Defining Agroecosystems and Suitability Analysis in the Tana Beles Sub Basin, Ethiopia

Defining Agroecosystems and Suitability Analysis in the Tana Beles Sub Basin, 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 Defining Agroecosystems and Suitability Analysis in the Tana Beles Sub Basin, Ethiopia Tesfay Mekonnen Weldegerima This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3893422/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study focuses on the imperative role of farming systems research in addressing agricultural challenges amid climate change. The primary aim is to assess agroecosystems and their suitability for farming practices within the Tana Beles Sub Basin (TBSB) in Ethiopia. Climate characterization involves utilizing observed rainfall and temperature data from the National Meteorology Agency (NMA) and gridded data from the WorldClim2 database for agro-climate zone delineation. Soil information is derived from the Harmonized World Soil Database version 1 (HWSD1), while agroecological data is collected from the regional Agriculture and Rural Development Bureau. Various analytical techniques, including agroclimate zoning, soil classification, farming classifications, and agro-ecosystem analysis, are employed. The TBSB is categorized into seven agroecosystems, with Moist Midlands (AES2) dominating the region, covering approximately 50% of the area. Altitudinal variation in AES2 ranges from 1500 m to over 2800 m, and key soils include Euteric Vertisol, Euteric Fulvisol, and Hapelic Luvisol. The topography spans flat plains to hilly mountains with slopes ranging from 0% to over 45%. AES2 supports diverse crops like Teff, Maize, Wheat, Barley, Rice, Chatt, and coffee, with the potential for irrigation schemes along the rivers. Suitability analysis reveals that AES2 is highly suitable for agriculture, AES4 and AES5 are moderately suitable, AES1, AES3, and AES6 are marginally suitable, and AES7 is unsuitable. This comprehensive assessment provides valuable insights into the diverse agroecological conditions within the TBSB, facilitating informed decision-making for sustainable agricultural development in the face of climate change. Soil properties Farming systems Agroecosystem analysis Agro ecosystem suitability and Tana Beles Sub Basin Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Climate change and variability have gained great attention in global discussion in recent times. This attention to climate change and variability is due to its adverse impacts on the economic, social, and political livelihoods of mankind. Though the issues have taken center stage in global development discussions, however when it comes to some developmental issues like poverty, discussions are normally associated with developing countries. Developing countries, particularly countries in Africa are much concerned about climate change due to their vulnerability and/or low adaptive capacity. Climate change and variability impede development and affect agricultural production, food security, and livelihoods in Sub-Saharan Africa due to rain-dependent agricultural production systems (DERESSA et al., 2011 ),(Nhemachena, 2009 ). As a result, the efforts of African countries like Ethiopia, to achieve the Millennium Development Goals may be seen as a mirage if the adverse effects of climate change are not addressed. Because of the complex topographical and geographical features, the climate of Ethiopia exhibits strong spatiotemporal variations and different rainfall regimes(NMSA, 1996 ; Slingo et al., 2005 ; Tsidu, 2012 ). The frequency and intensity of droughts have increased in recent years, severely affecting the livelihoods of millions of people (Simane, 2011 ). Within the active agricultural zones, investment capacity and access to production-enhancing technologies will influence farmers’ ability to maintain yields and conserve soil resources under changing climate conditions. Moreover, changes in agriculture induced either by new policies or by technical innovations, are unlikely to result in absolute improvements for all stakeholders and social actors involved, nor in absolute improvements on all the scales (soil, farm fields, and watershed, regional, global) on which the side effects of agricultural production can be described. The reinvestment efforts in agriculture since 2008 are also providing a green revolution with little or no emphasis on ecological farming methods that improve food production and farmers’ incomes, while also protecting the soil, water, and climate(De Schutter & Vanloqueren, 2011 ). The main aim of this study is to analyze the agroecosystems and assess the agricultural sustainability of the identified agroecosystems in the TBSB. (Simane et al., 2013 ) Remarks that the structure of an agroecosystem is a consequence of its environmental setting (e.g., climate, soil, topography, various organisms in the area), agricultural technologies and practices, and farmers’ social setting (e.g., human values, institutions, and skills). Mapping of an agroecosystem is important to separate areas with similar sets of potentials and constraints for development. Assessments of agricultural suitability in each agroecosystem can also help farmers and policymakers in selecting appropriate cropping and development planning. 2. Climatology and Agricultural practices in the TBSB As in many other parts of Ethiopia, the climate of the TBSB is highly correlated with the seasonal propagation of the Intertropical Convergence Zone (ITCZ). The climate is generally characterized as tropical in the lowlands and temperate at higher elevations. Based on the 1979–2015 averages, the mean annual rainfall and temperature for the TBSB are 1412.16 mm and 19.34 o C respectively Fig. 1 . The annual rainfall shows no clear trend while noticeable annual variability is observed. The area receives minimum annual rainfall in 1982 and maximum annual rainfall in 2014. Similarly, the average annual temperature shows variability from year to year and an increasing trend that intensified from 1998–2015. The Temperature in the study area reached its peak in 2015. Reports show the year 2015 is globally recorded as the warmest year ever in history. Owing to the complex topography, the regional rainfall and temperature distribution are not uniform in place and time. The evaporation rate of the TBSB is low in spring due to soil water constraints and high in summer when the rainy season starts, the evaporation rate increases, reaching its peak in October just after the rainy season when soils are all still wet, and solar radiation is stronger than in August and September due to reduced cloud cover (Watch, 2006 ). Generally, the topography of the Blue Nile (where the study area is located) is composed of highlands, hills, valleys, and occasional rock peaks (Tesemma et al., 2010 ). This Complex topography makes for strong local contrasts in precipitation and temperature, and soils are deeply weathered and erodible over most of the highland areas. Previous studies also show that rains around and/or in the current study region are characteristically intense and erosive (Nyssen et al., 2005 ; Worku, 2015 ). Soils in most of the Tana basin (Upper TBSB) are derived from the weathered basalt profiles and are highly variable. In low-lying areas particularly north and east of Lake Tana (the biggest freshwater natural reservoir in Ethiopia), and along parts of the Gilgel Abbay, soils have been developed on alluvial sediments. In the Beles catchment (lower TBSB) soils have largely been derived from basalts and the basement rocks (Watch, 2006 ). Owing to the sloppy mountains and intensive rains particularly during the rainy season it can be noted that erosive rains may affect the region. The agricultural practices of the TBSB are diverse; however, they can be classified generally as crop production and livestock raising. Most of the rural inhabitants in the area are farmers employed in rain-fed substance agriculture cultivating crops such as cereals (rice, teff, millet, maize, wheat, and barley), pulses, oil seed, and vegetables. However, crop production in the TBSB is under threat from climate-related and other environmental effects. These include effects of increased temperatures, changes in soil water balance, changes in the length of growing period (LGP), increased soil erosion and land degradation due to increased rainfall intensities, increased incidence of floods in flood-prone areas, and increased incidence and expansion of crop pests, diseases, and weeds mainly associated with increasing temperatures(Bewket et al., 2015 ). Traditional tillage practices particularly on steep slopes, combined with overgrazing and deforestation are also contribute to soil erosion and soil fertility reduction leading to a gradual decline of production in the region. These pressures combined with the fast-growing population, hence, improper utilization of the natural resource base (land, water, and biodiversity) show that the TBSB is under threat from multi-directional sources. Climate change may be one of the major sources of contributing challenges to humans and their livelihoods because the livelihoods of farming communities face severe constraints related to intensive cultivation, overgrazing and deforestation, soil erosion and soil fertility decline, water scarcity, livestock feed, and fuel wood demand (Simane et al., 2013 ). Climate change will also have a profound impact on the availability and variability of fresh water as the frequency of climatic extremes such as heat waves, drought, and changes in rainfall patterns increases in response to global warming (Field et al., 2012 ). This uncertainty of the availability of water resources will affect agricultural production, challenge socio-economic systems, and threaten environmental sustainability. Furthermore, in the high-water reservoir in the region, the communities in the TBSB are held in diverse agricultural activities cereal crop production being dominant. However, low productivity remains the major constraint of cereals cultivation, where yields are less than one ton per hectare (Pender & Gebremedhin, 2006 ). Moreover, even where agricultural production is increased, this success may be short-lived if attention is not quickly diverted to side effects that threaten other equally important development goals (G. R. Conway, 1985 ). Hence, the boundary conditions for sustainability in the TBSB are closely linked to the climate as the productivity of agriculture is sensitive to the timing as well as the amount of rainfall and temperature. 3. Methodology 3.1. The Study Area The TBSB is located in the Abbay (Blue Nile) river basin of the northern highlands of Ethiopia. The geographic location of the TBSB extends from 10.2 o to 12.8 o longitude and 35.0 o to 38.2 o latitude. A detailed description of the present study area can be obtained from (Weldegerima et al., 2023 ). 3.2. Data sources The study uses both primary and secondary datasets. The observational climate data of rainfall and temperature used to characterize the climate of six meteorological stations in the study area for the period of 37 years were obtained from the National Meteorology Agency (NMA) of Ethiopia. According to the NMA, in Ethiopia, there are three seasons with four months each, based on climatological means of rainfall and temperature. These seasons are locally known as Bega (October, November, December, and January), Belg (February, March, April, and May), and Kiremt (June, July, August, and September) (Degefu, 1987 ; Gissila et al., 2004 ). Seasonal and annual climate characterization in this study is based on their classifications. The descriptive statistics in Table 1 , of the monthly and seasonal data reveal the mean values are higher through the months of June-September (Kiremt: main rainy season) and lower during December-February (Bega: the dry) seasons. On the other hand, based on the standard deviation (SD) and coefficients of variance (CV) values, rainfall in almost all stations is highly variable during the months of the dry season when compared to the months of the rainy season. Moreover, rainfall in Pawe shows a higher variability among the other stations, with a coefficient of variance of 3.9 and 3.0 during December and January respectively. Table 1 Statistical summary of the monthly and seasonal rainfall data, for the selected stations in the TBSB, during 1979–2015 Stations Statstics Bega rainfall(mm) Belg rainfall(mm) Kiremt rainfall(mm) Oct. Nov. Dec. Jan. Feb. Mar. Apr. May. Jun. Jul. Aug. Sep. Bahir Dar Mean 89 11.6 3.1 1.8 2.1 10.8 23.7 72.5 176.8 414.6 374.4 189.3 SD 51.8 13.3 6.3 3.4 5.7 20.3 26.4 564 71.2 107.0 98.8 47.6 CV 0.6 1.2 2.0 1.9 2.7 1.9 1.1 0.8 0.4 0.3 0.3 0.3 Bullen Mean 118.3 12.2 2.3 1.1 2.4 14.0 30.2 132.6 229.6 284.6 303.2 239.4 SD 76.4 13.5 4.6 1.4 3.6 22.7 29.2 62.4 83.0 91.5 116.2 91.7 CV 0.6 1.1 1.8 1.3 1.5 1.6 1.0 0.5 0.4 0.3 0.4 0.4 Dangla Mean 99.6 29.2 6.6 2.8 1.7 19.7 35.9 134.0 257.5 374.6 386.6 244.9 SD 67.2 32.5 9.8 7.0 2.5 25.6 36.2 77.2 80.5 84.1 96.9 69.6 CV 0.7 1.1 1.5 2.5 1.5 1.3 1.0 0.6 0.3 0.2 0.3 0.3 Debretabor Mean 79.1 30.6 11.5 6.6 5.1 32.3 40.2 100.8 163.3 401.3 415.5 189.9 SD 71.5 30.8 16.2 14.4 12.5 31.1 31.0 62.7 63.0 82.0 98.9 49.3 CV 0.9 1.0 1.4 2.2 2.4 1.0 0.8 0.6 0.4 0.2 0.2 0.3 Gondar Mean 68.2 20.3 7.0 3.0 3.3 15.9 33.4 79.2 152.1 281.2 278.6 106.3 SD 48.2 17.1 9.8 5.7 6.7 25.6 27.1 47.0 55 54.8 50.0 33.3 CV 0.7 0.8 1.4 1.8 2.1 1.3 0.8 0.6 0.4 0.2 0.2 0.3 Pawe Mean 116.9 12.5 14.5 1.9 1.0 32.3 27.8 126.6 273.1 362.4 386.5 238.7 SD 54.5 14.6 56.9 5.7 3.1 31.1 34.3 72.6 103.9 103.3 112.6 73.3 CV 0.5 1.6 3.9 3.0 3.1 1.0 1.2 0.6 0.4 0.3 0.3 0.3 The other groups of the data set are agro-climatic data sets namely: Altitude (m), mean annual rainfall (mm), and mean annual temperature ( o C). The altitude data was raised from the integrated mapping of the Digital Elevation Model (DEM) obtained from the Shuttle Radar Topography Mission (SRTM), (Cao et al., 2021 ); which was also used in an earlier mapping of different agroecological zones in Ethiopia. Whereas, the rainfall and temperature data used for agro climate zoning were downloaded from WorldClim 2.0; a high-resolution (1-km) interpolated gridded climate surface for global land areas, excluding Antarctica (Fick & Hijmans, 2017 ). The WorldClim 2.0 dataset and agro climate class are described in detail at (Weldegerima et al., 2023 ). Similarly, the soil and terrain parameters were obtained from the FAO database. The FAO Soil and Terrain Database Map of East Africa is a computer system for the storage, display, interpretation, and analysis of the Soil Map of East Africa at scale 1:1.000.000. However, the terrain parameters in this study are calculated from the DEM for the sec of consistency. We download the FAO Classification dominant soil in Ethiopia and further refine it to our study area. The database can also provide basic information which is important for agriculture, climate, and drought monitoring. Finally, the qualitative data used to combine the quantitative effects were obtained from various sources namely, Regional and National Agriculture and Rural Development Bureaus, the Ministry of Water Resources as well as surveys, previous works of literature, and FAO guidelines. 3.3. Data Analysis To classify the Agro ecosystems of the TBSB we combined the three layers (Agro climate Zones, Soil and Terrain, and farming systems). Agricultural activities take place within a complex mess of multi-scalar, multi-dimensional interactions. This implies that in any analysis of a defined farming system, one will always find legitimate and contrasting perspectives concerning the effects of changes in the system. In each distinct agroecosystem, the biological and physical boundaries of the system become more clearly defined and Linkages with other systems become limited. Agroecosystem suitability can therefore be formulated to the distinct agroecosystem. As Agro climate zoning is available in the previous work of the authors (Weldegerima et al., 2023 ), the other layers will be described hereafter. 3.3.1. Soil and Terrain Analysis Soil and terrain analysis were performed to improve current and future land potential productivity; to identify land and water suitability and capability and to assess land degradations, particularly soil erosion. The Harmonized World Soil Database (HWSD version 1.0) was used for this study (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012 ). The HWSD was reconstructed in the context of the need to combine existing regional and national updates of soil information worldwide and incorporate these with the information former FAO-UESCO soil map of the world that was supposed to lack reflecting the actual state of the soil resources. The HWSD is of immediate use in the context of the Climate Change Convention and the Kyoto Protocol for soil carbon measurements and the FAO/IIASA Global Agro-ecological Assessment studies (Fischer et al., 2021 ). In the present study the HWSD textural classes for the topsoil (0–30cm), are simplified into three textural FAO classes as follows; Coarse textured sands, loamy sands, and sandy loams with less than 18% clay and more than 65% sand. Medium textured sandy loams, loams, sandy clay loams, silt loams, silt, silty clay loams, and clay loams with less than 35% clay and less than 65% sand; the sand fraction may be as high as 82% if a minimum of 18% of clay is present. Fine textured clays, silty clays, sandy clays, clay loams, and silty clay loams with more than 35%t clay. The HWSD is available at higher spatial coverage above national resolution the Soil and Terrain Databases (SOTER) have the highest regional reliability (Southern Africa, northeastern Africa, Latin America, and the Caribbean, Central and Eastern Europe). However, we use the arc map 10.2 tools, particularly spatial analysis tools to extract and cover the TBSB. Moreover, further classification considering local information and analysis was performed using the same version of Arc Gis. Several primary and secondary terrain attributes were derived using Terrain Analysis Systems. The definitions and methods used to derive the values and the units of the terrain attributes used in this research are given in the following equations. Assuming that any topographic surface can be described as a continuous function, where z is the elevation and x and y are the Cartesian coordinates: $$z=f\left(x,y\right) \left(1\right)$$ Where the size of y is constant for all grid cells. However, the size of x depends on latitude and is calculated separately for each row of a tile. Elevation which is the simplest measure of topography is defined as the height above sea level and the method to analyze elevation is using the DEM. It is important for the visibility of topographic features and the distribution of temperature, rainfall, or vegetation. Slope and aspect are also the most important primary attributes that can be derived from a DEM and are used widely in geomorphometric analysis and hydrological modeling. Slope is the rate of change of elevation in the direction of the steepest descent. It is the basic element for analyzing and visualizing landform characteristics(Ogunkunle et al., 2006 ; Sachin, 2011 ). $$Slope=100\sqrt{{\left(dz/dx\right)}^{2}+{\left(dz/dy\right)}^{2}} \left(2\right)$$ Aspect The direction of the line of the steepest descent, i.e. the orientation of the slope gradient, starting from north (0 degrees) and going clock-wise (whereby 0° is equal to North and 180° is equal to South), is calculated using the variables from above, as follows $$Slope=arctan\sqrt{\left(dz/dx\right) / \left(dz/dy\right)} \left(3\right)$$ The aspect map is reclassified following the division of 360 degrees into quadrants and sub-dials producing 8 possible orientation classes. It determines measures of insulation, temperature, vegetation, soil characteristics, and moisture. Automatically generated DEMs often have artifacts, that represent local alteration of the land surfaces, arising from different factors such as feature matching techniques, coarse spatial resolution, or reconditioning by anthropic structures, i.e. buildings or bridges. These are often just a few pixels large, but they can cause problems with hydrological modeling. (Wang & Liu, 2006 ) Have provided an accurate description of the nature/behavior of surface depressions. Therefore, an algorithm called “ Fill Sinks ” in ArcGIS has been applied to a DEM to prevent faulty results. 