Land suitability analysis for farm mechanization using GIS-based analytical hierarchy process and multicriteria decision-making method in Ethiopia

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Busse This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7396673/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 Farm mechanization plays a crucial part in improving productivity, reducing poverty, and optimizing resources. Farming in sub-Saharan Africa, including Ethiopia, is the least mechanized, less than one percent of agricultural land plowed with a tractor. This is mostly due to lack of adequate scientific and technical research in the area of mechanization, including studies in land suitability and selecting appropriate agricultural machinery. This study aimed to categorize and map farmlands suitable for farm mechanization. In this study, GIS integrated with analytical hierarchy process (AHP) and multicriteria decision-making method was used. Seven different factors, slope, soil texture, depth, drainage, road access, rainfall, and land use, were analyzed using a GIS-based weighted-overlay analysis. The results exhibited a large proportion (88.1%) of the zone, which is 844,689 ha, was found to be in different suitability range for farm mechanization, with 541,279 ha (56.4%) and 219,285 ha (22.9%) being highly, moderately, and marginally suitable, respectively. Approximately 470 ha (0.05%) unsuitable and the remaining (11.9%) or 113,898 ha is restricted for and settlement area. This study highlights the potential for expanding farm mechanization in the zone and the effect of terrain and soil physical factors on land suitability for farm mechanization. Biological sciences/Ecology Earth and environmental sciences/Ecology Physical sciences/Engineering Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Environmental social sciences Slope Soil texture Pair-wise comparison Overlay analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction The growing demand for food production, driven by population growth, industrialization, and urbanization, emphasizes the need for agricultural intensification. Farm mechanization is vital for enhancing productivity, reducing poverty, and optimizing strategic use of resource to achieve food security and sustainable development 1 . It contributes an important part in improving labor efficiency, reducing costs, and alleviating the physical burden on farmers, particularly women, while fostering rural development, job creation 2 . Sustainable farm mechanization minimizes environmental impacts by reducing soil erosion and improving land reclamation by incorporating environmentally friendly technologies such as precision agriculture 3 . Farming systems in Africa appears to be the least mechanized specially in sub-Sahara countries 4 . Ethiopia is among these countries, where the farm mechanization level of is very low, with less than one percent of agricultural land plowed with a tractor. Animal traction and handheld tools are the dominant power sources for smallholder farmers 5 . This is mostly due to lack of adequate scientific and technical research in the area of mechanization, which includes study in land suitability and selection appropriate agricultural machineries 6 . Assessing land suitability for farm mechanization in an important part of mechanizing a farm. Lots of studies have been done on farmland suitability 7 , 8 . In the assessment of farmland suitability, different factors, such as the topography of the area, soil properties, infrastructure (e.g., road network), and other relevant factors, have been considered 9 . Researchers have used different analytical methods to study farmland suitability for different agricultural practices 10 . In this method, qualitative descriptions, spatial data analysis, and other techniques are used. Analytic hierarchical process (AHPs) models and multicriteria decision method analysis (MCDM) are used in many assessments to identify farmland suitability 8 . Geographic information system (GIS) and remote sensing are preferred by scholars in the area of spatial data acquisition and analysis in land suitability studies for their significant advancements 11 , 12 . Materials and Methods Study area description The study was conducted in East-Shewa zone of Ethiopia (Fig. 1 ). It extends from 7°30′ N to 9°15′ N and from 38°00′ E to 40°00′ E, and a large proportion of the zone is situated in the Rift Valley. Ecologically, the zone is characterized by arid climatic conditions with ample water sources. The zone comprises diverse agro-ecological zones, mostly midland and highland, which are characterized by moderate to high elevations and receive variable amounts of rainfall. The zone experiences mean seasonal rainfall amounts of 300 mm over the northeast and 660 mm over the northwest 13 . Agriculture is the major economic source, with both rain-fed and irrigation-based cropping systems in the region. Data sources and types All relevant data from different sources were collected for this study. Land use land cover with a high resolution of 10 m Sentail-2 map and digital elevation model with 30 m resolution was downloaded from Environmental system research institute (ESRI) and the U.S Geographic survey websites, respectively, to generate the raster maps. A precipitation raster map generated by taking point data from different meteorology stations acquired from the National meteoroidal agency (NMA) of Ethiopia was downloaded from the EthioGIS-3 website. Digital road network and soil maps of the zone were obtained from the Ethiopian Water and Land Resources Information System (WALRIS) downloaded from the EthioGIS-3 website. Methods Analytical methods used To categorize and map the study area, multicriteria decision-making (MCDM) together with analytical hierarchy process (AHP) was used with GIS as the data acquisition, processing, and analysis tool. ArcGIS 10.5 software was used to prepare raster data layers of seven factors, that potentially influence the suitability of the study area for farm mechanization. Factors in the analysis of land suitability for farm mechanization vary depending on the location being studied. In this study, seven factors highly related to farm mechanization were considered depending on evaluation of earlier studies on farm mechanization, availability of data, expert opinions, and bio-physical conditions. These factors include slope, rainfall, land use land cover, soil texture, soil drainage, soil depth, and road network (Fig. 2 ). Methods used to prepare land suitability factors map In the ArcGIS environment, the spatial analyst tools were used to produce the slope map directly from the digital elevation model (DEM) of the study area. Rainfall map generated by interpolating rainfall point data from the seven meteorological stations obtained from the National meteorological agency of Ethiopia was down loaded from EthioGIS-3 website. The land use land cover map of the zone was extracted from the map downloaded from the ESRI website. Then the number assigned class were renamed to their respective land used land cover names using ArcGIS software. Soil-related factor map layers of the study area were extracted from the digital soil map obtained from the Ethiopian water and land resource information system (WALRIS). The clipping tool in ArcGIS 10.5 was used to extract the vector soil map and convert it to a raster. All the raster maps of the factors were then rescaled to a uniform resolution of 30 m using the resampling tool in ArcGIS. The factors with uniform resolution were then reclassified into a measurement scale of 1 to 5, being 1 very low and 5 very high, respectively. This was done depending on the intensity gradient of the factors map using the reclassify tool in ArcGIS. Method for land suitability map preparation Analytical hierarchy process (AHP) is effective and commonly used method in multicriteria decision-making (MCDM). It is mostly used to evaluate the relative influence of the factors in a study. Several studies 12 , 14 – 16 involving different land-related studies have used this method. According to 17 , relative influence weight among the factors was assigned to 1 to 9 scale, and pairwise comparison of the factors was conducted. According to the scale, a value of 9 and 1 denotes very high importance, and equal importance, respectively. Then, normalized pairwise comparison was computed by dividing the values in each column by column sum of the pairwise comparison table. Relative weight of the factors was then identified by dividing the sum of each row by the number of factors in the normalized pairwise comparison table. The consistency index was used to checked the constancy for pairwise comparison, which was calculated by equations (Eq. 1 ) according to 17 . $$\:CI=\frac{{{\lambda\:}}_{max}-n}{n-1}$$ 1 Where CI: consistency index n: the number of factors and λ𝑚𝑎𝑥: the highest eigenvalue The value of maximum eigenvalue (λ𝑚𝑎𝑥) was calculated asper the procedures given by 17 . As suggested by 17 equation (Eq. 2 ) was used for computing consistency ratio (CR) to check the comparison consistency. The value of CR should be below 0.1, otherwise; the pairwise comparison is not accepted considering it to be inconsistent. $$\:CR=\frac{CI}{RI}$$ 2 Where CR: the consistency ratio CI: consistency index RI: random index To prepare the suitability map, the reclassified spatial layers were integrated and overlaid using weighted overlay analysis. Weighted overlay analysis was done using the spatial data analysis in ArcGIS environment based on (Eq. 3 ) below. This was done by multiplying the number of pixels in the raster map layers found from the AHP analysis with their influence weight percentage and adding them. This technique has been used in different studies 18 , 19 to conduct different land-related studies, including land suitability, flood vulnerability, and site selection for different purposes in the ArcGIS environment. $$\:MS=\sum\:_{i=0}^{n}\left({X}_{i}\right){W}_{i}\:$$ 3 Where 𝑀𝑆: mechanization suitability n: number of decision criteria Xi: normalized factor Wi: weight of factor Results and Discussion Land suitability factors processing After reclassification of the factors (Figs. 3 and 4 ), based on the AHP analysis using pairwise comparison matrix, each factor was assigned with a relative influence weight (Table 1 ). As per 20 , the normalization (Table 2 ) and consistency (Table 4 ) of the pairwise comparison was computed and checked. Table 1 Pairwise comparison matrix Factors Slope Soil type Soil depth Soil drainage Rainfall Road network Land use Slope 1 3 5 7 8 9 9 Soil Type 1/3 1 3 4 7 8 7 Soil Depth 1/5 1/3 1 3 4 5 5 Soil Drainage 1/7 1/4 1/3 1 3 4 3 Rainfall 1/8 1/7 1/4 1/3 1 3 5 Road Network 1/9 1/8 1/5 1/4 1/3 1 3 Land Cover 1/9 1/7 1/5 1/3 1/5 1/3 1 Sum 2.02 4.99 9.98 15.92 23.53 