Assessing the Availability and Utilization of Agricultural Machinery and Implements in East Wallaga Zone, Oromia Regional State

Assessing the Availability and Utilization of Agricultural Machinery and Implements in East Wallaga Zone, Oromia Regional State | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing the Availability and Utilization of Agricultural Machinery and Implements in East Wallaga Zone, Oromia Regional State Hika Endalu Chibsa, Siraj Kedir Busse, Sintayehu Legesse Zeleke This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8554432/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 Agricultural mechanization is essential for enhancing productivity and efficiency in farming; however, there is no comprehensive study conducted on the availability and utilization of agricultural machinery in the East Wallaga Zone of Oromia Regional State. This study sought to assess the availability and use of agricultural machinery, identify factors affecting its adoption, and evaluate its impact on productivity. Data were gathered from 380 participants, including 360 farmers and 20 agricultural specialists, through structured questionnaires, interviews, and focus group discussions. Statistical analysis was performed using SPSS version 22 to uncover trends and relationships regarding machinery availability, utilization, and influencing factors. The findings indicated that 68% of farmers continued to depend on traditional farming practices. Access to agricultural machinery was limited to only 11% of farmers, which perpetuated reliance on outdated methods and restricted productivity. Additionally, several challenges hindered machinery use, including fragmented land holdings (with an average size of 2.1 hectares), financial constraints (75% of respondents), high costs of machinery (60%), and a scarcity of local suppliers (72%). Other obstacles included difficult terrain (56%), lack of maintenance services, and unavailability of replacement parts. Farmers who utilized machinery experienced a 35% increase in crop yields compared to those who relied on traditional methods, with mechanization significantly alleviating labor demands during peak periods. The study underscores the urgent need for policy interventions aimed at improving access to machinery, enhancing affordability, and establishing support systems to empower smallholder farmers in boosting productivity and achieving sustainable agricultural development. Agricultural mechanization Agricultural machinery Smallholder farmers East Wallaga Zone Barriers to adoption Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Mechanization technologies are critical, and they are among the most important inputs in agricultural enterprises (Emami et al., 2018 ). Unless and until the problem of farm electricity is solved and addressed realistically in Sub-Saharan African countries (SSA), the region risks increased poverty and famine (Paul & wa Gĩthĩnji, 2018 ). In areas where farming is a major source of rural livelihood, the agricultural sector is essential for the socioeconomic growth of nations (Terentyev et al., 2019 ). Agriculture is a key economic pillar in the East Wallaga Zone, making a substantial contribution to the general well-being and food security of the population (Ajama and Kibi, 2022). However, contemporary agricultural practices, such as the availability and effective use of agricultural technology, must be adopted if the industry is to realize its full potential. Farming technology has transformed agricultural methods worldwide by allowing farmers to boost yields, improve productivity, and make the greatest use of available resources(Dhanaraju et al., 2022 ). Modern equipment and technology are essential for agricultural operations in many regions. In addition to strengthening agricultural product chains, the implementation of equipment at different phases of farm operations will increase rural job prospects and farming profitability (Dhanaraju et al., 2022 ). However, inadequate access to and usage of agricultural machinery continues to impede the sector's development in less-developed regions, such as the East Wallaga Zone (Berhanu & Dubiso, 2022 ). Land is essential for rural communities because it is a deeply ingrained way of life. The size of the landholding has a considerable impact on agricultural machinery utilization, allowing for contemporary practices and technology such as tractors and combine harvesters (Andrew et al., 2015). Smaller land plots, on the other hand, may struggle to justify the cost of such gear, restricting their ability to reap its advantages. In rural areas, the size of the landholding influences mechanization, agricultural efficiency, and wealth (Alemu et al., 2017 ; Giller et al., 2021 ). The importance of this study is highlighted by the shortage of literature and research on agricultural machinery in the East Wallaga Zone. This study aims to add to the body of knowledge on agricultural growth in the area by performing a thorough analysis and providing useful recommendations. This study aims to provide a comprehensive analysis of the current status of agricultural machinery availability and utilization in the East Wallaga Zone. By understanding the challenges and opportunities faced by farmers in adopting mechanized practices, the research aims to shed light on potential areas for improvement. Additionally, this study seeks to propose recommendations that can enhance the accessibility and effectiveness of agricultural machinery, thereby fostering sustainable agricultural development in the region. Methods and materials Study Area Description This study was conducted in the Oromia regional state of East Wallaga Zone, which is located 328 kilometers west of the capital city of Addis Ababa. It is located at 07°02' N and 38°28' E. The area has an average yearly rainfall of 800–1200 mm and an average annual temperature of 27°C. It features diverse agroecology and agricultural systems. The district's agroecological zones are Dega, Woyina Dega, and Kola. Agricultural produce in the area is diversified, and the principal source of income for the rural population. Data Collection A mixed-methods research strategy was used for this study, which incorporated qualitative and quantitative data-gathering techniques. The use of mixed methods allows for a comprehensive understanding of the availability and utilization of agricultural machinery in East Wallaga Zone, as it incorporates the perspectives of farmers, agricultural machinery providers, and local authorities while also utilizing relevant secondary data. Structured questionnaires were administered to farmers across different areas of the East Wallaga Zone to gather information on their access to, and usage of, agricultural machinery. The surveys focused on the types of machinery used, the frequency of usage, and the challenges faced. Using a stratified random selection strategy, a representative sample of farmers was selected from various locations within the East Wallaga Zone. Using stratification, it is possible to understand that the sample adequately reflects both well-served and underserved areas. In-depth interviews were conducted with key stakeholders, including agricultural machinery providers and local authorities. Group discussions were organized with farmers to explore their perceptions, experiences, and suggestions related to agricultural machinery access and utilization. Government reports, agricultural organizations, and non-governmental sources were used to supplement the original data. This information was used to provide a more thorough context for the study and improve the analysis of trends and patterns. Sources of Data This study collected and used both primary and secondary data. Data obtained comprised socioeconomic and hypothesized factors influencing the adoption of agricultural mechanization technologies, as well as the types of agricultural mechanization technologies employed in the area. Data Collection Methods Field observations, interviews, structured and unstructured questionnaires, and document analysis were used to collect qualitative and quantitative data. To collect the necessary primary data, household surveys using semi-structured questionnaires with both open-ended and closed-ended questions were used. Similarly, data were collected from the sample respondents using a structured questionnaire and via face-to-face interviews. Data Acquisition Procedure Questionnaires were administered, and interviews with the farmers and owners of the farm machinery and equipment were conducted. Direct observation of the available agricultural machinery, tools, and implements was done in the study areas, and the data collected through questionnaires and interviews was analyzed statistically. The questionnaires were focused on the availability and utilization of agricultural machinery and implements. Personal interview The interview and questionnaires were conducted for owners, non-owners, user and non-user of agricultural machinery and implements. The responses of the selected samples were documented with the information gathered from the questionnaires. The questions asked during the personal interview and questionnaires incudes: availability of agricultural machinery and implements, repair and maintenance work shop, availability of spare parts in the localities, acceptability of the agricultural new technology/mechanization. The data from this survey was subjected to statistical analysis using descriptive statistics that will reflect the respondent frequency and percentage of existence. Sampling Design Four districts and twelve sample kebeles were chosen. Purposive and random sampling was used to pick sample farmers from the kebele. The basic sampling units were zone selections from the four Wallaga zones, then East Wallaga was chosen using purposive sampling based on the presence of agricultural machinery. Second, four Woredas, (Guto Gida, Sasiga, Diga, and Gidda Ayana) were chosen by the purposive sampling from the east Wallaga zone based. In the third sampling unit, three kebeles were chosen from each woreda also by Purposive and random sampling, based on farmers' knowledge and responses to agricultural machinery. The selection of respondents in the kebele was chosen both randomly and purposively; purposive is used here to identify the machine owner since they are small in number. Purposive sampling is more used because in addition to the history of the machinery in the area, depending on the peace problem in the study area the purposive is required to collect data in a safe way. The sample size was calculated using the standard sampling approach employed in numerous surveys. As a result, the following formula was used to compute the sample size for this investigation, which is the most commonly used standard in both qualitative and quantitative data gathering from unknown populations (Ali, 2014), and related to this study. n = [Z 2 p (1-p)]/d 2 (1) Were: n = the to-be-determined sample size Z = the standard normal deviation, which is set to 1.96 and corresponds to the 95% confidence interval. p = the estimated proportion of a population, which equals 50% or (0.5). d = 5% or (0.05) the amount of sampling error between the sample and the population So, to compute sample size; n= ((1.96) 2 ×0.5× (1-0.5))/ (0.05) 2 n = 384 Then adding 10% of the non-response rate, we get; n = 384(1 + 0.1) = 422 At the district level, a proportional sampling technique will be employed to calculate the sample size. The required sample sizes at the kebele level will equally be allocated to each kebele. № of respondent at kebele = \(\:\:\frac{Total\:№\:of\:respondants}{Total\:№\:ofkebele}\) ………………………………….