Vol. 44. Negash, M. and Achalu, N (2008) History of Indigenous Agroforestry in Gedeo, Southern Ethiopia, Based on Local Community Interviews: Vegetation Diversity and Structure in the Land Use Systems. Ethiopian Journal of Natural Resources 10(1):31–52. Negash, M., Yirdaw, E., & Luukkanen, O (2011) Potential of Indigenous Multistrata Agroforests for Maintaining Native Floristic Diversity in the South-Eastern Rift Valley Escarpment, Ethiopia. Agroforestry Systems 85(1):9–28. doi: https://doi.org/10.1007/s10457-011-9408-1. Nigatu, D., Tiki, L., Kewessa, G., & Datiko, D (2017) Traditional coffee management practices and their effects on woody species structure and regeneration in Bale Eco-region, Ethiopia. International Journal of Advanced Research , 5(6), 591-598. DOI: 10.21474/IJAR01/4454 Ong, C. K., & Leakey, R. R. B (1999) Why tree-crop interactions in agroforestry appear at odds with tree-grass interactions in tropical savannahs. Agroforestry Systems, 45(1-3), 109-129. https://doi.org/10.1023/A:1006243032538 Perfecto, I., & Vandermeer, J (2010) The agroecological matrix as alternative to the land-sparing/agriculture intensification model. Proceedings of the National Academy of Sciences, 107(13), 5786-5791. Pickett, S. T. A., & White, P. S (1985) The Ecology of Natural Disturbance and Patch Dynamics . Academic Press. Rahbek, C (1995) The elevation gradient of species richness: a uniform pattern?. Ecography, 18(2), 200-205. http://dx.doi.org/10.1111/j.1600-0587.1995.tb00341.x Regassa Debelo, A., Legesse, A., Milstein, T., & Orkaydo, O. O (2017) Tree Is Life’: The Rising of Dualism and the Declining of Mutualism among the Gedeo of Southern Ethiopia. Frontiers in Communication. 2. https://doi.org/10.3389/fcomm.2017.00007. Reshad, M., Asfaw, Z. & Mohammed, M. Perennial plant species diversity and its socio-ecological determinants in selected agroforestry practices at eastern escarpments of Chercher Massive, Ethiopia. Agroforest Syst 97 , 1627–1643 (2023). https://doi.org/10.1007/s10457-023-00884-6 Rodrigues, P., Shumi, G., Dorresteijn, I., Schultner, J., Hanspach, J., Hylander, K., Fischer (2018) Coffee Management and the Conservation of Forest Bird Diversity in Southwestern Ethiopia. Biological Conservation 217:131–139. https://doi.org/10.1016/j.biocon.2017.10.036. Rusdi Evizal, Sugiatno, Fembriarti Erry Prasmatiwi, and Indah Nurmayasari (2018) Shade Tree Species Diversity and Coffee Productivity in Sumberjay West Lampung, Indonesia. Biodiversitas. 17(1):234-240. DOI: 10.13057/biodiv/d170134. Scrosati, R.A., Knox, A.S., Valdivia, N. et al. (2021) Species richness and diversity across rocky intertidal elevation gradients in Helgoland: testing predictions from an environmental stress model. Helgol Mar Res. 65, 91–102. https://doi.org/10.1007/s10152-010-0205-4 Shannon, C. E., & Weaver, W (1949) The Mathematical Theory of Communication. Urbana, IL: The University of Illinois Press. Sharma, R., Mina, U., & Kumar, B. M 2022 Homegarden agroforestry systems in achievement of Sustainable Development Goals. Agronomy for Sustainable Development, 42, 44. Simpson, E. H (1949) Measurement of Diversity.” Nature 163(4148):688–688. https://doi.org/10.1038/163688a0. Snowdon P., Raison J., Keith H., Ritson P., Grierson P., Adams M., Montagu K., Bi HQ Burrows, W. &. Eamus D (2002) Protocol for Sampling Tree and Stand Biomass. National Carbon Accounting System, Technical Report No. 31. Canberra: Australian Greenhouse Office. Tadesse (2002) Five Thousand Years of Sustainability? A Case Study on Gedeo Land Use (Southern Ethiopia). PhD Dissertation. Wageningen Agricultural University, Wageningen. Tadesse, E., Negash, M. & Asfaw, Z. Impacts of traditional agroforestry practices, altitudinal gradients and households’ wealth status on perennial plants species composition, diversity, and structure in south-central Ethiopia. Agroforest Syst 95 , 1533–1561 (2021). https://doi.org/10.1007/s10457-021-00659-x Tamrat S (2011) Study of Useful Plants in and around Gate Uduma (Traditional Gedeo Homegardens) in Kochere Woreda of Gedeo Zone, SNNPR, Ethiopia: An Ethnobotanical Approach’, PhD Thesis, Addis Ababa University, Ethiopia. 144. Tesfay, H. M., Oettel, J., Lapin, K., & Negash, M (2024) Plant Diversity and Conservation Role of Three Indigenous Agroforestry Systems of Southeastern Rift-Valley Landscapes, Ethiopia. Diversity , 16 (1), 64. https://doi.org/10.3390/d16010064The Farming Insider. (2024). Biodiversity: Key to Sustainable Farm Success. Tolera, M., Asfaw, Z., Lemenih, M., & Karltun, E (2008) Woody Species Diversity in a Changing Landscape in the South-Central Highlands of Ethiopia.” Agriculture, Ecosystems & Environment, 128(102):52–58. https://doi.org/10.1016/j.agee.2008.05.001. Tscharntke, T., Clough, Y., Wanger, T. C., Jackson, L., Motzke, I., Perfecto, I. Whitbread, A (2012) Global food security, biodiversity conservation and the future of agricultural intensification. Biological Conservation, 151(1), 53-59. doi:10.1016/j.biocon.2012.01.068 Tzuc-Martínez, R., Casanova-Lugo, F., Caamal-Maldonado, A., Tun-Garrido, J., González-Valdivia, N., & Cetzal-Ix, W (2017) Influence of woody species in the dynamics of weeds in agroforestry systems in Yucatan, Mexico. Agrociencia, 51(3), 315-328. Udawatta, R. P., Rankoth, L., & Jose, S (2019) “Agroforestry and Biodiversity.” Sustainability 11(10):2879. doi: https://doi.org/10.3390/su11102879. Ulman, Y., Singh, M., Kumar, A., Sharma, M (2021) Conservation of Plant Diversity in Agroforestry Systems in a Biodiversity Hotspot Region of Northeast India.” Agric. Res. https://doi.org/10.1007/s40003-020-00525-9. Uwera, M. H., Mugunga, C. P., & Mukangango, M. (2022). Assessing agroforestry species, practices, uses, and tree diversity in two contrasting agro-ecological zones of rwanda. Forestist, December 12, 2022. DOI:10.5152/forestist. 2022.22048. Wang, J., Ren, C., Cheng, H., Zou, Y., Bughio, M. A., & Li, Q (2017) Conversion of Rainforest into Agroforestry and Monoculture Plantation in China: Consequences for Soil Phosphorus Forms and Microbial Community.” Science of the Total Environment 595:769–778. https://doi.org/10.1016/j.scitotenv.2017.04.012. Whittaker, Robert J (1993) Holocene Book Reviews: Vegetation Description and Analysis: A Practical Approach M. Kent and P. Coker, London: Belhaven Press. Hardback. ISBN 1-85293-006-3. The Holocene 3(4):379. Worku, M., Lindner, A. & Berger, U. Management Effects on Woody Species Diversity and Vegetation Structure of Coffee-based Agroforestry Systems in Ethiopia. Small-scale Forestry 14 , 531–551 (2015). https://doi.org/10.1007/s11842-015-9305-y Yamane T. 1967. Statistics: An Introductory Analysis. 2nd Ed. New York: Harper and Row. Yirefu Tefera, Wendawek Abebe, and Bogale Teferi (2016) Woody Plants Species Diversity of Home Garden Agroforestry in Three Agroecological Zones of Dilla Zuria District, Gedeo Zone, Southern Ethiopia. (3). International Journal of Fauna and Biological Studies. 3(3):98-106. Additional Declarations No competing interests reported. Supplementary Files Appendix1.docx Appendix2SpeciesDensityBasalareaandnumberofindividulas.docx Appendix.3docx3.docx Cite Share Download PDF Status: Published Journal Publication published 24 Dec, 2024 Read the published version in Agroforestry Systems → Version 1 posted Editorial decision: Revision requested 08 Nov, 2024 Reviews received at journal 07 Nov, 2024 Reviewers agreed at journal 28 Oct, 2024 Reviewers agreed at journal 28 Oct, 2024 Reviews received at journal 01 Sep, 2024 Reviewers agreed at journal 23 Aug, 2024 Reviewers invited by journal 21 Aug, 2024 Editor assigned by journal 21 Aug, 2024 Submission checks completed at journal 15 Aug, 2024 First submitted to journal 10 Aug, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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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-4893436","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":348041360,"identity":"117333f2-f472-4a15-8bd9-e6ae528c9b38","order_by":0,"name":"Sileshi 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2","display":"","copyAsset":false,"role":"figure","size":67613,"visible":true,"origin":"","legend":"\u003cp\u003eFamilies and their respective number of species in the study\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4893436/v1/3052bc5b7b093b5f04cecf48.jpg"},{"id":64302702,"identity":"2d507b86-7df2-4725-ae7c-c9d22378a878","added_by":"auto","created_at":"2024-09-11 12:04:40","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":76941,"visible":true,"origin":"","legend":"\u003cp\u003eThe economic (Figure-3a) and Ecological (Figure-3b) preferred perennial plant species percentage in the study area\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4893436/v1/117e60258f4bf91e2d41b658.jpg"},{"id":64300891,"identity":"39b0028d-c02e-4cd1-88ab-cebb35587612","added_by":"auto","created_at":"2024-09-11 11:40:40","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":55794,"visible":true,"origin":"","legend":"\u003cp\u003eThe DBH class distributions across individual perennial plant species in a) Enset-tree-based b) coffee-Enset-tree c) Coffee-fruit-tree- based agroforestry In Gedeo, Southern Ethiopia\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4893436/v1/a75f7893d241dfad3caefcfd.jpg"},{"id":72640248,"identity":"8aded238-4747-49ab-9bf6-260435c88377","added_by":"auto","created_at":"2024-12-30 16:03:15","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1046640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4893436/v1/e20229d5-d3cc-46ff-8a79-631c0373989f.pdf"},{"id":64302258,"identity":"5591e494-0a0a-4e46-a79e-dc4d321f8978","added_by":"auto","created_at":"2024-09-11 11:56:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":26438,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4893436/v1/14c71d00d00abdc021713002.docx"},{"id":64300867,"identity":"e61a33ed-d3e1-4701-8565-12f6b4da3599","added_by":"auto","created_at":"2024-09-11 11:40:40","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":19000,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix2SpeciesDensityBasalareaandnumberofindividulas.docx","url":"https://assets-eu.researchsquare.com/files/rs-4893436/v1/95f30a9e971705bb161cd5a0.docx"},{"id":64301439,"identity":"f07836b5-ede1-4ba8-83e0-26f7de0ceb0f","added_by":"auto","created_at":"2024-09-11 11:48:40","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":42157,"visible":true,"origin":"","legend":"","description":"","filename":"Appendix.3docx3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4893436/v1/0b792ab652c2583ac3fa7d5d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Effects of Management Practices and Socio-physical Factors on Perennial Plant Diversity of Agroforestry Systems of Gedeo landscapes, Southern Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eAgroforestry is a dynamic, ecological based, natural resource management system through integration of trees on farms and agricultural landscapes that diversifies and sustains production for the purpose of increasing social, economic, and environmental benefits for land users at all levels (ICRAF \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). Agroforestry systems (AFSs), are suggested as one of promising approach for biodiversity conservation complementing strict forest protection, while simultaneously enhancing livelihoods of poor farmers without compromising agricultural productivity (Udawatta et al.2019) It deliver key provisioning and regulating ecosystem services at both farm and landscape scale (Kuyah et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The system integrates crops, timbers, and medicinal plants in semi-domesticated ecosystems (Legesse \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, numerous valuable species are safeguarded within agroforestry systems, encompassing a variety of woody and non-woody plants. Particularly in Gedeo agroforestry, there has been a surge in recognition of its significance as a land use system that provides diverse ecosystem services due to its rich biodiversity (Tadesse \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Tesfay et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThese systems play a crucial role in preserving indigenous woody species diversity, climate regulation, local livelihoods, raw materials, genetic resources, and soil erosion control (Alambo \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Negash \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The quality and extent of those ecosystem services depend on the structural complexity, species richness, and management intensity of the AFS (Kennedy et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Laurance et al. 2014) Furthermore Agroforestry systems are frequently praised for their ability to protect biodiversity, but their main purpose is to enhance the well-being of farmers by boosting productivity, profitability, and sustainability (Atangana et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eHowever, a key challenge lies in balancing the conservation of biodiversity with the need for agricultural productivity within traditional agroforestry practices. Research indicates that the intensity of management practices significantly influences woody species diversity in these systems, primarily by altering plant population structures (Cerda et al. 2017; Miles et al. 2006) For instance, studies have shown that the intensification of coffee management in Ethiopia has led to structural degradation, specifically impacting shade tree diversity and density (Hundera et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Studies have shown that agroforestry systems that prioritize market-demanded crops tend to have lower diversity indices compared to those that maintain a mix of native and economically valuable species (Reshad et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eManagement techniques, such as intensive weeding, cutting of indigenous tree species, and focusing on market-demanded shrub and herb species, along with increasing exotic tree species, can lead to a decrease in woody species degradation, increased soil erosion, low adaptation to climate change, and unsustainable production systems (Hundera et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).Additionally, practices like pruning, thinning, mulching, fertilization, irrigation, and establishing certain crop-shade tree associations can significantly impact plant productivity, crop yields, and economic performance (Jezeer et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Meylan et al. 2017). These management practices play a crucial role in enhancing agricultural sustainability and productivity. Theoretical