In Ethiopia, agricultural technology adoption is an important strategy to improve agricultural production and productivity of smallholder farmers. However, the effect of age on farmers’ agricultural technology adoption is a controversial issue. To evaluate the total effect, a systematic review and meta-analysis are designed through systematically searching studies by following a PRISMA guideline. Finally, 25 articles were included. Using STATA version 17 software, the inconsistency indices tau², I², and H² were used to estimate the heterogeneity across the studies. The results of the random effect model indicated that farmers’ adoption of technology was constrained by the influence of aging by 1.38 units (95% CI: −2.48, −0.27). Furthermore, the meta-regression result revealed that distance from the market, extension contact, education, and family size are important moderators that determine heterogeneity in the effect of age on farmers’ agricultural technology adoption. It is concluded that building public social networking platforms, cooperative organizations, and mutual aid agencies within the community are crucial to supporting older farmers and reducing the negative effect of age on farmers’ agricultural technology adoption. Therefore, in order to increase the rate of agricultural technology adoption in Ethiopia, the government must take into account factors, such as age variations in adoption, the skills of older farmers, and the features of agricultural technology used by smallholder farmers.

1. Introduction

Ethiopia is one of the top-performing economies in Sub-Saharan Africa. In the year 2004–2017, this nation was able to record an average growth rate of around 11%, but since then, it has been falling to less than 6.6%. For the fiscal year 2022, GDP projections were only 3% due to multiple shocks with differing magnitude and nature, including internal conflicts, the COVID-19 pandemic, droughts, crop pests like the locust manifestations, and the effect of the Ukraine crisis, among others [1].

Agriculture is the mainstay of the economy, and exports are almost entirely dependent on agricultural commodities, such as coffee, oil seeds, dried pulses, hide, and skin, as well as live animals, which take the largest source of foreign revenue [2]. It has a 32.5% share of the National Gross Domestic Product (GDP) and contributes 81.5% of the export earnings for the year 2018/2019 to the country [3].

The policy in Ethiopia is designed to upgrade agricultural production and productivity to reduce rural poverty and ensure household food security [4]. However, its productivity is highly affected by different factors, which include drought, insufficient technology, reduction of natural resources, lack of institutional support, pests, and diseases [5, 6].

In developing countries, technology adoption by smallholder farmers is an important strategy to improve production and productivity. However, demographic factors like age influence farmers’ decisions to adopt new technologies [7].

Although many agricultural technologies are available that have the potential to improve the quality of life through increasing yields, reducing cost of production, and maximizing income, farmer adoption remains very low in Ethiopia. For instance, in the 2014/2015 production season, the adoption of chemical fertilizers, improved seed, pesticides, and irrigation were 55.06%, 8.55%, 22.32%, and 6.15%, respectively [8]. However, some factors, such as information access, and perceptions about the significance of the technology and its impact on yield, are significantly higher for the technology adoption decisions among smallholder farmers.

Farmers’ technology adoption, intellectual ability, and agricultural investments are affected by age, which limits farmers’ technology acceptance. However, the negative impact of aging can be reduced by building public social network platforms, cooperative organizations, mutual aid agencies, activity centers, and associations in the community, which are important conditions for the younger farmers to assist the older farmers [9]. As a result, the technology adoption rate and the human capital of the aged labor force’s contribution to the economy could increase.

Older adults have lower rates of technology adoption relative to their functions and ability to live [10]. Although we are now part of the digital world, older people have less access to information, fewer opportunities for dealing with government agencies, and lower interaction with development agents.

There are many controversial findings available across the studies in Ethiopia about the effect of age on technology adoption. Some authors argued that the proportions of farmers adopting agricultural technology increase with age [11–13] with the hypothesis that older farmers have more experience and resource endowment. However, other authors believed that older adults are less likely to adopt new and emerging technologies as compared to younger adults [14–18] with the hypothesis of limited access to information and less energetic and risk-averse behavior of older adults.

To have a good understanding, it is necessary to review researchers’ inconsistent findings arising from different authors for further intervention strategies. Therefore, this review provides answers to the following questions: (i) how many and what type of adopted agricultural technology studies are available in each region of Ethiopia? and (ii) what is the effect of age on the farmers’ technology adoption in Ethiopia? To answer these questions, a systematic review and meta-analysis were designed. Through identifying solutions, the review article could be an essential resource for global organizations, policymakers, farmers, and researchers to implement appropriate intervention strategies and to make farmers more productive. Besides, it is important to ensure that older farmers have equal opportunities to access farm technologies through an understanding of demographic factors that influence technology adoption.