30.33 33 The final criteria weight for each land suitability factor (Table 2 ) was calculated to be, (42%) for slope, (24%) for soil type, (13%) for soil depth, (8%) for soil drainage, (6%) for annual rainfall, (4%) for distance from access road, and (3%) for land cover. To compute consistency ratio (CR), random index (RI) a value 1.32 was used with influencing factors of seven (Table 4 ). The (RI) value varies depending on the number of influencing factors according to the literature 21 . The consistency index (CI) was found to be 0.125 by computing using (Eq. 1 ) with highest eigenvalue (λ max ) = 7.75 and number of factors (n = 7) (Table 3 ). Then, consistency ratio (CR), was calculated to be 0.09 using (Eq. 2 ). The weighted overlay analysis was performed depending on the value of the consistency ratio, which was within the acceptable range of 0 to 0.1(10%). Table 2 Calculated criteria weight and normalized pairwise comparison Factors S ST SD SDR RF RN LULC Sum CW CW (%) S 0.4942 0.6007 0.5008 0.4398 0.3399 0.2967 0.2727 2.94 0.42 42 ST 0.1647 0.2002 0.3005 0.2513 0.2975 0.2637 0.2121 1.69 0.24 24 SD 0.0988 0.0667 0.1002 0.1885 0.17 0.1648 0.1515 0.94 0.13 13 SDR 0.0706 0.0501 0.0334 0.0628 0.1275 0.1319 0.0909 0.57 0.08 8 RF 0.0618 0.0286 0.025 0.0209 0.0425 0.0989 0.1515 0.43 0.06 6 RN 0.0549 0.0250 0.02 0.0157 0.0142 0.033 0.0909 0.25 0.04 4 LULC 0.0549 0.0286 0.02 0.0209 0.0085 0.011 0.0303 0.17 0.02 3 S: slope, ST: soil texture, SD: soil depth, SDR: soil drainage, RF: annual rainfall, RN: road network, and LULC: land use and land cover, CW: criteria weight Table 3 Pairwise comparison consistency check result table (CR = 0.09) Factors S ST SD SDR RF RN LULC WSV CW WS/CW S 0.4207 0.7243 0.6718 0.5671 0.4906 0.3262 0.2241 3.4249 0.4207 8.14 ST 0.1402 0.2414 0.4031 0.3241 0.4293 0.29 0.1743 2.0024 0.2414 8.29 SD 0.0841 0.0805 0.1344 0.2431 0.2453 0.1812 0.1245 1.0931 0.1344 8.14 SDR 0.0601 0.0604 0.0448 0.081 0.184 0.145 0.0747 0.6499 0.081 8.02 RF 0.0526 0.0345 0.0336 0.027 0.0613 0.1087 0.1245 0.4422 0.0613 7.21 RN 0.0467 0.0302 0.0269 0.0203 0.0204 0.0362 0.0747 0.2554 0.0362 7.05 LULC 0.0467 0.0345 0.0269 0.0270 0.0123 0.0121 0.0249 0.1844 0.0249 7.4 λ.max 7.75 S: slope, ST: soil texture, SD: soil depth, SDR: soil drainage, RF: annual rainfall, RN: road network, and LULC: land use land cover, WSV: weighted sum value, CW: criteria weight, and WS: weighted sum Table 4 RI values for (n = 1 to 15) number of criteria factors n 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 RI 0 0 0.58 0.9 1.12 1.24 1.32 1.41 1.45 1.49 1.51 1.48 1.56 1.57 1.59 Analysis of factors Slope The slope of farmland highly influences the type, efficiency, safety, and utilization of farm mechanization 19 . Land terrain with slopes larger than 15 °is generally not suitable for farmland and farm operations due to high risks of machinery instability and rollover 22 . Gentle slopes (typically less than 8%) are more suitable for farm mechanization, as they are less prone to erosion risk and facilitate better soil retention 23 . The reclassified raster map (Fig. 3 c) shows approximately 97.76% (966,616 ha) of the land has a range of slope between 0 °to 15°, which is in the suitable range of land for farm mechanization. Of these, approximately 50.23% (496,612 ha), 31.79% (314,332 ha), 9.41% (93053 ha), 6.33% (62619 ha) and 2.24% (22156 ha) were considered as very highly (0–2°), highly (2–5°) moderately (5–8°) low (8–15°), and unsuitable (15-40.4°) for farm mechanization, respectively (Table 5 ). As indicated in the Figure, most of the land in the area falls under a suitable range of slopes, except for some patches in different parts of the zone. Rainfall In an area, rainfall distribution dictates the type of crop cultivated 24 . Rainfall distribution determines soil workability, which is affected by soil moisture content. The duration of growing season is determined by the onset of rainfall and its duration 25 . Rainfall dictates machinery management and planning; in regions where rainfall is intense in specific months, farmers should plan their planting and harvesting schedules accordingly 24 . The mean annual rainfall of the zone ranges from 507.91 to 1201.71 mm. The rainfall distribution pattern was then reclassified into five classes with (507.91–704.85 mm), (704.85–824.46 mm), (824.46–917.12 mm), (917.12–1001.96 mm), and (1001.96–1201.71mm), representing very low, low, moderate, high and very highly suitable for farm mechanization, respectively. As shown in (Fig. 3 b), most of the northern part of the study area is highly suitable for farm mechanization in terms of annual rainfall availability. As a result, 12.69%, 22.39%, 27.92%, 25.63%, and 11.37% of the area found to be very low, low, moderate, high and very highly suitable for farm mechanization in relation to annual rainfall (Table 5 ) Distance from access road A well-developed road network is crucial for the easy transportation of agricultural inputs and farm products to farms and harvested crops to markets 19 . Proximity to roads is predominantly significant in mechanized farming, where timely operations affect crop yields by significantly reducing transportation costs with reduced travel time and distance 19 . Without adequate road access, farmers are limited to smaller and less efficient equipment. Good road networks facilitate the movement of modern and larger machinery 26 . Farmlands within 1000 m from an access road were classified as very suitable, while those within a distance of 2000, 3000,4000, and > 4000 m were considered to have very high, high, moderate, low and very low suitability for farm mechanization, respectively (Table 5 ). As shown in (Fig. 3 a), the study areas have a reasonably good road network, except for some peripheral areas, which make the zone more suitable for farm machinery with respect to the availability of road infrastructure. Land use and land cover The type of land cover significantly affects the efficiency and utilization of agricultural machinery in that area. Understanding these challenges is critical for decision-making regarding farm mechanization 27 . It often requires significant investments for clearing, soil improvement, and infrastructure development to transform land from less suitable land covers (such as woodland or shrubland) to cropland 28 . Based on the land use land cover map (Fig. 3 d), the zone was categorized as very high (cropland) with an area of 546,134.94 ha (55.4%), high (bare land) 77,405.67 ha (7.85%), moderate (grassland) 98,415.45 ha (9.98%), low (shrubland) 121,338.45 ha (12.31%), and very low (woodland) 22,924.53 ha (2.33%) in terms of land suitability for farm mechanization (Table 5 ). Other LULC types that cannot be used as farmland, including wetlands, water bodies, forests, and settlements, have been designated as restricted in the map. Table 5 Suitability factors (Slope, Rainfall, Distance to Road and Land cover) and their status Factor Class Suitability Rating Class pixels Area (ha) Percent (%) Slope (degree) 0–2 Very high 5 5,517,912 496,612 50.23 2–5 High 4 3,492,583 314,332 31.79 5–8 Moderate 3 1,033,921 93,053 9.41 8–15 Low 2 695,769 62,619 6.33 15–40.3521 Not suitable 1 246,173 22,156 2.24 Rainfall (mm) 507.91-704.85 Very low 1 1,387,602 124,884 12.69 704.85-824.46 Low 2 2,448,647 220,378 22.39 824.46-917.12 Moderate 3 3,053,399 274,806 27.92 917.12–1002 High 4 2,802,357 252,212 25.63 1001.96-1201.7 Very high 5 1,242,862 111,858 11.37 Distance to Road 0–1000 Very high 5 6,078,883 547,099 55.5 1000–2000 High 4 2,339,170 210,525 21.36 2000–3000 Moderate 3 1,204,755 108,428 11 3000–4000 Low 2 592,589 53,333 5.41 4000–9240 Very low 1 737,453 66,371 6.73 Land cover Forest Restricted 0 1,054,301 94,887 9.63 Woodland Very low 1 254,717 22,924.5 2.33 Shrub/bush Low 2 1,348,205 121,338.5 12.31 Grassland Moderate 3 1,093,505 98,415.5 9.98 Barren land High 4 860,063 77,405.7 7.85 Cropland Very high 5 6,068,166 546,135 55.4 Wetland Restricted 0 42,917 3862.5 0.39 Water body Restricted 0 80,978 7,288 0.74 Settlements Restricted 0 150,013 13,501 1.37 Soil texture Soil texture significantly affect the performance of agricultural machinery. According to research by 29 , loamy soils (a mix of sand, silt, and clay) provide optimal conditions for machine operation due to their balance of drainage and nutrient retention. In contrast, clay soils become compacted under heavy machinery operations, affecting machinery performance 30 . Soil compaction due to agricultural machinery traffic varies for different soil textures 31 . Fine to medium textured soils are more susceptible to compaction, which can hamper root depth and reduce soil aeration 30 . As shown in (Fig. 4 c) there are five different soil texture classes in the zone which are classified in to five different class as very low clay (light), which accounts about 262,387ha (26.65%), low (clay loam) 298,897 ha (30.35%), moderately suitable (sandy clay loam) 39,406 ha (4%), highly suitable (sandy loam) 86,700 ha (8.8%), and very highly suitable (loam) 297,332 ha (30.19%). Approximately 42.99% of the zone is characterized as moderately to very highly suitable for farm mechanization. The remaining 57% of the zone is categorized as low and very low in terms of farm mechanization suitability (Table 6 ). Table 6 Suitability factors (Soil Texture Class, Soil Drainage & Soil Depth) and their status Factor Class Suitability Rating Class pixel Area (ha) Percent (%) Soil Texture Class clay (light) Very low 1 2,915,410 262,387 26.65 clay loam Low 2 3,321,082 298,897 30.35 sandy clay loam Moderate 3 437,845 39,406 4 sandy loam High 4 963,329 86,700 8.8 loam Very high 5 3,303,688 297,332 30.19 Soil Drainage Poor Very low 1 1,884,810 169,633 17.23 Imperfect Low 2 2,489,374 224,044 22.75 Moderate Moderate 3 6,567,170 591,045 60.02 Soil Depth 0–10 Very low 1 1,566,964 141,027 14.27 10–34 Low 2 878,948 79,105 8.01 34–61 Moderate 3 1,396,805 125,712 12.72 61–86 High 4 2,809,513 252,856 25.59 86–100 Very high 5 4,324,836 389,235 39.4 Soil depth Soil depth is an important factor that influences land suitability for farm mechanization. Deeper soils favor more effective tillage practices, reducing soil resistance and improving workability 26 . Shallow soils hinder the use of certain machinery and may not be suitable for the desired tillage depth 32 . As shown in (Fig. 4 a), the soil depth up to 100 cm was considered and categorized into five different classes with the range very low (0–10 cm), low (10–34 cm), moderate (34–61 cm), high (61–86 cm), and very high (86–100 cm) in terms of their suitability for farm mechanization. Where 141,027 ha (14.27%) account for very low, 79,105 ha (8.01%) low, 125,712 ha (12.72%) moderate 252,856 ha (25.59%) and 389,235 ha (39.4%) with respect to suitability for farm mechanization (Table 6 ) Soil drainage Soil drainage is a significant