frameworks, such as the Intermediate\u003c/p\u003e \u003cp\u003eFor instance, the composition, structure, and regeneration of moist evergreen Afromontane forests was affected by coffee management intensity (Rusdi et al. 2018), and the demand of food crops by rapidly growing and impoverished human populations (Franks et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The study conducted in Bale Eco-Region, Ethiopia reveals that Traditional forest coffee management refers to all practices carried out by local people to minimize the effect of woody and herbaceous plant species on existence and productivity of coffee plants i.e., carried out slashing of undergrowth (herbs, seedlings and saplings), hoeing under coffee plants, decreasing density of shade trees through thinning, planting of seedlings of improved coffee varieties, cutting of woody plants and girdling of trees (Nigatu et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The Disturbance Hypothesis underscores the importance of maintaining a balance in disturbance regimes to promote biodiversity. Excessive management practices, by disrupting this balance, can simplify ecosystems, reduce the diversity of available niches, and lead to homogenization and species loss. In contrast, moderate disturbances can help maintain a dynamic and diverse ecosystem (Pickett and White \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e1985\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTraditional agroforestry management practices, including intensified weeding and tillage, planting valuable perennial crops, and selectively removing or thinning indigenous tree species, impact woody diversity in various ways. Intensified weeding and tillage can reduce woody species diversity by favoring specific crops and limiting habitats for diverse tree species, often resulting in the dominance of a few pioneer species and a decline in overall biodiversity (Worku et al. \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). The introduction of crops such as coffee, enset, and cacao has mixed effects on woody diversity. While these crops can coexist with various tree species, their management often involves selectively removing less economically valuable trees, which can decrease native woody species diversity but also promote the growth of species compatible with these crops (Tadesse et al. \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Uwera et al. \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Selective removal or thinning of indigenous tree species can significantly alter species composition and reduce woody diversity. This practice often targets specific species, leading to a less diverse tree population, but it can also create opportunities for the growth of other species, depending on management intensity and objectives (Makhubele et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Chalite \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Socioeconomic factors also influence species diversity. Wealthier households typically have better management practices and resources, supporting higher species richness and diversity. In contrast, poorer households may rely more on intensive practices that reduce diversity.\u003c/p\u003e \u003cp\u003eThus, local management intensification, which simplifies and reduces canopy cover, negatively impacts species richness, diversity and density in a given agroforestry systems. Therefore, Balancing agricultural production goals with the preservation of biodiversity is crucial for ensuring the continued benefits that agroforestry systems provide to both people and the environment (Matthias et al. 2016). The integration of socio-ecological frameworks in recent studies emphasizes the need to consider both management practices and socio-physical factors to promote sustainable agroforestry systems that balance economic needs with biodiversity conservation (Reshad et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2023\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eBecause of their extremely rich biodiversity, Gedeo agroforestry are important on both local and global level in terms of provided ecosystem services (Tadesse \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The study area features three main Agroforestry Practices (AFPs): Enset-tree-based, Coffee-enset-tre- based, and Coffee-fruit-tree-based ((Negash et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e)). The Enset-based system, found at elevations of 2300\u0026ndash;2550 meters, involves Ensete ventricosum, a crucial crop for food security and livelihoods in Ethiopia (Borrell et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The Coffee-Enset-tree-based system, at 1650\u0026ndash;1990 meters, includes coffee and enset intercropped with annual crops\u0026sup3;. The Coffee-Fruit-tree-based system is located in the lowlands at 1500\u0026ndash;1750 meters (Mebrate \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). They play a very important role in, for example, in conservation indigenous woody species diversity (Negash \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2013\u003c/span\u003e), climate regulation, local provisioning of livelihoods, raw materials and genetic resources, and local control of soil erosion (Alambo \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The practice of agroforestry in the Gedeo community is considered exemplary and has been practiced for centuries. The Gedeo community primarily practices a home-garden type of agroforestry, where subsistence crops are grown alongside trees Tadese 2002). This practice is believed to be one of the oldest agricultural systems in the region, dating back to Neolithic times (Mesele and Achalu \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) suggest that the Gedeo community developed these agroforestry practices through the domestication of natural forests and the intensification of agriculture, resulting in the presence of mostly indigenous woody species in the area. But, Gedeo indigenous agroforestry systems have faced non-stop challenges.\u003c/p\u003e \u003cp\u003eSmall-scale farmers in Gedeo, are constantly faced with the difficult decision of balancing numerous social, economic, and environmental objectives as well as the pressure to increase their farming practices sustainably and produce more efficiently (IR3S/UTIAS \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). To navigate this challenge, farmers must make tough decisions, prioritizing goals based on their household situations and considering the trade-offs between short-term gains and long-term objectives. Due to increased land fragmentation and landscape homogenization, agricultural expansion is also indirectly affecting the remaining woody species, with negative effects on the remaining biodiversity and the functioning of the ecosystem of the Gedeo agroforestry systems (Erenso, F., \u0026amp; Andemo \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e) Therefore One possible strategy to supply and sustain agricultural productivity while preserving biodiversity and ecosystem service sustainability, is safeguarding and perpetuating of management schemes in agricultural landscapes in agro-ecosystems had to be designed (Getahun et al. 2017).\u003c/p\u003e \u003cp\u003eThe task at hand is to enhance agroforestry output to meet the growing needs of humans for food and non-food goods, like wood, rubber, and fibers, while still staying within the limits of what agroforestry systems can sustainably support. This requires a paradigm shift from maximizing production at the expense of nature, to farming with biodiversity; where nature drives agriculture rather than suffering from it. Small farmers in Gedeo, Ethiopia must juggle multiple social, economic, and environmental goals while also trying to improve their farming methods and increase efficiency. Thus, the negative impacts on local and regional biodiversity, as well as on the overall environmental quality, continue when management on existing agricultural lands becomes more intensified (Bastin et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAgroforestry systems, which integrate trees and shrubs with crops and livestock, have been recognized for their potential to enhance ecosystem services, increase agricultural productivity, and promote environmental sustainability. Understanding the diversity, richness, and composition of plant species within these systems is essential for effective management and conservation efforts. Compared to what's popular regarding the plant parts of Gedeo agroforestry systems and practices, little or no is understood regarding existing ground management practices in meeting their wants and their production area unit notable goals and regarding the challenges farmers face that limit their capability to develop ground tree resources in their agroforestry systems. For instance, the reduction in shade tree diversity and its concomitant decline in leaf-litter diversity also results in structural simplification. Such change can affect soil microbiota (Wang et al. \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) as well as microclimate (Makkonen et al. \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) with interacting effects on nutrient cycling processes.\u003c/p\u003e \u003cp\u003eThus, the study aims to assess and compare the variability in species diversity, richness, and abundance within agroforestry systems across management practices (such as weeding and tillage frequency), socioeconomic factors (like wealth and age), physical factors (such as elevation and proximity to major markets), the increase in abundance of marketable perennial species, and the removal of indigenous canopy trees affect species diversity and richness. This research will enhance our understanding of how management practices impact plant species diversity in agroforestry, aiding in the development of strategies to maintain high biodiversity. Ultimately, the findings could influence policy decisions, guide agroforestry management practices, and support biodiversity conservation in agroforestry landscapes.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eStudy area description\u003c/p\u003e\n\u003cp\u003eThe study was conducted in indigenous agroforestry systems of Gedeo zone, Dilla Zuria District, Southern Ethiopia, lies approximately 396 km south of the Addis Ababa (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The study district, Dilla Zuria, is situated between 6\u0026ordm;15\u0026rsquo;05\u0026rsquo;\u0026rsquo; N- 6\u0026ordm;26\u0026rsquo;35\u0026rsquo;\u0026rsquo;N latitude and 38\u0026ordm; 15\u0026rsquo;55\u0026rsquo;\u0026rsquo;E and 38\u0026ordm; 24\u0026rsquo;02\u0026rsquo;\u0026rsquo;E longitudes (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e), covering an area of 120 km\u003csup\u003e2\u003c/sup\u003e, with different land-use types such as agricultural land and AF accounting for 95%, while grassland, wetland, plantations, and others covered the remaining 5% (Mebrate \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e). The altitudinal range of the district ranges between 1350 m to 2550 m with a slope of 39.4\u0026ndash;51.5% (Yirefu and Wendawek 2016). The district is characterized by three agro-ecological zones (AEZ), namely highland (Dega) constitutes 23%, Midland (Woyna Dega) cover 70%, and Lowland (Qolla) embraced 7% of the total area. The total population of Dilla Zuria was 137,715 (M\u0026thinsp;=\u0026thinsp;71262, F\u0026thinsp;=\u0026thinsp;66453).\u003c/p\u003e\n\u003cp\u003eStudy design\u003c/p\u003e\n\u003cp\u003eSampling method and sample size determination\u003c/p\u003e\n\u003cp\u003eA multi-stage sampling approach was used to gather data on vegetation diversity and management practices. The Dilla Zuria District in Gedeo, Southern Ethiopia, known for its agroforestry systems (Kassa et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Tadesse \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e), was chosen first. The area was divided into three elevation categories: lower (1500-1750m), middle (1650-1990m), and upper (2300-2550m). Kebeles within each elevation were stratified by agroforestry types. Three Kebeles\u0026mdash;Michile Girisa (higher elevation), Michile Sisota (middle elevation), and Chichu (lower elevation)\u0026mdash;were randomly selected. Finally, 286 households were randomly chosen from a master list of 1006 households using proportional allocation (Yamane T \u003cspan class=\"CitationRef\"\u003e1967\u003c/span\u003e) across three agroforestry practices (Table-1).\u0026nbsp;\u003c/p\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eName of Study Sites, agroforestry practices, populations from which sample were drawn and Sample Households of the study area in the Southern Ethiopia, Gedeo\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSite name (Kebeles)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAgroforestry practices\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal Household\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003esample\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eElevation gradients\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMichile-Sitota\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCoffee-Enset-tree based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1650\u0026ndash;1990\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMichile-Girisa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEnset-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2300\u0026ndash;2550\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChichu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFruit-coffee-tree-based\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1500\u0026ndash;1750\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e286\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cp\u003eMethods of data collection\u003c/p\u003e\n\u003cp\u003eSample Layout and plant inventory\u003c/p\u003e\n\u003cp\u003eA vegetation inventory was conducted on 286 randomly selected farms, with one plot per farm. Farms were divided into 10 by 10 plots using ocular estimation, and one plot was randomly chosen (Negash et al., \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). If a household had multiple farms of the same type, only one was selected\u003csup\u003e1\u003c/sup\u003e. Quadrat sizes and sampling methods followed recommended practices (Hafte et al., 2024). Due to the small size of farms and considerations of cost and time, the quadrat size was limited to 100 m\u0026sup2;. Ethiopia\u0026rsquo;s agro-ecology is divided into three zones: Dega (highland), Woina-Dega (mid-highland), and Kolla (lowland) (Bekele, 2007). The Dega Zone includes highlands over 2,300 meters, while the Woina-Dega Zone covers areas between 1,500 and 2,300 meters\u003csup\u003e4\u003c/sup\u003e. Locally, this classification is divided into lower midland (1,500-1,750 meters) and upper midland (1,650-1,900 meters). Thus, the study area was stratified into upper, middle, and lower midland agroforestry farms.