3.3.2. Defining farming systems A farming system is defined as a population of individual farm systems that have broadly similar resource bases, enterprise patterns, household livelihoods, and constraints, and for which similar development strategies and interventions would be appropriate. Defining farming systems is important in agroecosystem analysis; it helps to understand the constraints farmers face and explore possible development pathways. The use of the Farming System Approach (FSA) as an analytical framework became common in the 1970s, and it has contributed to a paradigm change in rural development thinking. In the farming system definition approach, a system is a group of subsystems that interact according to some kind of process (Odum, 1983 ). This Farming System Approach considers both biophysical dimensions (such as soil nutrients and water balances) and socio-economic aspects (such as gender, food security, and profitability) at the level of the farm – where most agricultural production and consumption decisions are taken. The power of the approach lies in its ability to integrate multi-disciplinary analyses of production and its relationship to the key biophysical and socio-economic determinants of a farming system. However, it is critical to place the boundary between sub-systems and their environment accurately. In the present study farming system definition was based on the dominant type of resource base and the dominant livelihood pattern of farm households as presented in (FAO, 2007 ). The FAO classification reflects key distinguishing attributes, notably: (i) water resource availability, e.g. irrigated, rain-fed, moist, dry; (ii) climate, e.g. tropical, temperate, cold; (iii) landscape relief/altitude, e.g. highland, lowland; (iv) farm size, e.g. large scale; (v) production intensity, e.g. intensive, extensive, sparse; (vi) dominant livelihood source, e.g. root crop, maize, tree crop, artisanal fishing, pastoral; (vii) dual crop livelihoods, e.g. cereal-root, rice-wheat (note that crop-livestock integration is denoted by the term mixed); and (viii) location, e.g. forest-based, coastal, urban-based. The current study defines farming systems in the TBSB based on Climate and Dominant livelihood sources (crops). 4. Results and Discussions 4.1. Soil Classes and Characteristics The role of topography in the bio-physical process is very important to characterize the spatial distributions of soils and their properties. This is because it influences endogenic and exogenic soil-forming factors and processes. The spatial variability of landscape features such as topography, soils, and vegetation defines the spatial pattern of hydrological state variables like soil moisture, runoff, evapotranspiration, and groundwater flow (Melesse & Abtew, 2016 ). In the present study, we found it the characterization and investigation of the spatial distribution of soils and their properties, i.e. soil survey, is advancing due to the increasing need for knowledge about soils, triggered by their importance in the environmental well-being and agricultural activities. Hence, the Physical and chemical properties of soils in the TBSB are characterized based on the HWSD, which is available in recognition of the urgent need for improved soil information worldwide, particularly in the context of the Climate Change Convention and the Kyoto Protocol for soil carbon measurements and global agro-ecological assessments. The HWSD attribute database provides information on the soil unit composition for each of the 15773 global soil mapping units (FAO/IIASA/ISRIC/ISS-CAS/JRC, 2012 ). The database shows the composition of each soil mapping unit and standardized soil parameters for top and sub-soils globally and regionally. We therefore down-scaled the global and regional HWSD into our study area based on the morphological properties of these soil databases. The soils of the TBSB were then further classified into twelve homogeneous soil classes (Fig. 2 and Table 2 ). The soil characteristics at various topographies are different and are the result of geological and other natural processes. The general descriptions of the top three dominant soils in the TBSB are following: The dominant group of soils in the TBSB; Hapelic Nitosols : are deep, dark red, brown, or yellow clayey soils having a pronounced shiny, nut-shaped structure. These soils have maximum area coverage of 24.92% which is one-fourth of the total geographical area of the TBSB. These soils are mostly distributed in the North-central and western parts of the TBSB. Hapelic luvisols These are also genetically similar soils (share similar chemical and physical properties) with Cromic Luvisols. These soils cover 14.74% of the total area of the TBSB. These soils can be found around south central, southeastern, and northern parts of the TBSB. Chromic Luvisols are soils with subsurface accumulation of high activity clays and high base saturation. This type of soils covers 11.7% of the total geographical area of the sub basins. They are mostly confined around the central parts of the TBSB near to Bahir Dar town. Previous studies on soil distribution properties in and around the current study area also reveal similar results. For instance, (Gashaw et al., 2018 ) in their study of erosion risk assessments in the Gelleda watershed of the Upper Blue Nile basin suggests that the major soil types of the watershed are Luvic Calcisols (61.7%), Chromic Luvisols (32.3%), Eutric Leptosols (3.3%) and Haplic Luvisols (2.7%). Similarly, (Van Beek, n.d.) found that the major soil types of the Lake Tana basin are Nitisols, Vertisols, Luvisols, Regosols, and Phaeozems with a dominant presence of the Vertisols and Nitisols. While soil distribution is essential for agricultural activities, it is the soil property (Chemical and physical) that is vital for soil characterization and fertility management on small farms. Moreover, understanding soil fertility status is a prerequisite to implementing appropriate soil management practices for sustainable agricultural production and productivity. The physical and chemical characteristics of soils (top soil ~ 30cm) for approximate coordinates in the TBSB are described in Table 2 . Understanding these properties of soils is important to improve the estimation of current and future land potential productivity. According to Table 2 , the soils of the study area could be categorized under moderately well (mostly) and poor drainage class for the 0-0.5% slope. Moderate and well drainage classes facilitate plant growth while poor and excess drainage inhibits plant growth (REES, 1995 ). Soil texture tells the presence of sand, silt, and clay in the soil under consideration. It affects the physical and chemical properties of the soil. The particle size determination showed that the soils of the study area are clay loam texture; except for the Hapelic Luvisols (~ 11.23N & 36.59E). The clay loam distribution varies from 20–65 % and generlly increases in different depths. On the other hand, the sand and silt distribution vary from 11–54% and 22–60%. These textural differentiations might be caused by an alluvial accumulation of clay, predominant pedogenetic formation of clay in the subsoil, destruction of clay in the surface horizon, selective surface erosion of clay, upward movement of coarser particles due to swelling and shrinking, biological activity, and a combination of two or more of these different processes (Sotelo Ruíz et al., 2011 ). Similarly, the bulk density of the topsoil in the study area ranges from 1.21–1.51 kgdm 3- . This value is relatively good for the cultivated land, which could be attributed to compaction created due to cultivation. It is generally desirable to have soil with a low BD (< 1.5 g/cm 3 ) (Gilkes & Hunt, 1992 ), For optimum movement of air and water through the soil. While an increase in soil bulk density by about 21.42% could be observed due to deforestation and subsequent cultivation (Mojiri et al., 2012 ). On the other hand, the Chemical properties of the topsoil of the TBSB were described by soil pH, organic carbon, and salinity. Consequently, the soil pH (H 2 O) of the topsoil (surface soil) ranges from 5.4 to 7.5, indicating that the soils are moderately acidic to slightly alkaline. Acidic soils can restrict microbial activity; reduce the availability of essential nutrients and cause aluminum toxicity in the subsurface which retards root growth, and restricts access to water and nutrients (Horneck et al., 2011 ). Hence, Crops will display varying sensitivities to acidity and alkalinity. The Organic carbon (OC) content of the topsoil in the TBSB ranges from 0.39–1.45 %, which is categorizd under low to moderate organic carbon content. In general Soils that are very poor in organic carbon (< 0.6%), consistently need organic or inorganic fertilizer application to be productive. The organic carbon content values decrease with depth and the low content of soil OC is attributed to the warmer climate, which enhances the rapid rate of mineralization (Esayas, 2005 ). The topsoil salinity of the TBSB reveals a range of 0-0.4 (ECe)(dS/m). This salinity value can be categorized as very low to safe salinity (Lane, 1985 ). However, salinity can occur naturally where drainage is poor, in inland areas that were once inundated by seawater, or in areas with low rainfall and high evaporation. Table 2 Physical and chemical properties of soils in the TBSB Soil type Coordinates Area (%) Drainage class (0-0.5% slope) Soil texture Sand (%) Silt (%) Clay (%) Bulk density (kg/dm 3 ) OC (%) pH (H 2 O) salinity (ECe)(dS/m) Associated soil Lon. Lat. Chromic Luvisols 11.39 37.28 11.27 moderately well sandy clay loam 51 22 27 1.45 0.63 6.4 0 Euteric Vertisol Eutric Cambisols 11.3 36.42 8.58 poor clay loam 20 24 56 1.51 1.07 6.9 0.3 Humic Nitosols Euteric Fluviols 11.25 36.28 10.95 moderately well clay loam 44 33 23 1.33 0.73 7 0.1 Euteric Leptosols 12.18 37.39 10.95 moderately well clay loam 50 30 20 1.35 0.72 6.5 0.1 Chromic cambisol Euteric Regosols 11.30 36.57 0.19 medium loam 47 34 49 1.21 0.97 6.4 0.1 Lethic liptosol Euteric Vertisols 12.23 37.14 7.10 poor clay 21 25 54 1.51 1.07 6.9 0.3 Humic Nitosols Hapelic Acrisols 10.50 36.16 4.08 moderately well sandy clay loam 54 25 21 1.43 0.6 6.5 0 Humic Nitisols Hapelic Alisols 11.80 37.14 3.84 moderately well sandy clay loam 47 27 26 1.51 0.62 7.4 0 Lethic Liptosols Hapelic luvisols 11.23 36.59 14.74 moderately well silt loam 20 60 20 1.4 1.06 5.4 0 Euteric vertisol Hapelic Nitosols 10.50 35.20 24.92 moderately well clay loam 45 24 31 1.35 0.72 6.5 0.1 Euteric leptosol Lithic Liptosols 11.33 37.10 1.64 moderately well clay loam 43 29 28 1.31 0.39 7.5 0.4 Eutheric liptosol Rhodic Nitosols 11.21 36.16 1.73 moderately Well Clay (heavy) 11 24 65 1.21 1.45 5.4 0 Humic Nitosol 4.2. Terrain Characteristics In the present study, some of the most common terrain components (Altitude, slope, and aspect) associated with characteristics of landforms and related parent material are described. The entire study area was divided into five altitudinal classes which range from about 500 to 4000 masl (Fig. 3 a). The mean altitude calculated for the whole study area was about 1597 m and the standard deviation was 554. It appears that most of the area (36.5%) is located between 1716 to 2296 m above mean sea level (Fig. 3 b). A previous study by (Lemann et al., 2017 ) in the Upper Blue Nile Basin Basin (Where the TBSB is found) which covers a large part of the Ethiopian Highlands (175,000 km 2 ), was also discussed to have a topographic variation from an altitude of less than 500 meters above sea level at the Sudanese border to more than 4200 meters above sea level in the center and the eastern escarpment of the Ethiopian Highlands. Generally, the variance of altitude was increasing with higher altitudinal zones signifying an increase in the surface roughness in the mountainous region of the Northern tips of the Sub Basins. The other terrain components, Slope and Aspect are the most important primary attributes that are also derived from a DEM and are used widely in geomorphometric analysis and hydrological modeling. The slope is one of the most important terrain factors in representing ground surface properties and is commonly applied in geographical research and land use planning. A slope map for the study area is generated from the DEM and is shown in Fig. 4 a. The terrain classification based on slope was able to identify nine classes. It can be seen that the slope varies from 0 to 102%. The calculated mean slope is 4.6 and the standard deviation is 7.3. The slope of the study area is relatively low in the western parts of the basin relative to the eastern parts of the sub-basin, indicating that the area is dominated by flat lands with significant ridges and hills in some parts. However, the variation of slope being high also indicates that the terrain is rugged in topography. Generally, more than 75% of the area has a slope value of less than 15%. During a previous study on the Spatial analysis of the Beles River Basin by (A. H. Tefera, 2017 ) the mean slope calculated for the Beles watershed was 3%. The result of the classified slope reveals that the steepest slopes (greater than 45%) are located in the North, Northeastern, Northwestern, and central parts of the subbasin. An aspect map, for the same part as for the slope, is also derived from the DEM and is shown in Fig. 4 b. Aspect shows the direction of the maximum slope which is essential for the hydrological process simulation. In the present study, eight possible orientations (Namely; North, North East, East, South, South East, South West, West, and North West) with 45 o apart each based on the FAO standards have been considered to describe the distribution of aspect. Most of the degree measures of the slope direction (Aspect) in the current study area are predominantly inclined to the North-South and North-West-South East angles. In addition to the surface patterns, the Slope/Aspect class can give information about the condition of the region important for agricultural and hydrological assessments. For example, since the amount of moisture along the hill slope tends to increase from upslope to downslope, additional moisture contributed from upslope as the catchment area increases to the bottom of the basins. Spatial distribution day landscape forming processes such as Surface runoff and soil erosion processes can also be extracted. According to FAO guidelines (Rossiter, 2009 ), land suitability for agriculture can be classified into five categories: highly suitable, moderately suitable, marginally suitable, currently unsuitable, and permanently unsuitable. Elevations above 3,700 m is classified as ‘high wurch’ (frosty-alpine) and thus unsuitable for agricultural purposes according to the agroecological zoning of Ethiopia (Mengistu, 2003 ). Consequently, the agricultural suitability of different slope classes for the study area is defined in Table 5 . Hence, since no specific crop suitability is assumed, an elevation value lower than 3700 m a.s.l. is taken to be unsuitable for agriculture. Moreover, Population increase and the need for more food production, in regions such as the Blue Nile basin, have resulted in deforestation and the expansion of arable land to steep slope terrains (Melesse & Abtew, 2016 ). As a result, severe soil erosion is observed and attempts to control or reduce erosion are usually not successful. Rugged mountainous volcanic terrain, moderate to gentle terrain in volcanic rocks with some isolated hills, escarpment, and plain constitute the physiographic units of the TBSB. 4.3. Farming system definition of the TBSB In the present study classification of the farming systems, as specified herein, has been based on some of the number of key factors, including: (i) the available natural resource base; (ii) the dominant pattern of farm activities and household livelihoods, including relationship to markets; and, (iii) the intensity of production activities. In general, the farming systems in the TBSB are identified to be classified under two major Groups: the mixed crop-livestock farming (where both livestock and crop production take place within the same locality), 78% and the pastoral/agro-pastoral systems 22%, where up to 50% of household revenue is obtained from livestock and its products. The mixed farming system can be divided into three subsystems (Erkossa et al., 2009 ): the cereal-based, coffee-tree crops complex, and the inset root crops complex. In the present study, owing to the diversified agricultural activity of the study area and Ethiopia in general, we present our analysis of the cereal-based rained farming system. This farming system covers about 80% of the upper part of the Nile Basin. These major systems are then further classified into finer farming systems in the current study area. Accordingly, we classify the TBSB into eleven major farming classes as presented in Fig. 5 . However, the urban and water body-based farming systems are not mapped due to the lack of consistent urban-based agricultural practices and full information in the present study. The characteristics of the cereals (maize, tef. barley, and sorghum) based farming system are described in Table 3 . In addition, complex crops such as finger millet, cotton, sesame, groundnut, pumpkin, ginger, Chat, etc., also are characterized to some extent. Generally, the Wiena Dega Teff-based farming system covers the largest area in the sub-basins while the Dega Barley-based shifting cultivation covers a small area in the highlands. Many factors influence the variability in production across farming systems. Amongst them, climate variability has a high degree of influence on production in Ethiopian farming systems, particularly in drought-prone areas. Because, the factors that determine the suitability of different crops and pastures, such as rainfall and temperature that influence the length of growing seasons, played a critical role in the classification adopted by (Dixon et al., 2001 ). Hence, during the definition of farming systems, we also define an agro climate zone where the specific crop is growing. This approach could provide a more effective basis for organizing information and identifying knowledge gaps and development opportunities. Though large distributions of Forest Farming System in the Upper Blue Nile basin and Ethiopia, the distribution in the TBSB is negligible. This could be associated with the Farmer's practice of shifting cultivation; clearing a new field from the forest every year. Forest clearance activity is mostly practiced in the western, northwestern, and central parts of the basin following the Abay River (Erkossa et al., 2009 ). It is generally practiced in maize and sorghum-based farming. However other crops like Finger millet, cotton, sesame, groundnut, pumpkin, ginger, etc., also grow to some extent through such activities. In addition to the effects of climate change and climate variability Crop productivity is constrained by insect pests, disease, and weeds as there are weak extension services, while animal diseases impede livestock development (Johansen et al., 2012 ). Generally, the farming system classifications of the present study are consistent with previous studies in and around the current study area. For example, during their study of the farming system framework for investment planning and priority setting in Ethiopia, (Aranguiz & Creemers, 2019 ) presented most parts of the Lake Tana subbasin and the upper part of the Beles subbasin as High land (Weinadega) Teff mixed system and Highland (Weinadega) wheat livestock mixed system. They also classify the lower Beles sub-basin as a Lowland (Kolla) maize mixed system and a Highland (Weinadega) maize mixed system. Similarly, previously a study by (Erkossa et al., 2009 ), on the characterization and productivity assessment of the farming systems in the upper part of the Nile basin (Abay, Tekeze, and Baro Akobo River basins) using different approaches classified most parts of the TBSB as Teff based single cropping, Maize based single cropping and pastoral complex. Both studies reveal that Teff-based farming is dominant similar to the current findings; this could be due to the adaptive capacity of Teff to a varying environment ranging from low land to highlands. Moreover, it can grow in a wide range of environmental conditions (1000–3000masl; although most Teff cultivars are more suitably grown between 1500 and 2500m a.s.l.