2. Materials and Methods

2.1. Review Protocol

Full-text articles were searched to address the objective of this study based on the eligibility criteria (exclusion and inclusion criteria). Therefore, studies related to farmers’ technology adoption in Ethiopia were critically identified by considering the intervention variable.

Exclusion criteria were articles that were not written in English, articles published before 2014, review articles, studies done outside of Ethiopia, book chapters, books, time series, and panel data types of articles, categorical measures for the intervention variable, articles that have no full information, and duplicated articles to maintain procedural similarities between studies throughout article browsing.

Furthermore, the specified inclusion criteria were articles that were written in English, studies done only in Ethiopia, a cross-sectional type of research design, primary outcome were farmers’ technology adoption, and studies done within the time frame of 2014–2024 years.

The study followed the proposed checklists of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines for the screening process [19]. Using four-stage article selection procedures (i.e., identification, screening, eligibility, and included), duplicate articles were removed, then, through reading titles and/or abstracts, only relevant articles were screened, and finally, 25 appropriate full-text articles were included for the data extraction.

2.2. Data Browsing Strategy

The article searching process was done systematically using electronic databases, such as AGRIS, Research4Life (AGORA), ScienceDirect, and Google Scholar using keywords and advanced search strategies with the help of boolean operators (AND/OR) to combine keywords. Moreover, data were retrieved from reference lists of eligible articles to increase the chance of getting relevant studies. Keywords like Agriculture OR Farmers AND Technology adoption OR Agricultural technology adoption AND Ethiopia were used to get the required full-text articles that could respond to the review questions. The database search was conducted from October 10, 2023 to May 10, 2024.

2.3. Article Selection

As shown in Figure 1 below, using different database searching engines, a total of 7487 studies from ScienceDirect, 822 studies from Research4Life (AGORA), 13 studies from AGRIS, 22,200 studies from Google Scholar, and 12 studies through referencing of eligible studies were initially recorded. Finally, through applying different eligibility criteria and PRISMA guidelines, only 25 relevant studies were included for qualitative and quantitative (meta-analysis) analysis. Moreover, the selected articles’ used to conduct meta-analysis is shown in Table 1 below.

Details are in the caption following the image — **Figure 1**
Open in figure viewer PowerPoint

PRISMA flow diagram to show articles screening for systematic review and meta-analysis. *Note*: *Source*: Author computation based on the available online search.

Table 1. The total included articles in systematic review and meta-analysis.

Reference number	Authors’ name	Publication year	Study region	Study design	Total sample size	No. of adopters	Model used
[11]	Beri and Degefu	2024	Oromia	Cross-sectional survey	118	64	Logit
[20]	Nigus et al.	2024	Oromia	>>	385	270	Probit
[21]	Belissa	2024	SNNPR	>>	692	149	Difference-in-difference
[22]	Tekeste et al.	2023	Amhara	>>	299	142	Double hurdle model
[23]	Workie and Tasew	2023	SNNPR	>>	201	110	Logit and PSM
[12]	Jebessa et al.	2023	Oromia	>>	266	140	PSM
[24]	Mume et al.	2023	Oromia and Amhara	>>	288	130	Logit
[25]	Negera et al.	2022	Amhara	>>	404	202	PSM
[26]	Fasika et al.	2022	Oromia	>>	335	197	Probit
[27]	Mulatu et al.	2021	SNNPR	>>	89	33	Logit
[28]	Milkias and Muleta	2021	Oromia	>>	150	89	Logit
[29]	Solomon et al.	2021	Amhara	>>	796	543	Logit
[29]	Solomon et al.	2021	Amhara	>>	796	184	Logit
[29]	Solomon et al.	2021	Amhara	>>	796	344	Logit
[30]	Mekuria et al.	2020	Oromia	>>	196	104	Tobit
[31]	Milkias	2020	Amhara	>>	144	78	Double hurdle model
[32]	Teshome and Tegegne	2020	Amhara	>>	150	72	Logit and PSM
[33]	Fentie and Beyene	2019	SNNPR	>>	260	104	Logit
[34]	Feleke et al.	2019	Amhara	>>	146	52	PSM
[35]	Sileshi et al.	2019	Benishangul Gumuz	>>	408	200	Logit
[36]	Woldie	2019	Oromia	>>	111	35	Probit
[37]	Asfaw et al.	2018	Oromia	>>	384	323	Tobit
[38]	Milkias and Abdulahi	2018	Amhara	>>	150	104	Logit
[39]	Gebresilassie and Bekele	2015	Tigray	>>	160	118	Tobit
[40]	Hailu. et al.	2014	Tigray	>>	270	93	Probit

Note: Source: Author computation based on the available data.