\u003c/p\u003e\n\u003cp\u003eAll woody species in the plots, whether single-stemmed (Negash et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e) or multi-stemmed (Snowdon et al. \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e), with a DBH of at least 2.5 cm and a height of at least 1.5 m, were measured for their height and DBH. Their uses and growth habits were identified and recorded to the species level with the assistance of local informants.\u003c/p\u003e\n\u003cp\u003eTo measure the coffee shrubs in each plot, the DBH was measured at a height of 40 cm (Negash et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e). For Enset, the basal diameter of the pseudostem of all Enset found in the quadrates was measured at a height of 10 cm (Tesfay et al. \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). The scientific and vernacular names of plant species, local names, their family, genus, and other relevant information were recorded on the data sheet. For identifying the selectively removed trees number and species in the plots involves a combination of direct observation, counting, identifying species from the stamps left behind, and discussions with farmers.\u003c/p\u003e\n\u003cp\u003eSocio-economic data collection\u003c/p\u003e\n\u003cp\u003eA mixed-methods approach was used to collect socioeconomic data through face-to-face and key informant interviews, and focus group discussions (FGDs). Structured questionnaires were administered to 286 rural households, gathering information on socioeconomic and demographic factors, management practices, species use, wealth status, and agroforestry product utilization. Key informant interviews with community leaders, development agents, and knowledgeable farmers provided insights into community wealth status (Crabtree \u0026amp; Miller, 1992). Snowball sampling broadened the sample size. FGDs, involving six to twelve participants each, were conducted in three agroecological settings, focusing on plant diversity, management practices, and socioeconomic factors influencing agroforestry changes. Researchers also reviewed existing research to understand the historical and future dynamics of Gedeo agroforestry.\u003c/p\u003e\n\u003cp\u003eData analysis\u003c/p\u003e\n\u003cp\u003eFloristic composition and diversity\u003c/p\u003e\n\u003cp\u003eAll recorded data of each agroforestry practices were pooled and the total number of species and individuals were tallied. Using the pooled data, number of individual plants, number of species and their scientific names, Name of families, life form per species (trees, shrubs and herbs), identity (Native/Non-Native) and major uses of each species across agroforestry systems were recorded in the plots using supplementary field guide (Bekele-Tesemma \u003cspan class=\"CitationRef\"\u003e2007\u003c/span\u003e), and help of knowledgably key informants (Appendix-I).\u003c/p\u003e\n\u003cp\u003eThe mean diameter, density, and total basal area, including number of individuals per species, ecologically and economically preferred species by farming households were calculated for each species found in sample plots across three agroforestry systems (Appendix-).\u003c/p\u003e\n\u003cp\u003ePerennial plant species diversity (species richness and Shannon\u0026ndash;Wiener diversity, and Simpson Indices) and abundances of individuals.\u003c/p\u003e\n\u003cp\u003eShannon Diversity Index (H\u003csup\u003e\u0026rsquo;\u003c/sup\u003e)\u003c/p\u003e\n\u003cp\u003eShannon diversity index, Simpson\u0026apos;s index, and evenness were calculated for all sampled plots to evaluate the richness and diversity of shade trees in the two systems. The Shannon index H\u0026rsquo; (Shannon and Weaver \u003cspan class=\"CitationRef\"\u003e1949\u003c/span\u003e) is a measure of the number of species S and their even distribution according to the proportion of species pi and can be calculated as:\u003c/p\u003e\n\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:{\\text{H}}^{{\\prime\\:}}=-\\sum\\:_{\\text{i}}^{\\text{S}}\\text{p}\\text{i}\\text{*}\\left(\\text{L}\\text{N}\\text{p}\\text{i}\\right)$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe Simpson index (D)\u003c/p\u003e\n\u003cp\u003eThe Simpson index D is measure of diversity, which takes into account the number of species S and the relative abundance of each species with values starting from one. Higher values obtained with the Simpson index indicate greater diversity. It is calculated as (Simpson \u003cspan class=\"CitationRef\"\u003e1949\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:\\text{D}=\\:\\:\\frac{1}{{\\sum\\:}_{\\text{i}}^{\\text{S}}{\\text{P}\\text{i}}^{2}}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eThe number of species in each plot, known as species richness, was determined by counting the different species present in the area.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFloristic structure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe floristic structure of all perennial plant species was examined in terms of density, frequency, basal area, important value index, and distribution by diameter class. Microsoft Excel was used to conduct the analysis.\u003c/p\u003e\n\u003cp\u003eBasal area\u003c/p\u003e\n\u003cp\u003eIn order to compare the basal area and density between different DBH classes, we grouped the life form of trees, shrubs and herbs based on the mean DBH classes (0\u0026ndash;15, 16\u0026ndash;30, 31\u0026ndash;45, 46\u0026ndash;60, 61\u0026ndash;70) of stems for each of agroforestry system.\u003c/p\u003e\n\u003cp\u003eWe then calculated the mean of the basal area and density for each of the DBH classes for trees, shrubs and herbs.\u003c/p\u003e\n\u003cdiv id=\"Equc\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e$$\\:\\text{B}\\text{a}\\text{s}\\text{a}\\text{l}\\:\\text{a}\\text{r}\\text{e}\\text{a}\\:\\left(\\text{B}\\text{A}\\right)\\:\\left(\\frac{\\text{m}2}{\\text{h}\\text{a}}\\right)=\\frac{\\text{s}\\text{u}\\text{m}\\:\\text{o}\\text{f}\\:\\text{c}\\text{r}\\text{o}\\text{s}\\text{s}\\:\\text{s}\\text{e}\\text{c}\\text{t}\\text{i}\\text{o}\\text{n}\\text{a}\\text{l}\\:\\text{a}\\text{r}\\text{e}\\text{a}\\:\\text{f}\\text{o}\\text{r}\\:\\text{a}\\text{l}\\text{l}\\:\\text{t}\\text{r}\\text{e}\\text{e}\\text{s}\\:\\text{i}\\text{n}\\:\\text{p}\\text{l}\\text{o}\\text{t}\\text{s}}{\\text{s}\\text{t}\\text{u}\\text{d}\\text{y}\\:\\text{p}\\text{l}\\text{o}\\text{t}\\:\\text{a}\\text{r}\\text{e}\\text{a}\\:\\text{i}\\text{n}\\:\\left(\\text{h}\\text{a}\\right)}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u0026nbsp;\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{C}\\text{r}\\text{o}\\text{s}\\text{s}\\:\\text{s}\\text{e}\\text{c}\\text{t}\\text{i}\\text{o}\\text{n}\\text{a}\\text{l}\\:\\text{a}\\text{r}\\text{e}\\text{a}\\:\\text{o}\\text{f}\\:\\text{a}\\:\\text{t}\\text{r}\\text{e}\\text{e}={\\pi\\:}\\:\\left(\\frac{\\text{D}\\text{B}\\text{H}\\:\\left(\\text{c}\\text{m}\\right)}{200}\\right)2\\)\u003c/span\u003e\u0026nbsp;\u003c/span\u003e= 0.00007854* (DBH cm)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eWhere\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eHa stands for hectare; and\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eDBH for diameter at breast height in cm\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eArea in ha\u0026thinsp;=\u0026thinsp;area of plots in ha\u0026thinsp;=\u0026thinsp;0.01ha (100m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eDensity\u003c/p\u003e\n\u003cp\u003eDensity was determined by adding up all the stems found in each sample plot and then converting this total into a measurement per hectare (Mengistu and Asfaw \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e)\u003c/p\u003e\n\u003cdiv id=\"Equd\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equd\" name=\"EquationSource\"\u003e$$\\:\\text{D}\\text{e}\\text{n}\\text{s}\\text{i}\\text{t}\\text{y}=\\frac{\\text{t}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{a}\\text{l}\\text{l}\\:\\text{t}\\text{r}\\text{e}\\text{e}\\text{s}}{\\text{s}\\text{a}\\text{m}\\text{p}\\text{l}\\text{e}\\:\\text{s}\\text{i}\\text{z}\\text{e}\\:\\text{i}\\text{n}\\:\\text{h}\\text{e}\\text{c}\\text{t}\\text{a}\\text{r}\\text{e}\\:}$$\u003c/div\u003e\n\u003c/div\u003e\n\u003cp\u003eImportant Value Index (IVI)\u003c/p\u003e\n\u003cp\u003eIt was calculated by summing up the relative dominance, relative Frequency and relative abundance of the species (Mueller-Dombois and Ellenberg \u003cspan class=\"CitationRef\"\u003e1974\u003c/span\u003e). It shows the degree of a certain plant species\u0026apos; dominance, occurrence, and abundance in comparison to other nearby specie (Whittaker 1993). It is calculated as follows:\u003c/p\u003e\n\u003cdiv id=\"Eque\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Eque\" name=\"EquationSource\"\u003e$$\\:\\text{R}\\text{D}\\text{o}=\\frac{\\text{B}\\text{a}\\text{s}\\text{a}\\text{l}\\:\\text{a}\\text{r}\\text{e}\\text{a}\\:\\text{o}\\text{f}\\:\\:\\text{s}\\text{p}\\text{e}\\text{c}\\text{i}\\text{e}\\text{s}\\:\\text{A}}{\\text{A}\\text{r}\\text{e}\\text{a}\\:\\text{o}\\text{f}\\:\\text{s}\\text{a}\\text{m}\\text{p}\\text{l}\\text{e}\\text{d}\\:}\\text{*}100\\text{%}$$\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eRF= \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{i}\\text{n}\\text{d}\\text{i}\\text{v}\\text{i}\\text{d}\\text{u}\\text{a}\\text{l}\\text{s}\\:\\text{i}\\text{n}\\:\\text{a}\\text{l}\\text{l}\\:\\text{q}\\text{u}\\text{a}\\text{d}\\text{r}\\text{a}\\text{t}\\text{e}\\text{s}}{\\text{T}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{n}\\text{u}\\text{m}\\text{b}\\text{e}\\text{r}\\:\\text{o}\\text{f}\\:\\text{q}\\text{u}\\text{a}\\text{d}\\text{r}\\text{a}\\text{t}\\text{s}\\:\\text{i}\\text{n}\\:\\text{w}\\text{h}\\text{i}\\text{c}\\text{h}\\:\\text{t}\\text{h}\\text{e}\\:\\text{s}\\text{p}\\text{e}\\text{c}\\text{i}\\text{e}\\text{s}\\:\\text{o}\\text{c}\\text{c}\\text{u}\\text{r}\\text{r}\\text{e}\\text{d}}\\:\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003cp\u003eRA=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\text{a}\\text{b}\\text{u}\\text{n}\\text{d}\\text{a}\\text{n}\\text{c}\\text{e}\\:\\text{f}\\text{o}\\:\\text{o}\\text{n}\\text{e}\\:\\text{s}\\text{p}\\text{e}\\text{c}\\text{i}\\text{e}\\text{s}\\:}{\\text{t}\\text{o}\\text{t}\\text{a}\\text{l}\\:\\text{a}\\text{b}\\text{u}\\text{n}\\text{d}\\text{a}\\text{n}\\text{c}\\text{e}\\:\\text{o}\\text{f}\\:\\text{a}\\text{l}\\text{l}\\:\\text{s}\\text{p}\\text{c}\\text{e}\\text{i}\\text{s}\\:}\\text{x}100\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Equf\" class=\"Equation\"\u003e\u003cdiv class=\"mathdisplay\" id=\"FileID_Equf\" name=\"EquationSource\"\u003e$$\\:\\text{I}\\text{V}\\text{I}=\\text{R}\\text{D}0+\\text{R}\\text{F}+\\text{R}\\text{A}$$\u003c/div\u003e\u003c/div\u003e\u003cp\u003eWhere, IVI is importance value index, RD is relative Dominance, RF is relative frequency and RA is relative abundance of a species\u003c/p\u003e\u003cp\u003eData Analysis of Effects of agroforestry systems, management practices and socio-environmental factors on species Diversity and richness\u003c/p\u003e\u003cp\u003eThe research analyzed of the impacts of AFSs, management practices, abundance of plants (specifically maintained for economic purpose: dividing the number of economically important perennial plants by the total number of perennial plants in each site (Rodrigues et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e), removal of canopy tree species (recorded the number of canopy trees removed per plot across AFSs with visual observation at farm level and interview with household heads), Altitude (m.a.s.l), Age, Land size, Educational status, and Wealth status of farm households on species diversity and abundance of woody and non-woody plant species in the study area. The ratio of economically oriented perennial plants in AFSs by categorizing associated plants into Economic (plants maintained for income and domestic needs by AF farming households) and for ecological purpose (Asigbaase et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e) were analyzed .\u003c/p\u003e\u003cp\u003eStatistical analysis\u003c/p\u003e\u003cp\u003eDescriptive statistics are employed to detail the demographic and socioeconomic characteristics of the household sample, including measures like the average, percentage, standard deviation, and frequency. One-way ANOVA tests were conducted to compare the statistical differences between different types of AFPs, as well as between different life forms (Trees, shrubs, and herbs) for normally distributed data. Prior to each test all data was tested for normality by running and plotting a Shapiro-Wilk normality test with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and a Levene\u0026rsquo;s test with a significance level of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was performed to test for homogeneity of variance. A post hoc LSD test was carried out when significant differences were found among the AFPs. Where the data did not follow a normal distribution, a Kruskal-Wallis one-way ANOVA test was used.\u003c/p\u003e\u003cp\u003eFurther, a separate multiple linear regression model was built to analysis the impacts of agroforestry systems, management practices and social and environments factors (independent variables). This model is appropriate for investigating the impact of multiple independent variables on a single dependent variable (Montgomery et al., \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e). The model described the effects of the types AFSs, management activities carried out in the study area, abundance of economically managed species, and other socioeconomic factors (such as land size, wealth status, educational status, and age) and environmental factors (altitude: Different altitudes offer varying climatic conditions, impacting species composition and growth on species richness, diversity indices (Shannon and Simpson) and abundance of woody and non-woody species.