(H. Tefera et al., 2001 ). Barley is also commonly grown in highlands at elevations between 1800 and 3000 m. The entire TBSB exhibits a wide spectrum of altitude, temperature, rainfall, humidity, and aridity ranges, thus giving rise to the diversity of agro-climatic zones agriculture farming systems, and a range of agricultural products. Farmers in the area use the traditional farming system except in a few investment areas that use modern farming machines. Traditional farming involves frequent plowing by oxen-drawn implements, three to four times for most cereals and in some localities, up to nine times for Teff grown on Vertisols (Erkossa & Ayele, 2003 ). While crop rotation with no definite sequence is common, the use of artificial fertilizers is often limited to Teff, Wheat, and Maize. Moreover, the problems of soil erosion, shortage and unreliability of rainfall in some places, shortage of arable land, and low input use need to be addressed for enhanced and sustained productivity. 4.4. Identified Agroecosystems This section describes about the identified specific agro-ecosystem classes and characteristics in the TBSB. A system is defined here as an assemblage of elements contained within a boundary such that the elements within the boundary have strong functional relationships with each other, but limited, weak, or nonexistent relationships with elements in other assemblages; the combined outcome of the strong functional relationships within the boundary is to produce a distinctive behavior of the assemblage such that it tends to respond to stimuli as a whole, even if the stimulus is only applied to one part. The behavior of agroecosystems as discussed in the introduction part; can be described by four broad system properties: productivity, stability, sustainability, and equitability (G. R. Conway, 1985 )(Simane et al., 2013 ). The author defines each term as; Productivity is the yield or net income per unit of resource: Stability is the degree to which productivity is constant in the face of small disturbances caused by the normal fluctuations of climate and other environmental variables: Sustainability is the ability of a system to maintain productivity in spite of a major disturbance, such as caused by intensive stress or a large perturbation, and equitability expresses how evenly the products of an agro ecosystem are distributed among its human beneficiaries. A relatively recent study by (Simane et al., 2013 ) also used the above methodology to analyze the agroecosystems of the Choke Mountain watersheds, in the Blue Nile Highlands of Ethiopia. their analysis includes; the identification of six major agroecosystems, mapping of the agroecosystems, analysis of the productivity potential of each agroecosystem, identification of agricultural productivity, and recommendation of various adaptation strategies. The present study also follows a similar approach to identify the seven agroecosystems of the TBSB presented in Fig. 6 ; unless it gives attention to the climate-related constraints to the agricultural productivity and other livelihoods relative to other studies that include land degradation, soil acidity, and deforestation etc. This is due to the reason that Ethiopia is often cited as one of the most extreme examples, concerning the famines of the 1980s to warn of the disasters that may result from anthropogenic climate change (D. Conway & Schipper, 2011 ). Moreover, owing to the nature of Ethiopia’s agriculture primarily rain-fed, means that production is sensitive to fluctuations in rainfall. Agroecosystem analysis (AEA) is a methodology for analyzing ecosystems with various approaches and different objectives. In this study, the agroecosystem analysis is used for agricultural livelihood systems and planning and prioritizing research and development activities. Many studies reveal that climate change is likely to have profound effects on both natural and man-made ecosystems. Assessments of ecosystem vulnerability are crucial for identifying adaptation needs and for avoiding the worst possible consequences of climate change, for both human and natural systems (Luedeling et al., 2013 ). While such effects may well be beneficial in certain cases, many ecosystems are assumed to be vulnerable to climate change, in the sense that effects will be predominantly negative. Table 3 summarizes the specific characteristics of the identified agroecosystems in the TBSB. Table 3 Characteristics the identified Agro ecosystem classes in the TBSB Agro ecosystem Area (%) Agro climate class Major Soils Farming systems Major crops AES1: Moist Lowlands 19.3 Wet & Moist Kolla Hapelic Nitosol & Euteric Leptosol Maize based mixed Maize, Sorghum, teff, haricot bean AES2: Moist Mid Lands 50.6 Wet & Moist Weina dega Euter Vertisol, Euteric fulvisol & Hapelic Luvisol Teff based mixed Maize, wheat, Teff AES3: Moist High Lands 1.4 Wet & Moist Dega Chromic Luvisol & Hapelic Luvisol Barley Based shifting cultivation Barley,wheat potato, AES4: Sub Humid Low Lands 9.1 Wet Kolla Hapelic Nitosol & Hapelic Arcisol Sorghum based mixed Sorghum, Maize and Sesame AES5: Sub Humid Mid Lands 17.6 Wet Kolla & Wet Weina Dega Euteric Cambisol & Hapelic acrisols Maize based complex Maize, wheat, Teff AES6: Sub Moist Mid Lands 2.0 Moist Weina Dega & Moist Dega Euteric Fulvisol & Chromic Luvisol Barley based mixed Barley, wheat, Teff AES7: Sub Moist High lands 0.2 Moist Dega Euteric Liptososl Barley based shifting cultivation Dominantly Barley The seven identified AES are described as follows: Low Land Moist (AES1) This agroecosystem includes the lowlands in the western and northwestern parts of the TBSB, encompassing the draining area of the Beles River in the TBSB boundary. The altitude of this agroecosystem ranges from 554m in the westernmost tips to 1200m in the eastern parts of the Beles sub-basin. The topography is relatively characterized as flat plain with slope generally ranging from 0–7%. The dominant soils in this agroecosystem are Hapelic Nitosols, which are less acidic soils and are suitable for almost all crops. However, due to the lower and erratic rainfall associated with the higher temperatures which reach above 27 o C crop production is low. Alternatively, farmers used to grow sheep and goats as a source of income for their livelihoods. However, the dominant crop cultivated here includes Maize, sorghum, haricot bean, and Teff to some extent. Owing to the higher temperature and favorable climatic conditions (Moist) for the reproduction of mosquitos, Malaria is a major health-related constraint in this area. Mid-Land Moist (AES2) AES2 is found in the central parts of the Lake Tana sub basin circling the Lake from all directions. This is the largest component of the agroecosystem in the TBSB, covering around 51% of the total area of the TBSB. The altitudinal variation of this agroecosystem is diversified from 1500 to above 2800 m. The dominant soils in this agroecosystem are Euteric Vertisol, Euteric Fulvisol, and Hapelic Luvisol. Similarly, the topography ranges from flat plains to hilly mountains, sloping from 0 in the flat areas to greater than 45% in the hilly mountains. Owing to the range of agroecologies and optimum rainfall and temperature distributions, a variety of crops such as Teff, Maize, Wheat, Barley, and Rice as well as, cash crops such as Chatt and coffee are adapted to this agroecosystem. Generally, this AES is also known for its potential irrigation scheme associated with the rivers that flow through from and to Lake Tana. High Land Moist (AES3) AES3 is found on the eastern tips of the TBSB dispatched at three highland areas totaling only 1.4% of the TBSB. This agroecosystem is detached by the midland moist agroecosystems (AES 2) and midland sub-humid (AES 6). The altitude of this agroecosystem ranges from 2500 to about 3500 m. Owing to the appearance of an association to the other agroecosystems the topography is generally hilly and mountainous with a slope of above 45%. The dominant soils are Chromic Luvisol & Hapelic Luvisol. Though the climate here is favorable for a variety of crops with rainfall rates relatively high, the shallowness of soils in this AES, combined with the rapid drainage characteristics of the sloppy topography, can result in low production potential. The rainfall in the AES3 ranges from 1500 to 2000 mm and the temperature reaches as low as 6.5 o C. Low temperature, soil erosion, and deforestation leading to water management problems are the major constraints on production in this agroecosystem. The major crops grown in this AES are Barley, wheat, potato, and pulses to a smaller extent. Though AES3 is not appropriate for high-intensity agriculture, it does have a high potential for traditional forestry, including bamboo, potato, and barley production with appropriate mountain agricultural land management. Low Land Sub Humid (AES4) AES4 is located at the interior parts of the Beles sub-basin extending south of the Gilgele Beles River. This lowland is filled with Hapelic Nitosol & and Hapelic Arcisol, soils with moderately well drainage classes. These soils are among the most productive soils of the humid tropics appropriate for a wide variety of crops. The elevation of AES 4 falls in the lowlands class which varies between 550 and 1200 m. The Agro-climate of this AES is Wet Kolla characterized as sub-humid receiving a rainfall varying from 1250 to 1800 mm and temperature varying from 20 to 25 o C. Maize and sorghum as well as wheat-based farming systems dominate in the agro ecosystem. It is also a potential area for pulses and oil crops. Mid Land Sub Humid (AES5) The Midland sub-humid agroecosystem is found in the southern toe of the TBSB, extending from southwest of the Lake Tana sub-basin to the Beles sub-basin. Soils in this AES are predominantly Euteric Cambisol & and Haplic acrisols. The first group of soils is characteristically poor in drainage class, clay loam in texture and neutral in acidity. Whereas the second one is moderately well in drainage class, silt loam in texture and less acidic. The altitude of this AES ranges from 1500 to 2500m. The topography of this AES is highly sloppy, particularly in the sub-basins divide and in the west. Most of the aspect in this AES falls to the North West. The climate of this AES as the name indicates is sub-humid with a rainfall rate of 1000 to 1800 mm and a temperature range from 15 to 22 o C. Poor drainage class of the soils and the highly rugged landform, are major constraints for production of this AES. The main crops produced in this AES include Maize, wheat, and Teff. Mid-Land Sub Moist (AES6) AES is found in the western part of the Lake Tana Sub basin. The topography of this AES ranges from 2200 to 2800m and soils are predominantly Euteric Fulvisol & and Chromic Luvisol. These groups of soils are generally characterized as clay loam in texture, moderately well drainage class, and neutral in acidity. The agro climate of the AES is moist Weina dega with rainfall 900 to 1200 mm and temperature varies from 15 to 20 o C. The major crops grown in this AES include Barley, wheat, Teff, and potato. Highland Sub Moist (AES7) The highland sub-moist agroecosystem is found in the eastern periphery of the TBSB next to AES 6; it is confined in a small area of 0.2%. Euteric Liptosols are the dominant soils and the topography is generally characterized as sloppy. The agro climate of this AES is moist dega with average rainfall of 1000 mm and temperatures as cold as 7 o C. Though the soils are fairly good for various crops the coldest temperature associated with the limited rainfall and hilly topography makes it difficult for a wide range of crops to grow. Cold weather crops (Dominantly Barley) are the major agricultural production in this AES. Most parts of such AES are used as Rangeland (grazing or pasture land). 4.5. AES Suitability for Agriculture AES suitability evaluation is important for planning strategies to increase agricultural productivity and to identify priority areas for potential management and policy interventions. Agricultural land suitability evaluation does not specify any particular evaluation methodology. In this study, the criteria used for AES suitability assessments for agriculture were classified based on various literature, field investigations, and following the FAO guidelines for agricultural land use evaluations. The Food and Agricultural Organization(Kutter et al., 1997 ) for example recommended an approach for land suitability evaluation for crops in terms of suitability ratings ranging from highly suitable to not suitable based on climatic and terrain data and soil properties. Plenty of criteria for land suitability evaluation for agriculture such as soil type, soil depth, soil water content, soil stoniness slope elevation and proximity to towns, roads and water resources are available in the vast literatures. However, since an AES in the present study represents the intersection of; a set of agriculturally relevant climatic factors (the agroecological zone), soils, and physiographic variables relevant to crop production, a prevailing set of cropping practices; we select the soil type, elevation, slope and climate to characterize the suitability of an AES for agriculture in the TBSB (Table 4 ). According to the FAO guidelines land suitability for agriculture is classified into five categories (1) highly suitable, (2) moderately suitable, (3), marginally suitable, (4) currently unsuitable and, (5) permanently unsuitable. This criterion is further customized and reclassified by (Sileshi et al., 2019 ) into four classes designating, 4 = highly suitable; 3 = moderately suitable; 2 = marginally suitable; and 1 = unsuitable. The unsuitable represents both the permanently unsuitable and currently unsuitable categories of the FAO method. The present study also follows the criteria used in (Sileshi et al., 2019 ) with some customizations to evaluate the suitability of AES for agriculture in the TBSB. During the AES classifications, we do not find considerable forest and protected areas hence our evaluation does not consider such areas. Moreover, such land cover may not apply to agricultural practices in crop production. Table 4 Suitability class of the various parameters for agriculture in the TBSB Criteria Suitability score Details 4 3 2 1 Soil type EVertisol, EFulvisol, CLuvisol and ELeptosols ECambisol, HAlisol and HNitosol HAcrisol RNitosol and LLeptosol ERegosl and HLuvisol (Nations, 2007) Elevation (m) 1500–2500 1000–1500 and 2500–3000 500 − 100 and 3000–3500 3500 (Mengistu, 2003 ) Slope class (%) 0–7 7–15 15–25 > 25 (Nations, 2007),(Sileshi et al., 2019 ) Agro Climate Moist and Wet weinadega, Moist and Wet kolla Moist and wet dega moist wurch, wet wurch and wet alpine wurch (Hurni, 1998; Kutter et al., 1997 ) NB: extended names of the soil type can be obtained from Table 2 , eg., EVertisol represents to Euteric Vertisol. The analysis of AES suitability conducted following different criteria of the different layers with their respective priority is summarized in Table 5 . According to these criteria, most of the TBSB is characterized as suitable for agricultural productions. Numerically it was identified that 50.65% of the study area is highly suitable, 26.7% is moderately suitable, 22.7% is marginally suitable and only 0.2 is unsuitable. previous studies in the upper Blue Nile basin also show About 50% of the Tana catchment (North) was classified as ‘highly suitable’, which is a consistent result of the current studies. A recent study by (Gashaw et al., 2018 ) on land capability in Geleda watershed, Upper Blue Nile basin, also found land suitability for cultivation which is higher than the land currently under cultivation. Their research indicates the major account of cultivatable land in their study site. Where in the present study the highlands Particularly AES6 and AES7, are characterized as unsuitable for agricultural practices of various crops. This is associated with the low temperatures in the mountainous areas. Similarly, according to the agroecological zoning of Ethiopia (FAO 2003): Elevation above 3,700 m is classified as ‘high wurch’ (frosty-alpine) and thus unsuitable for agricultural purposes. Table 5 Summary of agricultural suitability map of AES in the TBSB AES Total area (km 2 ) Area (%) Suitability class AES 1 5,636 19.3 Marginally suitable AES 2 14,788 50.6 Highly suitable AES 3 410 1.4 Marginally suitable AES 4 2,651 9.1 Moderately suitable AES 5 5,135 17.6 Moderately suitable AES 6 583 2.0 Marginally suitable AES 7 49 0.2 Unsuitable Generally, the AES suitability of the TBSB is summarized in the tables; the productivity potential of a specific AES can be decreased or enhanced depending on the constraints and inputs. While several constraints such as water logging, soil acidity, deforestation, and climate variability are the factors to reduce the AES suitability for agriculture, various inputs like water conservation techniques, climate information, introducing technologies and quality fertilizers can be used for potential productivity. The main variable in the study, climate change; is a major threat to the suitability of the AESs and the potential productivity of crops, as it can also affect the other variables directly or indirectly by influencing the spatial and temporal patterns of rainfall and temperature. Moreover, the higher variations in suitability among AESs could be associated with the highly variable topography in addition to the aforementioned constraints and imputes. 