(2) № of the respondent at each kebeles = \(\:\:\frac{422}{12}\) =35.16 we take 35 № of the respondent at woreda = No of kebeles x No of respondents at kebeles………….(3) Table 1 Sample size distribution in the study area. Sample Weradas from East Wallaga № of the selected kebeles in the woreda № of respondents in each kebele Total № of respondents Guto Gida 3 35 105 Diga 3 35 105 Sasiga 3 35 105 Gida kiramu 3 35 105 Total 12 35 420 Finally, the required sample sizes were allocated to those selected kebeles as indicated in Figure below. Figure 2. Schematic presentations of equal and proportional allocations of sample size Data Analysis To generate descriptive statistics and identify patterns, quantitative data from surveys were statistically examined (descriptive statistics). Qualitative data from focus groups and interviews were transcribed and thematically analyzed to extract significant ideas. MS Excel 2016 was used for data coding and entry, while SPSS (Statistical Software Package for Social Sciences) version 26 was used for data analysis. To summarize the socioeconomic characteristics of farmers and other data received from farmers, and other government and non-government sources, descriptive statistics such as tabulation, percentage, maximum, and minimum were used. All methods were performed in accordance with the relevant guidelines and regulations, including the Declaration of Helsinki (2013 revision) for research involving human participants and the ethical research guidelines of Adama Science and Technology University. Results and Discussions The first section reflects the social and demographic characteristics of the sample households. The characteristics include age, sex, education, family size, farming experience, land size, and number of economically active household members. Table 2 Respondent’s age description Age intervals (N = 360) Age 18–25 26–35 36–45 > 45 Frequency 0 15 186 159 The findings revealed that 52% of respondents were middle-aged, which may have a good impact on agricultural productivity. Table 3 Sex and educational status of respondents Sex Illiterate Adult education Grade 1-5 Grade 5-10 Level Diploma Degree Male 82 33 180 53 0 0 18 Female 0 0 8 4 0 0 2 Total 82 33 188 57 0 0 20 The result revealed that 15% of the respondents were female while 85% were male which shows that males were more involved in farming practices than females in the study area. Traditionally, ladies are more responsible for interior tasks than males are for outdoor activities such as farming. When males are absent from a family in a rural setting, females may lack access to information about new farming technology and machinery used to boost productivity(Neway & Zegeye, 2022 ). Agriculture accounts for 85% of the Ethiopian economy (Paul & Gĩthĩnji, 2018 ). This is corroborated by this study, which found that all farmers in the study region were involved in agriculture. The academic qualifications of the respondents showed that out of the interviewed households, 21.6% were illiterate, 8.6% were categorized as literate, 49.5% were primary (first cycle) educated, about 10% and 5% were above grade five and grade ten complete respectively. The degree holders of the respondents are those from the office at zone, woreda, and Das, which are a total of 5%. Despite the fact that the number of illiterate and uncertified persons was far higher than the national average, the research region lies within the educationally beneficial Wallaga zones. Most farmers can read and write and are likely to be familiar with agricultural mechanization, which is critical to maximizing their output. Table 4 The farmers work experience (year) Experience (in year) 5–10 11–15 16–20 21–25 > 25 № of respondents 10 72 171 95 12 % 2.8 20 47.5 26.4 3.3 The findings indicated that 2.8% of farmers had 5–10 years of agricultural experience, whereas 20%, 47.5%, and 26.4% had 11–15, 16–20, and 21–25 years of farming experience, respectively. 3.3% of those polled have greater experience, accounting for more than 25 years of agricultural expertise. The survey found that all of the respondents in the study region owned land; however, the amount of the property varied substantially across households and among respondents. The average size of a land holding was two hectares, with the smallest and largest observed holdings per family being 0.4 and 9 hectares, respectively. An improvement in farm human capital through agricultural mechanization is important to enhance farm productivity (Oduma et al., 2021 ). Availability of machinery is the basic tool that should be insured to rural farmers in order to increase productivity and income from agriculture (Eskeziaw et al., 2021 ). One of the significant issues with farm power in the zone is the lack of the appropriate types of Agricultural machinery, hence mechanization utilization remains low. For example, walking tractors are not utilized in the study area; as a result, farmers do not prefer to purchase and no one is supplied. Consequently, farm power availability is always insufficient for performing farm operations that are difficult to undertake without mechanical assistance. To promote agricultural machinery availability and utilization among smallholder farmers in the country as well as in the area, agricultural machinery-hiring schemes have been established and primarily managed by farmer organizations(Van Loon et al., 2020 ). Table 5 Farmland possession of farmers (hectare) Land size (ha) № of respondents % Land size (ha) № of respondents % 0.4 4 1.1 3.0 10 2.8 0.5 30 8.3 3.2 9 2.5 0.6 12 3.3 3.4 9 2.5 0.8 26 7.2 3.5 6 1.7 1.0 22 6.1 3.8 6 1.7 1.3 28 7.8 4.0 7 1.9 1.4 32 8.9 4.4 5 1.4 1.5 24 6.7 4.7 5 1.4 1.7 14 3.9 5.0 3 0.8 1.8 18 5 6.0 2 0.6 2.0 22 6.1 7.0 1 0.3 2.3 19 5.3 8.0 1 0.3 2.4 20 5.6 9.0 1 0.3 2.5 13 3.6 Total 360 100.0 2.7 11 3.1 Summary : Mean 2.00 ha Std. Dev. 1.18 ha Minimum 0.4 ha Maximum 9.0 ha Availability of Agricultural Machinery In the study area, agricultural machinery is distributed in two ways. That is done through the MoA, and individuals through cash payments. To solve the problem of availability and distribution with demand and supply, the government and institution or organization working on the developments of agricultural mechanization have to incorporate with a company of different machinery and implement to distribute lower or discounted costs with a long-term credit facility and work to access the providers and brokers and introduce the appropriate equipment suitable for the different situation of farm activity and affordable for small-scale farms (Sims & Kienzle, 2017 ). The agricultural machinery and implements found in the study area are tractors, irrigation pumps, maize shellers, groundnut shellers, and implements such as moldboard, disc plows, are found. Table 6 The availability of equipment in study area Woredas 4WTs Irrigation pump Implements Gida ayana 31% 36% 33% Guto gida 29% 36% 35% Diga 10% 60% 30% Sasiga 8% 42% 50% Total 20% 44% 37% Among the available machinery and implements in this study area, tractors (20%), irrigation pump (44%), and implements (maize and groundnut sheller, disc plow, mold bold) account 37%. Cereal crop mechanization, such as tractors, combines, and irrigation pumps, has been adopted in some sections of Oromia, notably Wallaga Zones, in the last 10 years, albeit not in all parts of Wallaga. Table 7. Tractors and combine harvesters in the study area Machinery Quantity (№) Brand Horsepower (hp) Tractors 75hp 120hp 42 Natfaa 34 8 21 John Deere 21 5 WYito 5 78 Horsus 72 6 7 New Holland 7 56 Massey Ferguson 35 21 98 Velarus 98 27 Turbo 27 6 Agro track 6 Combine harvester 2 John deer 2 1 New Holland 1 Table 6 shows that there were roughly 343 registered agricultural machines in the research region, with tractors accounting for 99% and combined harvesters accounting for 1%. These machines are owned by a variety of organizations, including farmer organizations, youth groups, PLCs, investors, and farmer unions. Factors Affecting Availability The purpose of this study was to identify the variables influencing the accessibility, use, and distribution of agricultural machinery in the study area, such as capital, distance from the center, topography, income from farm or product, government consideration, farmer perception, land size, and product aim. during the study, the survey and respondent states revealed that the major factors hindering the availability and utilization of agricultural machinery in the study area were: The accessibility to different services Extension, promotion, and advice have a direct influence through the provision of information and training on the available agricultural technology. The research center, Institute of agricultural mechanization, and agricultural machinery and implement providers are important sources of information on agricultural innovations including encouraging and advising farmers and supplying adopted technologies. The availability of these technology centers initiates the farmer’s machinery and availability by initiating those who have the capital to purchase and adopt new technology. Credit service is one of the economic variables that influence the availability of agricultural machinery technologies. Access to credit services enables the farmer with liquid assets to purchase different inputs and machinery due to that machinery becoming available in the area. Lack of capital Smallholder farmers are virtually resource-poor by definition, and they frequently struggle to invest in physical assets in general and Agricultural machinery in particular. As the respondents reported, low own-starting capital or purchase capital was the major hindrance that hampered the existing agricultural machinery not being available in the study area. Lack of Government/institution consideration The other reason that inhibited the availability of agricultural machinery in the study area stated by respondents is there was no consideration given to the area by the government or NGOs regarding the development of agricultural technology/machinery. Training and extension, as well as research and development (R&D), support services, infrastructure development, etc. in developing countries, the government must undertake because the private sector is not yet developed enough. However, government institutions are usually also very weak and largely ineffective and inefficient in the mechanization technology in the study area, and as the area far from the center mechanization center was so important but there is no yet. Also, there are no Agricultural development supporters and advisory activities are critical to the success of any mechanization and the promotion of new sustainable agricultural production technology in the farming system. Cost of machinery and implement In many areas, farmers cannot afford to purchase equipment, and financial support through subsidies or financing schemes is limited. The financial services sector shies away from providing credit to smallholders because of a lack of eligible collateral and a perception of the high risk involved with agriculture. As reported by the sample respondents, the machinery and other mechanization inputs are expensive and increasing (from 1.6 to 2.7 million difference in one year), cost of the tractor to the respondent and agricultural bureau data; which, affecting their availability, even though the local farmers can’t purchase them. Brokers As the influence of brokers is high in the availability of farm machinery and implements, by making the supply process easier and quicker, the absence of brokers in the area affects the availability of machinery and implements. As the area is far from the center and agricultural machinery suppliers are only found in the larger towns and cities, as the perceived low demand in rural areas for equipment does not