\u003c/p\u003e"},{"header":"3. Results of the study","content":"\u003cp\u003eSpecies composition and vegetation structure\u003c/p\u003e \u003cp\u003eSpecies composition\u003c/p\u003e \u003cp\u003eThe vegetation inventory conducted in Gedeo agroforestry systems identified a total of 78 woody and non-woody species across three different agroforestry systems (AFSs). Among these, there were 60 tree species, 11 shrub species, and 7 herb species (Appendix-1). The most dominant plant family found was Fabaceae, represented by nine species. Other notable families included Rutaceae, Moraceae, Boraginaceae, and Euphorbiaceae, each with five species. The Coffee-Fruit-Tree-based agroforestry system was richest in species from the Fabaceae, Moraceae, and Rutaceae families. In the Coffee-Enset-based system, the dominant families were Euphorbiaceae, Fabaceae, and Bignoniaceae, while the Enset-based system was dominated by Euphorbiaceae, Fabaceae, and Rosaceae families. Additionally, 20 plant families were represented by only one species each, and 10 families had 2\u0026ndash;3 species each (Figure-2). A survey of the Coffee-Enset agroforestry system recorded 1208 individuals from 43 species and 29 families. In the Enset-Tree system, there were 1303 individuals from 40 species across 27 families. The Coffee-Fruit-Tree system had the highest number of individuals, with 2027 belonging to 58 species in 38 families.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn a study of the top 12 economically preferred perennial plant species by agroforestry farmers, Coffee arabica was found to be the most widely planted, making up 39.86% of the total stems, followed by Ensete ventricosum at 32.33%. Other species, such as Persea americana Mill, Mangifera indica L., and Carica papaya L., accounted for 0.90\u0026ndash;2.49%. Less frequently planted species, including Musa \u0026times; paradisiaca L., Rhamnus prenoides L\u0026rsquo;h\u0026eacute;r, and Aningeria altissima (A.Chev.), ranged from 0.22\u0026ndash;0.64% (Figure-3a). This shows a clear preference for economically valuable species like Coffee arabica and Enset. Additionally, Millettia ferruginea was the most commonly chosen species among the other plants, constituting 5.62% of the total, followed by Cordia africana at 2.58% and Erythrina brucei Schweinf at 2.09% (Figure-3b).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVegetation structure\u003c/p\u003e \u003cp\u003eDBH, Height, Density and Basal area\u003c/p\u003e \u003cp\u003eThe overall mean value in DBH, height and density were significantly (P\u0026thinsp;\u0026lt;\u0026thinsp;0.001) different among three AFPs (Table-3). The higher mean DBH were observed in the Enset-tree AF (30.220\u0026thinsp;\u0026plusmn;\u0026thinsp;12.761), while no difference were observed between Coffee-Enset-tree and Enset-tree AF (Table-2), whereas the lowest mean DBH were observed in the Coffee-Fruit-Tree-based (15.840\u0026thinsp;\u0026plusmn;\u0026thinsp;7.900). From the three AFSs\u0026rsquo; the highest mean height were recorded in Enset-tree AF. The overall mean value of species density were significantly different across AFSs (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The highest densities were observed in Coffee-fruit-tree AF and the lowest were in Enset-tree. There were significant variations in mean basal area (m\u003csup\u003e2\u003c/sup\u003e ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) across AFSs, with the highest mean basal area found in the Enset-tree-based (126.648\u0026thinsp;\u0026plusmn;\u0026thinsp;33.579) (Table-2). The species with the lowest basal areas were Stellaria sennii Chiov and Spathodea campanulata (Appendix-2).\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 DBH, height, basal area and density for each agroforestry in Gedeo Ethiopia, followed by SD\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFS\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDBH(cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHeight(m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBasal area(m\u003csup\u003e2\u003c/sup\u003e/ha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDensity(Nha\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEnset-tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.220\u0026thinsp;\u0026plusmn;\u0026thinsp;12.761\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.618\u0026thinsp;\u0026plusmn;\u0026thinsp;1.272\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126.648\u0026thinsp;\u0026plusmn;\u0026thinsp;33.579\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1497.70\u0026thinsp;\u0026plusmn;\u0026thinsp;244.94\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee-Enset-tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16.334\u0026thinsp;\u0026plusmn;\u0026thinsp;11.92\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.234\u0026thinsp;\u0026plusmn;\u0026thinsp;0.914\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e51.016\u0026thinsp;\u0026plusmn;\u0026thinsp;18.518\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1589.47\u0026thinsp;\u0026plusmn;\u0026thinsp;239.76\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoffee-Fruit-tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15.840\u0026thinsp;\u0026plusmn;\u0026thinsp;7.900\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.122\u0026thinsp;\u0026plusmn;\u0026thinsp;0.917\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40.542\u0026thinsp;\u0026plusmn;\u0026thinsp;14.183\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1647.97\u0026thinsp;\u0026plusmn;\u0026thinsp;249.37\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: *, **, *** Significant (F test) at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lt; 0.01, \u0026lt; 0.001, respectively\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe distribution of individuals across diameter classes varied significantly among the different AFSs. The majority of perennial plant individuals in all AFS were found in the 0\u0026ndash;15 cm DBH class (nearly 50%). In the Coffee-fruit-tree-based agroforestry system, there was a more balanced distribution of individuals across diameter classes, with a significant number of individuals in the 0\u0026ndash;15 cm DBH class (Figure-4a). However, in the coffee-Enset-Tree-based AFS, there was a gradual decrease in the number of individuals across diameter classes, with very few individuals in the 61\u0026ndash;70 cm DBH class (Figure-4b). In contrast, in the Enset-Tree-based AFS, there was a notable increase in the number of individuals from the 0\u0026ndash;15 cm to the 16\u0026ndash;30 cm DBH class, before decreasing in the larger diameter classes (Figure-4c).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMean Comparison of species diversity (Shannon and Simpson), richness and abundance across agroforestry practices, Management activities, and socio-physical variables\u003c/p\u003e \u003cp\u003eThe study compared species diversity (Shannon and Simpson indices), richness, and abundance of woody and non-woody plants across various factors (Appendix-3). The Coffee-Fruit-Tree agroforestry system (AFS) had the highest diversity (Shannon index: 1.482), richness (6.618), Simpson index (0.731), and abundance (16.48). The Coffee-Enset-Tree AFS showed intermediate values, while the Enset-Tree AFS had the lowest diversity and abundance. These differences were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Lowland farms had the highest diversity (Shannon index: 1.378), richness (6.164), Simpson index (5.161), and abundance (16.349). Midland farms had moderate values, and highland farms had the lowest diversity and abundance, with significant differences across elevations (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table-4). Plots tilled once a year had the highest diversity (Shannon index: 1.391), richness (6.240), and abundance (16.560). Those hoed twice a year had moderate values, while plots ploughed three times a year had the lowest diversity, richness, and abundance, with all differences being statistically significant. The study found that weeding once or twice a year did not significantly affect plant diversity indices (Shannon, species richness, Simpson) or abundance. However, weeding three times a year reduced these diversity indices, though abundance remained similar. Wealth status significantly influenced plant diversity, with rich households having the highest diversity and slightly higher abundance, followed by medium wealth households, and poor households with the lowest diversity and abundance. Farmland size also impacted diversity: small farmlands had the highest diversity and species richness, medium-sized farmlands had moderate diversity and richness, and larger farmlands had the lowest diversity and species richness but the highest abundance. Wealthier households\u0026rsquo; farms had lower perennial plant species richness compared to those of middle-class and poor households (Tablel-\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMean (\u0026plusmn;\u0026thinsp;SD) trees and other perennial plant Shannon diversity (H\u0026rsquo;), species richness (S\u0026rsquo;), Simpson index (D) and abundance across categories of agroforestry systems, elevation gradients (altitude), Management activities, and socioeconomic variables in the study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eH\u0026rsquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eS\u0026rsquo;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAbundance\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAFSs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoffee-Fruit-Tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.482\u0026thinsp;\u0026plusmn;\u0026thinsp;0.193\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.618\u0026thinsp;\u0026plusmn;\u0026thinsp;1.113\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.731\u0026thinsp;\u0026plusmn;\u0026thinsp;0.074\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.48\u0026thinsp;\u0026plusmn;\u0026thinsp;2.45\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCoffee-Enset- Tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.972\u0026thinsp;\u0026plusmn;\u0026thinsp;0.228\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.276\u0026thinsp;\u0026plusmn;\u0026thinsp;0.810\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.531\u0026thinsp;\u0026plusmn;\u0026thinsp;0.124\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.03\u0026thinsp;\u0026plusmn;\u0026thinsp;2.33\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnset-Tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.788\u0026thinsp;\u0026plusmn;\u0026thinsp;0.160\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.874\u0026thinsp;\u0026plusmn;\u0026thinsp;0.606\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.424\u0026thinsp;\u0026plusmn;\u0026thinsp;0.0.0101\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.44\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLowland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.378\u0026thinsp;\u0026plusmn;\u0026thinsp;0.294\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.164\u0026thinsp;\u0026plusmn;\u0026thinsp;1.435\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.161\u0026thinsp;\u0026plusmn;\u0026thinsp;1.563\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.349\u0026thinsp;\u0026plusmn;\u0026thinsp;2.382\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMidland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.993\u0026thinsp;\u0026plusmn;\u0026thinsp;0.215\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.298\u0026thinsp;\u0026plusmn;\u0026thinsp;0.689\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.686\u0026thinsp;\u0026plusmn;\u0026thinsp;0.129\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.170\u0026thinsp;\u0026plusmn;\u0026thinsp;2.632\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHighland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.788\u0026thinsp;\u0026plusmn;\u0026thinsp;0.160\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.874\u0026thinsp;\u0026plusmn;\u0026thinsp;0.606\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.545\u0026thinsp;\u0026plusmn;\u0026thinsp;0.113\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.966\u0026thinsp;\u0026plusmn;\u0026thinsp;2.442\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTillage frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eonce a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.391\u0026thinsp;\u0026plusmn;\u0026thinsp;0.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.240\u0026thinsp;\u0026plusmn;\u0026thinsp;1.469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.692\u0026thinsp;\u0026plusmn;\u0026thinsp;0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.560\u0026thinsp;\u0026plusmn;\u0026thinsp;2.447\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwice a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.106\u0026thinsp;\u0026plusmn;\u0026thinsp;0.371\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.030\u0026thinsp;\u0026plusmn;\u0026thinsp;1.535\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.564\u0026thinsp;\u0026plusmn;\u0026thinsp;0.175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.940\u0026thinsp;\u0026plusmn;\u0026thinsp;2.337\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etrice a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.898\u0026thinsp;\u0026plusmn;\u0026thinsp;0.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.180\u0026thinsp;\u0026plusmn;\u0026thinsp;0.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.495\u0026thinsp;\u0026plusmn;\u0026thinsp;0.