2.4. Data Extraction Process

Articles were screened by two reviewers independently based on the specified eligibility criteria. Before excluding the study, disagreements between reviewers were resolved through discussion. Microsoft Excel spreadsheet forms were used for the data extraction to keep the data accurate and consistent. The necessary data extraction process was done from the included articles for systematic review and meta-analysis. Data were extracted based on the intervention variable, the year of publication, study design, study region of Ethiopia, total sample size, and number of adopters. After the data were extracted, meta-analysis was conducted to know the average effect of age on farmers technology adoption.

2.5. Statistical Analysis

Meta-analysis was applied using STATA version 17 software. Results were reported through a forest plot diagram to visualize the pooled effect size with a 95% confidence interval (CI) and to estimate the statistical heterogeneity between the included studies.

In meta-analysis, due to the variations in the effect of the intervention variable on the outcome variable, heterogeneity could occur across studies because of the variations in the household head’s age, the type of adopted technology, the study region, the study design, the total sample size, the number of adopters, and the model employed. The two very common measures of heterogeneity across studies (tau² and I²) [41], were applied. Note that, H² is a transformation of I². Therefore, after checking the level of variation in effect sizes, the appropriate meta-analysis model is selected even if meta-analysis includes random effect, fixed effect, and mixed effect models [42]. The benchmarks for I² values are 25%, 50%, and 75% for low, moderate, and high heterogeneity, respectively [43]. As a result, high heterogeneity leads us to use a random effect model, whereas low heterogeneity leads us to use either a fixed effect or combined effect model. For this study, random effect model was applied since there is high heterogeneity.

The random effect model is specified as;

where, yi is the efrunfect size, b₀ is the constant term, s_i is a study-specific deviation from the overall mean for ith study, it is normally distributed with the between-study variance, often referred to as 𝜏², Note that weights for this model are 1/(𝜏² + v_i). e_i is the error term.

For this study, the mean age of the household head was the intervention variable (continuous), while the adoption of farmers’ technology was the outcome variable (dummy) to maintain methodological consistency. The mean difference was used as the measure of the effect size for all included studies of meta-analysis.

Moreover, identifying the sources of heterogeneity in meta-regression analysis was conducted using meta-effect size as the dependent variable and four covariates as independent variables to explain the level of heterogeneity. Publication bias tests for small-study effects in the meta-analysis were done using the regression-based Egger’s test and nonparametric rank correlation (Begg’s) test.

3. Results and Discussion

3.1. Agricultural Technology Adoption Studies by Region

The bar graph in Figure 2 below portrays the number of studies on agricultural technology adoption in Ethiopia. Of 25 included studies, about 9 studies were conducted in Amhara Region, 8 studies in Oromia Region, 4 studies in South Nation Nationality People Region (SNNPR), 2 studies in Tigray, 1 study in both Oromia and Amhara Regions, and 1 study was conducted in Benishangul Gumuz Region. The descriptions in these regions indicated that there is no sufficient number of studies conducted, especially in Benishangul Gumuz, Tigray, and SNNPR Regions, related to farmers’ technology adoption. Although, Amhara and Oromia Regions are the two largest regions in Ethiopia in both population and area coverage, the studies were not that much large in number. Therefore, the studies must be conducted, especially in the rest of the regions to know the status of farmers and fill the gaps related to farmers’ technology adoption.

3.2. Adopted Agricultural Technologies in Ethiopia

There were different types of agriculture technologies adopted by farmers in Ethiopia [44]. On the basis of the included studies, the pie chart in Figure 3 below portrays the share of agricultural technologies adopted by smallholder farmers. Adoption of improved crop varieties had the highest share, which was 48%. Adoption of high yielding crop varieties was the primary concern in Ethiopia to solve food security problems.