always justify the establishment of distribution networks the availability of the complimentary actors involved with manufacturing, supply, distribution and after-sales service not there in the study area. Table 8 Descriptive statistics for factors affecting the availability of machinery. Variable N Mean SE Mean Std. Dev Lack of differen t services 380 0.72 0.02 0.45 Lack of capital 380 0.63 0.02 0.48 Cost unaffordability 380 0.69 0.02 0.46 Lack of Government consideration 380 0.48 0.03 0.50 Absence of brokers 380 0.67 0.02 0.47 In the study are as the respondent's results and Table 8 revealed that the availability of machinery is highly affected by the government considers as it’s possible to understand from standard and farmers or end users have no capital to facilitate appropriate mechanization technology, and the lack of capital is secondly affected as shown also from standard deviations of the following to the cost of machinery and lack of brokers. Utilization of Machinery The result further revealed that pre-harvest technology utilization was greatly varying across districts and there is no harvest and post-harvest technology available in all parts of the study area except the maize sheller. Next to the irrigation pump from the total number of machineries in the area four-wheel tractor is utilized. It can be revealed from the respondents and data from the experts the technology utilization of the study area summarizes as 42% (N = 145) were user and 58% (N = 215) are non-user of the agricultural machinery and implements when see the total user and non-user in the study area. If credit was granted, respondents' need for their own AMT was considered. The region's utilization interest in current machinery is as strong (85%), according to research area respondents, including owners and non-owners. Farmers rent implements and agricultural machinery (tractors, combine harvesters, and other equipment) from machinery and implement owners during regular and peak working seasons (Challa, 2016 ). Respondents' farmers’ households were interested in the credit-based provision of intermediate upgraded agricultural mechanization technology. The survey revealed that due to non-availability, about 40% of farmers in the study area used the least modern technology in addition to other local and intermediate implements, and were no respondents who were only using either tractor or other intermediate technologies for agricultural activities. While the rest 60% of the farmers were purely using local/traditional tools and systems in the agricultural activity starting from primary tillage to harvest and threshing, due to the lack of affordable machinery and implements as the capacity of farmers demand and the non-availability of appropriate machines to meet the farmers’ needs due to the farm size, agroecology, and capital. Factors Affecting Utilization As a result, it is necessary to continue to promote agricultural new technologies by developing them to address farmer problems and needs. Farmers' use of new agricultural technology is influenced by a variety of factors that vary by area (Sims et al., 2016). In the study area, it was possible to identify the major reasons why farmers did not utilize agricultural technology or Agricultural machinery. The sample respondents had different reasons for not utilizing the existing technology in the country. Land size and fragmentation The great problem in mechanization of small-scale farm is the mismatch between the economies of scale of machines and farm size. More of the food produced originates from small family farms (Diriba, 2020) and many of these consist of separate and dispersed fields and are thus poorly suited for larger machinery. The amount of cultivated land per household significantly affects the technology use status of the household. The total land size and its continuity or fragmentations affect the utilization of even available machinery by the rental service. Productivity; Level of farm income per year The level of annual income should be greater than the expenditure for farm inputs consisting of farm power and implements. Better output encourages farmers to invest in agricultural inputs, including the purchase of small machines or using available hire services. An adequate income could be used as funding to purchase or rent the mechanization services. Higher-income farm households are more likely than lower-income ones to use equipment and implements for farming tasks. Service Providers The services for rental utilization are significantly influenced by the local service providers' accessibility. The use of rental services is impacted by the lack of machinery service providers in the farmers' immediate area. Mechanization services will be easier to acquire if there are more Agricultural machinery service providers in the area. Family size Agriculture is a labor-intensive industry that needs access to family and/or hired labor. A larger family can provide more labor than a smaller one. The number of economically active labour forces in the family was discovered to have a negative relationship with agricultural machinery utilization, especially when small-scale and fragmented land is handled since it is unsuited for machinery. Households with a higher proportion of economically active labour force members are less likely to hire machinery, according to the data, because extra labour is used for fieldwork. Topography The utilization of Agricultural machinery requires a flat and straight area. The efficiency and productivity of the machinery are also higher in straight topography than in the up and down areas (Li et al., 2023). Therefore, the undulating of the area affects the utilization of Agricultural machinery. Table 9 Descriptive statistics for factors affecting the utilization of machinery. Factors N Mean Std. Error of Mean Std. Deviation Land size 380 0.02 0.47 0.37 Productivity 380 0.02 0.47 0.46 Absence of provider 380 0.02 0.47 0.48 Topography 380 0.02 0.47 0.45 Family size 380 0.02 0.47 0.46 Goa of production 380 0.66 0.02 0.47 As the evaluated response of respondent indicates (Table 9 ) above the degree by which factors affect the utilization of agricultural technology was varied. The factors with high std. deviation were significantly affect the utilization of mechanization technology in the study area. From the Table 9 seen that the absence of provider affects by 0.48 Std. deviations rather than another factor as respondent’s response. Productivity Impact Using a tractor to plow one hectare of land takes a few hours, whereas using animals can take more than seven days. One tractor can plow seven to eight hectares of land per day with one operator, while the traditional method needs a minimum of seven people and seven pairs of oxen to plow one hectare. Farmers in the study reported that it took an average of three workers to thresh one quintal of crops using a thresher, compared to the six laborers required in the traditional threshing system. This demonstrates that using a thresher reduces the need for labor and resources. Mechanized farming reduces the time required for operations while also improving the quality of the work produced. A study found that there is a 35% difference in productivity between farmers who use agricultural machinery and those who do not. The study indicates that increased productivity from utilizing agricultural machinery leads to increased yield, higher work quality, reduced labor, and time savings. Farmers also cited the benefit of generating income by renting out machinery or profiting from rental services. Increased efficiency and output : Tractors can plow significantly more land in a day compared to traditional animal plowing methods. A single tractor can plow 7–8 hectares per day, whereas using animals can take more than seven days to plow one hectare. This efficiency translates to increased output and the potential for higher yields. Reduced labor requirements: Using agricultural machinery significantly reduces the need for manual labor. For example, threshing one quintal of crops with a thresher requires three workers compared to six laborers using traditional methods. Time savings: Mechanization allows farmers to complete tasks faster, freeing up time for other activities or enabling them to cultivate more land. Improved quality of work: Agricultural machinery often produces higher quality work compared to manual methods. This can lead to better crop yields and higher profits. Higher profits: Increased efficiency, reduced labor costs, and improved quality of work can result in higher profitability for farmers. Increased income from renting out machinery: Farmers who own agricultural machinery can generate additional income by renting out their equipment to others. Conclusion This research work presents the availability and utilization of agricultural machinery in the Oromia region of the East Wallaga zone with four targeted Woredas: Guto Gida, Diga, Sasiga, and Gida Ayana. Hence, the availability and utilization of machinery and implement, as well as factors affecting the mechanization technology in the study area, were evaluated and analyzed by the descriptive statistical method. The findings of this study indicate that there is a low degree of availability and utilization of implements and equipment, and that commercial organizations are more involved in the ownership of agricultural mechanization than the government and people. Farmers' low levels of capital and income, as well as a lack of external support and the highest percentage of farmers' lack of technical know-how and machinery/implements availability, impede the adoption and utilization of modern agricultural technology and make it difficult to increase land and labour productivity. Declarations Funding No funding received for this article Acknowledgments The authors gratefully acknowledge the support of Adama Science and Technology University (ASTU) for facilitating this research. We also thank the East Wallaga Zone agricultural offices and all farmers and experts who participated in this study. Author contributions Siraj Kedir Busse conceived and designed the study. Hika E. Chibsa collected the data and drafted the manuscript. Sintayehu analysed and interpreted the data. All authors reviewed and approved the final manuscript. Clinical trial number Not applicable. Consent to participate All participants were informed about the objectives of the study, their right to withdraw at any time, and the voluntary nature of participation. Freely given, informed consent was obtained from all participants prior to data collection. Consent to publish The authors confirm that informed consent for publication of anonymized data was obtained from all study participants. No data that could identify individual participants are included in the manuscript. Conflicts of interest The author declares that there is no conflict of interest. Ethical approval Ethical clearance for this study was obtained from the Ethical Review Committee of Adama Science and Technology University, Ethiopia . Data collection was conducted in compliance with the approved ethical standards. Data availability statements The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request. References Ajama G and K. G. Evaluation and Demonstration of Engine Driven Soybean Thresher in East Wallaga Zone, Ethiopia. J Biology Agric Healthc. 2022;12(2):12–9. https://doi.org/10.7176/jbah/12-2-03 . Alemu GT, Ayele B, Z., Abelieneh Berhanu A. (2017). Effects of Land Fragmentation on Productivity in Northwestern Ethiopia. Advances in Agriculture , 2017 . https://doi.org/10.1155/2017/4509605 Andrew S, Charles B, M. Mechanized Agriculture: Machine Adoption, Farm Size Displacement. AgBioForum. 