factor affecting land suitability for farm mechanization. It affects various aspects of farming operations 33 . Well-drained soils are easily accessible to agricultural machinery and are less prone to compaction, even after rainfall 34 . Poorly drained fields become muddy and inaccessible, which can cause delays in field operations. This can affect crop yields and profitability because of missed planting windows or late harvests 35 . As shown in (Fig. 4 b), the study area has three different levels of soil drainage, which are categorized as poor, imperfect, and moderate soil drainage. These were classified as very low, low, and moderate, respectively, in terms of their suitability for farm mechanization. Where 169633 ha (17.23%) account for very low, 224044 ha (22.75%) low, and 591045 ha (60.02%) moderate respectively with respect to farm mechanization suitability (Table 6 ). Land suitability map of the study area for farm mechanization As discussed above the farm mechanization suitability map of the study area (Fig. 5 ) was generated by considering seven farm mechanization influencing factors. Table 7 Land suitability in area coverage and percentage of the zone No Description Cell count (30x30) Area (ha) Percentage (%) 1 Restricted 1,265,537 113,898 11.9 2 Not suitable 5,223 470 0.05 3 Marginally suitable 2,436,500 219,285 22.9 4 Moderately suitable 6,014,210 541,279 56.4 5 Highly suitable 934,719 84,125 8.8 Sum 10,656,189 959,057 100 Based on the analysis results, the study area was categorized into five suitability class of restricted, not suitable, marginally suitable, moderately suitable, and highly suitable. As indicated in (Table 7 ), the zone has a large proportion of area about 844,689 ha (88.1%) that falls under different suitability range for farm mechanization, which covers a suitability range from marginal to high. Of these, 541,279 ha (56.4%) are moderately, 84,125 ha (8.8%) are highly, and 219,285 ha (22.9%) are marginally suitable for farm mechanization. Only approximately 470 ha (0.05%) of the land was found to be unsuitable for farm mechanization. The remaining (11.9%) or 113,898 ha is area that is restricted for forest, wet lands, and water bodies and used for settlements areas. Conclusion This study provides useful information that can help investors in crop production, farmers, extension workers, and policymakers in promoting farm mechanization in the zone. The study highlighted, that the zone has good potential for mechanized farming. Most of the highly suitable land found in the southern part of the study area, while the majority of the lands fall under moderately suitable range. In this study, although slope is the major influencing factor, the slope of arable lands in the zone mostly falls within the suitable range for farming. Regardless, the land suitability of the zone for farm mechanization was mostly influenced by soil factors, such as sol texture, soil depth and soil drainage. Hence, to improve the suitability of farmlands for farm mechanization, soil priority improvement activities, such as improving drainage and soil organic content. In addition to its local significance, this study provides important insights for land suitability study in the area of agriculture and land management. The method improves the accuracy and interpretability of land suitability identification by combining a wide range of bio-physical, geomorphological, climatic, and soil parameters into a clear and repeatable AHP framework. This makes it a practical decision-support tool for investors, engineers, planners, and policymakers aiming to effectively implement mechanized farming. On a broader scale, the methodology and findings contribute to the expanding global discourse on land suitability studies related to agriculture. The study is limited to analysis of the spatial data; further we plan to extend validation practices by incorporating a comprehensive physical validation work on the ground based on the results indicated in this research result. These validation work will provide a deeper and more accurate balanced understanding for reliable land suitability analysis for farm mechanization and other farming activities. Declarations Competing interests The authors declare no competing interests. Funding This research work was funded by the Ethiopian Institute of Agricultural Research (EIAR) as part of a PhD dissertation work. Author Contribution Yonas Mulatu: conceptualization, methodology, data analysis, visualization, mapping, and writing-original draft. Mihret Dananto: review and editing, methodology. Siraj K. Busse: resources, visualization, writing-review, and editing. 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Multi-criteria decision based geospatial mapping of flood susceptibility and temporal hydro-geomorphic changes in the Subarnarekha basin, India. Geosci. Front. 12 (5), 101206 (2021). Saaty, R. W. The analytic hierarchy process-what it is and how it is used. Math. Model. 9 (3–5), 161–176 (1987). Cogato, A. et al. A GIS-based multicriteria index to evaluate the mechanisability potential of Italian vineyard area. Land 9 (11), 1–17 (2020). Yang, H., Ma, W., Liu, T. & Li, W. Assessing farmland suitability for agricultural machinery in land consolidation schemes in hilly terrain in China: A machine learning approach. Front. Plant. Sci. 14 (March), 1–16 (2023). Saaty, T. L. How to make a decision: The analytic hierarchy process. Eur. J. Oper. Res. 48 (1), 9–26 (1990). AYDIN MC, SEVGİ BİRİNCİOĞLU E, B. Ü. Y. Ü. K. S. A. R. A. Ç. A. CBS tabanlı AHP yöntemi kullanılarak Bitlis İlinin Heyelan Duyarlılık Haritalaması. Turkish J. Remote Sens. GIS . 3 (September), 160–171 (2022). Bietresato, M. & Mazzetto, F. Increasing the safety of agricultural machinery operating on sloping grounds by performing static and dynamic tests of stability on a new-concept facility. Int. J. Saf. Secur. Eng. 8 (1), 77–89 (2018). Magdić, I. et al. Effect of slope position on soil properties and soil moisture regime of Stagnosol in the vineyard. ;(2017):62–73. (2022). Bedane, H. R., Beketie, K. T., Fantahun, E. E. & Feyisa, G. L. The impact of rainfall variability and crop production on vertisols in the central highlands of Ethiopia. Environ Syst Res [Internet]. ; (2022). Available from: https://doi.org/10.1186/s40068-022-00275-3 Gornall, J. et al. Implications of climate change for agricultural productivity in the early twenty-first century. ;2973–2989. (2010). Scholten, T., Nabiollahi, K., Rasoli, L. & Kerry, R. Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models. Agronomy. ;1–20. (2020). Li, J., Rodriguez, D. & Tang, X. Effects of land lease policy on changes in land use, mechanization and agricultural pollution. Land use policy [Internet]. ;64(1):405–13. (2017). Available from: http://dx.doi.org/10.1016/j.landusepol.2017.03.008 Peng, J., Zhao, Z. & Liu, D. Impact of Agricultural Mechanization on Agricultural Production, Income, and Mechanism. Evid. Hubei . 10 (February), 1–15 (2022). Fahim, N. S., Khan, B., Rahman, M. S. & Hossain, A. Effect of Soil Texture on Agricultural Machine Performance in Sylhet, Bangladesh. Am. J. Agric. Sci. Eng. Technol. ; 8 . (2024). KigwangBaek, L. E., Hyungtae, C., Cho, M., Yunsung, C. & SangkyunHan Impact on Soil Physical Properties Related to a High Mechanization Level in the Row Thinning of a Korean Pine Stand. Land. ; (2022). Lin, L. et al. Influence of soil physical and chemical properties on mechanical characteristics under different cultivation durations with Mollisols. Soil. Tillage Res. 224 , 105520 (2022). Seifu, Y., Hiremath, S. S., Tola, S. & Wako, A. Depth and soil physiochemical properties effects on soil compaction in agricultural field. ; 19 (2):170–177. (2023). Crosson, P. R. & Haas, R. B. Agricultural land. Curr. Issues Nat. Resour. Policy ;253–282. (2016). Manik, S. M. N. et al. Soil and crop management practices to minimize the impact of waterlogging on crop productivity. Front. Plant. Sci. 10 (February), 1–23 (2019). Shaheb, M. R., Venkatesh, R. & Shearer, S. A. A Review on the Effect of Soil Compaction and its Management for Sustainable Crop Production. J Biosyst Eng [Internet]. ;46(4):417–39. (2021). Available from: https://doi.org/10.1007/s42853-021-00117-7 Additional Declarations No competing interests reported. Supplementary Files Spatialdata.pdf 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. 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Busse","email":"","orcid":"","institution":"Adama Science and Technology University","correspondingAuthor":false,"prefix":"","firstName":"Siraj","middleName":"K.","lastName":"Busse","suffix":""}],"badges":[],"createdAt":"2025-08-18 07:08:34","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7396673/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7396673/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":90389784,"identity":"8566d48b-5cbd-41c1-82c1-f8b14794730b","added_by":"auto","created_at":"2025-09-02 08:18:07","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":198106,"visible":true,"origin":"","legend":"\u003cp\u003eLocation map of the zone\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7396673/v1/235786898d4f31ae61b0cff3.jpg"},{"id":90390681,"identity":"38b7dae8-7961-4f72-9a18-ca7c6a91542e","added_by":"auto","created_at":"2025-09-02 08:26:07","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":300411,"visible":true,"origin":"","legend":"\u003cp\u003eFlowchart of the Methods used to prepare land suitability factors map\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7396673/v1/aed48a38c62af0b7f175eb6c.png"},{"id":90388606,"identity":"422e7ea6-4526-4b75-b8cc-4ffdf3bc7e6f","added_by":"auto","created_at":"2025-09-02 08:02:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2158098,"visible":true,"origin":"","legend":"\u003cp\u003eReclassified maps of road network (a), rainfall (b), slope and (c), land cover(d)\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7396673/v1/661d513e169bf6e6fc78b7fc.png"},{"id":90388610,"identity":"23cd67db-e2d1-41ef-ab68-bc4c330014d5","added_by":"auto","created_at":"2025-09-02 08:02:07","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":677310,"visible":true,"origin":"","legend":"\u003cp\u003eReclassified maps of soil depth (a), soil drainage (b) and soil texture (c)\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7396673/v1/b85be062e9cf042dbd797e6d.png"},{"id":90388612,"identity":"2c4878a2-772b-4f7d-8dfd-1545a4575594","added_by":"auto","created_at":"2025-09-02 08:02:07","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":26133,"visible":true,"origin":"","legend":"\u003cp\u003eLand suitability for farm mechanization result map\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7396673/v1/8a34c8a21b39e94536d03271.jpg"},{"id":96452772,"identity":"db66c4a6-5eec-491b-a582-90ce0e2cf004","added_by":"auto","created_at":"2025-11-21 09:43:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4053705,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7396673/v1/fa21b82f-f80f-4afd-8551-81c87d01ad62.pdf"},{"id":90388611,"identity":"1940e3f4-0b88-4ff4-bdcb-fd085fab87a0","added_by":"auto","created_at":"2025-09-02 08:02:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":295048,"visible":true,"origin":"","legend":"","description":"","filename":"Spatialdata.