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.950\u0026thinsp;\u0026plusmn;\u0026thinsp;2.812\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeeding frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eonce a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.176\u0026thinsp;\u0026plusmn;\u0026thinsp;0.391\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.194\u0026thinsp;\u0026plusmn;\u0026thinsp;1.559\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.606\u0026thinsp;\u0026plusmn;\u0026thinsp;0.171\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.582\u0026thinsp;\u0026plusmn;\u0026thinsp;2.714\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwice a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.187\u0026thinsp;\u0026plusmn;\u0026thinsp;0.369\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.440\u0026thinsp;\u0026plusmn;\u0026thinsp;1.643\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.601\u0026thinsp;\u0026plusmn;\u0026thinsp;0.165\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.209\u0026thinsp;\u0026plusmn;\u0026thinsp;2.474\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003etrice a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.022\u0026thinsp;\u0026plusmn;\u0026thinsp;0.308\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.694\u0026thinsp;\u0026plusmn;\u0026thinsp;1.328\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.533\u0026thinsp;\u0026plusmn;\u0026thinsp;0.160\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.659\u0026thinsp;\u0026plusmn;\u0026thinsp;2.373\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.870\u0026thinsp;\u0026plusmn;\u0026thinsp;0.288\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.127\u0026thinsp;\u0026plusmn;\u0026thinsp;1.123\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.462\u0026thinsp;\u0026plusmn;\u0026thinsp;0.153\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.982\u0026thinsp;\u0026plusmn;\u0026thinsp;2.313\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.173\u0026thinsp;\u0026plusmn;\u0026thinsp;0.364\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.292\u0026thinsp;\u0026plusmn;\u0026thinsp;1.548\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.597\u0026thinsp;\u0026plusmn;\u0026thinsp;0.167\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.105\u0026thinsp;\u0026plusmn;\u0026thinsp;2.626\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.271\u0026thinsp;\u0026plusmn;\u0026thinsp;0.305\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.733\u0026thinsp;\u0026plusmn;\u0026thinsp;1.539\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.650\u0026thinsp;\u0026plusmn;\u0026thinsp;0.124\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.150\u0026thinsp;\u0026plusmn;\u0026thinsp;2.177\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSmall\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.254\u0026thinsp;\u0026plusmn;\u0026thinsp;0.354\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.651\u0026thinsp;\u0026plusmn;\u0026thinsp;1.562\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.635\u0026thinsp;\u0026plusmn;\u0026thinsp;0.157\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.009\u0026thinsp;\u0026plusmn;\u0026thinsp;2.591\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.069\u0026thinsp;\u0026plusmn;\u0026thinsp;0.353\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.890\u0026thinsp;\u0026plusmn;\u0026thinsp;1.470\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.553\u0026thinsp;\u0026plusmn;\u0026thinsp;0.168\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e15.779\u0026thinsp;\u0026plusmn;\u0026thinsp;2.522\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLarge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.035\u0026thinsp;\u0026plusmn;\u0026thinsp;0.358\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.706\u0026thinsp;\u0026plusmn;\u0026thinsp;1.759\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.533\u0026thinsp;\u0026plusmn;\u0026thinsp;0.153\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e16.353\u0026thinsp;\u0026plusmn;\u0026thinsp;1.869\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: *, **, *** Significant at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lt; 0.01, \u0026lt; 0.001, respectively\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe Influences of management practices and socio-physical conditions on species diversity and Richness\u003c/p\u003e \u003cp\u003eA multiple linear regression model was used to assess how different factors influence species diversity and richness. The study found significant interactions between management practices and socio-economic factors affecting species diversity and richness. The model was statistically significant (F\u0026thinsp;=\u0026thinsp;51.975, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and explained 65.4% of the variance in species diversity (R\u0026sup2; = 0.654) (Tabel-5). It showed that a higher ratio of economically oriented woody and non-woody plants negatively impacted species diversity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In contrast, selective tree removal at the plot level positively influenced species diversity (β\u0026thinsp;=\u0026thinsp;0.125, t\u0026thinsp;=\u0026thinsp;3.106, p\u0026thinsp;=\u0026thinsp;0.002), suggesting that plots where trees are removed tend to have higher species diversity. A total of 623 trees were removed for different reasons by agroforestry farm households. The highest mean number of trees removal was observed in Enset-based AF (2.41), followed by Coffee-Enset-tree and the least was observed in Coffee-fruit-tree-based.\u003c/p\u003e \u003cp\u003eAdditionally, the age of the household head had a negative impact on species diversity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), implying that older household heads are associated with lower species diversity. Altitude had a strong negative effect (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with higher altitudes correlating with lower species diversity. The frequency of tillage by hand tools per year negatively affected species diversity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), as did the frequency of weeding by slashing per year (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Farmers in the focus group discussions indicated that frequent tillage with hand tools and weeding by slashing often result in the unintentional cutting of seedlings and saplings. This unselective clearing reduces the number of young plants that could mature into trees, thereby leading to a decline in species diversity in the Gedeo agroforestry systems.\u003c/p\u003e \u003cp\u003eThe distance from home to the main market had a significant negative effect (p\u0026thinsp;=\u0026thinsp;0.004), suggesting that greater distances might reduce species diversity (Table \u0026minus;\u0026thinsp;5).\u003c/p\u003e \u003cp\u003eAn analysis also found that various factors significantly impacted species richness, explaining 57.5% of the variance (F\u0026thinsp;=\u0026thinsp;37.216, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, R\u0026sup2; = 0.575). The study revealed that a higher proportion of economically maintained woody and non-woody perennial plants negatively affected species richness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that for every unit increase in these plants, species richness decreases by 0.148 units. Conversely, tree removal at the plot level positively impacted species richness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with species richness increasing by 0.202 units per unit increase in tree removal. Wealth status and educational attainment were not significant predictors of species richness. The age of the household head had a significant negative effect on species richness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), suggesting that older household heads manage plots with lower species richness. Lastly, land size did not significantly influence species richness (p\u0026thinsp;=\u0026thinsp;0.271) (Tabel-5). The study found that the distance from home to the main market did not significantly impact species richness (p\u0026thinsp;=\u0026thinsp;0.806). Altitude, however, had a significant negative effect on species richness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with species richness decreasing by 0.526 units for every unit increase in altitude. More frequent tillage negatively affected species richness (p\u0026thinsp;=\u0026thinsp;0.013), with species richness decreasing by 0.115 units for each additional tillage frequency per year. Similarly, increased weeding frequency also reduced species richness (p\u0026thinsp;=\u0026thinsp;0.019), with a 0.094 unit decrease in species richness for each additional weeding instance per year (Table-5).\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 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe Multiple linear regression coefficients describing species richness and diversity (H\u0026rsquo;) and Simpson indices with Management and socio-environment factors\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDependent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePredictors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eSpecies diversity (Shannon)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbundance of plants maintained for Economic purpose\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.197\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.654\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e51.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-5.354\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelective Removal of tree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.125\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTillage frequency per year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.104\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeeding Frequency per Year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.133\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWealth Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.025\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.558(NS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.125\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducation status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.847 (NS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand Size in Ha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.282 (NS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from home to main market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.664\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.597\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eSpecies richness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEconomic goal plants ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e37.216\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.632\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRemoval of tree at plot level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.202\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTillage frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWeeding Frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.019**\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWealth Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.003\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.943(NS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge of HHH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.185\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-4.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEducational Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.566\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.572 (NS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLand Size in Ha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.047\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.271(NS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDistance from home to main market\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-0.246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.806 (NS)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAltitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.526\u003c/p\u003e \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=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-7.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: *, **, *** Significant (F test) at P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026lt; 0.01, \u0026lt; 0.001, respectively\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSpecies composition, structure, and determinant of diversity and abundance of woody and non-woody plant species in Gedeo AFSs\u003c/p\u003e \u003cp\u003eEfforts to conserve biodiversity in many countries have traditionally focused on natural forests, but there is growing recognition of the potential of Agroforestry Systems (AFS) to contribute to biodiversity conservation outside of traditional forest areas (Fahad et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The ecosystem services provided by AFSs at both the farm and landscape scale depend on the structural complexity, species richness, and management intensity of the system (Kuyah et al. 2014). Research conducted by Udawatta et al. (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) has shown that AFS can help combat tree cover loss and support biodiversity. While biodiversity in AFS may be lower than in natural forests, it is generally higher than in Monoculture Systems (MCS) (Haggar et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe list of 78 plant species in the study area shows the AFSs are rich in diversity and can be considered to be among the highly diverse agricultural land in Ethiopia (Tadesse \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). The result was comparable with other study results outside the study area. The highest plant species richness was reported in Southern Ethiopia, with a range of 50\u0026ndash;198 plant species (Abebe et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2006\u003c/span\u003e ;Asfaw and Woldu \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Negash and Achalu \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Tamrat \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). This was followed by South-west Ethiopia, Central Ethiopia with a range of 27\u0026ndash;114 plant species (Mengesha \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Tolera et al. \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e2008\u003c/span\u003e) reported 90 woody species in south-eastern Ethiopia, including native tree species such as Juniperus procera Hochst. ex, Podocarpus falcatus, and Hagenia abyssinica. Moreover, research has documented a diverse array of plant species in tropical home gardens, such as 475 species in north-eastern India (Ulman et al. \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) and 419 species in south-western Bangladesh (Kabir and Webb \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). In Uganda, research by Boffa et al. (2005) revealed that farmers preserved around 25% of the woody species found in a protected natural area, with half of these species deliberately planted due to their perceived benefits. This highlights the role of farmers in actively managing and conserving biodiversity in AFS.