2015;18(3):278–96. Berhanu S, Dubiso G. Using farmers evaluation criteria as an essential constituent of variety popularization: The case of improved finger millet variety demonstration in selected districts of East Wallaga Zone. Int J Agricultural Sci Food Technol. 2022;8(2):083–9. https://doi.org/10.17352/2455-815x.000149 . Challa TG. (2016). Prospects and Challenges of Agricultural Mechanization in Oromia Regional State-Ethiopia, Policy Perspectives . 4 (5), 118–127. https://doi.org/10.11648/j.ajaf.20160405.12 Dhanaraju M, Chenniappan P, Ramalingam K, Pazhanivelan S, Kaliaperumal R. Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agric (Switzerland). 2022;12(10):1–26. https://doi.org/10.3390/agriculture12101745 . Emami M, Almassi M, Bakhoda H, kalantari I. Agricultural mechanization, a key to food security in developing countries: Strategy formulating for Iran. Agric Food Secur. 2018;7(1):1–12. https://doi.org/10.1186/s40066-018-0176-2 . Eskeziaw MY, Ketema M, Haji J, Bekele K. Production Efficiency of Major Crops Among Smallholder Farmers in Central Ethiopia. J Agribusiness Rural Dev. 2021;61(3). https://doi.org/10.17306/j.jard.2021.01391 . Giller KE, Delaune T, Silva JV, Descheemaeker K, van de Ven G, Schut AGT, van Wijk M, Hammond J, Hochman Z, Taulya G, Chikowo R, Narayanan S, Kishore A, Bresciani F, Teixeira HM, Andersson JA, van Ittersum MK. The future of farming: Who will produce our food? Food Secur. 2021;13(5):1073–99. https://doi.org/10.1007/s12571-021-01184-6 . Neway MM, Zegeye MB. Gender differences in the adoption of agricultural technology in North Shewa Zone, Amhara Regional State, Ethiopia. Cogent Social Sci. 2022;8(1). https://doi.org/10.1080/23311886.2022.2069209 . Oduma O, Agu CS, Stephen BO, Ehiomogue P, Eze JI, Nnadi DC, Udensi KN. Effect of soil type and operational speed on performance of some selected agricultural field machinery in south east Nigeria. Agricultural Eng International: CIGR J. 2021;23(1):17–27. Paul M, wa Gĩthĩnji M. Small farms, smaller plots: land size, fragmentation, and productivity in Ethiopia. J Peasant Stud. 2018;45(4):757–75. https://doi.org/10.1080/03066150.2016.1278365 . Sims B, Kienzle J. Sustainable agricultural mechanization for smallholders: What is it and how can we implement it? Agric (Switzerland). 2017;7(50):1–21. https://doi.org/10.3390/agriculture7060050 . Terentyev S, Kuchumov A, Sapozhnikova S. (2019). Assessment of State and Prospects for Development of Regional Agricultural Sector and Rural Areas. KnE Life Sciences , 2019 , 165–172. https://doi.org/10.18502/kls.v4i14.5602 Van Loon J, Woltering L, Krupnik TJ, Baudron F, Boa M, Govaerts B. (2020). Scaling agricultural mechanization services in smallholder farming systems: Case studies from sub-Saharan Africa, South Asia, and Latin America. Agricultural Systems , 180 (December 2018), 102792. https://doi.org/10.1016/j.agsy.2020.102792 Yigezu Wendimu G. The challenges and prospects of Ethiopian agriculture. Cogent Food Agric. 2021;7(1). https://doi.org/10.1080/23311932.2021.1923619 . Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8554432","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":591540825,"identity":"2498f4bd-8b21-41a4-ac47-5c9ba1ee1026","order_by":0,"name":"Hika Endalu Chibsa","email":"","orcid":"","institution":"Wallaga University, Faculty of Technology, Department of Agricultural Engineering","correspondingAuthor":false,"prefix":"","firstName":"Hika","middleName":"Endalu","lastName":"Chibsa","suffix":""},{"id":591540827,"identity":"c9cd56a4-5e7a-40a7-b675-35e7746dcdba","order_by":1,"name":"Siraj Kedir Busse","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYDACZiCWYDgAYoEICRlStLAlgJg8xNoF0sJjAGIR1iLfzvvwgwXDncT57T2fX92oseBhYD98dAM+LQaH2Y0lJBieJW44c3abdc4xoMN40tJu4NXCzMYA1HI4cYNE7jbjHDagFgkeM7xa5JvZmH+AtMyf/+aZcc4/IrQwHGZjA9vScIOH+XFuGxFaDIBaLCQMnhlvOJNmxpzbJ8HDRsgv8v3HmG9LVNyRnd9++PHnnG91cvzsh4/hdxgQMEuAY4SBTQJMElIOAowfoFo/EKN6FIyCUTAKRh4AAFcYQ3gv4Kx1AAAAAElFTkSuQmCC","orcid":"","institution":"Adama Science and Technology University","correspondingAuthor":true,"prefix":"","firstName":"Siraj","middleName":"Kedir","lastName":"Busse","suffix":""},{"id":591540828,"identity":"8f449e17-e1c1-478c-8a24-6e3f89479caf","order_by":2,"name":"Sintayehu Legesse Zeleke","email":"","orcid":"","institution":"Melba Printing, Publishing and Packaging S.C","correspondingAuthor":false,"prefix":"","firstName":"Sintayehu","middleName":"Legesse","lastName":"Zeleke","suffix":""}],"badges":[],"createdAt":"2026-01-08 18:38:16","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8554432/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8554432/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102962741,"identity":"0414efc5-0464-4395-903d-d1bebea33f49","added_by":"auto","created_at":"2026-02-19 04:10:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":341718,"visible":true,"origin":"","legend":"\u003cp\u003eStudy Areas Descriptions Map\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8554432/v1/66c30e4c5de0abf717437136.png"},{"id":102962702,"identity":"8b2628b8-691d-4ef6-af03-e67b8d8d8314","added_by":"auto","created_at":"2026-02-19 04:10:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":54455,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic presentations of equal and proportional allocations of sample size\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8554432/v1/2063932bfbe5412184392afd.png"},{"id":102828048,"identity":"d4299f18-5f40-41cc-b6c1-4128725faa50","added_by":"auto","created_at":"2026-02-17 09:21:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":43454,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of the availability of machinery and Implement in the study area.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-8554432/v1/7b0d46c73b4b65ed30dd0476.png"},{"id":102828046,"identity":"c5c032d1-39b0-4ec0-81d8-54c02c841386","added_by":"auto","created_at":"2026-02-17 09:21:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":31938,"visible":true,"origin":"","legend":"\u003cp\u003eAgricultural machinery utilization status\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-8554432/v1/46f51fbe6e13e9cb0fdd7a4b.png"},{"id":107486095,"identity":"07e25aa3-d468-465f-ab27-3f3feddfd5a8","added_by":"auto","created_at":"2026-04-22 02:37:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1180311,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8554432/v1/8d37c7c5-e7d2-408e-9548-acd56944d7e4.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Assessing the Availability and Utilization of Agricultural Machinery and Implements in East Wallaga Zone, Oromia Regional State","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMechanization technologies are critical, and they are among the most important inputs in agricultural enterprises (Emami et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Unless and until the problem of farm electricity is solved and addressed realistically in Sub-Saharan African countries (SSA), the region risks increased poverty and famine (Paul \u0026amp; wa Gĩthĩnji, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). In areas where farming is a major source of rural livelihood, the agricultural sector is essential for the socioeconomic growth of nations (Terentyev et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Agriculture is a key economic pillar in the East Wallaga Zone, making a substantial contribution to the general well-being and food security of the population (Ajama and Kibi, 2022). However, contemporary agricultural practices, such as the availability and effective use of agricultural technology, must be adopted if the industry is to realize its full potential.\u003c/p\u003e \u003cp\u003eFarming technology has transformed agricultural methods worldwide by allowing farmers to boost yields, improve productivity, and make the greatest use of available resources(Dhanaraju et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Modern equipment and technology are essential for agricultural operations in many regions. In addition to strengthening agricultural product chains, the implementation of equipment at different phases of farm operations will increase rural job prospects and farming profitability (Dhanaraju et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). However, inadequate access to and usage of agricultural machinery continues to impede the sector's development in less-developed regions, such as the East Wallaga Zone (Berhanu \u0026amp; Dubiso, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eLand is essential for rural communities because it is a deeply ingrained way of life. The size of the landholding has a considerable impact on agricultural machinery utilization, allowing for contemporary practices and technology such as tractors and combine harvesters (Andrew et al., 2015). Smaller land plots, on the other hand, may struggle to justify the cost of such gear, restricting their ability to reap its advantages. In rural areas, the size of the landholding influences mechanization, agricultural efficiency, and wealth (Alemu et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Giller et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). The importance of this study is highlighted by the shortage of literature and research on agricultural machinery in the East Wallaga Zone. This study aims to add to the body of knowledge on agricultural growth in the area by performing a thorough analysis and providing useful recommendations. This study aims to provide a comprehensive analysis of the current status of agricultural machinery availability and utilization in the East Wallaga Zone. By understanding the challenges and opportunities faced by farmers in adopting mechanized practices, the research aims to shed light on potential areas for improvement. Additionally, this study seeks to propose recommendations that can enhance the accessibility and effectiveness of agricultural machinery, thereby fostering sustainable agricultural development in the region.\u003c/p\u003e"},{"header":"Methods and materials","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Area Description\u003c/h2\u003e \u003cp\u003eThis study was conducted in the Oromia regional state of East Wallaga Zone, which is located 328 kilometers west of the capital city of Addis Ababa. It is located at 07\u0026deg;02' N and 38\u0026deg;28' E. The area has an average yearly rainfall of 800\u0026ndash;1200 mm and an average annual temperature of 27\u0026deg;C. It features diverse agroecology and agricultural systems. The district's agroecological zones are Dega, Woyina Dega, and Kola. Agricultural produce in the area is diversified, and the principal source of income for the rural population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eA mixed-methods research strategy was used for this study, which incorporated qualitative and quantitative data-gathering techniques. The use of mixed methods allows for a comprehensive understanding of the availability and utilization of agricultural machinery in East Wallaga Zone, as it incorporates the perspectives of farmers, agricultural machinery providers, and local authorities while also utilizing relevant secondary data. Structured questionnaires were administered to farmers across different areas of the East Wallaga Zone to gather information on their access to, and usage of, agricultural machinery. The surveys focused on the types of machinery used, the frequency of usage, and the challenges faced. Using a stratified random selection strategy, a representative sample of farmers was selected from various locations within the East Wallaga Zone.