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7396673/v1/99cb1698452517faff19f9c6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Land suitability analysis for farm mechanization using GIS-based analytical hierarchy process and multicriteria decision-making method in Ethiopia","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe growing demand for food production, driven by population growth, industrialization, and urbanization, emphasizes the need for agricultural intensification. Farm mechanization is vital for enhancing productivity, reducing poverty, and optimizing strategic use of resource to achieve food security and sustainable development\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. It contributes an important part in improving labor efficiency, reducing costs, and alleviating the physical burden on farmers, particularly women, while fostering rural development, job creation\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Sustainable farm mechanization minimizes environmental impacts by reducing soil erosion and improving land reclamation by incorporating environmentally friendly technologies such as precision agriculture\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eFarming systems in Africa appears to be the least mechanized specially in sub-Sahara countries\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Ethiopia is among these countries, where the farm mechanization level of is very low, with less than one percent of agricultural land plowed with a tractor. Animal traction and handheld tools are the dominant power sources for smallholder farmers\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. This is mostly due to lack of adequate scientific and technical research in the area of mechanization, which includes study in land suitability and selection appropriate agricultural machineries\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAssessing land suitability for farm mechanization in an important part of mechanizing a farm. Lots of studies have been done on farmland suitability\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. In the assessment of farmland suitability, different factors, such as the topography of the area, soil properties, infrastructure (e.g., road network), and other relevant factors, have been considered\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Researchers have used different analytical methods to study farmland suitability for different agricultural practices\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. In this method, qualitative descriptions, spatial data analysis, and other techniques are used. Analytic hierarchical process (AHPs) models and multicriteria decision method analysis (MCDM) are used in many assessments to identify farmland suitability\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. Geographic information system (GIS) and remote sensing are preferred by scholars in the area of spatial data acquisition and analysis in land suitability studies for their significant advancements\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eStudy area description\u003c/h2\u003e\u003cp\u003eThe study was conducted in East-Shewa zone of Ethiopia (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It extends from 7\u0026deg;30\u0026prime; N to 9\u0026deg;15\u0026prime; N and from 38\u0026deg;00\u0026prime; E to 40\u0026deg;00\u0026prime; E, and a large proportion of the zone is situated in the Rift Valley. Ecologically, the zone is characterized by arid climatic conditions with ample water sources. The zone comprises diverse agro-ecological zones, mostly midland and highland, which are characterized by moderate to high elevations and receive variable amounts of rainfall. The zone experiences mean seasonal rainfall amounts of 300 mm over the northeast and 660 mm over the northwest\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Agriculture is the major economic source, with both rain-fed and irrigation-based cropping systems in the region.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eData sources and types\u003c/h3\u003e\n\u003cp\u003eAll relevant data from different sources were collected for this study. Land use land cover with a high resolution of 10 m Sentail-2 map and digital elevation model with 30 m resolution was downloaded from Environmental system research institute (ESRI) and the U.S Geographic survey websites, respectively, to generate the raster maps. A precipitation raster map generated by taking point data from different meteorology stations acquired from the National meteoroidal agency (NMA) of Ethiopia was downloaded from the EthioGIS-3 website. Digital road network and soil maps of the zone were obtained from the Ethiopian Water and Land Resources Information System (WALRIS) downloaded from the EthioGIS-3 website.\u003c/p\u003e\n\u003ch3\u003eMethods\u003c/h3\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eAnalytical methods used\u003c/h2\u003e\u003cp\u003eTo categorize and map the study area, multicriteria decision-making (MCDM) together with analytical hierarchy process (AHP) was used with GIS as the data acquisition, processing, and analysis tool. ArcGIS 10.5 software was used to prepare raster data layers of seven factors, that potentially influence the suitability of the study area for farm mechanization. Factors in the analysis of land suitability for farm mechanization vary depending on the location being studied. In this study, seven factors highly related to farm mechanization were considered depending on evaluation of earlier studies on farm mechanization, availability of data, expert opinions, and bio-physical conditions. These factors include slope, rainfall, land use land cover, soil texture, soil drainage, soil depth, and road network (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMethods used to prepare land suitability factors map\u003c/h3\u003e\n\u003cp\u003eIn the ArcGIS environment, the spatial analyst tools were used to produce the slope map directly from the digital elevation model (DEM) of the study area. Rainfall map generated by interpolating rainfall point data from the seven meteorological stations obtained from the National meteorological agency of Ethiopia was down loaded from EthioGIS-3 website. The land use land cover map of the zone was extracted from the map downloaded from the ESRI website. Then the number assigned class were renamed to their respective land used land cover names using ArcGIS software. Soil-related factor map layers of the study area were extracted from the digital soil map obtained from the Ethiopian water and land resource information system (WALRIS).\u003c/p\u003e\u003cp\u003eThe clipping tool in ArcGIS 10.5 was used to extract the vector soil map and convert it to a raster. All the raster maps of the factors were then rescaled to a uniform resolution of 30 m using the resampling tool in ArcGIS. The factors with uniform resolution were then reclassified into a measurement scale of 1 to 5, being 1 very low and 5 very high, respectively. This was done depending on the intensity gradient of the factors map using the reclassify tool in ArcGIS.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMethod for land suitability map preparation\u003c/h2\u003e\u003cp\u003eAnalytical hierarchy process (AHP) is effective and commonly used method in multicriteria decision-making (MCDM). It is mostly used to evaluate the relative influence of the factors in a study. Several studies\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e involving different land-related studies have used this method.\u003c/p\u003e\u003cp\u003eAccording to\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e, relative influence weight among the factors was assigned to 1 to 9 scale, and pairwise comparison of the factors was conducted. According to the scale, a value of 9 and 1 denotes very high importance, and equal importance, respectively. Then, normalized pairwise comparison was computed by dividing the values in each column by column sum of the pairwise comparison table. Relative weight of the factors was then identified by dividing the sum of each row by the number of factors in the normalized pairwise comparison table. The consistency index was used to checked the constancy for pairwise comparison, which was calculated by equations (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) according to\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e.\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\:CI=\\frac{{{\\lambda\\:}}_{max}-n}{n-1}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere\u003c/p\u003e\u003cp\u003eCI: consistency index\u003c/p\u003e\u003cp\u003en: the number of factors and\u003c/p\u003e\u003cp\u003eλ\u0026#119898;\u0026#119886;\u0026#119909;: the highest eigenvalue\u003c/p\u003e\u003cp\u003eThe value of maximum eigenvalue (λ\u0026#119898;\u0026#119886;\u0026#119909;) was calculated asper the procedures given by\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. As suggested by\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e equation (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was used for computing consistency ratio (CR) to check the comparison consistency. The value of CR should be below 0.1, otherwise; the pairwise comparison is not accepted considering it to be inconsistent.\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$$\\:CR=\\frac{CI}{RI}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere\u003c/p\u003e\u003cp\u003eCR: the consistency ratio\u003c/p\u003e\u003cp\u003eCI: consistency index\u003c/p\u003e\u003cp\u003eRI: random index\u003c/p\u003e\u003cp\u003eTo prepare the suitability map, the reclassified spatial layers were integrated and overlaid using weighted overlay analysis. Weighted overlay analysis was done using the spatial data analysis in ArcGIS environment based on (Eq.\u0026nbsp;\u003cspan refid=\"Equ3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) below. This was done by multiplying the number of pixels in the raster map layers found from the AHP analysis with their influence weight percentage and adding them. This technique has been used in different studies \u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e to conduct different land-related studies, including land suitability, flood vulnerability, and site selection for different purposes in the ArcGIS environment.