\u003c/p\u003e \u003cp\u003eIn fact, in Ethiopia, Enset ventricosum is arguably the most important crop for food security and rural livelihoods for more than one fourth of the country\u0026rsquo;s population has been depend on it (Borrell et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Persea americana Mill and Mangifera indica (two major fruit crops mainly maintained for income sources) and Millettia Ferringson and Cordia africana (maintained for mainly for soil fertility, shade and livestock feed) also common species observed. This finding is consistent with previous research by (Ereso \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), who reported almost similar numbers of woody species in agroforestry systems.\u003c/p\u003e \u003cp\u003eRegarding species diversity, the study revealed that the coffee-fruit-tree AF had the highest diversity of species, which aligns with the results of (Negash et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and (Tesfay et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) in the same study area. Coffee-Fruit-tree based AF were more diverse in terms of shade tree species than the coffee and enset based AF farms. The results of the present study are comparable to studies that investigated coffee-Enset agroforestry (Tesfay et al. \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) Enset-based agroforestry (Negash et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) and Coffee-fruit \u0026ndash;tree-based agroforestry (Asfaw et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) found significantly higher flora diversity compared to the other two (Negash et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study highlights a strong emphasis on economically valuable perennial plants, particularly Coffee-fruit-tree-based agroforestry systems, in the research area. Coffee arabica and Enset ventricosum were the dominant species, making up over 72% of the total individual plants in the three agroforestry systems. About 222 (77.62%) sample households have removed old indigenous trees to make room for more marketable perennial crops through practices like selective canopy thinning. This aligns with the research suggesting that Farmers actively cultivated both annual and perennial herbs, as well as various woody species, in order to fulfill their subsistence and economic needs. The result is aligned with several studies. For instance, according (Talemos et al. 2013), 159 plant species were identified in the homegarden, with 70.44 percent of them considered beneficial to the local community. The other finding suggested that shift towards intensifying the cultivation of marketable and consumption crops at the expense of biodiversity, despite the well-documented advantages of biodiverse ecosystems in terms of resilience and resource efficiency (Tilman et al. 2014), climate regulation, agricultural support, and cultural significance (Karp et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe Gedeo agroforestry system has been recognized for its unique characteristics, combining sustainable practices with the conservation of endangered woody species and wildlife (IR3S/UTIAS \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). The traditional agroforestry practiced of the Gedeo people consists of a diverse mix of trees, shrubs, and annuals closely planted to form a continuous vegetation cover, making it a model land-use system in the region (Negash et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eCurrently, most members of the Gedeo community practice a home-garden type of agroforestry, where subsistence crops are grown alongside trees (Legesse \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Despite its potential, the Gedeo agroforestry system is facing challenges due to increasing population pressure, leading to the deterioration of agroforestry practices and the degradation of forest species essential to the system (Bishaw et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Cash crops like khat have encroached on the cultivation of staple crops like enset and coffee in the region, as farmers strive to increase coffee production to meet global demands and accommodate the growing human population (Abebe et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). The main component crops, enset (\u003cem\u003eEnsete ventricosum\u003c/em\u003e an herbaceous monocarpic banana-like plant) and coffee (\u003cem\u003eCoffea arabica\u003c/em\u003e L.), are the pillars of food security (Negash et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The result of study revealed that, economically maintained perennial crops abundance was covered about 80% of their farmland.\u003c/p\u003e \u003cp\u003eTo achieve higher yields, farmers are clearing competing understorey vegetation and selectively cutting trees, often favoring shade-providing species while sacrificing overall vegetation structure and diversity (Aerts et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Furthermore, The Gedeo youth, engaged in cutting trees for firewood, charcoal, and construction materials, and the truck drivers, loading the resulting lumber to sell in nearby towns (Regassa et al. 2017). Besides, the proportion of land use and land cover change of agroforestry area account for 72.8% during 1988, which was reduced to 49.5% and 45.1% during 2002 and 2018, respectively (Erenso and Andemo \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThis all indicating that intensive management practices can decrease woody plant biodiversity and hinder tree regeneration (Hundera et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Tree composition of high-intensity plantations is dominated by species that fulfill landowners\u0026rsquo; economic and dietary needs, which reduce the number of species (Erenso and Andemo \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Competing understorey vegetation is removed and trees are selectively cut, systematically taking out the tallest individuals or unwanted species and selecting for preferred shade providing species. This gradual development of monocropping (high-intensity management crops) plots within the integrated multistory systems of Gedeo AF involves a negative overall trend in terms of crop diversity (Linger \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2014\u003c/span\u003e), can lead to microclimatic changes, such as increased light radiation, temperatures, and wind speeds, which can further\u003c/p\u003e \u003cp\u003eThe impacts of agroforestry types, management practices, Socioeconomic and environmental factors on species diversity and richness\u003c/p\u003e \u003cp\u003eThe findings of this study underscore the intricate relationships between agroforestry management practices, socio-economic factors, and environmental conditions on species diversity within the Gedeo agroforestry systems. The significant negative impact of the economically motivated intensification of marketable perennial plants on species diversity (β = -0.197, t = -5.354, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) aligns with findings from other studies that highlight the trade-offs between economic plant cultivation and biodiversity. For instance, a systematic review by Apriyani et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) found that the increasing demand for high-value tropical commodities often leads to reduced biodiversity in rainforest areas.\u003c/p\u003e \u003cp\u003eThe result also depicted that selective tree removal at the plot level positively influence species diversity (β\u0026thinsp;=\u0026thinsp;0.125, t\u0026thinsp;=\u0026thinsp;3.106, p\u0026thinsp;=\u0026thinsp;0.002). This result suggests that certain management practices, such as selective tree removal, can enhance biodiversity. This finding is supported by research indicating that managed tree removal can create a more heterogeneous environment, promoting species diversity (National Academy of Sciences \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The negative impact of the household head\u0026rsquo;s age on species diversity (β = -0.125, t = -3.344, p\u0026thinsp;=\u0026thinsp;0.001) suggests that older household heads may be less likely to adopt biodiversity-friendly practices. This is consistent with studies that show younger farmers are often more open to innovative and sustainable agricultural practices (FAO \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The strong negative effect of altitude on species diversity (β = -0.597, t = -8.9, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) highlights the challenges of maintaining diversity at higher elevations. Higher altitudes often have harsher environmental conditions, which can limit species diversity (Allen et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A study on rocky intertidal elevation gradients found that environmental stress affects species richness and diversity, although the relationship can be complex and context-dependent (Scrosati et al. 2011).\u003c/p\u003e \u003cp\u003eThe results also indicated that a significant negative effect of the distance from home to the main market on species diversity. This suggests that as the distance increases, species diversity decrease, and the effect is statistically significant at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 level. For instance, a study on plant species found that island area and distance to the mainland significantly affected species richness and composition (Aggemyr et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). By understanding the complex interactions between agricultural practices, socio-economic factors, and environmental conditions, stakeholders can develop strategies that promote sustainable agriculture and biodiversity conservation. This might be because the intrinsic ecological characteristics and specific management practices have a more direct impact on biodiversity (Tscharntke et al. \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings our study also revealed the impact of agroforestry farm management, socioeconomic, and environmental factors on species richness in the Gedeo agroforestry landscapes provide valuable insights into the complex dynamics influencing biodiversity in these systems. The negative impact of the economically derived motives suggests that prioritizing economically valuable plants may reduce diversity. This aligns with findings from other studies indicating that monoculture or low-diversity systems focused on economic gain can lead to a decline in species richness (Perfecto and Vandermeer \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).Older household heads may be more inclined to traditional practices that do not prioritize diversity. Similar trends have been noted in other regions where older farmers are less likely to adopt biodiversity-friendly practices (Kassie et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The strong negative impact of altitude on species richness is consistent with ecological principles that higher altitudes often have harsher conditions, leading to lower biodiversity. This is supported by studies showing that species richness generally decreases with increasing altitude due to factors like temperature and oxygen availability (Rahbek \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1995\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBoth tillage frequency (β = -0.115, t = -2.508, p\u0026thinsp;=\u0026thinsp;0.013) and weeding frequency (β = -0.094, t = -2.368, p\u0026thinsp;=\u0026thinsp;0.019) negatively affected species richness. \u003cb\u003eTillage\u003c/b\u003e is a common agricultural practice used to prepare the soil for planting and manage weeds. However, frequent tillage can have negative impacts on species richness. The result of our study found that for every unit increase in tillage frequency, species richness decreases by 0.115 units. Frequent tillage can damage the root systems of woody plants. For instance, in agroforestry systems where trees like Cordia africana and Acacia tortilis are integrated with crops, tillage can sever roots, reducing the trees' ability to absorb water and nutrients (Gebirehiwot et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This can lead to stunted growth and even tree mortality. Tillage increases soil erosion, which can expose the roots of woody plants and make them more susceptible to environmental stress. In areas like the Gurawa District in Oromia, Ethiopia, soil erosion due to tillage has been shown to negatively impact the distribution and composition of woody plant species (Abdurezak \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Repeated tillage can lead to soil compaction, which reduces the soil's ability to retain water and nutrients. This can be particularly detrimental to young woody plants that have less developed root systems. For example, in parkland agroforestry systems, soil compaction has been observed to hinder the growth of trees like Croton macrostachyus (Fahad et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Tillage disrupts the soil's microbial community, which plays a crucial role in nutrient cycling and soil health. This disruption can negatively affect the growth of woody plants that rely on symbiotic relationships with soil microbes. Studies have shown that reduced microbial activity due to tillage can lead to poorer growth outcomes for trees in agroforestry systems (Kun et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In agroforestry systems, tillage is often used to prepare the soil for annual crops. This can create competition for resources between the crops and woody plants. For example, in systems where maize is grown alongside trees, tillage can favor the crops at the expense of the trees, reducing the overall species richness (Ong et al. 1999). By understanding these impacts, farmers can adopt practices that minimize tillage and promote the health and diversity of woody plant species in agroforestry systems. Techniques like reduced tillage or no-till farming can help maintain soil structure and health, benefiting both crops and trees.\u003c/p\u003e \u003cp\u003eWeeding is an essential practice in traditional agroforestry systems of Gedeo to manage unwanted plants, favoring economically valuable perennial plants for income and household consumption. The study indicates that increased weeding frequency, specifically through slashing with hand tools, is associated with a reduction in species richness, with a decrease of 0.094 units for every unit increase in weeding frequency. Research on the dynamics of weeds in agroforestry systems indicates that the presence of diverse woody species can influence weed cover and biomass (Tzuc-Mart\u0026iacute;nez et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Reduced species richness can lead to a loss of biodiversity, which is crucial for ecosystem stability and resilience (Sharma et al. 2022). Diverse woody species provide various ecosystem services, such as soil fertility, microclimate regulation, and erosion control. A decrease in species richness can diminish these benefits (Maria et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). For smallholder farmers, the loss of diverse woody species can affect the availability of timber, firewood, and other forest products, impacting their livelihoods (Tzuc-Mart\u0026iacute;nez et al., \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Balancing weeding practices to manage unwanted plants while preserving species richness is essential for the sustainability of traditional agroforestry systems. Encouraging practices that maintain or enhance woody species diversity can help achieve both ecological and economic goals (Sharma et al. 2022).