\u003c/p\u003e \u003cp\u003eUsing stratification, it is possible to understand that the sample adequately reflects both well-served and underserved areas. In-depth interviews were conducted with key stakeholders, including agricultural machinery providers and local authorities. Group discussions were organized with farmers to explore their perceptions, experiences, and suggestions related to agricultural machinery access and utilization. Government reports, agricultural organizations, and non-governmental sources were used to supplement the original data. This information was used to provide a more thorough context for the study and improve the analysis of trends and patterns.\u003c/p\u003e\n\u003ch3\u003eSources of Data\u003c/h3\u003e\n\u003cp\u003eThis study collected and used both primary and secondary data. Data obtained comprised socioeconomic and hypothesized factors influencing the adoption of agricultural mechanization technologies, as well as the types of agricultural mechanization technologies employed in the area.\u003c/p\u003e\n\u003ch3\u003eData Collection Methods\u003c/h3\u003e\n\u003cp\u003eField observations, interviews, structured and unstructured questionnaires, and document analysis were used to collect qualitative and quantitative data. To collect the necessary primary data, household surveys using semi-structured questionnaires with both open-ended and closed-ended questions were used. Similarly, data were collected from the sample respondents using a structured questionnaire and via face-to-face interviews.\u003c/p\u003e\n\u003ch3\u003eData Acquisition Procedure\u003c/h3\u003e\n\u003cp\u003eQuestionnaires were administered, and interviews with the farmers and owners of the farm machinery and equipment were conducted. Direct observation of the available agricultural machinery, tools, and implements was done in the study areas, and the data collected through questionnaires and interviews was analyzed statistically. The questionnaires were focused on the availability and utilization of agricultural machinery and implements.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003ePersonal interview\u003c/h2\u003e \u003cp\u003eThe interview and questionnaires were conducted for owners, non-owners, user and non-user of agricultural machinery and implements. The responses of the selected samples were documented with the information gathered from the questionnaires. The questions asked during the personal interview and questionnaires incudes: availability of agricultural machinery and implements, repair and maintenance work shop, availability of spare parts in the localities, acceptability of the agricultural new technology/mechanization. The data from this survey was subjected to statistical analysis using descriptive statistics that will reflect the respondent frequency and percentage of existence.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSampling Design\u003c/h3\u003e\n\u003cp\u003eFour districts and twelve sample kebeles were chosen. Purposive and random sampling was used to pick sample farmers from the kebele. The basic sampling units were zone selections from the four Wallaga zones, then East Wallaga was chosen using purposive sampling based on the presence of agricultural machinery. Second, four Woredas, (Guto Gida, Sasiga, Diga, and Gidda Ayana) were chosen by the purposive sampling from the east Wallaga zone based. In the third sampling unit, three kebeles were chosen from each woreda also by Purposive and random sampling, based on farmers' knowledge and responses to agricultural machinery. The selection of respondents in the kebele was chosen both randomly and purposively; purposive is used here to identify the machine owner since they are small in number. Purposive sampling is more used because in addition to the history of the machinery in the area, depending on the peace problem in the study area the purposive is required to collect data in a safe way. The sample size was calculated using the standard sampling approach employed in numerous surveys. As a result, the following formula was used to compute the sample size for this investigation, which is the most commonly used standard in both qualitative and quantitative data gathering from unknown populations (Ali, 2014), and related to this study.\u003c/p\u003e \u003cp\u003en = [Z\u003csup\u003e2\u003c/sup\u003e p (1-p)]/d\u003csup\u003e2\u003c/sup\u003e (1)\u003c/p\u003e \u003cp\u003eWere:\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;the to-be-determined sample size\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eZ\u0026thinsp;=\u0026thinsp;the standard normal deviation, which is set to 1.96 and corresponds to the 95% confidence interval.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;the estimated proportion of a population, which equals 50% or (0.5).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003ed\u0026thinsp;=\u0026thinsp;5% or (0.05) the amount of sampling error between the sample and the population So, to compute sample size;\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003en= ((1.96)\u003csup\u003e2\u003c/sup\u003e\u0026times;0.5\u0026times; (1-0.5))/ (0.05)\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003cp\u003en\u0026thinsp;=\u0026thinsp;384\u003c/p\u003e \u003cp\u003eThen adding 10% of the non-response rate, we get; n\u0026thinsp;=\u0026thinsp;384(1\u0026thinsp;+\u0026thinsp;0.1)\u0026thinsp;=\u0026thinsp;422\u003c/p\u003e \u003cp\u003eAt the district level, a proportional sampling technique will be employed to calculate the sample size. The required sample sizes at the kebele level will equally be allocated to each kebele.\u003c/p\u003e \u003cp\u003e№ of respondent at kebele =\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\frac{Total\\:№\\:of\\:respondants}{Total\\:№\\:ofkebele}\\)\u003c/span\u003e\u003c/span\u003e\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.(2)\u003c/p\u003e \u003cp\u003e№ of the respondent at each kebeles = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:\\frac{422}{12}\\)\u003c/span\u003e\u003c/span\u003e=35.16 we take 35\u003c/p\u003e \u003cp\u003e№ of the respondent at woreda\u0026thinsp;=\u0026thinsp;No of kebeles x No of respondents at kebeles\u0026hellip;\u0026hellip;\u0026hellip;\u0026hellip;.(3)\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\u003eSample size distribution in the study area.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample Weradas from\u003c/p\u003e \u003cp\u003eEast Wallaga\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e№ of the selected\u003c/p\u003e \u003cp\u003ekebeles in the woreda\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e№ of respondents in\u003c/p\u003e \u003cp\u003eeach kebele\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal № of\u003c/p\u003e \u003cp\u003erespondents\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuto Gida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSasiga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGida kiramu\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e12\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003e35\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e420\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFinally, the required sample sizes were allocated to those selected kebeles as indicated in Figure below. \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eFigure 2. Schematic presentations of equal and proportional allocations of sample size\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eTo generate descriptive statistics and identify patterns, quantitative data from surveys were statistically examined (descriptive statistics). Qualitative data from focus groups and interviews were transcribed and thematically analyzed to extract significant ideas. MS Excel 2016 was used for data coding and entry, while SPSS (Statistical Software Package for Social Sciences) version 26 was used for data analysis. To summarize the socioeconomic characteristics of farmers and other data received from farmers, and other government and non-government sources, descriptive statistics such as tabulation, percentage, maximum, and minimum were used.\u003c/p\u003e \u003cp\u003eAll methods were performed in accordance with the relevant guidelines and regulations, including the \u003cb\u003eDeclaration of Helsinki (2013 revision)\u003c/b\u003e for research involving human participants and the ethical research guidelines of Adama Science and Technology University.\u003c/p\u003e \u003c/div\u003e "},{"header":"Results and Discussions","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003cp\u003eThe first section reflects the social and demographic characteristics of the sample households. The characteristics include age, sex, education, family size, farming experience, land size, and number of economically active household members.\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\u003eRespondent\u0026rsquo;s age description\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\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eAge intervals (N\u0026thinsp;=\u0026thinsp;360)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26\u0026ndash;35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36\u0026ndash;45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFrequency\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\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e159\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 findings revealed that 52% of respondents were middle-aged, which may have a good impact on agricultural productivity.\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\u003eSex and educational status of respondents\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdult\u003c/p\u003e \u003cp\u003eeducation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003cp\u003e1-5\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGrade\u003c/p\u003e \u003cp\u003e5-10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eDiploma\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDegree\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e188\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e20\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 result revealed that 15% of the respondents were female while 85% were male which shows that males were more involved in farming practices than females in the study area. Traditionally, ladies are more responsible for interior tasks than males are for outdoor activities such as farming. When males are absent from a family in a rural setting, females may lack access to information about new farming technology and machinery used to boost productivity(Neway \u0026amp; Zegeye, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Agriculture accounts for 85% of the Ethiopian economy (Paul \u0026amp; Gĩthĩnji, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). This is corroborated by this study, which found that all farmers in the study region were involved in agriculture.\u003c/p\u003e \u003cp\u003eThe academic qualifications of the respondents showed that out of the interviewed households, 21.6% were illiterate, 8.6% were categorized as literate, 49.5% were primary (first cycle) educated, about 10% and 5% were above grade five and grade ten complete respectively. The degree holders of the respondents are those from the office at zone, woreda, and Das, which are a total of 5%. Despite the fact that the number of illiterate and uncertified persons was far higher than the national average, the research region lies within the educationally beneficial Wallaga zones. Most farmers can read and write and are likely to be familiar with agricultural mechanization, which is critical to maximizing their output.