5. Conclusion and Recommendation Agro-ecosystem analysis could be important for classifying the landscape and for identifying agro-ecosystem-specific opportunities and constraints for climate change adaptation. The present study examines the agroecosystems in the TBSB and their suitability for the agricultural practices of various crops. The analysis was started by characterizing various variables that are believed to be components of an AES in the TBSB. These variables are; the agro climate of the study area, the soil characteristics, the topography (elevation, slope, and aspect), and farming systems. These variables' characteristics were categorized based on various classification and characterization guidelines. The distinct characteristics of these variables were input to the definition of the agroecosystems and characterization of their suitability for agriculture. The analysis reveals that the TBSB is comprised of twelve homogeneous soil classes. The topsoil (~ 30 cm) characteristics generally show moderately well drainage class, moderately acidic to less alkaline, and clay loam in texture properties. The analysis also reveals the topography of the TBSB, which ranges from 500 to above 4000 m in Elevation, from flat Plains (0 ~ 7%) to hilly mountains (> 50%) in slope and North to North West general aspect. The topography is generally characterized as flat in the west (Beles sub-basin) and hilly and ragged in the North and North East (Tana sub-basin). During the analysis of farming systems, nine crop-based farming systems are identified and Teff, Maize, Wheat, and Barley are among the dominant cereal crops cultivated in the TBSB. Moreover, mixed-crop livestock was recognized as the dominant farming system in the TBSB. Finally, seven agroecosystems namely; Moist Lowlands (AES1), Moist Midlands (AES2), Moist Highlands (AES3), Sub Humid Lowlands (AES4), Sub Humid Midlands (AES5), Sub Moist Mid Lands (AES6) and Sub Moist Highlands (AES7) were defined in the TBSB. The agroecosystems AES2, AES1, and AES5 are the dominant agroecosystems in the TBSB covering 50.6%, 19.3%, and 17.6% of the total area; whereas, AES7 is the smallest agroecosystem class identified which covers only 0.2% of the total TBSB. The analysis also includes a suitability assessment of the agro-ecosystems to agriculture particularly crop production of the major crops following FAO guidelines. Accordingly, the Moist Midlands are highly suitable, the Sub Humid Lowlands and Sub Humid Midlands are moderately suitable, the Moist Lowlands, Moist Highlands, and Sub Humid Midlands are marginally suitable and the Sub Moist Highlands are unsuitable. It is noted that the suitability of an AES can be modified by the intervention of the constraints such as adaptations to climate change and adding inputs such as fertilizers. The current study has provided the Agroecosystem classes and their suitability for agriculture under current land use land cover and climatic conditions. Though several types of agroecological practices can be used to improve agricultural production and natural capital in and around agro ecosystems; current classification suggests, Conservation tillage: which reduces the amount of tillage, sometimes to zero, so that soil can be conserved and available moisture used more efficiently; Water harvesting in the dry land areas, which can mean formerly abandoned and degraded lands can be cultivated, and additional crops grown on small patches of irrigated land owing to better rain water retention. However, future land uses, climate, and agricultural production are not known with certainty. Understanding that Intensification of agricultural production, considering the challenges and opportunities of the different agroecosystems, can ensure food security and contribute to climate change adaptations. For example, the application of improved seeds, fertilizer application, irrigation, soil, and water conservation measures can contribute greatly to increasing the economic benefit of agroecosystems. Agro-ecosystem assessments that incorporate high-quality data and multiple variables followed by feasible adaptation strategies are our recommendations to be worked ahead to overcome the climate change impacts and to benefit from opportunities. Declarations Conflicts of Interest: Not applicable Funding: This research received no external funding Author Contribution The author designed and analyzed the results. he also writes and reads the whole contents of the Manuscript. Acknowledgments: I would like to thank institutions, Ethiopian Meteorology, World Clim, and HWSD for the data availability Data Availability Statement: The data presented in this study are available on request from the author. References Aranguiz AA, Creemers J (2019) Quick scan of Ethiopia’s forage sub-sector . 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Field Crops Research 132:18–32 Kutter A, Nachtergaele FO, Verheye WH (1997) The new FAO approach to land use planning and management, and its application in Sierra Leone. ITC J 3(4):278–283 Lane M (1985) Salt damage in container plants. Aust Hortic, 96–100 Lemann T, Roth V, Zeleke G (2017) Impact of precipitation and temperature changes on hydrological responses of small-scale catchments in the Ethiopian Highlands. Hydrol Sci J 62(2):270–282 Luedeling E, Muthuri CW, Kindt R (2013) Ecosystem vulnerability to climate change: a literature review Melesse AM, Abtew W (2016) Landscape dynamics, soils and hydrological processes in varied climates. Springer Mengistu A (2003) Country pasture/forage resource profiles, Ethiopia. FAO Web Site: Http://Www.Fao.Org i>/Ag/Agp/Agpc/Doc/Counprof/Ethiopia/Ethiopia. Htm. Mojiri A, Aziz HA, Ramaji A (2012) Potential decline in soil quality attributes as a result of land use change in a hillslope in Lordegan, Western Iran. Afr J Agric Res 7(4):577–582 Nhemachena C (2009) Agriculture and future climate dynamics in Africa: Impacts and adaptation options. University of Pretoria NMSA (1996) Climatic and agroclimatic resources of Ethiopia. Natl Meteorol Serv Agency of Ethiopia Meteorol Res Rep Ser 1(1):1–137 Nyssen J, Vandenreyken H, Poesen J, Moeyersons J, Deckers J, Haile M, Salles C, Govers G (2005) Rainfall erosivity and variability in the Northern Ethiopian Highlands. J Hydrol 311(1–4):172–187 Odum HT (1983) Systems Ecology; an introduction Ogunkunle AO, Oluwatosin GA, Adeyolanu OD, Idowu OJ (2006) From land capability classification to soil quality: an assessment. Trop Subtropical Agroecosystems 6(2):45–55 Pender J, Gebremedhin B (2006) Land management, crop production, and household income in the highlands of Tigray, Northern Ethiopia: An econometric analysis. Strategies for Sustainable Land Management in the East African Highlands, 107–139 REES DB (1995) A LAND CAPABILITY STUDY OF THE CASSILIS VALLEY, SWIFTS CREEK Rossiter DG (2009) Land evaluation: towards a revised framework; Land and Water Discussion Paper 6, FAO. FAO, Rome (2007), 107 pp., ISSN: 1729 – 0554; Only available in PDF format as i>www.fao.org/nr/lman/docs/lman_070601_en. i>pdf; free. Elsevier. Sachin P (2011) Land capability classification for integrated watershed development by applying remote sensing and GIS techniques. J Agricultural Biol Sci 6(4):46–55 Sileshi M, Kadigi R, Mutabazi K, Sieber S (2019) Determinants for adoption of physical soil and water conservation measures by smallholder farmers in Ethiopia. Int Soil Water Conserv Res 7(4):354–361 Simane B (2011) Building resilience to climate change and green economy in mountain ecosystems of Ethiopia: Integrating research, capacity building and sustainable development activities. Proceedings of the Stakeholders Workshop Simane B, Zaitchik BF, Ozdogan M (2013) Agroecosystem analysis of the Choke Mountain watersheds, Ethiopia. Sustainability 5(2):592–616 Slingo J, Spencer H, Hoskins B, Berrisford P, Black E (2005) The meteorology of the Western Indian Ocean, and the influence of the East African Highlands. Philosophical Trans Royal Soc A: Math Phys Eng Sci 363(1826):25–42 Sotelo Ruíz ED, González Hernández A, Cruz Bello G, Moreno Sánchez F, Cruz Cárdenas G (2011) Los suelos del Estado de México y su actualización a la base referencial mundial del recurso suelo 2006. Revista Mexicana de Ciencias Forestales 2(8):71–84 Tefera AH (2017) Characterization of Beles River Basin of Blue Nile sub-Basin in North-Western Ethiopia using Arc-Hydro tools in Arc-GIS. Int J Water Resour Environ Eng 9(5):113–120 Tefera H, Belay G, Sorrells M (2001) Narrowing the Rift. Tef research and development Tesemma ZK, Mohamed YA, Steenhuis TS (2010) Trends in rainfall and runoff in the Blue Nile Basin: 1964–2003. Hydrol Process 24(25):3747–3758 Tsidu GM (2012) High-resolution monthly rainfall database for Ethiopia: Homogenization, reconstruction, and gridding. J Clim 25(24):8422–8443 Van Beek C (n.d.) (ed) Soil Classification in Yigossa Watershed, Lake Tana Basin, Highlands of Northwestern Ethiopia-Gizachew Ayalew1, Yihenew G. Selassie2, Eyasu Elias3&. Our Vision Our Mission , 76 Wang L, Liu H (2006) An efficient method for identifying and filling surface depressions in digital elevation models for hydrologic analysis and modelling. Int J Geogr Inf Sci 20(2):193–213 Watch W (2006) Remote sensing studies of Tana-Beles sub-basins. MoWR, Addis Ababa, Ethiopia Weldegerima TM, Birhanu BS, Zeleke TT (2023) Zoning and agro-climatic characterization of hotspots in the Tana-Beles Sub-Basin–Ethiopia Worku LY (2015) Climate change impact on variability of rainfall intensity in the Upper Blue Nile Basin. Proceedings of the International Association of Hydrological Sciences , 366 , 135–136 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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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-3893422","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":269253395,"identity":"3d01af2f-a1a9-4d1c-a244-1a3de5cbc153","order_by":0,"name":"Tesfay Mekonnen Weldegerima","email":"data:image/png;base64,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","orcid":"","institution":"Arba Minch University","correspondingAuthor":true,"prefix":"","firstName":"Tesfay","middleName":"Mekonnen","lastName":"Weldegerima","suffix":""}],"badges":[],"createdAt":"2024-01-24 08:15:49","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3893422/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3893422/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50325854,"identity":"6c43ce7f-e744-497c-8e31-1ea273daf939","added_by":"auto","created_at":"2024-01-29 19:48:22","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24213,"visible":true,"origin":"","legend":"\u003cp\u003eMean annual rainfall (Bars) and Average Temperature (line) of the TBSB for the period 1979-2015\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-3893422/v1/65f0f99319ed0179c4f2a2c1.png"},{"id":50325857,"identity":"4afb74c3-7bc4-4d4a-88e8-acd582ba42d6","added_by":"auto","created_at":"2024-01-29 19:48:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":67998,"visible":true,"origin":"","legend":"\u003cp\u003eSoil classes and their distribution in the TBSB\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-3893422/v1/ef9a27a60133e3e90967d8c6.png"},{"id":50325853,"identity":"460255f1-e56c-4b57-8719-343fdbfa0d93","added_by":"auto","created_at":"2024-01-29 19:48:22","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":66072,"visible":true,"origin":"","legend":"\u003cp\u003eAltitudinal classes of the Study Area after classified using equal intervals a) spatial distribution and b) area coverage of specific class in percentage\u003c/p\u003e","description":"","filename":"F3.png","url":"https://assets-eu.researchsquare.com/files/rs-3893422/v1/09f570cbd45ab16b0e8f646d.png"},{"id":50325855,"identity":"abb4e99b-6f8b-49bf-aa03-5ba2ef7f448d","added_by":"auto","created_at":"2024-01-29 19:48:22","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":122721,"visible":true,"origin":"","legend":"\u003cp\u003eslope class (a), and Aspect class (b) of the TBSB computed form the digital elevation model\u003c/p\u003e","description":"","filename":"F4.png","url":"https://assets-eu.researchsquare.com/files/rs-3893422/v1/c646021e1a74d9ea34cc6333.png"},{"id":50325858,"identity":"8e23596a-db34-44d4-86a8-6d9bd9184bf1","added_by":"auto","created_at":"2024-01-29 19:48:22","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":98160,"visible":true,"origin":"","legend":"\u003cp\u003eSpatial distribution of the major farming systems in the TBSB\u003c/p\u003e","description":"","filename":"F5.png","url":"https://assets-eu.researchsquare.com/files/rs-3893422/v1/e7ac1f8010ca63b6236043a7.png"},{"id":50325856,"identity":"e125389e-8889-40a3-80be-e2264e3a9e61","added_by":"auto","created_at":"2024-01-29 19:48:22","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":52450,"visible":true,"origin":"","legend":"\u003cp\u003eThe Agro ecosystem classes of the TBSB identified by the study\u003c/p\u003e","description":"","filename":"F6.png","url":"https://assets-eu.researchsquare.com/files/rs-3893422/v1/5f30e72a8900cfe184ee547c.png"},{"id":72037327,"identity":"bde7b339-43b0-404f-b0df-5a54ae976c73","added_by":"auto","created_at":"2024-12-21 00:16:21","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1382617,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3893422/v1/8d6765e8-d306-45f2-82b8-5197aaf44d5b.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Defining Agroecosystems and Suitability Analysis in the Tana Beles Sub Basin, Ethiopia","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eClimate change and variability have gained great attention in global discussion in recent times. This attention to climate change and variability is due to its adverse impacts on the economic, social, and political livelihoods of mankind. Though the issues have taken center stage in global development discussions, however when it comes to some developmental issues like poverty, discussions are normally associated with developing countries. Developing countries, particularly countries in Africa are much concerned about climate change due to their vulnerability and/or low adaptive capacity. Climate change and variability impede development and affect agricultural production, food security, and livelihoods in Sub-Saharan Africa due to rain-dependent agricultural production systems (DERESSA et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2011\u003c/span\u003e),(Nhemachena, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). As a result, the efforts of African countries like Ethiopia, to achieve the Millennium Development Goals may be seen as a mirage if the adverse effects of climate change are not addressed.\u003c/p\u003e \u003cp\u003eBecause of the complex topographical and geographical features, the climate of Ethiopia exhibits strong spatiotemporal variations and different rainfall regimes(NMSA, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e1996\u003c/span\u003e; Slingo et al., \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Tsidu, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The frequency and intensity of droughts have increased in recent years, severely affecting the livelihoods of millions of people (Simane, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Within the active agricultural zones, investment capacity and access to production-enhancing technologies will influence farmers\u0026rsquo; ability to maintain yields and conserve soil resources under changing climate conditions. Moreover, changes in agriculture induced either by new policies or by technical innovations, are unlikely to result in absolute improvements for all stakeholders and social actors involved, nor in absolute improvements on all the scales (soil, farm fields, and watershed, regional, global) on which the side effects of agricultural production can be described. The reinvestment efforts in agriculture since 2008 are also providing a green revolution with little or no emphasis on ecological farming methods that improve food production and farmers\u0026rsquo; incomes, while also protecting the soil, water, and climate(De Schutter \u0026amp; Vanloqueren, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe main aim of this study is to analyze the agroecosystems and assess the agricultural sustainability of the identified agroecosystems in the TBSB. (Simane et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) Remarks that the structure of an agroecosystem is a consequence of its environmental setting (e.g., climate, soil, topography, various organisms in the area), agricultural technologies and practices, and farmers\u0026rsquo; social setting (e.g., human values, institutions, and skills). Mapping of an agroecosystem is important to separate areas with similar sets of potentials and constraints for development. Assessments of agricultural suitability in each agroecosystem can also help farmers and policymakers in selecting appropriate cropping and development planning.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"2. Climatology and Agricultural practices in the TBSB","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAs in many other parts of Ethiopia, the climate of the TBSB is highly correlated with the seasonal propagation of the Intertropical Convergence Zone (ITCZ). The climate is generally characterized as tropical in the lowlands and temperate at higher elevations. Based on the 1979\u0026ndash;2015 averages, the mean annual rainfall and temperature for the TBSB are 1412.16 mm and 19.34 \u003csup\u003eo\u003c/sup\u003eC respectively Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The annual rainfall shows no clear trend while noticeable annual variability is observed. The area receives minimum annual rainfall in 1982 and maximum annual rainfall in 2014. Similarly, the average annual temperature shows variability from year to year and an increasing trend that intensified from 1998\u0026ndash;2015. The Temperature in the study area reached its peak in 2015. Reports show the year 2015 is globally recorded as the warmest year ever in history. Owing to the complex topography, the regional rainfall and temperature distribution are not uniform in place and time. The evaporation rate of the TBSB is low in spring due to soil water constraints and high in summer when the rainy season starts, the evaporation rate increases, reaching its peak in October just after the rainy season when soils are all still wet, and solar radiation is stronger than in August and September due to reduced cloud cover (Watch, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerally, the topography of the Blue Nile (where the study area is located) is composed of highlands, hills, valleys, and occasional rock peaks (Tesemma et al., \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). This Complex topography makes for strong local contrasts in precipitation and temperature, and soils are deeply weathered and erodible over most of the highland areas. Previous studies also show that rains around and/or in the current study region are characteristically intense and erosive (Nyssen et al., \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Worku, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Soils in most of the Tana basin (Upper TBSB) are derived from the weathered basalt profiles and are highly variable. In low-lying areas particularly north and east of Lake Tana (the biggest freshwater natural reservoir in Ethiopia), and along parts of the Gilgel Abbay, soils have been developed on alluvial sediments. In the Beles catchment (lower TBSB) soils have largely been derived from basalts and the basement rocks (Watch, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Owing to the sloppy mountains and intensive rains particularly during the rainy season it can be noted that erosive rains may affect the region.