\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eSubject: Authorship Contributions StatementThe contributions of the authors are as follows:\u0026bull; Sileshi Lemma conceived the research idea and developed the original draft of the manuscript.\u0026bull; Sileshi Lemma and Dr. Zebene Asfaw Developed The research design and methodology \u0026bull; Sileshi Lemma conducted the formal analysis, prepared the original draft, and carried out the investigation.\u0026bull; All authors participated in reviewing and finalizing the manuscript.\u0026bull; Supervision was provided by Dr. Zebene Asfaw.Sileshi Lemma With Best Regards\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eData is provided within the manuscript or supplementary information files (Appendix-1;Appendix-2 and Appendix-3)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbdurezak, I (2023) Soil and land use management practices and its effects on woody plant species composition and distribution in Gurawa District, East Hararghe Zone, Oromia, Ethiopia (Master\u0026rsquo;s thesis, Haramaya University).\u003c/li\u003e\n\u003cli\u003eAbebe, T., Wiersum, K. F., \u0026amp; Bongers, F (2009) Spatial and Temporal Variation in Crop Diversity in Agroforestry Homegardens of Southern Ethiopia. Agroforestry Systems, 78(3):309\u0026ndash;322. https://doi.org/10.1007/s10457-009-9246-6.\u003c/li\u003e\n\u003cli\u003eAbebe, T., Wiersum, K. F., Bongers, F., \u0026amp; Sterck, F (2006) Diversity and Dynamics in Homegardens of Southern Ethiopia. Advances in Agroforestry. 123\u0026ndash;142. https://doi.org/10.1007/978-1-4020-4948-4_8.\u003c/li\u003e\n\u003cli\u003eAerts, R., Hundera, K., Berecha, G., Gijbels, P., Baeten, M., Van Mechelen, M., \u0026amp; Honnay, O (2011) Semi-Forest Coffee Cultivation and the Conservation of Ethiopian Afromontane Rainforest Fragments. Forest Ecology and Management. 261(6):1034-1041. DOI:10.1016/j.foreco.2010.12.025.\u003c/li\u003e\n\u003cli\u003eAggemyr, E., Auffret, A.G., J\u0026auml;derg\u0026aring;rd, L. et al (2018) Species richness and composition differ in response to landscape and biogeography. Landscape Ecol 33, 2273\u0026ndash;2284. https://doi.org/10.1007/s10980-018-0742-9\u003c/li\u003e\n\u003cli\u003eAlambo, F. I (2020) Agroforestry-Based Livelihoods in the Face of Cultural and Socio-Economic Dynamics in Rural Gedeo, Southern Ethiopia. The Journal of Rural and Community Development. 15(3):113\u0026ndash;132.\u003c/li\u003e\n\u003cli\u003eAllen, T., Prosperi, P., Cogill, B., \u0026amp; Flichman, G (2014) Agricultural biodiversity, social\u0026ndash;ecological systems and sustainable dietsProceedings of the Nutrition Society.\u003c/li\u003e\n\u003cli\u003eApriyani, V., Holle, M. J. M., \u0026amp; Mumbunan, S (2024) A systematic map of evidence on the relationship between agricultural production and biodiversity in tropical rainforest areas Environmental Evidence.\u003c/li\u003e\n\u003cli\u003eAsfaw, Z. Mulata, Y. Assefa, B. Abebe, T. Duna, S. Mulugeta, G. Mebrahten, H. and Kassa, H (2015) Enhancing the Role of Forestry in Building Climate Resilient Green Economy in Ethiopia: Strategy for Scalling up Effective Forest Management Practices in S.\u003c/li\u003e\n\u003cli\u003eAsfaw, Z., and Woldu, Z (1997) Crop Associations of Home Gardens in Welayta and Gurage in Southern Ethiopia.\u0026rdquo; SINET: Ethiopian Journal of Science 20(1). https://doi.org/10.4314/sinet.v20i1.18092.\u003c/li\u003e\n\u003cli\u003eAsigbaase, M., Sjogersten, S., Lomax, B. H., \u0026amp; Dawoe, E (2019) Tree Diversity and Its Ecological Importance Value in Organic and Conventional Cocoa Agroforests in Ghana. PLOS ONE 14(1):0210557. https://doi.org/10.1371/journal.pone.0210557.\u003c/li\u003e\n\u003cli\u003eAtangana, A., Khasa, D., Chang, S., \u0026amp; Degrande, A (2014) Agroforestry and Biodiversity Conservation in Tropical Landscapes, In Tropical Agroforestry (pp. 227-232). Dordrecht: Springer. \u003cbr\u003e https://doi.org/10.1007/978-94-007-7723-1_11\u003c/li\u003e\n\u003cli\u003eAtangana, A., Khasa, D., Chang, S., Degrande A (2014) Tropical Agroforestry, in: Tropical Agroforestry. Netherlands, Dordrecht. Springer.\u003c/li\u003e\n\u003cli\u003eBastin, J. F., Finegold, Y., Garcia, C., Mollicone, D., Rezende, M., Routh, D et al. (2019) The Global Tree Restoration Potential.Science 365(6448):76\u0026ndash;79. https://doi.org/10.1126/science.aax0848.\u003c/li\u003e\n\u003cli\u003eBekele-Tesemma (2007) Useful Trees of Ethiopia: Identification, Propagation and Management in 17 Agroecological Zones. Edited by S. D. and P. M. Bo Tengn\u0026auml;s, Ensermu Kelbesa. Nirobi: Nairobi: RELMA in ICRAF Project.\u003c/li\u003e\n\u003cli\u003eBishaw, Badege, Henry Neufeldt, Jeremias Mowo, Abdu Abdelkadir, Jonathan Muriuki et al. (2013) Farmers\u0026rsquo; Strategies for Adapting to and Mitigating Climate Variability and Change through Agroforestry in Ethiopia and Kenya. Forestry Communications Group, Oregon State University, Corvallis, edited by and A. D. Caryn M. Davis, Bryan Bernart. Oregon.\u003c/li\u003e\n\u003cli\u003eBorrell, J. S., Biswas, M. K., Goodwin, M., Blomme, G., Schwarzacher, T., Heslop-Harrison, Wilkin et.al. (2019) Enset in Ethiopia: A Poorly Characterized but Resilient Starch Staple. Annals of Botany.\u0026rdquo; 123(5):747\u0026ndash;766. https://doi.org/10.1093/aob/mcy214.\u003c/li\u003e\n\u003cli\u003eCerda Clementine Allinne, Christian Gary, Philippe Tixier, et al. (2017) Effects of Shade, Altitude and Management on Multiple Ecosystem Services in Coffee Agroecosystems.\u0026rdquo; Europ. J. Agronomy. 82:308\u0026ndash;319. doi: Doi.org/10.1016/j.eja.2016.09.019.\u003c/li\u003e\n\u003cli\u003eChalite, G. C. (2022). Management and the influence of socioeconomic factors on tree species diversity in traditional agroforestry practices in Demba Gofa District, South Ethiopia. \u003cem\u003eResearch Aspects in Biological Science\u003c/em\u003e, 2, 18-26. https://doi.org/10.9734/bpi/rabs/v2/2765C\u003c/li\u003e\n\u003cli\u003eCrabtree, B.F. \u0026amp; MiUer, W (1992) Doing qualitative research. Newbury Park, CA: Sage.\u003c/li\u003e\n\u003cli\u003eErenso, F., \u0026amp; Andemo, A (2022) Vulnerability of Agrobiodiversity and Agroforestry Settings to Climate Change in Gedeo Zone, Ethiopia. International Journal of Agronomy 15(3):1\u0026ndash;13. https://doi.org/10.1155/2022/8738482.\u003c/li\u003e\n\u003cli\u003eEreso, T (2023) Woody Species Conservation, Management, and Its Socioeconomic Importance of Agroforestry Practice in Ethiopia. 1(8). https://doi.org/https://doi.org/10.1155/2023/2623291.\u003c/li\u003e\n\u003cli\u003eFahad, Shah, Sangram Bhanudas Chavan, Akash Ravindra Chichaghare et al. (2022) Agroforestry Systems for Soil Health Improvement and Maintenance. Sustainability 14(22):1\u0026ndash;25. https://doi.org/10.3390/su142214877.\u003c/li\u003e\n\u003cli\u003eFAO (2019) The State of the World\u0026rsquo;s Biodiversity for Food and Agriculture FAO Commission on Genetic Resources for Food and Agriculture.\u003c/li\u003e\n\u003cli\u003eFranks, P., Hou Jones, X., Fikreyesus, D., Sintayehu, M., Mamuye, S., Danso, E., Meshack, C., McNicol, I. and Soesbergen, A (2017) Reconciling Forest Conservation with Food Production in Sub-Saharan Africa: Case Studies from Ethiopia, Ghana and Tanzania. London. IIED.\u003c/li\u003e\n\u003cli\u003eGebirehiwot, H. T., Kedanu, A. A., \u0026amp; Adugna, M. T 2021) The role of woody plant functional traits for sustainable soil management in the agroforestry system of Ethiopia. In L. Hufnagel (Ed.), Biodiversity of Ecosystems. IntechOpen. https://doi.org/10.5772/intechopen.99296\u003c/li\u003e\n\u003cli\u003eGetahun Haile, Mulugeta Lemenih, Feyera Senbeta, Fisseha Itanna (2017) Plant Diversity and Determinant Factors across Smallholder Agricultural Management Units in Central Ethiopia.\u0026rdquo; Agroforest Syst. 91(4):677\u0026ndash;695. doi: Doi: 10.1007/s10457-016-0038-5.\u003c/li\u003e\n\u003cli\u003eHaggar, J., Pons, D., Saenz, L., Vides, M., 2019 (2019) Contribution of Agroforestry Systems to Sustaining Biodiversity in Fragmented Forest Landscapes.\u0026rdquo; Agric. Ecosyst. Environ. 283. doi: https://doi.org/10.1016/j.agee.2019.06.006.\u003c/li\u003e\n\u003cli\u003eHundera K., Aerts R., Fontaine A., Van Mechelen M., Gijbels P., Honnay O., Muys B (2013 )Effects of Coffee Management Intensity on Composition, Structure, and Regeneration Status of Ethiopian Moist Evergreen Afromontane Forests.\u0026rdquo; Environ. Manag. 51(3):801\u0026ndash;809. Doi: 10.1007/s00267-012-9976-5.\u003c/li\u003e\n\u003cli\u003eICRAF (1997) International Centre for Research in Agroforestry (ICRAF) Medium Term Plan 1998-2000.\u003c/li\u003e\n\u003cli\u003eIR3S/UTIAS, UNU-IAS \u0026amp;. 2016. Socio-Ecological Production Landscapes and Seascapes (SEPLS) in Africa. United Nations University Institute for the Advanced Study of Sustainability, Tokyo.\u003c/li\u003e\n\u003cli\u003eJezeer R. Rosalien E, Santos Maria J, Boot Ren\u0026eacute; G A, Junginger Martin, Verweij Pita A (2018) Effects of Shade and Input Management on Economic Performance of Small-Scale Peruvian Coffee Systems. Agricultural Systems.\u0026rdquo; Agricultural Systems 162:179\u0026ndash;190. doi: Doi.org/10.1016/j.agsy.2018.01.014.\u003c/li\u003e\n\u003cli\u003eKabir, M. E., \u0026amp; Webb, E. L (2007) Can Homegardens Conserve Biodiversity in Bangladesh? Biotropica 40(1):95\u0026ndash;103. https://doi.org/10.1111/j.1744-7429.2007.00346.x.\u003c/li\u003e\n\u003cli\u003eKarp, D. S., Mendenhall, C. D., Sand\u0026iacute;, R. F., Chaumont, N., Ehrlich, P. R., Hadly, E. A., \u0026amp; Daily, G. C (2013) Forest Bolsters Bird Abundance, Pest Control and Coffee Yield. Ecology Letters 16(11):1339\u0026ndash;1347. doi: https://doi.org/10.1111/ele.12173.\u003c/li\u003e\n\u003cli\u003eKassa, Gezahegn, Tesfaye Abebe, and Zeleke Ewnetu (2015) Diversity, Density and Management of Trees in Different Agro-Forestry Practices of Yem Special District, Southern Ethiopia. SINET: Ethiop. J. Sci 38(1):1\u0026ndash;16.\u003c/li\u003e\n\u003cli\u003eKassie, M., Jaleta, M., Shiferaw, B., Mmbando, F., \u0026amp; Mekuria, M 2013 Adoption of interrelated sustainable agricultural practices in smallholder systems: Evidence from rural Tanzania. Technological Forecasting and Social Change, 80(3), 525-540. https://doi.org/10.1016/j.techfore.2012.08.007\u003c/li\u003e\n\u003cli\u003eKennedy C.M., Daniela A. Miteva, Leandro Baumgarten, Peter L. Hawthorne, Kei Sochi, Stephen Polasky, James R. Oakleaf, Elizabeth M. Uhlhorn, and Joseph Kiesecker (2016) Bigger Is Better: Improved Nature Conservation and Economic Returns from Landscape-Level Mitigation. Science Advance. 2(7):107-116. doi: Doi: 10.1126/sciadv.1501021.\u003c/li\u003e\n\u003cli\u003eKun, \u0026Aacute;., Simon, B., Zalai, M., Kolozsv\u0026aacute;ri, I., Boz\u0026aacute;n, C., Jancs\u0026oacute;, M., K\u0026ouml;r\u0026ouml;sparti, J. T., Kov\u0026aacute;cs, G. P., Gyuricza, C., \u0026amp; Bakti, B (2023) Effect of mulching on soil quality in an agroforestry system irrigated with reused water. Agronomy, 13(6), 1622. https://doi.org/10.3390/agronomy13061622\u003c/li\u003e\n\u003cli\u003eKuyah, S., \u0026Ouml;born, I., Jonsson, M., Dahlin, A. S., Barrios, E., Muthuri, C., Sinclair (2016) Trees in Agricultural Landscapes Enhance Provision of Ecosystem Services in Sub-Saharan Africa. International Journal of Biodiversity Science, Ecosystem Services \u0026amp; Management 1\u0026ndash;19. https://doi.org/10.1080/21513732.2016.1214178.\u003c/li\u003e\n\u003cli\u003eLaurance, W. F., Carolina Useche, D., Rendeiro, J., Kalka, M., Bradshaw, C. J. A., Sloan, S. P., Zamzani, F (2012) Averting Biodiversity Collapse in Tropical Forest Protected Areas.\u0026rdquo; Nature 489(7415). doi: https://doi.org/10.1038/nature11318.\u003c/li\u003e\n\u003cli\u003eLegesse, Abiyot (2013) The Dynamics of Indigenous Knowledge Pertaining to Agroforestry Systems of Gedeo: Implications for Sustainability. PhD Dissertation. University of South Africa.\u003c/li\u003e\n\u003cli\u003eLinger, E (2014) Agro-Ecosystem and Socio-Economic Role of Homegarden Agroforestry in Jabithenan District, North-Western Ethiopia: Implication for Climate Change Adaptation. SpringerPlus 3(1). https://doi.org/10.1186/2193-1801-3-154.\u003c/li\u003e\n\u003cli\u003eMakhubele, L., Araia, M.G. \u0026amp; Chirwa, P.W. Harvesting distance effect on tree species diversity in traditional agroforestry landscape: a case of Vhembe Biosphere Reserve in South Africa. \u003cem\u003eBiodivers Conserv\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 3397\u0026ndash;3421 (2023). https://doi.org/10.1007/s10531-023-02671-2\u003c/li\u003e\n\u003cli\u003eMakkonen, M., Berg, M. P., van Logtestijn, R. S. P., van Hal, J. R., \u0026amp; Aerts, R (2013) Do Physical Plant Litter Traits Explain Non-Additivity in Litter Mixtures? A Test of the Improved Microenvironmental Conditions Theory. Oikos, 122. doi: //doi.org/10.1111/j.1600-0706.2012.20750.x.\u003c/li\u003e\n\u003cli\u003eMaria, D. M., Chagas, K. P. T., da Silva Lins, T. R., de Cademartori, P. H. G., \u0026amp; Vidaurre, G. B (2024) A review on the quality of wood from agroforestry systems. Agroforestry Systems, 98, 715-737.\u003c/li\u003e\n\u003cli\u003eMatthias De Beenhouwer, Lore Geeraert, Jan Mertens, Maarten Van Geel, Raf Aerts, Koen Vanderhaegen, Olivier Honnay (2016) Biodiversity and Carbon Storage Co-Benefits of Coffee Agroforestry across a Gradient of Increasing Management Intensity in the SW E. Agriculture, Ecosystems and Environment 222:193\u0026ndash;199. Doi.org/10.1016/j.agee.2016.02.017.\u003c/li\u003e\n\u003cli\u003eMebrate (2007) Agroforestry Practices in Gedeo Zone, Ethiopia: A Geographical Analysis. Ph.D. Dissertation, Panjab University, Chandigarh, India.\u003c/li\u003e\n\u003cli\u003eMengesha, B (2010) Alternative Technologies for Sustainable Agricultural Production and Agroecosystem Conservation in Arsi Highlands, Southeastern Ethiopia\u0026rsquo;, PhD Thesis. Addis Ababa University, Ethiopia.