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe farmers work experience (year)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExperience (in year)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u0026ndash;10\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11\u0026ndash;15\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16\u0026ndash;20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e21\u0026ndash;25\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;25\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e№ of respondents\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe findings indicated that 2.8% of farmers had 5\u0026ndash;10 years of agricultural experience, whereas 20%, 47.5%, and 26.4% had 11\u0026ndash;15, 16\u0026ndash;20, and 21\u0026ndash;25 years of farming experience, respectively. 3.3% of those polled have greater experience, accounting for more than 25 years of agricultural expertise. The survey found that all of the respondents in the study region owned land; however, the amount of the property varied substantially across households and among respondents. The average size of a land holding was two hectares, with the smallest and largest observed holdings per family being 0.4 and 9 hectares, respectively.\u003c/p\u003e \u003cp\u003eAn improvement in farm human capital through agricultural mechanization is important to enhance farm productivity (Oduma et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Availability of machinery is the basic tool\u003c/p\u003e \u003cp\u003ethat should be insured to rural farmers in order to increase productivity and income from\u003c/p\u003e \u003cp\u003eagriculture (Eskeziaw et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). One of the significant issues with farm power in the zone is the lack of the appropriate types of Agricultural machinery, hence mechanization utilization remains low. For example, walking tractors are not utilized in the study area; as a result, farmers do not prefer to purchase and no one is supplied. Consequently, farm power availability is always insufficient for performing farm operations that are difficult to undertake without mechanical assistance. To promote agricultural machinery availability and utilization among smallholder farmers in the country as well as in the area, agricultural machinery-hiring schemes have been established and primarily managed by farmer organizations(Van Loon et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFarmland possession of farmers (hectare)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand size (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e№ of respondents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLand size (ha)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e№ of respondents\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\u003e0.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e360\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100.0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSummary\u003c/b\u003e:\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e2.00 ha\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eStd. Dev.\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1.18 ha\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMinimum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0.4 ha\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMaximum\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e9.0 ha\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\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=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eAvailability of Agricultural Machinery\u003c/h2\u003e \u003cp\u003eIn the study area, agricultural machinery is distributed in two ways. That is done through the MoA, and individuals through cash payments. To solve the problem of availability and distribution with demand and supply, the government and institution or organization working on the developments of agricultural mechanization have to incorporate with a company of different machinery and implement to distribute lower or discounted costs with a long-term credit facility and work to access the providers and brokers and introduce the appropriate equipment suitable for the different situation of farm activity and affordable for small-scale farms (Sims \u0026amp; Kienzle, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The agricultural machinery and implements found in the study area are tractors, irrigation pumps, maize shellers, groundnut shellers, and implements such as moldboard, disc plows, are found.\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\u003eThe availability of equipment in study area\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWoredas\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4WTs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIrrigation pump\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eImplements\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGida ayana\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGuto gida\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e60%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSasiga\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e42%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37%\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\u003eAmong the available machinery and implements in this study area, tractors (20%), irrigation pump (44%), and implements (maize and groundnut sheller, disc plow, mold bold) account 37%. Cereal crop mechanization, such as tractors, combines, and irrigation pumps, has been adopted in some sections of Oromia, notably Wallaga Zones, in the last 10 years, albeit not in all parts of Wallaga.\u003c/p\u003e \u003cp\u003eTable 7. Tractors and combine harvesters in the study area\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"99%\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMachinery\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQuantity (№)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBrand\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 31px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHorsepower (hp)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"10\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTractors\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 22px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e75hp\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e120hp\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e42\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eNatfaa\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e34\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e21\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eJohn Deere\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e5\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eWYito\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e78\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eHorsus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e72\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e7 \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eNew Holland\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eMassey Ferguson\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e35\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e98\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eVelarus\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e27\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eTurbo\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e6\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eAgro track\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCombine harvester\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eJohn deer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e2\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 22px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003eNew Holland\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 21px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10px;\"\u003e\n \u003cp\u003e1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e shows that there were roughly 343 registered agricultural machines in the research region, with tractors accounting for 99% and combined harvesters accounting for 1%. These machines are owned by a variety of organizations, including farmer organizations, youth groups, PLCs, investors, and farmer unions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eFactors Affecting Availability\u003c/h2\u003e \u003cp\u003eThe purpose of this study was to identify the variables influencing the accessibility, use, and distribution of agricultural machinery in the study area, such as capital, distance from the center, topography, income from farm or product, government consideration, farmer perception, land size, and product aim. during the study, the survey and respondent states revealed that the major factors hindering the availability and utilization of agricultural machinery in the study area were:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eThe accessibility to different services\u003c/strong\u003e Extension, promotion, and advice have a direct influence through the provision of information and training on the available agricultural technology. The research center, Institute of agricultural mechanization, and agricultural machinery and implement providers are important sources of information on agricultural innovations including encouraging and advising farmers and supplying adopted technologies. The availability of these technology centers initiates the farmer\u0026rsquo;s machinery and availability by initiating those who have the capital to purchase and adopt new technology. Credit service is one of the economic variables that influence the availability of agricultural machinery technologies. Access to credit services enables the farmer with liquid assets to purchase different inputs and machinery due to that machinery becoming available in the area.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLack of capital\u003c/strong\u003e \u003cp\u003eSmallholder farmers are virtually resource-poor by definition, and they frequently struggle to invest in physical assets in general and Agricultural machinery in particular. As the respondents reported, low own-starting capital or purchase capital was the major hindrance that hampered the existing agricultural machinery not being available in the study area.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLack of Government/institution consideration\u003c/strong\u003e \u003cp\u003eThe other reason that inhibited the availability of agricultural machinery in the study area stated by respondents is there was no consideration given to the area by the government or NGOs regarding the development of agricultural technology/machinery. Training and extension, as well as research and development (R\u0026amp;D), support services, infrastructure development, etc. in developing countries, the government must undertake because the private sector is not yet developed enough. However, government institutions are usually also very weak and largely ineffective and inefficient in the mechanization technology in the study area, and as the area far from the center mechanization center was so important but there is no yet. Also, there are no Agricultural development supporters and advisory activities are critical to the success of any mechanization and the promotion of new sustainable agricultural production technology in the farming system.