Climate-smart agricultural technologies and soil and water conservation (SWC) practices were ranked second in the adoption of technologies, each accounting for 16%. In Ethiopia, adoptions of climate smart agricultural technologies have been familiar as the best alternative to reduce the adverse effect of climate change. However, the adoption of smallholder farmers remains very low.

The third technology adopted by Ethiopian farmers’ was livestock technology which accounted for 8%. Although Ethiopia has the largest livestock population in Africa [45], their productivity was very low due to the use of indigenous/local breeds since local breeds have low yield potential. Therefore, the government and the concerned stakeholders should give high attention to increasing the intensity of the adoption of livestock technologies.

The other technology used by farmers was the adoption of small scale biogas technology. It accounted for the percentage share of 4%. In Ethiopia, biogas technology is made from cow dung, and biogas slurry is a by-product that is used as an organic fertilizer. The technology is one of the most precious alternatives to chemical fertilizers. The by-product of the technology (bio-slurry) is very important to increase soil fertility and crop production, to assure food security, and to increase household income [26]. But adoption of this important technology was very low, almost insignificant in Ethiopia. Therefore, to benefit smallholder farmers’, the use of biogas technology should be included in the national agricultural input policy.

Furthermore, the other relevant adopted agricultural technology in Ethiopia is urban agriculture (UA), which accounted for 4%. In Ethiopia, UA practices were applied through keeping livestock (sheep, and poultry), growing rain-fed crops and vegetables nearby to their homes [46]. Even though it is influenced by land shortages, lack of credit availability and poor access to agriculture extension services, it is considered an important livelihood strategy specifically for urban residents [20]. To expand UA practices, the local administrator, agricultural offices, and NGOs should collaborate with urban farmers to increase extension contacts, financial support, and, information access to boost the adoption of UA practices.

Finally, the other adopted farm technology in Ethiopia is chemical fertilizers (Urea and NPS), which accounted for 4%. Adoption of chemical fertilizers are crucial to maximize crop yield by improving plant growth since the level of soil fertility status is very low due to soil erosion, overgrazing, overcultivation, etc. Therefore, adopting chemical fertilizers are indispensable to increasing the productivity of crops in Ethiopia.

3.3. The Effect of Age on Farmers’ Technology Adoption

The random effect model results of meta-analysis are shown in Figure 4. The model describes the effect of age on farmers’ technology adoption in Ethiopia. The mean difference explains the average effect size. The forest plot diagnostic test shows the estimated pooled effect and its 95% CI for the true effect size. The forest plot revealed that, a 1-year increase in the age of farmers’ will reduce the probability of adopting agricultural technology by 1.38 units with a 95% CI: −2.48, −0.27. The reason is that in Ethiopia, older farmers are unable to read and write, risk-averse, less exposed to information about the existing agricultural technologies, prefer to stick to farming practices that already existed previously, and are also sometimes more conservative to accepting new technologies as compared to younger farmers, Based on the included articles for meta-analysis, more than 85% of the studies agreed that age of farmers and technology adoption had an inverse relationship. This finding is in line with the previous studies [22, 23, 25]. In the other viewpoint, younger farmers in Ethiopia are more likely to adopt agriculture technologies since they have formal education, are less risk-averse, have more willingness, and have greater flexibility in accepting new technologies. These findings are consistent with [47].

In addition, the presence of the variations in effect sizes across the studies were assessed using tau², I², and H² statistics. The random effect model results explained that there were a strong heterogeneity across the included studies (tau² = 5.75, I² = 77.31%, and H² = 4.41 with p = 0.01). This meta-analysis implies that the effect of age on farmers’ technology adoption is heterogeneous. To determine the sources of heterogeneity, meta-regression analysis was conducted as shown in Table 2 below.

Table 2. Random effect meta-regression results.

Effect size	Coefficient	Std. err.	T	P> \|t\|	(95% conf. interval)
Distance from the market	0.027 ^∗	0.013	2.010	0.058	−0.001	0.055
Extension contact	−0.750 ^∗∗∗	0.200	3.750	0.001	−1.166	−0.333
Education	−0.396	0.228	1.740	0.097	−0.870	0.079
Family size	0.123	0.377	0.330	0.748	−0.664	0.910
Constant	−0.401	2.457	0.160	0.872	−5.526	4.723

Number of observation = 25						—

Residual heterogeneity: tau² = 2.246 I² (%) = 56.68 H² = 2.31			Adj R-squared = 0.5038 Model F (3,21) = 4.56 Prob > F = 0.0089

Note: Source: Author computation based on the available data. Test of residual homogeneity: Q_res = chi² (20) = 56.67 Prob > Q_res = 0.0000 ^∗∗∗p < 0.01, ^∗∗p < 0.05 and ^∗p < 0.1.