\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\:MS=\\sum\\:_{i=0}^{n}\\left({X}_{i}\\right){W}_{i}\\:$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eWhere\u003c/p\u003e\u003cp\u003e\u0026#119872;\u0026#119878;: mechanization suitability\u003c/p\u003e\u003cp\u003en: number of decision criteria\u003c/p\u003e\u003cp\u003eXi: normalized factor\u003c/p\u003e\u003cp\u003eWi: weight of factor\u003c/p\u003e\u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eLand suitability factors processing\u003c/h2\u003e\u003cp\u003eAfter reclassification of the factors (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003e), based on the AHP analysis using pairwise comparison matrix, each factor was assigned with a relative influence weight (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). As per\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, the normalization (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) and consistency (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) of the pairwise comparison was computed and checked.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\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\u003ePairwise comparison matrix\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSoil type\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSoil depth\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSoil drainage\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRainfall\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRoad network\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLand use\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/td\u003e\u003c/tr\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\u003e1/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil Depth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil Drainage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1/7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRainfall\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1/7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1/4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRoad Network\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1/9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1/8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1/5\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\u003e1/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand Cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1/9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1/7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1/5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1/3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4.99\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e9.98\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e15.92\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e33\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\u003eThe final criteria weight for each land suitability factor (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) was calculated to be, (42%) for slope, (24%) for soil type, (13%) for soil depth, (8%) for soil drainage, (6%) for annual rainfall, (4%) for distance from access road, and (3%) for land cover. To compute consistency ratio (CR), random index (RI) a value 1.32 was used with influencing factors of seven (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The (RI) value varies depending on the number of influencing factors according to the literature \u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e. The consistency index (CI) was found to be 0.125 by computing using (Eq.\u0026nbsp;\u003cspan refid=\"Equ1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) with highest eigenvalue (λ\u003csub\u003emax\u003c/sub\u003e)\u0026thinsp;=\u0026thinsp;7.75 and number of factors (n\u0026thinsp;=\u0026thinsp;7) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Then, consistency ratio (CR), was calculated to be 0.09 using (Eq.\u0026nbsp;\u003cspan refid=\"Equ2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The weighted overlay analysis was performed depending on the value of the consistency ratio, which was within the acceptable range of 0 to 0.1(10%).\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\u003eCalculated criteria weight and normalized pairwise comparison\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"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=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eST\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSDR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLULC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eSum\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eCW (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.4942\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6007\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.5008\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.4398\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.3399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2967\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.2727\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e2.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.42\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e42\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.1647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.3005\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.2513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.2975\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.2637\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.2121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e1.69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0988\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0667\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.1002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.1885\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1648\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.94\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.13\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0706\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.0334\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0628\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.1275\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.1319\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.08\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0618\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.025\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0425\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.0989\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1515\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.06\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0250\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0157\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0142\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.033\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0909\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLULC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e0.0549\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.0286\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.0209\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.0085\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e0.011\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.0303\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e\u003cp\u003e0.17\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e\u003cp\u003e0.02\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eS: slope, ST: soil texture, SD: soil depth, SDR: soil drainage, RF: annual rainfall, RN: road network, and LULC: land use and land cover, CW: criteria weight\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\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\u003ePairwise comparison consistency check result table (CR\u0026thinsp;=\u0026thinsp;0.09)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eST\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSDR\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eRF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRN\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eLULC\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003eWSV\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003eCW\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003eWS/CW\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.4207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.7243\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6718\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.5671\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4906\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.3262\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.2241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.4249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.4207\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eST\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.1402\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.2414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.4031\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.3241\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.4293\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1743\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.0024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.2414\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.29\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0841\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.1344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.2431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.2453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1812\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.0931\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.1344\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.14\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSDR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0601\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0604\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0448\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.184\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.145\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.6499\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.081\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e8.