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCost of machinery and implement\u003c/strong\u003e \u003cp\u003eIn many areas, farmers cannot afford to purchase equipment, and financial support through subsidies or financing schemes is limited. The financial services sector shies away from providing credit to smallholders because of a lack of eligible collateral and a perception of the high risk involved with agriculture. As reported by the sample respondents, the machinery and other mechanization inputs are expensive and increasing (from 1.6 to 2.7\u0026nbsp;million difference in one year), cost of the tractor to the respondent and agricultural bureau data; which, affecting their availability, even though the local farmers can\u0026rsquo;t purchase them.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eBrokers\u003c/strong\u003e \u003cp\u003eAs the influence of brokers is high in the availability of farm machinery and implements, by making the supply process easier and quicker, the absence of brokers in the area affects the availability of machinery and implements. As the area is far from the center and agricultural machinery suppliers are only found in the larger towns and cities, as the perceived low demand in rural areas for equipment does not always justify the establishment of distribution networks the availability of the complimentary actors involved with manufacturing, supply, distribution and after-sales service not there in the study area.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for factors affecting the availability of machinery.\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSE Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Dev\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of differen\u003cb\u003et\u003c/b\u003e services\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.72\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\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.63\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\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCost unaffordability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.69\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\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLack of Government consideration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence of brokers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.67\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\u003e0.47\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\u003eIn the study are as the respondent's results and Table\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e revealed that the availability of machinery is highly affected by the government considers as it\u0026rsquo;s possible to understand from standard and farmers or end users have no capital to facilitate appropriate mechanization technology, and the lack of capital is secondly affected as shown also from standard deviations of the following to the cost of machinery and lack of brokers.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eUtilization of Machinery\u003c/h2\u003e \u003cp\u003eThe result further revealed that pre-harvest technology utilization was greatly varying across districts and there is no harvest and post-harvest technology available in all parts of the study area except the maize sheller. Next to the irrigation pump from the total number of machineries in the area four-wheel tractor is utilized. It can be revealed from the respondents and data from the experts the technology utilization of the study area summarizes as 42% (N\u0026thinsp;=\u0026thinsp;145) were user and 58% (N\u0026thinsp;=\u0026thinsp;215) are non-user of the agricultural machinery and implements when see the total user and non-user in the study area. If credit was granted, respondents' need for their own AMT was considered. The region's utilization interest in current machinery is as strong (85%), according to research area respondents, including owners and non-owners. Farmers rent implements and agricultural machinery (tractors, combine harvesters, and other equipment) from machinery and implement owners during regular and peak working seasons (Challa, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Respondents' farmers\u0026rsquo; households were interested in the credit-based provision of intermediate upgraded agricultural mechanization technology.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe survey revealed that due to non-availability, about 40% of farmers in the study area used the least modern technology in addition to other local and intermediate implements, and were no respondents who were only using either tractor or other intermediate technologies for agricultural activities. While the rest 60% of the farmers were purely using local/traditional tools and systems in the agricultural activity starting from primary tillage to harvest and threshing, due to the lack of affordable machinery and implements as the capacity of farmers demand and the non-availability of appropriate machines to meet the farmers\u0026rsquo; needs due to the farm size, agroecology, and capital.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eFactors Affecting Utilization\u003c/h2\u003e \u003cp\u003eAs a result, it is necessary to continue to promote agricultural new technologies by developing them to address farmer problems and needs. Farmers' use of new agricultural technology is influenced by a variety of factors that vary by area (Sims et al., 2016). In the study area, it was possible to identify the major reasons why farmers did not utilize agricultural technology or Agricultural machinery. The sample respondents had different reasons for not utilizing the existing technology in the country.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eLand size and fragmentation\u003c/strong\u003e \u003cp\u003eThe great problem in mechanization of small-scale farm is the mismatch between the economies of scale of machines and farm size. More of the food produced originates from small family farms (Diriba, 2020) and many of these consist of separate and dispersed fields and are thus poorly suited for larger machinery. The amount of cultivated land per household significantly affects the technology use status of the household. The total land size and its continuity or fragmentations affect the utilization of even available machinery by the rental service.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eProductivity; Level of farm income per year\u003c/strong\u003e \u003cp\u003eThe level of annual income should be greater than the expenditure for farm inputs consisting of farm power and implements. Better output encourages farmers to invest in agricultural inputs, including the purchase of small machines or using available hire services. An adequate income could be used as funding to purchase or rent the mechanization services. Higher-income farm households are more likely than lower-income ones to use equipment and implements for farming tasks.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eService Providers\u003c/strong\u003e \u003cp\u003eThe services for rental utilization are significantly influenced by the local service providers' accessibility. The use of rental services is impacted by the lack of machinery service providers in the farmers' immediate area. Mechanization services will be easier to acquire if there are more Agricultural machinery service providers in the area.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eFamily size\u003c/strong\u003e \u003cp\u003eAgriculture is a labor-intensive industry that needs access to family and/or hired labor. A larger family can provide more labor than a smaller one. The number of economically active labour forces in the family was discovered to have a negative relationship with agricultural machinery utilization, especially when small-scale and fragmented land is handled since it is unsuited for machinery. Households with a higher proportion of economically active labour force members are less likely to hire machinery, according to the data, because extra labour is used for fieldwork.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eTopography\u003c/strong\u003e \u003cp\u003eThe utilization of Agricultural machinery requires a flat and straight area. The efficiency and productivity of the machinery are also higher in straight topography than in the up and down areas (Li et al., 2023). Therefore, the undulating of the area affects the utilization of Agricultural machinery.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab9\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 9\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics for factors affecting the utilization of machinery.\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=\"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 \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\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Error of Mean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eStd. Deviation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLand size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProductivity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbsence of provider\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTopography\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFamily size\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGoa of production\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.66\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\u003e0.47\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\u003eAs the evaluated response of respondent indicates (Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e) above the degree by which factors affect the utilization of agricultural technology was varied. The factors with high std. deviation were significantly affect the utilization of mechanization technology in the study area. From the Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e seen that the absence of provider affects by 0.48 Std. deviations rather than another factor as respondent\u0026rsquo;s response.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eProductivity Impact\u003c/h2\u003e \u003cp\u003eUsing a tractor to plow one hectare of land takes a few hours, whereas using animals can take more than seven days. One tractor can plow seven to eight hectares of land per day with one operator, while the traditional method needs a minimum of seven people and seven pairs of oxen to plow one hectare. Farmers in the study reported that it took an average of three workers to thresh one quintal of crops using a thresher, compared to the six laborers required in the traditional threshing system. This demonstrates that using a thresher reduces the need for labor and resources. Mechanized farming reduces the time required for operations while also improving the quality of the work produced. A study found that there is a 35% difference in productivity between farmers who use agricultural machinery and those who do not. The study indicates that increased productivity from utilizing agricultural machinery leads to increased yield, higher work quality, reduced labor, and time savings. Farmers also cited the benefit of generating income by renting out machinery or profiting from rental services.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIncreased efficiency and output\u003c/b\u003e: Tractors can plow significantly more land in a day compared to traditional animal plowing methods. A single tractor can plow 7\u0026ndash;8 hectares per day, whereas using animals can take more than seven days to plow one hectare. This efficiency translates to increased output and the potential for higher yields. Reduced labor requirements: Using agricultural machinery significantly reduces the need for manual labor. For example, threshing one quintal of crops with a thresher requires three workers compared to six laborers using traditional methods. Time savings: Mechanization allows farmers to complete tasks faster, freeing up time for other activities or enabling them to cultivate more land. Improved quality of work: Agricultural machinery often produces higher quality work compared to manual methods. This can lead to better crop yields and higher profits. Higher profits: Increased efficiency, reduced labor costs, and improved quality of work can result in higher profitability for farmers. Increased income from renting out machinery: Farmers who own agricultural machinery can generate additional income by renting out their equipment to others.