3.4. Meta-Regression Analysis

The meta-regression results in Table 2 describe the sources of heterogeneity. Based on the available results, more than 80% of the studies explained about the main factors influencing the effect of age on farmers’ technology adoption, which were distance to market, extension contact, education, and family size. These four covariates were employed as predictors to consider various treatment results. More or less, the differences between the studies appeared to have been caused by these factors. According to the result in Table 2, a residual I-squared value of 56.68%, a tau-squared value of 2.246, and an H-squared value of 2.31 showed that all moderators had explained the variation between the studies. A p-value of 0.0089 for each moderator in the joint test showed a possible association between the treatment effect and at least one moderator. Moreover, an adjusted R² value of 50.38% indicated that these four moderators were primarily responsible for the variation in the study.

The meta-regression results indicated that distance from the market site had a positive effect, which was significant at a 10% probability level. It showed that a unit increase in distance from the market site increases the effect size by 0.027; other things remain constant (ceteris paribus). This is consistent with the findings of [20, 48], who found that farmers who live far away from the market site have a higher chance of implementing agricultural technology than farmers who live close to the market location. Hence, farmers who live towards the border of the market site allocate and use the available land for other alternative income sources (or nonfarm activities, such as petty trading, cafes, restaurants, shops, etc.) in order to generate rent and these situations changes as one goes far away from the market site. Additionally, as compared to older farmers, younger farmers can travel to remote locations and access the market because of the availability of physical labor and formal education, which expands their capacity to attain the farm inputs easily.

Finally, at 95% confidence level, a significant negative effect was found between the extension contact and the effect size. The random effect meta-regression indicated that a unit increase in extension contact causes a decrease in the effect size by 0.75, ceteris paribus. The study revealed that there is no organized and quality extension service; extension workers have limited knowledge about agricultural technology and communication skill to share information and providing relevant support to farmers. This leads to a negative perception of extension services and hinders farmers’ technology adoption, even with extension contact. This finding is consistent with that of [49, 50], who identified that farmers who had contact with the extension agents on a sporadic basis have no greater likelihood of adopting the technology than those who did so regularly. Although many studies [20, 25, 35, 51] have found that the frequency of extension contact increases the possibility of adopting agricultural technology; extension agents can imitate the negative impacts of the agricultural technology due to inaccurate information, insufficient training and lack of formal education in the overall decision to adopt farm technologies. Furthermore, older farmers are less likely to accept the new agricultural technologies and misinformed; they rather prefer to practice the existing traditional farming systems.

3.5. Publication Bias Tests

Funnel plot for graphical diagnostics of small-study effects are often used to assess whether meta-analyses have a publication bias. However, this kind of visual interpretation is subjective [52]. Publication bias can be caused by poor methodological quality, chance, true heterogeneity, and reporting bias [53]. For this study, both regression-based Egger’s test and nonparametric rank correlation (Begg’s) statistical tests were used since they are recommended for continuous data. However, Peter and Harbord tests were recommended for only binary data.

As shown in Table 3 below, the statistical test showed the absence of publication bias. The Egger’s test result indicated that the standard error of beta1 is 1.27 with a p-value of 0.1353. This test suggests little evidence for the presence of small-study effects. As a result, smaller studies (those with larger standard errors) have no larger effect size, which did not prove publication bias. The Begg’s test result in Table 3 showed a standard error of score 42.817 with a p-value of 0.2158. This test also exhibited the absence of a small study effect, and confirmed the rationality of the study.

Table 3. Tests for small-study effects in meta-analysis.

Regression-based Egger’s test for small-study effects	Nonparametric rank correlation (Begg’s) test for small study effects
H₀: beta1 = 0; no small-study effects beta1 = −1.97 SE of beta1 = 1.270 t = −1.55 Prob > \|t\| = 0.1353	Kendall’s score = −54.00 SE of score = 42.817 Z = −1.28 Prob > \|Z\| = 0.2158

Note: Source: Author computation based on the available data.