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0526\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0336\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.1087\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.1245\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.4422\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0613\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.21\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRN\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0302\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0203\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0204\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0747\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.2554\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0362\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLULC\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.0467\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0.0345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.0269\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.0270\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e0.0123\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.0121\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e0.0249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.1844\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e0.0249\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"10\" nameend=\"c10\" namest=\"c1\"\u003e\u003cp\u003eλ.max\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e7.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"11\"\u003eS: slope, ST: soil texture, SD: soil depth, SDR: soil drainage, RF: annual rainfall, RN: road network, and LULC: land use land cover, WSV: weighted sum value, CW: criteria weight, and WS: weighted sum\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\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\u003eRI values for (n\u0026thinsp;=\u0026thinsp;1 to 15) number of criteria factors\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"16\"\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\u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003en\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e\u003cp\u003e9\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e\u003cp\u003e10\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c12\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c13\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c14\"\u003e\u003cp\u003e13\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c15\"\u003e\u003cp\u003e14\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c16\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.58\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.24\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.41\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.49\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c12\"\u003e\u003cp\u003e1.51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c13\"\u003e\u003cp\u003e1.48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c14\"\u003e\u003cp\u003e1.56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c15\"\u003e\u003cp\u003e1.57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c16\"\u003e\u003cp\u003e1.59\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\u003eAnalysis of factors\u003c/h2\u003e\u003cdiv id=\"Sec12\" class=\"Section3\"\u003e\u003ch2\u003eSlope\u003c/h2\u003e\u003cp\u003eThe slope of farmland highly influences the type, efficiency, safety, and utilization of farm mechanization\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Land terrain with slopes larger than 15 \u0026deg;is generally not suitable for farmland and farm operations due to high risks of machinery instability and rollover\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Gentle slopes (typically less than 8%) are more suitable for farm mechanization, as they are less prone to erosion risk and facilitate better soil retention\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eThe reclassified raster map (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003ec) shows approximately 97.76% (966,616 ha) of the land has a range of slope between 0 \u0026deg;to 15\u0026deg;, which is in the suitable range of land for farm mechanization. Of these, approximately 50.23% (496,612 ha), 31.79% (314,332 ha), 9.41% (93053 ha), 6.33% (62619 ha) and 2.24% (22156 ha) were considered as very highly (0\u0026ndash;2\u0026deg;), highly (2\u0026ndash;5\u0026deg;) moderately (5\u0026ndash;8\u0026deg;) low (8\u0026ndash;15\u0026deg;), and unsuitable (15-40.4\u0026deg;) for farm mechanization, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As indicated in the Figure, most of the land in the area falls under a suitable range of slopes, except for some patches in different parts of the zone.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eRainfall\u003c/h2\u003e\u003cp\u003eIn an area, rainfall distribution dictates the type of crop cultivated\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Rainfall distribution determines soil workability, which is affected by soil moisture content. The duration of growing season is determined by the onset of rainfall and its duration\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. Rainfall dictates machinery management and planning; in regions where rainfall is intense in specific months, farmers should plan their planting and harvesting schedules accordingly\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe mean annual rainfall of the zone ranges from 507.91 to 1201.71 mm. The rainfall distribution pattern was then reclassified into five classes with (507.91\u0026ndash;704.85 mm), (704.85\u0026ndash;824.46 mm), (824.46\u0026ndash;917.12 mm), (917.12\u0026ndash;1001.96 mm), and (1001.96\u0026ndash;1201.71mm), representing very low, low, moderate, high and very highly suitable for farm mechanization, respectively. As shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003eb), most of the northern part of the study area is highly suitable for farm mechanization in terms of annual rainfall availability. As a result, 12.69%, 22.39%, 27.92%, 25.63%, and 11.37% of the area found to be very low, low, moderate, high and very highly suitable for farm mechanization in relation to annual rainfall (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eDistance from access road\u003c/h2\u003e\u003cp\u003eA well-developed road network is crucial for the easy transportation of agricultural inputs and farm products to farms and harvested crops to markets\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Proximity to roads is predominantly significant in mechanized farming, where timely operations affect crop yields by significantly reducing transportation costs with reduced travel time and distance\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Without adequate road access, farmers are limited to smaller and less efficient equipment. Good road networks facilitate the movement of modern and larger machinery\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eFarmlands within 1000 m from an access road were classified as very suitable, while those within a distance of 2000, 3000,4000, and \u0026gt;\u0026thinsp;4000 m were considered to have very high, high, moderate, low and very low suitability for farm mechanization, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). As shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003ea), the study areas have a reasonably good road network, except for some peripheral areas, which make the zone more suitable for farm machinery with respect to the availability of road infrastructure.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eLand use and land cover\u003c/h2\u003e\u003cp\u003eThe type of land cover significantly affects the efficiency and utilization of agricultural machinery in that area. Understanding these challenges is critical for decision-making regarding farm mechanization\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. It often requires significant investments for clearing, soil improvement, and infrastructure development to transform land from less suitable land covers (such as woodland or shrubland) to cropland\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eBased on the land use land cover map (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e3\u003c/span\u003ed), the zone was categorized as very high (cropland) with an area of 546,134.94 ha (55.4%), high (bare land) 77,405.67 ha (7.85%), moderate (grassland) 98,415.45 ha (9.98%), low (shrubland) 121,338.45 ha (12.31%), and very low (woodland) 22,924.53 ha (2.33%) in terms of land suitability for farm mechanization (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Other LULC types that cannot be used as farmland, including wetlands, water bodies, forests, and settlements, have been designated as restricted in the map.\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\u003eSuitability factors (Slope, Rainfall, Distance to Road and Land cover) and their status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSuitability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRating\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClass pixels\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eArea (ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePercent (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSlope (degree)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e5,517,912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e496,612\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e50.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026ndash;5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3,492,583\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e314,332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e31.79\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5\u0026ndash;8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,033,921\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e93,053\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e8\u0026ndash;15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e695,769\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e62,619\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e15\u0026ndash;40.3521\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNot suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e246,173\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22,156\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.24\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRainfall (mm)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e507.91-704.85\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,387,602\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e124,884\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.69\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e704.85-824.