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis research work presents the availability and utilization of agricultural machinery in the Oromia region of the East Wallaga zone with four targeted Woredas: Guto Gida, Diga, Sasiga, and Gida Ayana. Hence, the availability and utilization of machinery and implement, as well as factors affecting the mechanization technology in the study area, were evaluated and analyzed by the descriptive statistical method. The findings of this study indicate that there is a low degree of availability and utilization of implements and equipment, and that commercial organizations are more involved in the ownership of agricultural mechanization than the government and people. Farmers' low levels of capital and income, as well as a lack of external support and the highest percentage of farmers' lack of technical know-how and machinery/implements availability, impede the adoption and utilization of modern agricultural technology and make it difficult to increase land and labour productivity.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo funding received for this article\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the support of Adama Science and Technology University (ASTU) for facilitating this research. We also thank the East Wallaga Zone agricultural offices and all farmers and experts who participated in this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSiraj Kedir Busse conceived and designed the study. Hika E. Chibsa collected the data and drafted the manuscript. Sintayehu analysed and interpreted the data. All authors reviewed and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll participants were informed about the objectives of the study, their right to withdraw at any time, and the voluntary nature of participation. Freely given, informed consent was obtained from all participants prior to data collection.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that informed consent for publication of anonymized data was obtained from all study participants. No data that could identify individual participants are included in the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;Ethical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical clearance for this study was obtained from the \u003cstrong\u003eEthical Review Committee of Adama Science and Technology University, Ethiopia\u003c/strong\u003e. Data collection was conducted in compliance with the approved ethical standards.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability statements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAjama G and K. G. Evaluation and Demonstration of Engine Driven Soybean Thresher in East Wallaga Zone, Ethiopia. J Biology Agric Healthc. 2022;12(2):12\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.7176/jbah/12-2-03\u003c/span\u003e\u003cspan address=\"10.7176/jbah/12-2-03\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlemu GT, Ayele B, Z., Abelieneh Berhanu A. (2017). Effects of Land Fragmentation on Productivity in Northwestern Ethiopia. \u003cem\u003eAdvances in Agriculture\u003c/em\u003e, \u003cem\u003e2017\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1155/2017/4509605\u003c/span\u003e\u003cspan address=\"10.1155/2017/4509605\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAndrew S, Charles B, M. Mechanized Agriculture: Machine Adoption, Farm Size Displacement. AgBioForum. 2015;18(3):278\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBerhanu S, Dubiso G. Using farmers evaluation criteria as an essential constituent of variety popularization: The case of improved finger millet variety demonstration in selected districts of East Wallaga Zone. Int J Agricultural Sci Food Technol. 2022;8(2):083\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17352/2455-815x.000149\u003c/span\u003e\u003cspan address=\"10.17352/2455-815x.000149\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChalla TG. (2016). \u003cem\u003eProspects and Challenges of Agricultural Mechanization in Oromia Regional State-Ethiopia, Policy Perspectives\u003c/em\u003e. \u003cem\u003e4\u003c/em\u003e(5), 118\u0026ndash;127. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.11648/j.ajaf.20160405.12\u003c/span\u003e\u003cspan address=\"10.11648/j.ajaf.20160405.12\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDhanaraju M, Chenniappan P, Ramalingam K, Pazhanivelan S, Kaliaperumal R. Smart Farming: Internet of Things (IoT)-Based Sustainable Agriculture. Agric (Switzerland). 2022;12(10):1\u0026ndash;26. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agriculture12101745\u003c/span\u003e\u003cspan address=\"10.3390/agriculture12101745\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmami M, Almassi M, Bakhoda H, kalantari I. Agricultural mechanization, a key to food security in developing countries: Strategy formulating for Iran. Agric Food Secur. 2018;7(1):1\u0026ndash;12. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s40066-018-0176-2\u003c/span\u003e\u003cspan address=\"10.1186/s40066-018-0176-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEskeziaw MY, Ketema M, Haji J, Bekele K. Production Efficiency of Major Crops Among Smallholder Farmers in Central Ethiopia. J Agribusiness Rural Dev. 2021;61(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.17306/j.jard.2021.01391\u003c/span\u003e\u003cspan address=\"10.17306/j.jard.2021.01391\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGiller KE, Delaune T, Silva JV, Descheemaeker K, van de Ven G, Schut AGT, van Wijk M, Hammond J, Hochman Z, Taulya G, Chikowo R, Narayanan S, Kishore A, Bresciani F, Teixeira HM, Andersson JA, van Ittersum MK. The future of farming: Who will produce our food? Food Secur. 2021;13(5):1073\u0026ndash;99. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12571-021-01184-6\u003c/span\u003e\u003cspan address=\"10.1007/s12571-021-01184-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeway MM, Zegeye MB. Gender differences in the adoption of agricultural technology in North Shewa Zone, Amhara Regional State, Ethiopia. Cogent Social Sci. 2022;8(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23311886.2022.2069209\u003c/span\u003e\u003cspan address=\"10.1080/23311886.2022.2069209\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOduma O, Agu CS, Stephen BO, Ehiomogue P, Eze JI, Nnadi DC, Udensi KN. Effect of soil type and operational speed on performance of some selected agricultural field machinery in south east Nigeria. Agricultural Eng International: CIGR J. 2021;23(1):17\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePaul M, wa Gĩthĩnji M. Small farms, smaller plots: land size, fragmentation, and productivity in Ethiopia. J Peasant Stud. 2018;45(4):757\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/03066150.2016.1278365\u003c/span\u003e\u003cspan address=\"10.1080/03066150.2016.1278365\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSims B, Kienzle J. Sustainable agricultural mechanization for smallholders: What is it and how can we implement it? Agric (Switzerland). 2017;7(50):1\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/agriculture7060050\u003c/span\u003e\u003cspan address=\"10.3390/agriculture7060050\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerentyev S, Kuchumov A, Sapozhnikova S. (2019). Assessment of State and Prospects for Development of Regional Agricultural Sector and Rural Areas. \u003cem\u003eKnE Life Sciences\u003c/em\u003e, \u003cem\u003e2019\u003c/em\u003e, 165\u0026ndash;172. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18502/kls.v4i14.5602\u003c/span\u003e\u003cspan address=\"10.18502/kls.v4i14.5602\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan Loon J, Woltering L, Krupnik TJ, Baudron F, Boa M, Govaerts B. (2020). Scaling agricultural mechanization services in smallholder farming systems: Case studies from sub-Saharan Africa, South Asia, and Latin America. \u003cem\u003eAgricultural Systems\u003c/em\u003e, \u003cem\u003e180\u003c/em\u003e(December 2018), 102792. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.agsy.2020.102792\u003c/span\u003e\u003cspan address=\"10.1016/j.agsy.2020.102792\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYigezu Wendimu G. The challenges and prospects of Ethiopian agriculture. Cogent Food Agric. 2021;7(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/23311932.2021.1923619\u003c/span\u003e\u003cspan address=\"10.1080/23311932.2021.1923619\" 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":"Agricultural mechanization, Agricultural machinery, Smallholder farmers, East Wallaga Zone, Barriers to adoption","lastPublishedDoi":"10.21203/rs.3.rs-8554432/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8554432/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAgricultural mechanization is essential for enhancing productivity and efficiency in farming; however, there is no comprehensive study conducted on the availability and utilization of agricultural machinery in the East Wallaga Zone of Oromia Regional State. This study sought to assess the availability and use of agricultural machinery, identify factors affecting its adoption, and evaluate its impact on productivity. Data were gathered from 380 participants, including 360 farmers and 20 agricultural specialists, through structured questionnaires, interviews, and focus group discussions. Statistical analysis was performed using SPSS version 22 to uncover trends and relationships regarding machinery availability, utilization, and influencing factors. The findings indicated that 68% of farmers continued to depend on traditional farming practices. Access to agricultural machinery was limited to only 11% of farmers, which perpetuated reliance on outdated methods and restricted productivity. Additionally, several challenges hindered machinery use, including fragmented land holdings (with an average size of 2.1 hectares), financial constraints (75% of respondents), high costs of machinery (60%), and a scarcity of local suppliers (72%). Other obstacles included difficult terrain (56%), lack of maintenance services, and unavailability of replacement parts. Farmers who utilized machinery experienced a 35% increase in crop yields compared to those who relied on traditional methods, with mechanization significantly alleviating labor demands during peak periods. The study underscores the urgent need for policy interventions aimed at improving access to machinery, enhancing affordability, and establishing support systems to empower smallholder farmers in boosting productivity and achieving sustainable agricultural development.\u003c/p\u003e","manuscriptTitle":"Assessing the Availability and Utilization of Agricultural Machinery and Implements in East Wallaga Zone, Oromia Regional State","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 09:21:26","doi":"10.21203/rs.3.rs-8554432/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":"3ff597b0-b85f-4bdc-8eb2-446a270117e6","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-20T08:28:16+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 09:21:26","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8554432","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8554432","identity":"rs-8554432","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}