4. Conclusion and Recommendations

Although there are many agricultural technologies in Ethiopia, their adoption status is influenced by demographic factors like age. The random effect model results indicated that a 1-year increase in the age of farmers, reduce the probability of adopting agricultural technology. This implied that in Ethiopia older farmers were unable read and write, risk-averse, less exposed to information about the existing technologies, preferred to stick to farming practices already existing previously, and also sometimes conservative to accept new farm technologies as compared to younger farmers.

Based on the meta-regression results, distance to market, extension contact, education, and family size were the primary factors in explaining variation in the effect of age on farmers’ technology adoption.

Therefore, the following recommendations have been forwarded:

•
Interventions should be undertaken to improve the effect of age on farmers’ technology adoption through building public social network platforms, cooperative organizations, mutual aid agencies, activity centers, and clubs in the community to create a positive effect on older farmers.
•
The government must ensure and work on the older farmers to have equal opportunity in the development policy and strategies of the country to access agricultural technologies through providing tarining, additional information and some affirmative actions.
•
The development agents must have enough knowledge about agricultural technology and effective information-sharing skills for providing relevant support to older farmers.
•
Furthermore, a systematic review focussing on problems of aged farmers’ technology adoption and coping strategies should be done. A better understanding of these issues will increase the contribution of the aged labor force.

Nomenclature

AGORA:: Agriculture, Forestry, Fisheries, Climate & Food Security
AGRIS:: International System for Agricultural Science and Technology
PRISMA:: Preferred reporting items for systematic reviews and meta-analyses
PSM:: Propensity score matching
SNNPR:: South Nation Nationality People Region

Conflicts of Interest

The authors declare no conflicts of interest.

Author Contributions

Abera Ayalew was responsible for conceptualization, data curation, investigation, methodology, validation, visualization, writing the original draft, submitting the article. Yohannes Girma was responsible for conceptualization, visualization, rewriting, editing.

Funding

No funding was received for this research.