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe agricultural practices of the TBSB are diverse; however, they can be classified generally as crop production and livestock raising. Most of the rural inhabitants in the area are farmers employed in rain-fed substance agriculture cultivating crops such as cereals (rice, teff, millet, maize, wheat, and barley), pulses, oil seed, and vegetables. However, crop production in the TBSB is under threat from climate-related and other environmental effects. These include effects of increased temperatures, changes in soil water balance, changes in the length of growing period (LGP), increased soil erosion and land degradation due to increased rainfall intensities, increased incidence of floods in flood-prone areas, and increased incidence and expansion of crop pests, diseases, and weeds mainly associated with increasing temperatures(Bewket et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Traditional tillage practices particularly on steep slopes, combined with overgrazing and deforestation are also contribute to soil erosion and soil fertility reduction leading to a gradual decline of production in the region.\u003c/p\u003e \u003cp\u003eThese pressures combined with the fast-growing population, hence, improper utilization of the natural resource base (land, water, and biodiversity) show that the TBSB is under threat from multi-directional sources. Climate change may be one of the major sources of contributing challenges to humans and their livelihoods because the livelihoods of farming communities face severe constraints related to intensive cultivation, overgrazing and deforestation, soil erosion and soil fertility decline, water scarcity, livestock feed, and fuel wood demand (Simane et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Climate change will also have a profound impact on the availability and variability of fresh water as the frequency of climatic extremes such as heat waves, drought, and changes in rainfall patterns increases in response to global warming (Field et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). This uncertainty of the availability of water resources will affect agricultural production, challenge socio-economic systems, and threaten environmental sustainability.\u003c/p\u003e \u003cp\u003eFurthermore, in the high-water reservoir in the region, the communities in the TBSB are held in diverse agricultural activities cereal crop production being dominant. However, low productivity remains the major constraint of cereals cultivation, where yields are less than one ton per hectare (Pender \u0026amp; Gebremedhin, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Moreover, even where agricultural production is increased, this success may be short-lived if attention is not quickly diverted to side effects that threaten other equally important development goals (G. R. Conway, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). Hence, the boundary conditions for sustainability in the TBSB are closely linked to the climate as the productivity of agriculture is sensitive to the timing as well as the amount of rainfall and temperature.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"3. Methodology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1. The Study Area\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe TBSB is located in the Abbay (Blue Nile) river basin of the northern highlands of Ethiopia. The geographic location of the TBSB extends from 10.2\u003csup\u003eo\u003c/sup\u003e to 12.8\u003csup\u003eo\u003c/sup\u003e longitude and 35.0\u003csup\u003eo\u003c/sup\u003e to 38.2\u003csup\u003eo\u003c/sup\u003e latitude. A detailed description of the present study area can be obtained from (Weldegerima et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Data sources\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe study uses both primary and secondary datasets. The observational climate data of rainfall and temperature used to characterize the climate of six meteorological stations in the study area for the period of 37 years were obtained from the National Meteorology Agency (NMA) of Ethiopia. According to the NMA, in Ethiopia, there are three seasons with four months each, based on climatological means of rainfall and temperature. These seasons are locally known as Bega (October, November, December, and January), Belg (February, March, April, and May), and Kiremt (June, July, August, and September) (Degefu, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e1987\u003c/span\u003e; Gissila et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2004\u003c/span\u003e). Seasonal and annual climate characterization in this study is based on their classifications.\u003c/p\u003e \u003cp\u003eThe descriptive statistics in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, of the monthly and seasonal data reveal the mean values are higher through the months of June-September (Kiremt: main rainy season) and lower during December-February (Bega: the dry) seasons. On the other hand, based on the standard deviation (SD) and coefficients of variance (CV) values, rainfall in almost all stations is highly variable during the months of the dry season when compared to the months of the rainy season. Moreover, rainfall in Pawe shows a higher variability among the other stations, with a coefficient of variance of 3.9 and 3.0 during December and January respectively.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eStatistical summary of the monthly and seasonal rainfall data, for the selected stations in the TBSB, during 1979\u0026ndash;2015\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eStatstics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBega rainfall(mm)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c10\" namest=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBelg rainfall(mm)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c14\" namest=\"c11\"\u003e \u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eKiremt rainfall(mm)\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOct.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNov.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDec.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJan.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eFeb.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMar.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eApr.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMay.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eJun.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003eJul.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eAug.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eSep.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBahir Dar\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e23.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e72.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e176.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e414.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e374.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e189.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e20.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e71.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e107.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e98.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e47.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eBullen\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e118.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e14.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e132.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e229.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e284.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e303.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e239.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e76.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e22.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e62.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e83.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e91.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e116.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e91.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDangla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e99.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e19.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e35.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e134.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e257.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e374.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e386.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e244.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e67.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e36.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e77.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e80.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e84.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e96.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e69.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eDebretabor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e 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\u003cp\u003e106.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e25.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e47.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e54.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e50.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e33.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003ePawe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e116.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e32.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e27.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e126.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e273.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e362.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e386.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e238.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e54.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e56.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e31.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e34.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e72.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e103.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e103.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e112.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e73.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e1.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe other groups of the data set are agro-climatic data sets namely: Altitude (m), mean annual rainfall (mm), and mean annual temperature (\u003csup\u003eo\u003c/sup\u003eC). The altitude data was raised from the integrated mapping of the Digital Elevation Model (DEM) obtained from the Shuttle Radar Topography Mission (SRTM), (Cao et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2021\u003c/span\u003e); which was also used in an earlier mapping of different agroecological zones in Ethiopia. Whereas, the rainfall and temperature data used for agro climate zoning were downloaded from WorldClim 2.0; a high-resolution (1-km) interpolated gridded climate surface for global land areas, excluding Antarctica (Fick \u0026amp; Hijmans, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The WorldClim 2.0 dataset and agro climate class are described in detail at (Weldegerima et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSimilarly, the soil and terrain parameters were obtained from the FAO database. The FAO Soil and Terrain Database Map of East Africa is a computer system for the storage, display, interpretation, and analysis of the Soil Map of East Africa at scale 1:1.000.000. However, the terrain parameters in this study are calculated from the DEM for the sec of consistency. We download the FAO Classification dominant soil in Ethiopia and further refine it to our study area. The database can also provide basic information which is important for agriculture, climate, and drought monitoring. Finally, the qualitative data used to combine the quantitative effects were obtained from various sources namely, Regional and National Agriculture and Rural Development Bureaus, the Ministry of Water Resources as well as surveys, previous works of literature, and FAO guidelines.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Data Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTo classify the Agro ecosystems of the TBSB we combined the three layers (Agro climate Zones, Soil and Terrain, and farming systems). Agricultural activities take place within a complex mess of multi-scalar, multi-dimensional interactions. This implies that in any analysis of a defined farming system, one will always find legitimate and contrasting perspectives concerning the effects of changes in the system. In each distinct agroecosystem, the biological and physical boundaries of the system become more clearly defined and Linkages with other systems become limited. Agroecosystem suitability can therefore be formulated to the distinct agroecosystem. As Agro climate zoning is available in the previous work of the authors (Weldegerima et al., \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), the other layers will be described hereafter.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Soil and Terrain Analysis\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eSoil and terrain analysis were performed to improve current and future land potential productivity; to identify land and water suitability and capability and to assess land degradations, particularly soil erosion. The Harmonized World Soil Database (HWSD version 1.0) was used for this study (FAO/IIASA/ISRIC/ISS-CAS/JRC, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The HWSD was reconstructed in the context of the need to combine existing regional and national updates of soil information worldwide and incorporate these with the information former FAO-UESCO soil map of the world that was supposed to lack reflecting the actual state of the soil resources. The HWSD is of immediate use in the context of the Climate Change Convention and the Kyoto Protocol for soil carbon measurements and the FAO/IIASA Global Agro-ecological Assessment studies (Fischer et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2021\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study the HWSD textural classes for the topsoil (0\u0026ndash;30cm), are simplified into three textural FAO classes as follows;\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCoarse textured\u003c/strong\u003e \u003cp\u003esands, loamy sands, and sandy loams with less than 18% clay and more than 65% sand.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMedium textured\u003c/strong\u003e \u003cp\u003esandy loams, loams, sandy clay loams, silt loams, silt, silty clay loams, and clay loams with less than 35% clay and less than 65% sand; the sand fraction may be as high as 82% if a minimum of 18% of clay is present.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFine textured\u003c/strong\u003e \u003cp\u003eclays, silty clays, sandy clays, clay loams, and silty clay loams with more than 35%t clay.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe HWSD is available at higher spatial coverage above national resolution the Soil and Terrain Databases (SOTER) have the highest regional reliability (Southern Africa, northeastern Africa, Latin America, and the Caribbean, Central and Eastern Europe). However, we use the arc map 10.2 tools, particularly spatial analysis tools to extract and cover the TBSB. Moreover, further classification considering local information and analysis was performed using the same version of Arc Gis. Several primary and secondary terrain attributes were derived using Terrain Analysis Systems. The definitions and methods used to derive the values and the units of the terrain attributes used in this research are given in the following equations. Assuming that any topographic surface can be described as a continuous function, where z is the elevation and x and y are the Cartesian coordinates:\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Equa\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$z=f\\left(x,y\\right) \\left(1\\right)$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eWhere the size of y is constant for all grid cells. However, the size of x depends on latitude and is calculated separately for each row of a tile.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eElevation\u003c/strong\u003e \u003cp\u003ewhich is the simplest measure of topography is defined as the height above sea level and the method to analyze elevation is using the DEM. It is important for the visibility of topographic features and the distribution of temperature, rainfall, or vegetation. Slope and aspect are also the most important primary attributes that can be derived from a DEM and are used widely in geomorphometric analysis and hydrological modeling.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eSlope\u003c/strong\u003e \u003cp\u003eis the rate of change of elevation in the direction of the steepest descent. It is the basic element for analyzing and visualizing landform characteristics(Ogunkunle et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Sachin, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equb\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e\n$$Slope=100\\sqrt{{\\left(dz/dx\\right)}^{2}+{\\left(dz/dy\\right)}^{2}} \\left(2\\right)$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eAspect\u003c/strong\u003e \u003cp\u003eThe direction of the line of the steepest descent, i.e. the orientation of the slope gradient, starting from north (0 degrees) and going clock-wise (whereby 0\u0026deg; is equal to North and 180\u0026deg; is equal to South), is calculated using the variables from above, as follows\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv id=\"Equc\" class=\"Equation\"\u003e \u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equc\" name=\"EquationSource\"\u003e\n$$Slope=arctan\\sqrt{\\left(dz/dx\\right) / \\left(dz/dy\\right)} \\left(3\\right)$$\u003c/div\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe aspect map is reclassified following the division of 360 degrees into quadrants and sub-dials producing 8 possible orientation classes. It determines measures of insulation, temperature, vegetation, soil characteristics, and moisture.\u003c/p\u003e \u003cp\u003eAutomatically generated DEMs often have artifacts, that represent local alteration of the land surfaces, arising from different factors such as feature matching techniques, coarse spatial resolution, or reconditioning by anthropic structures, i.e. buildings or bridges. These are often just a few pixels large, but they can cause problems with hydrological modeling. (Wang \u0026amp; Liu, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2006\u003c/span\u003e) Have provided an accurate description of the nature/behavior of surface depressions. Therefore, an algorithm called \u0026ldquo;\u003cem\u003eFill Sinks\u003c/em\u003e\u0026rdquo; in ArcGIS has been applied to a DEM to prevent faulty results.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Defining farming systems\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA farming system is defined as a population of individual farm systems that have broadly similar resource bases, enterprise patterns, household livelihoods, and constraints, and for which similar development strategies and interventions would be appropriate. Defining farming systems is important in agroecosystem analysis; it helps to understand the constraints farmers face and explore possible development pathways. The use of the Farming System Approach (FSA) as an analytical framework became common in the 1970s, and it has contributed to a paradigm change in rural development thinking. In the farming system definition approach, a system is a group of subsystems that interact according to some kind of process (Odum, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1983\u003c/span\u003e). This Farming System Approach considers both biophysical dimensions (such as soil nutrients and water balances) and socio-economic aspects (such as gender, food security, and profitability) at the level of the farm \u0026ndash; where most agricultural production and consumption decisions are taken. The power of the approach lies in its ability to integrate multi-disciplinary analyses of production and its relationship to the key biophysical and socio-economic determinants of a farming system. However, it is critical to place the boundary between sub-systems and their environment accurately.