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,448,647\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e220,378\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e824.46-917.12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3,053,399\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e274,806\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e27.92\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e917.12\u0026ndash;1002\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,802,357\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e252,212\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1001.96-1201.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,242,862\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e111,858\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11.37\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDistance to Road\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;1000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,078,883\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e547,099\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1000\u0026ndash;2000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,339,170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e210,525\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e21.36\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2000\u0026ndash;3000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,204,755\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e108,428\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3000\u0026ndash;4000\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e592,589\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53,333\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e5.41\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4000\u0026ndash;9240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e737,453\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e66,371\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e6.73\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLand cover\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eForest\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRestricted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,054,301\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e94,887\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWoodland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e254,717\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e22,924.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e2.33\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eShrub/bush\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,348,205\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e121,338.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.31\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrassland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,093,505\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e98,415.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e9.98\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBarren land\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e860,063\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e77,405.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e7.85\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCropland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,068,166\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e546,135\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e55.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWetland\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRestricted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e42,917\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e3862.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.39\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWater body\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRestricted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e80,978\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e7,288\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e0.74\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSettlements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRestricted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e150,013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e13,501\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.37\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=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eSoil texture\u003c/h2\u003e\u003cp\u003eSoil texture significantly affect the performance of agricultural machinery. According to research by\u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e, loamy soils (a mix of sand, silt, and clay) provide optimal conditions for machine operation due to their balance of drainage and nutrient retention. In contrast, clay soils become compacted under heavy machinery operations, affecting machinery performance\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e. Soil compaction due to agricultural machinery traffic varies for different soil textures\u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. Fine to medium textured soils are more susceptible to compaction, which can hamper root depth and reduce soil aeration\u003csup\u003e\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAs shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ec) there are five different soil texture classes in the zone which are classified in to five different class as very low clay (light), which accounts about 262,387ha (26.65%), low (clay loam) 298,897 ha (30.35%), moderately suitable (sandy clay loam) 39,406 ha (4%), highly suitable (sandy loam) 86,700 ha (8.8%), and very highly suitable (loam) 297,332 ha (30.19%). Approximately 42.99% of the zone is characterized as moderately to very highly suitable for farm mechanization. The remaining 57% of the zone is categorized as low and very low in terms of farm mechanization suitability (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSuitability factors (Soil Texture Class, Soil Drainage \u0026amp; Soil Depth) and their status\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFactor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eClass\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSuitability\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eRating\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eClass pixel\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eArea (ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003ePercent (%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil Texture Class\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eclay (light)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,915,410\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e262,387\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e26.65\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eclay loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3,321,082\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e298,897\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.35\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esandy clay loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e437,845\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e39,406\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003esandy loam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e963,329\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e86,700\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eloam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e3,303,688\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e297,332\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e30.19\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil Drainage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePoor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,884,810\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e169,633\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e17.23\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eImperfect\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,489,374\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e224,044\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e22.75\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e6,567,170\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e591,045\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e60.02\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSoil Depth\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u0026ndash;10\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,566,964\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e141,027\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e14.27\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026ndash;34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLow\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e878,948\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e79,105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e8.01\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e34\u0026ndash;61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eModerate\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e1,396,805\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e125,712\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e12.72\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e61\u0026ndash;86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHigh\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e2,809,513\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e252,856\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e25.59\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86\u0026ndash;100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVery high\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e4,324,836\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e389,235\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e39.4\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=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eSoil depth\u003c/h2\u003e\u003cp\u003eSoil depth is an important factor that influences land suitability for farm mechanization. Deeper soils favor more effective tillage practices, reducing soil resistance and improving workability\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Shallow soils hinder the use of certain machinery and may not be suitable for the desired tillage depth\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAs shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003ea), the soil depth up to 100 cm was considered and categorized into five different classes with the range very low (0\u0026ndash;10 cm), low (10\u0026ndash;34 cm), moderate (34\u0026ndash;61 cm), high (61\u0026ndash;86 cm), and very high (86\u0026ndash;100 cm) in