\u003c/li\u003e\n\u003cli\u003eMengistu, B., and Asfaw, Z. (2016) Woody Species Diversity and Structure of Agroforestry and Adjacent Land Uses in Dallo Mena District, South-East Ethiopia. Natural Resources 07(10):515\u0026ndash;34. https://doi.org/10.4236/nr.2016.710044.\u003c/li\u003e\n\u003cli\u003eMesele N., M., Achalu, N (2008) History of Indigenous Agroforestry in Gedeo, Southern Ethiopia, Based Onlocal Community Interviews: Vegetation Diversity and Structure in the Land Use Systems.\u0026rdquo; Ethiop. J. Nat.Resour. 10(1):31\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eMeylan Louise, Gary Christian, Allinne Cl\u0026eacute;mentine, Ortiz Jorge, Jackson Louise, Rapidel, Bruno (2017) Evaluating the Effect of Shade Trees on Provision of Ecosystem Services in Intensively Managed Coffee. 245:32\u0026ndash;42. Doi:10.1016/j.agee.2017.05.005.\u003c/li\u003e\n\u003cli\u003eMontgomery, D. C., Peck, E. A., \u0026amp; Vining, G. G (2012) Introduction to linear regression analysis. John Wiley \u0026amp; Sons. Nelder, J. A., \u0026amp; Wedderburn, R. W. M. (1972) Generalized Linear Models. Journal of the Royal Statistical Society: Series A (General), 135(3), 370-384.\u003c/li\u003e\n\u003cli\u003eMueller-Dombois, D., and Ellenberg, H (1974) Aims and Methods of Vegetation Ecology. John Wiley \u0026amp; Sons.\u003c/li\u003e\n\u003cli\u003eNational Academy of Sciences (2021) Agriculture\u0026rsquo;s Impacts on Biodiversity, the Environment, and Climate The Challenge of Feeding the World Sustainably: Summary of the US-UK Scientific Forum on Sustainable Agriculture.\u003c/li\u003e\n\u003cli\u003eNegash (2013) The Indigenous Agroforestry Systems of the South-Eastern Rift Valley Escarpment, Ethiopia: Their Biodiversity, Carbon Stocks, and Litterfall. Vol. 44.\u003c/li\u003e\n\u003cli\u003eNegash, M. and Achalu, N (2008) History of Indigenous Agroforestry in Gedeo, Southern Ethiopia, Based on Local Community Interviews: Vegetation Diversity and Structure in the Land Use Systems. Ethiopian Journal of Natural Resources 10(1):31\u0026ndash;52.\u003c/li\u003e\n\u003cli\u003eNegash, M., Yirdaw, E., \u0026amp; Luukkanen, O (2011) Potential of Indigenous Multistrata Agroforests for Maintaining Native Floristic Diversity in the South-Eastern Rift Valley Escarpment, Ethiopia. Agroforestry Systems 85(1):9\u0026ndash;28. doi: https://doi.org/10.1007/s10457-011-9408-1.\u003c/li\u003e\n\u003cli\u003eNigatu, D., Tiki, L., Kewessa, G., \u0026amp; Datiko, D (2017) Traditional coffee management practices and their effects on woody species structure and regeneration in Bale Eco-region, Ethiopia. \u003cem\u003eInternational Journal of Advanced Research\u003c/em\u003e, 5(6), 591-598. \u003cstrong\u003eDOI:\u003c/strong\u003e 10.21474/IJAR01/4454\u003c/li\u003e\n\u003cli\u003eOng, C. K., \u0026amp; Leakey, R. R. B (1999) Why tree-crop interactions in agroforestry appear at odds with tree-grass interactions in tropical savannahs. Agroforestry Systems, 45(1-3), 109-129. https://doi.org/10.1023/A:1006243032538\u003c/li\u003e\n\u003cli\u003ePerfecto, I., \u0026amp; Vandermeer, J (2010) The agroecological matrix as alternative to the land-sparing/agriculture intensification model. Proceedings of the National Academy of Sciences, 107(13), 5786-5791.\u003c/li\u003e\n\u003cli\u003ePickett, S. T. A., \u0026amp; White, P. S (1985) \u003cem\u003eThe Ecology of Natural Disturbance and Patch Dynamics\u003c/em\u003e. Academic Press.\u003c/li\u003e\n\u003cli\u003eRahbek, C (1995) The elevation gradient of species richness: a uniform pattern?. Ecography, 18(2), 200-205. http://dx.doi.org/10.1111/j.1600-0587.1995.tb00341.x\u003c/li\u003e\n\u003cli\u003eRegassa Debelo, A., Legesse, A., Milstein, T., \u0026amp; Orkaydo, O. O (2017) Tree Is Life\u0026rsquo;: The Rising of Dualism and the Declining of Mutualism among the Gedeo of Southern Ethiopia. Frontiers in Communication. 2. https://doi.org/10.3389/fcomm.2017.00007.\u003c/li\u003e\n\u003cli\u003eReshad, M., Asfaw, Z. \u0026amp; Mohammed, M. Perennial plant species diversity and its socio-ecological determinants in selected agroforestry practices at eastern escarpments of Chercher Massive, Ethiopia. \u003cem\u003eAgroforest Syst\u003c/em\u003e \u003cstrong\u003e97\u003c/strong\u003e, 1627\u0026ndash;1643 (2023). https://doi.org/10.1007/s10457-023-00884-6\u003c/li\u003e\n\u003cli\u003eRodrigues, P., Shumi, G., Dorresteijn, I., Schultner, J., Hanspach, J., Hylander, K., Fischer (2018) Coffee Management and the Conservation of Forest Bird Diversity in Southwestern Ethiopia. Biological Conservation 217:131\u0026ndash;139. https://doi.org/10.1016/j.biocon.2017.10.036.\u003c/li\u003e\n\u003cli\u003eRusdi Evizal, Sugiatno, Fembriarti Erry Prasmatiwi, and Indah Nurmayasari (2018) Shade Tree Species Diversity and Coffee Productivity in Sumberjay West Lampung, Indonesia. Biodiversitas. 17(1):234-240. DOI: 10.13057/biodiv/d170134.\u003c/li\u003e\n\u003cli\u003eScrosati, R.A., Knox, A.S., Valdivia, N. et al. (2021) Species richness and diversity across rocky intertidal elevation gradients in Helgoland: testing predictions from an environmental stress model. Helgol Mar Res. 65, 91\u0026ndash;102. https://doi.org/10.1007/s10152-010-0205-4\u003c/li\u003e\n\u003cli\u003eShannon, C. E., \u0026amp; Weaver, W (1949) The Mathematical Theory of Communication. Urbana, IL: The University of Illinois Press.\u003c/li\u003e\n\u003cli\u003eSharma, R., Mina, U., \u0026amp; Kumar, B. M 2022 Homegarden agroforestry systems in achievement of Sustainable Development Goals. Agronomy for Sustainable Development, 42, 44.\u003c/li\u003e\n\u003cli\u003eSimpson, E. H (1949) Measurement of Diversity.\u0026rdquo; Nature 163(4148):688\u0026ndash;688. https://doi.org/10.1038/163688a0.\u003c/li\u003e\n\u003cli\u003eSnowdon P., Raison J., Keith H., Ritson P., Grierson P., Adams M., Montagu K., Bi HQ Burrows, W. \u0026amp;. Eamus D (2002) Protocol for Sampling Tree and Stand Biomass. National Carbon Accounting System, Technical Report No. 31. Canberra: Australian Greenhouse Office.\u003c/li\u003e\n\u003cli\u003eTadesse (2002) Five Thousand Years of Sustainability? A Case Study on Gedeo Land Use (Southern Ethiopia). PhD Dissertation. Wageningen Agricultural University, Wageningen.\u003c/li\u003e\n\u003cli\u003eTadesse, E., Negash, M. \u0026amp; Asfaw, Z. Impacts of traditional agroforestry practices, altitudinal gradients and households\u0026rsquo; wealth status on perennial plants species composition, diversity, and structure in south-central Ethiopia. \u003cem\u003eAgroforest Syst\u003c/em\u003e \u003cstrong\u003e95\u003c/strong\u003e, 1533\u0026ndash;1561 (2021). https://doi.org/10.1007/s10457-021-00659-x\u003c/li\u003e\n\u003cli\u003eTamrat S (2011) Study of Useful Plants in and around Gate Uduma (Traditional Gedeo Homegardens) in Kochere Woreda of Gedeo Zone, SNNPR, Ethiopia: An Ethnobotanical Approach\u0026rsquo;, PhD Thesis, Addis Ababa University, Ethiopia. 144.\u003c/li\u003e\n\u003cli\u003eTesfay, H. M., Oettel, J., Lapin, K., \u0026amp; Negash, M (2024) Plant Diversity and Conservation Role of Three Indigenous Agroforestry Systems of Southeastern Rift-Valley Landscapes, Ethiopia. \u003cem\u003eDiversity\u003c/em\u003e, \u003cem\u003e16\u003c/em\u003e(1), 64. https://doi.org/10.3390/d16010064The Farming Insider. (2024). Biodiversity: Key to Sustainable Farm Success.\u003c/li\u003e\n\u003cli\u003eTolera, M., Asfaw, Z., Lemenih, M., \u0026amp; Karltun, E (2008) Woody Species Diversity in a Changing Landscape in the South-Central Highlands of Ethiopia.\u0026rdquo; Agriculture, Ecosystems \u0026amp; Environment, 128(102):52\u0026ndash;58. https://doi.org/10.1016/j.agee.2008.05.001.\u003c/li\u003e\n\u003cli\u003eTscharntke, T., Clough, Y., Wanger, T. C., Jackson, L., Motzke, I., Perfecto, I. Whitbread, A (2012) Global food security, biodiversity conservation and the future of agricultural intensification. Biological Conservation, 151(1), 53-59. doi:10.1016/j.biocon.2012.01.068\u003c/li\u003e\n\u003cli\u003eTzuc-Mart\u0026iacute;nez, R., Casanova-Lugo, F., Caamal-Maldonado, A., Tun-Garrido, J., Gonz\u0026aacute;lez-Valdivia, N., \u0026amp; Cetzal-Ix, W (2017) Influence of woody species in the dynamics of weeds in agroforestry systems in Yucatan, Mexico. Agrociencia, 51(3), 315-328.\u003c/li\u003e\n\u003cli\u003eUdawatta, R. P., Rankoth, L., \u0026amp; Jose, S (2019) \u0026ldquo;Agroforestry and Biodiversity.\u0026rdquo; Sustainability 11(10):2879. doi: https://doi.org/10.3390/su11102879.\u003c/li\u003e\n\u003cli\u003eUlman, Y., Singh, M., Kumar, A., Sharma, M (2021) Conservation of Plant Diversity in Agroforestry Systems in a Biodiversity Hotspot Region of Northeast India.\u0026rdquo; Agric. Res. https://doi.org/10.1007/s40003-020-00525-9.\u003c/li\u003e\n\u003cli\u003eUwera, M. H., Mugunga, C. P., \u0026amp; Mukangango, M. (2022). Assessing agroforestry species, practices, uses, and tree diversity in two contrasting agro-ecological zones of rwanda. Forestist, December 12, 2022. DOI:10.5152/forestist. 2022.22048.\u003c/li\u003e\n\u003cli\u003eWang, J., Ren, C., Cheng, H., Zou, Y., Bughio, M. A., \u0026amp; Li, Q (2017) Conversion of Rainforest into Agroforestry and Monoculture Plantation in China: Consequences for Soil Phosphorus Forms and Microbial Community.\u0026rdquo; Science of the Total Environment 595:769\u0026ndash;778. https://doi.org/10.1016/j.scitotenv.2017.04.012.\u003c/li\u003e\n\u003cli\u003eWhittaker, Robert J (1993) Holocene Book Reviews: Vegetation Description and Analysis: A Practical Approach M. Kent and P. Coker, London: Belhaven Press. Hardback. ISBN 1-85293-006-3. The Holocene 3(4):379.\u003c/li\u003e\n\u003cli\u003eWorku, M., Lindner, A. \u0026amp; Berger, U. Management Effects on Woody Species Diversity and Vegetation Structure of Coffee-based Agroforestry Systems in Ethiopia. \u003cem\u003eSmall-scale Forestry\u003c/em\u003e \u003cstrong\u003e14\u003c/strong\u003e, 531\u0026ndash;551 (2015). https://doi.org/10.1007/s11842-015-9305-y\u003c/li\u003e\n\u003cli\u003eYamane T. 1967. Statistics: An Introductory Analysis. 2nd Ed. New York: Harper and Row.\u003c/li\u003e\n\u003cli\u003eYirefu Tefera, Wendawek Abebe, and Bogale Teferi (2016) Woody Plants Species Diversity of Home Garden Agroforestry in Three Agroecological Zones of Dilla Zuria District, Gedeo Zone, Southern Ethiopia. (3). International Journal of Fauna and Biological Studies. 3(3):98-106.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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, Management, Diversity, richness, Multiple Linear regression","lastPublishedDoi":"10.21203/rs.3.rs-4893436/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4893436/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the influence of different agroforestry systems, management practices, and socio-physical factors on plant diversity and richness in Gedeo indigenous agroforestry systems in southern Ethiopia. It analyzed 286 sample plots (10m x 10m), collecting data on both woody and non-woody perennial species. Insights into management practices and socio-physical conditions were gathered through surveys, focus groups, and interviews. A total of 78 plant species were identified in the study area. The Coffee-Fruit-tree system showed the highest species richness (10 species per plot) and the highest Shannon (1.482) and Simpson (0.731) diversity indices. In contrast, the Coffee-Enset-tree and Enset-Tree systems had lower species richness (6 species per plot) and fewer stems (20 to 23 per plot). The study found significant differences in species diversity and abundance across elevations, with highland farms having the lowest values (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Plots tilled once a year showed the highest diversity, richness, and abundance, while those ploughed three times a year had the lowest. Weeding once or twice a year did not significantly affect diversity indices, but weeding three times a year reduced them. Wealthier households had lower perennial plant species richness compared to middle-class and poor households. The prevalence of economically focused plants had a detrimental effect on species diversity and richness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas selective tree removal had a positive impact on both. Additionally, the age of the household head and higher altitudes were associated with lower species diversity and richness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Increased frequency of tillage and weeding by slashing also led to reductions in species diversity and richness. The distance from home to the main market negatively influenced species diversity and richness (p\u0026thinsp;=\u0026thinsp;0.004), and altitude had a negative effect on both species richness and diversity.\u003c/p\u003e","manuscriptTitle":"Effects of Management Practices and Socio-physical Factors on Perennial Plant Diversity of Agroforestry Systems of Gedeo landscapes, Southern Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-11 11:40:35","doi":"10.21203/rs.3.rs-4893436/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-11-08T16:30:23+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-08T04:20:54+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"230782645268800090406845164546887476969","date":"2024-10-29T00:18:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"220730991099516144332278289702701109795","date":"2024-10-28T11:37:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-01T20:55:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"158977540344605182573804459231206248423","date":"2024-08-23T18:21:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-08-21T14:51:41+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-21T13:54:32+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-15T10:30:17+00:00","index":"","fulltext":""},{"type":"submitted","content":"Agroforestry Systems","date":"2024-08-11T02:14:40+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":"7556fa7b-ec97-4791-97b0-842dd2de9521","owner":[],"postedDate":"September 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2024-12-30T15:58:08+00:00","versionOfRecord":{"articleIdentity":"rs-4893436","link":"https://doi.org/10.1007/s10457-024-01104-5","journal":{"identity":"agroforestry-systems","isVorOnly":false,"title":"Agroforestry Systems"},"publishedOn":"2024-12-24 15:56:51","publishedOnDateReadable":"December 24th, 2024"},"versionCreatedAt":"2024-09-11 11:40:35","video":"","vorDoi":"10.1007/s10457-024-01104-5","vorDoiUrl":"https://doi.org/10.1007/s10457-024-01104-5","workflowStages":[]},"version":"v1","identity":"rs-4893436","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4893436","identity":"rs-4893436","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}