\u003c/p\u003e \u003cp\u003eIn the present study farming system definition was based on the dominant type of resource base and the dominant livelihood pattern of farm households as presented in (FAO, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The FAO classification reflects key distinguishing attributes, notably: (i) water resource availability, e.g. irrigated, rain-fed, moist, dry; (ii) climate, e.g. tropical, temperate, cold; (iii) landscape relief/altitude, e.g. highland, lowland; (iv) farm size, e.g. large scale; (v) production intensity, e.g. intensive, extensive, sparse; (vi) dominant livelihood source, e.g. root crop, maize, tree crop, artisanal fishing, pastoral; (vii) dual crop livelihoods, e.g. cereal-root, rice-wheat (note that crop-livestock integration is denoted by the term mixed); and (viii) location, e.g. forest-based, coastal, urban-based. The current study defines farming systems in the TBSB based on Climate and Dominant livelihood sources (crops).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Results and Discussions","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Soil Classes and Characteristics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe role of topography in the bio-physical process is very important to characterize the spatial distributions of soils and their properties. This is because it influences endogenic and exogenic soil-forming factors and processes. The spatial variability of landscape features such as topography, soils, and vegetation defines the spatial pattern of hydrological state variables like soil moisture, runoff, evapotranspiration, and groundwater flow (Melesse \u0026amp; Abtew, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In the present study, we found it the characterization and investigation of the spatial distribution of soils and their properties, i.e. soil survey, is advancing due to the increasing need for knowledge about soils, triggered by their importance in the environmental well-being and agricultural activities. Hence, the Physical and chemical properties of soils in the TBSB are characterized based on the HWSD, which is available in recognition of the urgent need for improved soil information worldwide, particularly in the context of the Climate Change Convention and the Kyoto Protocol for soil carbon measurements and global agro-ecological assessments.\u003c/p\u003e \u003cp\u003eThe HWSD attribute database provides information on the soil unit composition for each of the 15773 global soil mapping units (FAO/IIASA/ISRIC/ISS-CAS/JRC, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The database shows the composition of each soil mapping unit and standardized soil parameters for top and sub-soils globally and regionally. We therefore down-scaled the global and regional HWSD into our study area based on the morphological properties of these soil databases. The soils of the TBSB were then further classified into twelve homogeneous soil classes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The soil characteristics at various topographies are different and are the result of geological and other natural processes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe general descriptions of the top three dominant soils in the TBSB are following:\u003c/p\u003e \u003cp\u003eThe dominant group of soils in the TBSB; \u003cem\u003eHapelic Nitosols\u003c/em\u003e: are deep, dark red, brown, or yellow clayey soils having a pronounced shiny, nut-shaped structure. These soils have maximum area coverage of 24.92% which is one-fourth of the total geographical area of the TBSB. These soils are mostly distributed in the North-central and western parts of the TBSB.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHapelic luvisols\u003c/strong\u003e \u003cp\u003eThese are also genetically similar soils (share similar chemical and physical properties) with Cromic Luvisols. These soils cover 14.74% of the total area of the TBSB. These soils can be found around south central, southeastern, and northern parts of the TBSB.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eChromic Luvisols\u003c/strong\u003e \u003cp\u003eare soils with subsurface accumulation of high activity clays and high base saturation. This type of soils covers 11.7% of the total geographical area of the sub basins. They are mostly confined around the central parts of the TBSB near to Bahir Dar town.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePrevious studies on soil distribution properties in and around the current study area also reveal similar results. For instance, (Gashaw et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) in their study of erosion risk assessments in the Gelleda watershed of the Upper Blue Nile basin suggests that the major soil types of the watershed are Luvic Calcisols (61.7%), Chromic Luvisols (32.3%), Eutric Leptosols (3.3%) and Haplic Luvisols (2.7%). Similarly, (Van Beek, n.d.) found that the major soil types of the Lake Tana basin are Nitisols, Vertisols, Luvisols, Regosols, and Phaeozems with a dominant presence of the Vertisols and Nitisols. While soil distribution is essential for agricultural activities, it is the soil property (Chemical and physical) that is vital for soil characterization and fertility management on small farms. Moreover, understanding soil fertility status is a prerequisite to implementing appropriate soil management practices for sustainable agricultural production and productivity. The physical and chemical characteristics of soils (top soil\u0026thinsp;~\u0026thinsp;30cm) for approximate coordinates in the TBSB are described in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Understanding these properties of soils is important to improve the estimation of current and future land potential productivity.\u003c/p\u003e \u003cp\u003eAccording to Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the soils of the study area could be categorized under moderately well (mostly) and poor drainage class for the 0-0.5% slope. Moderate and well drainage classes facilitate plant growth while poor and excess drainage inhibits plant growth (REES, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e1995\u003c/span\u003e). Soil texture tells the presence of sand, silt, and clay in the soil under consideration. It affects the physical and chemical properties of the soil. The particle size determination showed that the soils of the study area are clay loam texture; except for the Hapelic Luvisols (~\u0026thinsp;11.23N \u0026amp; 36.59E). The clay loam distribution varies from 20\u0026ndash;65 % and generlly increases in different depths. On the other hand, the sand and silt distribution vary from 11\u0026ndash;54% and 22\u0026ndash;60%. These textural differentiations might be caused by an alluvial accumulation of clay, predominant pedogenetic formation of clay in the subsoil, destruction of clay in the surface horizon, selective surface erosion of clay, upward movement of coarser particles due to swelling and shrinking, biological activity, and a combination of two or more of these different processes (Sotelo Ru\u0026iacute;z et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Similarly, the bulk density of the topsoil in the study area ranges from 1.21\u0026ndash;1.51 kgdm\u003csup\u003e3-\u003c/sup\u003e. This value is relatively good for the cultivated land, which could be attributed to compaction created due to cultivation. It is generally desirable to have soil with a low BD (\u0026lt;\u0026thinsp;1.5 g/cm\u003csup\u003e3\u003c/sup\u003e) (Gilkes \u0026amp; Hunt, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e1992\u003c/span\u003e), For optimum movement of air and water through the soil. While an increase in soil bulk density by about 21.42% could be observed due to deforestation and subsequent cultivation (Mojiri et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOn the other hand, the Chemical properties of the topsoil of the TBSB were described by soil pH, organic carbon, and salinity. Consequently, the soil pH (H\u003csub\u003e2\u003c/sub\u003eO) of the topsoil (surface soil) ranges from 5.4 to 7.5, indicating that the soils are moderately acidic to slightly alkaline. Acidic soils can restrict microbial activity; reduce the availability of essential nutrients and cause aluminum toxicity in the subsurface which retards root growth, and restricts access to water and nutrients (Horneck et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Hence, Crops will display varying sensitivities to acidity and alkalinity. The Organic carbon (OC) content of the topsoil in the TBSB ranges from 0.39\u0026ndash;1.45 %, which is categorizd under low to moderate organic carbon content. In general Soils that are very poor in organic carbon (\u0026lt;\u0026thinsp;0.6%), consistently need organic or inorganic fertilizer application to be productive. The organic carbon content values decrease with depth and the low content of soil OC is attributed to the warmer climate, which enhances the rapid rate of mineralization (Esayas, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). The topsoil salinity of the TBSB reveals a range of 0-0.4 (ECe)(dS/m). This salinity value can be categorized as very low to safe salinity (Lane, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e1985\u003c/span\u003e). However, salinity can occur naturally where drainage is poor, in inland areas that were once inundated by seawater, or in areas with low rainfall and high evaporation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhysical and chemical properties of soils in the TBSB\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eCoordinates\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDrainage class (0-0.5% slope)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSoil texture\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSand (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSilt (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eClay (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBulk density\u003c/p\u003e \u003cp\u003e(kg/dm\u003csup\u003e3\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOC (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c12\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epH\u003c/p\u003e \u003cp\u003e(H\u003csub\u003e2\u003c/sub\u003eO)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003esalinity\u003c/p\u003e \u003cp\u003e(ECe)(dS/m)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAssociated soil\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLon.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLat.\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eChromic Luvisols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emoderately well\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003esandy clay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eEuteric Vertisol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEutric Cambisols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eclay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eHumic Nitosols\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEuteric Fluviols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emoderately well\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eclay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEuteric Leptosols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emoderately well\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eclay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eChromic cambisol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEuteric Regosols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eloam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eLethic liptosol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eEuteric Vertisols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003epoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eclay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eHumic Nitosols\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHapelic Acrisols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emoderately well\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003esandy clay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eHumic Nitisols\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHapelic Alisols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emoderately well\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003esandy clay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eLethic Liptosols\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHapelic luvisols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emoderately well\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003esilt loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eEuteric vertisol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHapelic Nitosols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e24.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emoderately well\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eclay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eEuteric leptosol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLithic Liptosols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e37.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emoderately well\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eclay loam\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e7.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eEutheric liptosol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eRhodic Nitosols\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emoderately Well\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eClay (heavy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003eHumic Nitosol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Terrain Characteristics\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the present study, some of the most common terrain components (Altitude, slope, and aspect) associated with characteristics of landforms and related parent material are described. The entire study area was divided into five altitudinal classes which range from about 500 to 4000 masl (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea). The mean altitude calculated for the whole study area was about 1597 m and the standard deviation was 554. It appears that most of the area (36.5%) is located between 1716 to 2296 m above mean sea level (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb). A previous study by (Lemann et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) in the Upper Blue Nile Basin Basin (Where the TBSB is found) which covers a large part of the Ethiopian Highlands (175,000 km\u003csup\u003e2\u003c/sup\u003e), was also discussed to have a topographic variation from an altitude of less than 500 meters above sea level at the Sudanese border to more than 4200 meters above sea level in the center and the eastern escarpment of the Ethiopian Highlands. Generally, the variance of altitude was increasing with higher altitudinal zones signifying an increase in the surface roughness in the mountainous region of the Northern tips of the Sub Basins.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe other terrain components, Slope and Aspect are the most important primary attributes that are also derived from a DEM and are used widely in geomorphometric analysis and hydrological modeling. The slope is one of the most important terrain factors in representing ground surface properties and is commonly applied in geographical research and land use planning. A slope map for the study area is generated from the DEM and is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003ea. The terrain classification based on slope was able to identify nine classes. It can be seen that the slope varies from 0 to 102%. The calculated mean slope is 4.6 and the standard deviation is 7.3. The slope of the study area is relatively low in the western parts of the basin relative to the eastern parts of the sub-basin, indicating that the area is dominated by flat lands with significant ridges and hills in some parts. However, the variation of slope being high also indicates that the terrain is rugged in topography. Generally, more than 75% of the area has a slope value of less than 15%. During a previous study on the Spatial analysis of the Beles River Basin by (A. H. Tefera, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) the mean slope calculated for the Beles watershed was 3%. The result of the classified slope reveals that the steepest slopes (greater than 45%) are located in the North, Northeastern, Northwestern, and central parts of the subbasin.\u003c/p\u003e \u003cp\u003eAn aspect map, for the same part as for the slope, is also derived from the DEM and is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eb. Aspect shows the direction of the maximum slope which is essential for the hydrological process simulation. In the present study, eight possible orientations (Namely; North, North East, East, South, South East, South West, West, and North West) with 45\u003csup\u003eo\u003c/sup\u003e apart each based on the FAO standards have been considered to describe the distribution of aspect. Most of the degree measures of the slope direction (Aspect) in the current study area are predominantly inclined to the North-South and North-West-South East angles.\u003c/p\u003e \u003cp\u003eIn addition to the surface patterns, the Slope/Aspect class can give information about the condition of the region important for agricultural and hydrological assessments. For example, since the amount of moisture along the hill slope tends to increase from upslope to downslope, additional moisture contributed from upslope as the catchment area increases to the bottom of the basins. Spatial distribution day landscape forming processes such as Surface runoff and soil erosion processes can also be extracted.\u003c/p\u003e \u003cp\u003eAccording to FAO guidelines (Rossiter, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), land suitability for agriculture can be classified into five categories: highly suitable, moderately suitable, marginally suitable, currently unsuitable, and permanently unsuitable. Elevations above 3,700 m is classified as \u0026lsquo;high wurch\u0026rsquo; (frosty-alpine) and thus unsuitable for agricultural purposes according to the agroecological zoning of Ethiopia (Mengistu, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). Consequently, the agricultural suitability of different slope classes for the study area is defined in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Hence, since no specific crop suitability is assumed, an elevation value lower than 3700 m a.s.l. is taken to be unsuitable for agriculture. Moreover, Population increase and the need for more food production, in regions such as the Blue Nile basin, have resulted in deforestation and the expansion of arable land to steep slope terrains (Melesse \u0026amp; Abtew, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). As a result, severe soil erosion is observed and attempts to control or reduce erosion are usually not successful. Rugged mountainous volcanic terrain, moderate to gentle terrain in volcanic rocks with some isolated hills, escarpment, and plain constitute the physiographic units of the TBSB.