terms of their suitability for farm mechanization. Where 141,027 ha (14.27%) account for very low, 79,105 ha (8.01%) low, 125,712 ha (12.72%) moderate 252,856 ha (25.59%) and 389,235 ha (39.4%) with respect to suitability for farm mechanization (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e)\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eSoil drainage\u003c/h2\u003e\u003cp\u003eSoil drainage is a significant factor affecting land suitability for farm mechanization. It affects various aspects of farming operations\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Well-drained soils are easily accessible to agricultural machinery and are less prone to compaction, even after rainfall\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Poorly drained fields become muddy and inaccessible, which can cause delays in field operations. This can affect crop yields and profitability because of missed planting windows or late harvests\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\u003cp\u003eAs shown in (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e4\u003c/span\u003eb), the study area has three different levels of soil drainage, which are categorized as poor, imperfect, and moderate soil drainage. These were classified as very low, low, and moderate, respectively, in terms of their suitability for farm mechanization. Where 169633 ha (17.23%) account for very low, 224044 ha (22.75%) low, and 591045 ha (60.02%) moderate respectively with respect to farm mechanization suitability (Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003eLand suitability map of the study area for farm mechanization\u003c/h2\u003e\u003cp\u003eAs discussed above the farm mechanization suitability map of the study area (Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e5\u003c/span\u003e) was generated by considering seven farm mechanization influencing factors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eLand suitability in area coverage and percentage of the zone\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCell count\u003c/p\u003e\u003cp\u003e(30x30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eArea (ha)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePercentage\u003c/p\u003e\u003cp\u003e(%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRestricted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1,265,537\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e113,898\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e11.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNot suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5,223\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e470\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMarginally suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2,436,500\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e219,285\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e22.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eModerately suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6,014,210\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e541,279\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e56.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eHighly suitable\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e934,719\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e84,125\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e8.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eSum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10,656,189\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e959,057\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e100\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\u003eBased on the analysis results, the study area was categorized into five suitability class of restricted, not suitable, marginally suitable, moderately suitable, and highly suitable.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs indicated in (Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), the zone has a large proportion of area about 844,689 ha (88.1%) that falls under different suitability range for farm mechanization, which covers a suitability range from marginal to high. Of these, 541,279 ha (56.4%) are moderately, 84,125 ha (8.8%) are highly, and 219,285 ha (22.9%) are marginally suitable for farm mechanization. Only approximately 470 ha (0.05%) of the land was found to be unsuitable for farm mechanization. The remaining (11.9%) or 113,898 ha is area that is restricted for forest, wet lands, and water bodies and used for settlements areas.\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides useful information that can help investors in crop production, farmers, extension workers, and policymakers in promoting farm mechanization in the zone. The study highlighted, that the zone has good potential for mechanized farming. Most of the highly suitable land found in the southern part of the study area, while the majority of the lands fall under moderately suitable range.\u003c/p\u003e\u003cp\u003eIn this study, although slope is the major influencing factor, the slope of arable lands in the zone mostly falls within the suitable range for farming. Regardless, the land suitability of the zone for farm mechanization was mostly influenced by soil factors, such as sol texture, soil depth and soil drainage. Hence, to improve the suitability of farmlands for farm mechanization, soil priority improvement activities, such as improving drainage and soil organic content.\u003c/p\u003e\u003cp\u003eIn addition to its local significance, this study provides important insights for land suitability study in the area of agriculture and land management. The method improves the accuracy and interpretability of land suitability identification by combining a wide range of bio-physical, geomorphological, climatic, and soil parameters into a clear and repeatable AHP framework. This makes it a practical decision-support tool for investors, engineers, planners, and policymakers aiming to effectively implement mechanized farming. On a broader scale, the methodology and findings contribute to the expanding global discourse on land suitability studies related to agriculture.\u003c/p\u003e\u003cp\u003eThe study is limited to analysis of the spatial data; further we plan to extend validation practices by incorporating a comprehensive physical validation work on the ground based on the results indicated in this research result. These validation work will provide a deeper and more accurate balanced understanding for reliable land suitability analysis for farm mechanization and other farming activities.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e\u003cp\u003eThis research work was funded by the Ethiopian Institute of Agricultural Research (EIAR) as part of a PhD dissertation work.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYonas Mulatu: conceptualization, methodology, data analysis, visualization, mapping, and writing-original draft. Mihret Dananto: review and editing, methodology. Siraj K. Busse: resources, visualization, writing-review, and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgment:\u003c/h2\u003e\u003cp\u003eTo the Ethiopian institute of agriculture management and researchers for supporting this research as part of my dissertation work. EIAR, Addis Ababa, Ethiopia.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data generated and analyzed will be made available when it is requested\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAkpabio, E. S., Akeju, K. F. \u0026amp; Omotoso, K. O. E-agriculture and food security in developing countries: beaming the searchlight on Nigeria. Smart Agric Technol. ;10(October 2024):100689. (2025).\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eFischer, G. et al. Gender and mechanization: Exploring the sustainability of mechanized forage chopping in Tanzania. \u003cem\u003eJ. 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Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s42853-021-00117-7\u003c/span\u003e\u003cspan address=\"10.1007/s42853-021-00117-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":"Slope, Soil texture, Pair-wise comparison, Overlay analysis","lastPublishedDoi":"10.21203/rs.3.rs-7396673/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7396673/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eFarm mechanization plays a crucial part in improving productivity, reducing poverty, and optimizing resources. Farming in sub-Saharan Africa, including Ethiopia, is the least mechanized, less than one percent of agricultural land plowed with a tractor. This is mostly due to lack of adequate scientific and technical research in the area of mechanization, including studies in land suitability and selecting appropriate agricultural machinery. This study aimed to categorize and map farmlands suitable for farm mechanization. In this study, GIS integrated with analytical hierarchy process (AHP) and multicriteria decision-making method was used. Seven different factors, slope, soil texture, depth, drainage, road access, rainfall, and land use, were analyzed using a GIS-based weighted-overlay analysis. The results exhibited a large proportion (88.1%) of the zone, which is 844,689 ha, was found to be in different suitability range for farm mechanization, with 541,279 ha (56.4%) and 219,285 ha (22.9%) being highly, moderately, and marginally suitable, respectively. Approximately 470 ha (0.05%) unsuitable and the remaining (11.9%) or 113,898 ha is restricted for and settlement area. This study highlights the potential for expanding farm mechanization in the zone and the effect of terrain and soil physical factors on land suitability for farm mechanization.\u003c/p\u003e","manuscriptTitle":"Land suitability analysis for farm mechanization using GIS-based analytical hierarchy process and multicriteria decision-making method in Ethiopia","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-02 08:02:02","doi":"10.21203/rs.3.rs-7396673/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":"4fb9215d-3ac2-41af-924b-b2662499a9eb","owner":[],"postedDate":"September 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":53773361,"name":"Biological sciences/Ecology"},{"id":53773362,"name":"Earth and environmental sciences/Ecology"},{"id":53773363,"name":"Physical sciences/Engineering"},{"id":53773364,"name":"Earth and environmental sciences/Environmental sciences"},{"id":53773365,"name":"Earth and environmental sciences/Environmental social sciences"}],"tags":[],"updatedAt":"2025-11-14T06:39:06+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-02 08:02:02","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7396673","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7396673","identity":"rs-7396673","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}