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Farming system definition of the TBSB\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eIn the present study classification of the farming systems, as specified herein, has been based on some of the number of key factors, including: (i) the available natural resource base; (ii) the dominant pattern of farm activities and household livelihoods, including relationship to markets; and, (iii) the intensity of production activities. In general, the farming systems in the TBSB are identified to be classified under two major Groups: the mixed crop-livestock farming (where both livestock and crop production take place within the same locality), 78% and the pastoral/agro-pastoral systems 22%, where up to 50% of household revenue is obtained from livestock and its products.\u003c/p\u003e \u003cp\u003eThe mixed farming system can be divided into three subsystems (Erkossa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e): the cereal-based, coffee-tree crops complex, and the inset root crops complex. In the present study, owing to the diversified agricultural activity of the study area and Ethiopia in general, we present our analysis of the cereal-based rained farming system. This farming system covers about 80% of the upper part of the Nile Basin. These major systems are then further classified into finer farming systems in the current study area. Accordingly, we classify the TBSB into eleven major farming classes as presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. However, the urban and water body-based farming systems are not mapped due to the lack of consistent urban-based agricultural practices and full information in the present study. The characteristics of the cereals (maize, tef. barley, and sorghum) based farming system are described in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. In addition, complex crops such as finger millet, cotton, sesame, groundnut, pumpkin, ginger, Chat, etc., also are characterized to some extent. Generally, the Wiena Dega Teff-based farming system covers the largest area in the sub-basins while the Dega Barley-based shifting cultivation covers a small area in the highlands.\u003c/p\u003e \u003cp\u003eMany factors influence the variability in production across farming systems. Amongst them, climate variability has a high degree of influence on production in Ethiopian farming systems, particularly in drought-prone areas. Because, the factors that determine the suitability of different crops and pastures, such as rainfall and temperature that influence the length of growing seasons, played a critical role in the classification adopted by (Dixon et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Hence, during the definition of farming systems, we also define an agro climate zone where the specific crop is growing. This approach could provide a more effective basis for organizing information and identifying knowledge gaps and development opportunities.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThough large distributions of Forest Farming System in the Upper Blue Nile basin and Ethiopia, the distribution in the TBSB is negligible. This could be associated with the Farmer's practice of shifting cultivation; clearing a new field from the forest every year. Forest clearance activity is mostly practiced in the western, northwestern, and central parts of the basin following the Abay River (Erkossa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). It is generally practiced in maize and sorghum-based farming. However other crops like Finger millet, cotton, sesame, groundnut, pumpkin, ginger, etc., also grow to some extent through such activities. In addition to the effects of climate change and climate variability Crop productivity is constrained by insect pests, disease, and weeds as there are weak extension services, while animal diseases impede livestock development (Johansen et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGenerally, the farming system classifications of the present study are consistent with previous studies in and around the current study area. For example, during their study of the farming system framework for investment planning and priority setting in Ethiopia, (Aranguiz \u0026amp; Creemers, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) presented most parts of the Lake Tana subbasin and the upper part of the Beles subbasin as High land (Weinadega) Teff mixed system and Highland (Weinadega) wheat livestock mixed system. They also classify the lower Beles sub-basin as a Lowland (Kolla) maize mixed system and a Highland (Weinadega) maize mixed system. Similarly, previously a study by (Erkossa et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), on the characterization and productivity assessment of the farming systems in the upper part of the Nile basin (Abay, Tekeze, and Baro Akobo River basins) using different approaches classified most parts of the TBSB as Teff based single cropping, Maize based single cropping and pastoral complex. Both studies reveal that Teff-based farming is dominant similar to the current findings; this could be due to the adaptive capacity of Teff to a varying environment ranging from low land to highlands. Moreover, it can grow in a wide range of environmental conditions (1000\u0026ndash;3000masl; although most Teff cultivars are more suitably grown between 1500 and 2500m a.s.l.(H. Tefera et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). Barley is also commonly grown in highlands at elevations between 1800 and 3000 m.\u003c/p\u003e \u003cp\u003eThe entire TBSB exhibits a wide spectrum of altitude, temperature, rainfall, humidity, and aridity ranges, thus giving rise to the diversity of agro-climatic zones agriculture farming systems, and a range of agricultural products. Farmers in the area use the traditional farming system except in a few investment areas that use modern farming machines. Traditional farming involves frequent plowing by oxen-drawn implements, three to four times for most cereals and in some localities, up to nine times for Teff grown on Vertisols (Erkossa \u0026amp; Ayele, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). While crop rotation with no definite sequence is common, the use of artificial fertilizers is often limited to Teff, Wheat, and Maize. Moreover, the problems of soil erosion, shortage and unreliability of rainfall in some places, shortage of arable land, and low input use need to be addressed for enhanced and sustained productivity.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Identified Agroecosystems\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis section describes about the identified specific agro-ecosystem classes and characteristics in the TBSB. A system is defined here as an assemblage of elements contained within a boundary such that the elements within the boundary have strong functional relationships with each other, but limited, weak, or nonexistent relationships with elements in other assemblages; the combined outcome of the strong functional relationships within the boundary is to produce a distinctive behavior of the assemblage such that it tends to respond to stimuli as a whole, even if the stimulus is only applied to one part. The behavior of agroecosystems as discussed in the introduction part; can be described by four broad system properties: productivity, stability, sustainability, and equitability (G. R. Conway, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1985\u003c/span\u003e)(Simane et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). The author defines each term as; Productivity is the yield or net income per unit of resource: Stability is the degree to which productivity is constant in the face of small disturbances caused by the normal fluctuations of climate and other environmental variables: Sustainability is the ability of a system to maintain productivity in spite of a major disturbance, such as caused by intensive stress or a large perturbation, and equitability expresses how evenly the products of an agro ecosystem are distributed among its human beneficiaries.\u003c/p\u003e \u003cp\u003eA relatively recent study by (Simane et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2013\u003c/span\u003e) also used the above methodology to analyze the agroecosystems of the Choke Mountain watersheds, in the Blue Nile Highlands of Ethiopia. their analysis includes; the identification of six major agroecosystems, mapping of the agroecosystems, analysis of the productivity potential of each agroecosystem, identification of agricultural productivity, and recommendation of various adaptation strategies. The present study also follows a similar approach to identify the seven agroecosystems of the TBSB presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e; unless it gives attention to the climate-related constraints to the agricultural productivity and other livelihoods relative to other studies that include land degradation, soil acidity, and deforestation etc. This is due to the reason that Ethiopia is often cited as one of the most extreme examples, concerning the famines of the 1980s to warn of the disasters that may result from anthropogenic climate change (D. Conway \u0026amp; Schipper, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Moreover, owing to the nature of Ethiopia\u0026rsquo;s agriculture primarily rain-fed, means that production is sensitive to fluctuations in rainfall.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAgroecosystem analysis (AEA) is a methodology for analyzing ecosystems with various approaches and different objectives. In this study, the agroecosystem analysis is used for agricultural livelihood systems and planning and prioritizing research and development activities. Many studies reveal that climate change is likely to have profound effects on both natural and man-made ecosystems. Assessments of ecosystem vulnerability are crucial for identifying adaptation needs and for avoiding the worst possible consequences of climate change, for both human and natural systems (Luedeling et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). While such effects may well be beneficial in certain cases, many ecosystems are assumed to be vulnerable to climate change, in the sense that effects will be predominantly negative. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e summarizes the specific characteristics of the identified agroecosystems in the TBSB.\u003c/p\u003e \u003c/div\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 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics the identified Agro ecosystem classes in the TBSB\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=\"char\" char=\".\" 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\u003eAgro ecosystem\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgro climate class\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMajor Soils\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eFarming systems\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMajor crops\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAES1: Moist Lowlands\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWet \u0026amp; Moist Kolla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHapelic Nitosol\u003c/p\u003e \u003cp\u003e\u0026amp; Euteric Leptosol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaize based mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaize, Sorghum, teff,\u003c/p\u003e \u003cp\u003eharicot bean\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAES2: Moist Mid Lands\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWet \u0026amp; Moist Weina dega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuter Vertisol,\u003c/p\u003e \u003cp\u003eEuteric fulvisol \u0026amp; Hapelic Luvisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTeff based mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaize, wheat,\u003c/p\u003e \u003cp\u003eTeff\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAES3: Moist High Lands\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWet \u0026amp; Moist Dega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eChromic Luvisol \u0026amp; Hapelic Luvisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBarley Based shifting cultivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBarley,wheat potato,\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAES4: Sub Humid Low Lands\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWet Kolla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHapelic Nitosol\u003c/p\u003e \u003cp\u003e\u0026amp; Hapelic Arcisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSorghum based mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSorghum, Maize and Sesame\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAES5: Sub Humid Mid Lands\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWet Kolla \u0026amp; Wet Weina Dega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuteric Cambisol \u0026amp; Hapelic acrisols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaize based complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMaize, wheat,\u003c/p\u003e \u003cp\u003eTeff\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAES6: Sub Moist Mid Lands\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoist Weina Dega \u0026amp; Moist Dega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuteric Fulvisol \u0026amp; Chromic Luvisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBarley based mixed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eBarley, wheat, Teff\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAES7: Sub Moist High lands\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoist Dega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEuteric Liptososl\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eBarley based shifting cultivation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eDominantly Barley\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe seven identified AES are described as follows:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLow Land Moist (AES1)\u003c/strong\u003e \u003cp\u003eThis agroecosystem includes the lowlands in the western and northwestern parts of the TBSB, encompassing the draining area of the Beles River in the TBSB boundary. The altitude of this agroecosystem ranges from 554m in the westernmost tips to 1200m in the eastern parts of the Beles sub-basin. The topography is relatively characterized as flat plain with slope generally ranging from 0\u0026ndash;7%. The dominant soils in this agroecosystem are Hapelic Nitosols, which are less acidic soils and are suitable for almost all crops. However, due to the lower and erratic rainfall associated with the higher temperatures which reach above 27\u003csup\u003eo\u003c/sup\u003eC crop production is low. Alternatively, farmers used to grow sheep and goats as a source of income for their livelihoods. However, the dominant crop cultivated here includes Maize, sorghum, haricot bean, and Teff to some extent. Owing to the higher temperature and favorable climatic conditions (Moist) for the reproduction of mosquitos, Malaria is a major health-related constraint in this area.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMid-Land Moist (AES2)\u003c/strong\u003e \u003cp\u003eAES2 is found in the central parts of the Lake Tana sub basin circling the Lake from all directions. This is the largest component of the agroecosystem in the TBSB, covering around 51% of the total area of the TBSB. The altitudinal variation of this agroecosystem is diversified from 1500 to above 2800 m. The dominant soils in this agroecosystem are Euteric Vertisol, Euteric Fulvisol, and Hapelic Luvisol. Similarly, the topography ranges from flat plains to hilly mountains, sloping from 0 in the flat areas to greater than 45% in the hilly mountains. Owing to the range of agroecologies and optimum rainfall and temperature distributions, a variety of crops such as Teff, Maize, Wheat, Barley, and Rice as well as, cash crops such as Chatt and coffee are adapted to this agroecosystem. Generally, this AES is also known for its potential irrigation scheme associated with the rivers that flow through from and to Lake Tana.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHigh Land Moist (AES3)\u003c/strong\u003e \u003cp\u003eAES3 is found on the eastern tips of the TBSB dispatched at three highland areas totaling only 1.4% of the TBSB. This agroecosystem is detached by the midland moist agroecosystems (AES 2) and midland sub-humid (AES 6). The altitude of this agroecosystem ranges from 2500 to about 3500 m. Owing to the appearance of an association to the other agroecosystems the topography is generally hilly and mountainous with a slope of above 45%. The dominant soils are Chromic Luvisol \u0026amp; Hapelic Luvisol. Though the climate here is favorable for a variety of crops with rainfall rates relatively high, the shallowness of soils in this AES, combined with the rapid drainage characteristics of the sloppy topography, can result in low production potential. The rainfall in the AES3 ranges from 1500 to 2000 mm and the temperature reaches as low as 6.5 \u003csup\u003eo\u003c/sup\u003eC. Low temperature, soil erosion, and deforestation leading to water management problems are the major constraints on production in this agroecosystem. The major crops grown in this AES are Barley, wheat, potato, and pulses to a smaller extent. Though AES3 is not appropriate for high-intensity agriculture, it does have a high potential for traditional forestry, including bamboo, potato, and barley production with appropriate mountain agricultural land management.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLow Land Sub Humid (AES4)\u003c/strong\u003e \u003cp\u003eAES4 is located at the interior parts of the Beles sub-basin extending south of the Gilgele Beles River. This lowland is filled with Hapelic Nitosol \u0026amp; and Hapelic Arcisol, soils with moderately well drainage classes. These soils are among the most productive soils of the humid tropics appropriate for a wide variety of crops. The elevation of AES 4 falls in the lowlands class which varies between 550 and 1200 m. The Agro-climate of this AES is Wet Kolla characterized as sub-humid receiving a rainfall varying from 1250 to 1800 mm and temperature varying from 20 to 25 \u003csup\u003eo\u003c/sup\u003eC. Maize and sorghum as well as wheat-based farming systems dominate in the agro ecosystem. It is also a potential area for pulses and oil crops.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMid Land Sub Humid (AES5)\u003c/strong\u003e \u003cp\u003eThe Midland sub-humid agroecosystem is found in the southern toe of the TBSB, extending from southwest of the Lake Tana sub-basin to the Beles sub-basin. Soils in this AES are predominantly Euteric Cambisol \u0026amp; and Haplic acrisols. The first group of soils is characteristically poor in drainage class, clay loam in texture and neutral in acidity. Whereas the second one is moderately well in drainage class, silt loam in texture and less acidic. The altitude of this AES ranges from 1500 to 2500m. The topography of this AES is highly sloppy, particularly in the sub-basins divide and in the west. Most of the aspect in this AES falls to the North West. The climate of this AES as the name indicates is sub-humid with a rainfall rate of 1000 to 1800 mm and a temperature range from 15 to 22 \u003csup\u003eo\u003c/sup\u003eC. Poor drainage class of the soils and the highly rugged landform, are major constraints for production of this AES. The main crops produced in this AES include Maize, wheat, and Teff.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eMid-Land Sub Moist (AES6)\u003c/strong\u003e \u003cp\u003eAES is found in the western part of the Lake Tana Sub basin. The topography of this AES ranges from 2200 to 2800m and soils are predominantly Euteric Fulvisol \u0026amp; and Chromic Luvisol. These groups of soils are generally characterized as clay loam in texture, moderately well drainage class, and neutral in acidity. The agro climate of the AES is moist Weina dega with rainfall 900 to 1200 mm and temperature varies from 15 to 20 \u003csup\u003eo\u003c/sup\u003eC. The major crops grown in this AES include Barley, wheat, Teff, and potato.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eHighland Sub Moist (AES7)\u003c/strong\u003e \u003cp\u003eThe highland sub-moist agroecosystem is found in the eastern periphery of the TBSB next to AES 6; it is confined in a small area of 0.2%. Euteric Liptosols are the dominant soils and the topography is generally characterized as sloppy. The agro climate of this AES is moist dega with average rainfall of 1000 mm and temperatures as cold as 7\u003csup\u003eo\u003c/sup\u003eC. Though the soils are fairly good for various crops the coldest temperature associated with the limited rainfall and hilly topography makes it difficult for a wide range of crops to grow. Cold weather crops (Dominantly Barley) are the major agricultural production in this AES. Most parts of such AES are used as Rangeland (grazing or pasture land).\u003c/p\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.5. AES Suitability for Agriculture\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAES suitability evaluation is important for planning strategies to increase agricultural productivity and to identify priority areas for potential management and policy interventions. Agricultural land suitability evaluation does not specify any particular evaluation methodology. In this study, the criteria used for AES suitability assessments for agriculture were classified based on various literature, field investigations, and following the FAO guidelines for agricultural land use evaluations. The Food and Agricultural Organization(Kutter et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1997\u003c/span\u003e) for example recommended an approach for land suitability evaluation for crops in terms of suitability ratings ranging from highly suitable to not suitable based on climatic and terrain data and soil properties. Plenty of criteria for land suitability evaluation for agriculture such as soil type, soil depth, soil water content, soil stoniness slope elevation and proximity to towns, roads and water resources are available in the vast literatures. However, since an AES in the present study represents the intersection of; a set of agriculturally relevant climatic factors (the agroecological zone), soils, and physiographic variables relevant to crop production, a prevailing set of cropping practices; we select the soil type, elevation, slope and climate to characterize the suitability of an AES for agriculture in the TBSB (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAccording to the FAO guidelines land suitability for agriculture is classified into five categories (1) highly suitable, (2) moderately suitable, (3), marginally suitable, (4) currently unsuitable and, (5) permanently unsuitable. This criterion is further customized and reclassified by (Sileshi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) into four classes designating, 4\u0026thinsp;=\u0026thinsp;highly suitable; 3\u0026thinsp;=\u0026thinsp;moderately suitable; 2\u0026thinsp;=\u0026thinsp;marginally suitable; and 1\u0026thinsp;=\u0026thinsp;unsuitable. The unsuitable represents both the permanently unsuitable and currently unsuitable categories of the FAO method. The present study also follows the criteria used in (Sileshi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) with some customizations to evaluate the suitability of AES for agriculture in the TBSB. During the AES classifications, we do not find considerable forest and protected areas hence our evaluation does not consider such areas. Moreover, such land cover may not apply to agricultural practices in crop production.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eSuitability\u003c/b\u003e class of the various parameters for agriculture in the TBSB\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCriteria\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eSuitability score\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDetails\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoil type\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEVertisol, EFulvisol, CLuvisol and ELeptosols\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eECambisol, HAlisol and HNitosol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHAcrisol\u003c/p\u003e \u003cp\u003eRNitosol and LLeptosol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eERegosl\u003c/p\u003e \u003cp\u003eand HLuvisol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Nations, 2007)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eElevation (m)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1500\u0026ndash;2500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1000\u0026ndash;1500 and 2500\u0026ndash;3000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e500\u0026thinsp;\u0026minus;\u0026thinsp;100 and 3000\u0026ndash;3500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;500 or/and \u0026gt;\u0026thinsp;3500\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Mengistu, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2003\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSlope class (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u0026ndash;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7\u0026ndash;15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Nations, 2007),(Sileshi et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAgro Climate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMoist and Wet weinadega,\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMoist and Wet kolla\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMoist and wet dega\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003emoist wurch, wet wurch and wet alpine wurch\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e(Hurni, 1998; Kutter et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e1997\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cem\u003eNB: extended names of the soil type can be obtained from\u003c/em\u003e Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cem\u003eeg., EVertisol represents to Euteric Vertisol.\u003c/em\u003e\u003c/p\u003e \u003cp\u003eThe analysis of AES suitability conducted following different criteria of the different layers with their respective priority is summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. According to these criteria, most of the TBSB is characterized as suitable for agricultural productions. Numerically it was identified that 50.65% of the study area is highly suitable, 26.7% is moderately suitable, 22.7% is marginally suitable and only 0.2 is unsuitable. previous studies in the upper Blue Nile basin also show About 50% of the Tana catchment (North) was classified as \u0026lsquo;highly suitable\u0026rsquo;, which is a consistent result of the current studies. A recent study by (Gashaw et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) on land capability in Geleda watershed, Upper Blue Nile basin, also found land suitability for cultivation which is higher than the land currently under cultivation. Their research indicates the major account of cultivatable land in their study site. Where in the present study the highlands Particularly AES6 and AES7, are characterized as unsuitable for agricultural practices of various crops. This is associated with the low temperatures in the mountainous areas. Similarly, according to the agroecological zoning of Ethiopia (FAO 2003): Elevation above 3,700 m is classified as \u0026lsquo;high wurch\u0026rsquo; (frosty-alpine) and thus unsuitable for agricultural purposes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of agricultural suitability map of AES in the TBSB\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAES\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal area (km\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eArea (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSuitability class\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAES 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarginally suitable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAES 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14,788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHighly suitable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAES 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e410\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarginally suitable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAES 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2,651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerately suitable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAES 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5,135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModerately suitable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAES 6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMarginally suitable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAES 7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eUnsuitable\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eGenerally, the AES suitability of the TBSB is summarized in the tables; the productivity potential of a specific AES can be decreased or enhanced depending on the constraints and inputs. While several constraints such as water logging, soil acidity, deforestation, and climate variability are the factors to reduce the AES suitability for agriculture, various inputs like water conservation techniques, climate information, introducing technologies and quality fertilizers can be used for potential productivity. The main variable in the study, climate change; is a major threat to the suitability of the AESs and the potential productivity of crops, as it can also affect the other variables directly or indirectly by influencing the spatial and temporal patterns of rainfall and temperature. Moreover, the higher variations in suitability among AESs could be associated with the highly variable topography in addition to the aforementioned constraints and imputes.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion and Recommendation","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAgro-ecosystem analysis could be important for classifying the landscape and for identifying agro-ecosystem-specific opportunities and constraints for climate change adaptation. The present study examines the agroecosystems in the TBSB and their suitability for the agricultural practices of various crops. The analysis was started by characterizing various variables that are believed to be components of an AES in the TBSB. These variables are; the agro climate of the study area, the soil characteristics, the topography (elevation, slope, and aspect), and farming systems. These variables' characteristics were categorized based on various classification and characterization guidelines. The distinct characteristics of these variables were input to the definition of the agroecosystems and characterization of their suitability for agriculture.\u003c/p\u003e \u003cp\u003eThe analysis reveals that the TBSB is comprised of twelve homogeneous soil classes. The topsoil (~\u0026thinsp;30 cm) characteristics generally show moderately well drainage class, moderately acidic to less alkaline, and clay loam in texture properties. The analysis also reveals the topography of the TBSB, which ranges from 500 to above 4000 m in Elevation, from flat Plains (0\u0026thinsp;~\u0026thinsp;7%) to hilly mountains (\u0026gt;\u0026thinsp;50%) in slope and North to North West general aspect. The topography is generally characterized as flat in the west (Beles sub-basin) and hilly and ragged in the North and North East (Tana sub-basin). During the analysis of farming systems, nine crop-based farming systems are identified and Teff, Maize, Wheat, and Barley are among the dominant cereal crops cultivated in the TBSB. Moreover, mixed-crop livestock was recognized as the dominant farming system in the TBSB.\u003c/p\u003e \u003cp\u003eFinally, seven agroecosystems namely; Moist Lowlands (AES1), Moist Midlands (AES2), Moist Highlands (AES3), Sub Humid Lowlands (AES4), Sub Humid Midlands (AES5), Sub Moist Mid Lands (AES6) and Sub Moist Highlands (AES7) were defined in the TBSB. The agroecosystems AES2, AES1, and AES5 are the dominant agroecosystems in the TBSB covering 50.6%, 19.3%, and 17.6% of the total area; whereas, AES7 is the smallest agroecosystem class identified which covers only 0.2% of the total TBSB. The analysis also includes a suitability assessment of the agro-ecosystems to agriculture particularly crop production of the major crops following FAO guidelines. Accordingly, the Moist Midlands are highly suitable, the Sub Humid Lowlands and Sub Humid Midlands are moderately suitable, the Moist Lowlands, Moist Highlands, and Sub Humid Midlands are marginally suitable and the Sub Moist Highlands are unsuitable. It is noted that the suitability of an AES can be modified by the intervention of the constraints such as adaptations to climate change and adding inputs such as fertilizers.\u003c/p\u003e \u003cp\u003eThe current study has provided the Agroecosystem classes and their suitability for agriculture under current land use land cover and climatic conditions. Though several types of agroecological practices can be used to improve agricultural production and natural capital in and around agro ecosystems; current classification suggests, Conservation tillage: which reduces the amount of tillage, sometimes to zero, so that soil can be conserved and available moisture used more efficiently; Water harvesting in the dry land areas, which can mean formerly abandoned and degraded lands can be cultivated, and additional crops grown on small patches of irrigated land owing to better rain water retention.\u003c/p\u003e \u003cp\u003eHowever, future land uses, climate, and agricultural production are not known with certainty. Understanding that Intensification of agricultural production, considering the challenges and opportunities of the different agroecosystems, can ensure food security and contribute to climate change adaptations. For example, the application of improved seeds, fertilizer application, irrigation, soil, and water conservation measures can contribute greatly to increasing the economic benefit of agroecosystems. Agro-ecosystem assessments that incorporate high-quality data and multiple variables followed by feasible adaptation strategies are our recommendations to be worked ahead to overcome the climate change impacts and to benefit from opportunities.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflicts of Interest:\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis research received no external funding\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eThe author designed and analyzed the results. he also writes and reads the whole contents of the Manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgments:\u003c/h2\u003e \u003cp\u003eI would like to thank institutions, Ethiopian Meteorology, World Clim, and HWSD for the data availability\u003c/p\u003e\u003ch2\u003eData Availability Statement:\u003c/h2\u003e \u003cp\u003eThe data presented in this study are available on request from the author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAranguiz AA, Creemers J (2019) \u003cem\u003eQuick scan of Ethiopia\u0026rsquo;s forage sub-sector\u003c/em\u003e. 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MoWR, Addis Ababa, Ethiopia\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeldegerima TM, Birhanu BS, Zeleke TT (2023) \u003cem\u003eZoning and agro-climatic characterization of hotspots in the Tana-Beles Sub-Basin\u0026ndash;Ethiopia\u003c/em\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorku LY (2015) Climate change impact on variability of rainfall intensity in the Upper Blue Nile Basin. \u003cem\u003eProceedings of the International Association of Hydrological Sciences\u003c/em\u003e, \u003cem\u003e366\u003c/em\u003e, 135\u0026ndash;136\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Soil properties, Farming systems, Agroecosystem analysis, Agro ecosystem suitability and Tana Beles Sub Basin","lastPublishedDoi":"10.21203/rs.3.rs-3893422/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3893422/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study focuses on the imperative role of farming systems research in addressing agricultural challenges amid climate change. The primary aim is to assess agroecosystems and their suitability for farming practices within the Tana Beles Sub Basin (TBSB) in Ethiopia. Climate characterization involves utilizing observed rainfall and temperature data from the National Meteorology Agency (NMA) and gridded data from the WorldClim2 database for agro-climate zone delineation. Soil information is derived from the Harmonized World Soil Database version 1 (HWSD1), while agroecological data is collected from the regional Agriculture and Rural Development Bureau. Various analytical techniques, including agroclimate zoning, soil classification, farming classifications, and agro-ecosystem analysis, are employed. The TBSB is categorized into seven agroecosystems, with Moist Midlands (AES2) dominating the region, covering approximately 50% of the area. Altitudinal variation in AES2 ranges from 1500 m to over 2800 m, and key soils include Euteric Vertisol, Euteric Fulvisol, and Hapelic Luvisol. The topography spans flat plains to hilly mountains with slopes ranging from 0% to over 45%. AES2 supports diverse crops like Teff, Maize, Wheat, Barley, Rice, Chatt, and coffee, with the potential for irrigation schemes along the rivers. Suitability analysis reveals that AES2 is highly suitable for agriculture, AES4 and AES5 are moderately suitable, AES1, AES3, and AES6 are marginally suitable, and AES7 is unsuitable. This comprehensive assessment provides valuable insights into the diverse agroecological conditions within the TBSB, facilitating informed decision-making for sustainable agricultural development in the face of climate change.\u003c/p\u003e","manuscriptTitle":"Defining Agroecosystems and Suitability Analysis in the Tana Beles Sub Basin, Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-29 19:48:18","doi":"10.21203/rs.3.rs-3893422/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab49d602-d241-4feb-9bca-9c80d18589cf","owner":[],"postedDate":"January 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-21T00:08:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-29 19:48:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3893422","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3893422","identity":"rs-3893422","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}