Open Research

Data Availability Statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

1 United Nation Development Program (UNDP), 2022, https://www.fao.org/ethiopia/fao-in-ethiopia/ethiopia-at-a-glance/fr/.
Google Scholar
2 Ethiopia at a Glance, https://www.fao.org/ethiopia/fao-in-ethiopia/.
Google Scholar
3 National Bank of Ethiopia (NBE), Annual Report of the National Bank of Ethiopia for the Year 2018/2019, 2020, Addis Ababa.
Google Scholar
4 Geffersa A. G., Agbola F. W., and Mahmood A., Improved Maize Adoption and Impacts on Farm Household Welfare: Evidence from Rural Ethiopia, The Australian Journal of Agricultural and Resource Economics. (2021) 66, no. 4, 860–886, https://doi.org/10.1111/1467-8489.12489.
10.1111/1467-8489.12489
Google Scholar
5 Yigezu Wendimu G., The Challenges and Prospects of Ethiopian Agriculture, Cogent Food & Agriculture. (2021) 7, no. 1, https://doi.org/10.1080/23311932.2021.1923619.
10.1080/23311932.2021.1923619
Google Scholar
6 Merga B. and Haji J., Factors Impeding Effective Crop Production in Ethiopia, Journal of Agricultural Science. (2019) 11, no. 10, https://doi.org/10.5539/jas.v11n10p98.
10.5539/jas.v11n10p98
Google Scholar
7 Anang B. T., Farm Technology Adoption by Smallholder Farmers in Ghana, Review of Agricultural and Applied Economics (RAAE). (2018) 21, no. 2, 41–47, https://doi.org/10.15414/raae.2018.21.02.41-47.
10.15414/raae.2018.21.02.41-47
Google Scholar
8 Shita A., Kumar N., and Singh S., Agricultural Technology Adoption and Its Determinants in Ethiopia: A Reviewed Paper, Asia Pacific Journal of Research. (2018) 1, no. 55, 99–104.
Google Scholar
9 Huang X., Lu Q., Wang L., Cui M., and Yang F., Does Aging and Off-Farm Employment Hinder Farmers’ Adoption Behavior of Soil and Water Conservation Technology in the Loess Plateau?, International Journal of Climate Change Strategies and Management. (2020) 12, no. 1, 92–107, https://doi.org/10.1108/IJCCSM-04-2019-0021, 2-s2.0-85073962064.
10.1108/IJCCSM-04-2019-0021
Google Scholar
10 Czaja S. J., Lee C. C., Nair S. N., and Sharit J., Older Adults and Technology Adoption, 52, Proceedings of the Human Factors and Ergonomics Society Annual Meeting, 2008, Los Angeles, CA, SAGE Publications, 139–143.
Google Scholar
11 Beri G. and Degefu S., Factors Affecting Adoption of Wheat Row Planting Technology in Girar Jarso Woreda, North Shewa Zone,Oromia Region, Ethiopia, Journal of Economic. (2024) 4, no. 2, 27–34.
Google Scholar
12 Jebessa G. M., Fikadu B., and Chalchisa T., et al.Impacts of Crossbreed Dairy Cow Adoption on Women Dietary Diversity in Southwestern Ethiopia, Journal of Agriculture and Food Research. (2023) 12, https://doi.org/10.1016/j.jafr.2023.100544.
10.1016/j.jafr.2023.100544
PubMed Google Scholar
13 Feyisa B. W., Determinants of Agricultural Technology Adoption in Ethiopia: A Meta-Analysis, Cogent Food & Agriculture. (2020) 6, no. 1, https://doi.org/10.1080/23311932.2020.1855817.
10.1080/23311932.2020.1855817
PubMed Google Scholar
14 Anderson M. and Perrin A., Tech Adoption Climbs Among Older Adults. Pew Research Center, 2017, Retrieved from http://assets.pewresearch.org/wpcontent/uploads/sites/14/2017/05/16170850/http://assets.pewresearch.org/wpcontent/uploads/sites/14/2017/05/16170850/PI_2017.05.17_Older-Americans-Tech_FINAL.pdf.
Google Scholar
15 Berkowsky R. W., Sharit J., and Czaja S. J., Factors Predicting Decisions about Technology Adoption among Older Adults, Innovation in Aging. (2017) 1, no. 3, https://doi.org/10.1093/geroni/igy002.
10.1093/geroni/igy002
Google Scholar
16 Rogers W. A., Mitzner T. L., Boot W. R., Charness N. H., Czaja S. J., and Sharit J., Understanding Individual and Age-Related Differences in Technology Adoption, Innovation in Aging. (2017) 1, no. (Suppl 1), 1026–1026, https://doi.org/10.1093/geroni/igx004.3733.
10.1093/geroni/igx004.3733
Google Scholar
17 Friemel T. N., The Digital Divide Has Grown Old: Determinants of a Digital Divide among Seniors, New Media & Society. (2016) 18, no. 2, 313–331, https://doi.org/10.1177/1461444814538648, 2-s2.0-84954418403.
10.1177/1461444814538648
Web of Science® Google Scholar
18 Choi N. G. and DiNitto D. M., The Digital Divide among Low-Income Homebound Older Adults: Internet Use Patterns, eHealth Literacy, and Attitudes toward Computer/Internet Use, Journal of Medical Internet Research. (2013) 15, no. 5, https://doi.org/10.2196/jmir.2645, 2-s2.0-84878685255.
10.2196/jmir.2645
Web of Science® Google Scholar
19 Page M. J., McKenzie J. E., Bossuyt P. M., Boutron I., Hoffmann T. C., and Mulrow C. D., et al.The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews, BMJ. (2021) 372, https://doi.org/10.1136/bmj.n71.
10.1136/bmj.n71
Google Scholar
20 Nigus G., Ketema M., Haji J., and Sileshi M., Determinants of Adoption of Urban Agricultural Practices in Eastern Haraghe Zone of Oromia Region and Dire Dawa City Administration, Eastern Ethiopia, Heliyon. (2024) 10, no. 4, https://doi.org/10.1016/j.heliyon.2024.e26758.
10.1016/j.heliyon.2024.e26758
Google Scholar
21 Belissa T. K., Effects of Weather Index Insurance Adoption on Household Food Consumption and Investment in Agricultural Inputs in Ethiopia, Journal of Agriculture and Food Research. (2024) 16, https://doi.org/10.1016/j.jafr.2024.101043.
10.1016/j.jafr.2024.101043
Google Scholar
22 Tekeste K., Degefa T., Admasu S., and Dawit A., Determinants of Adoption of Improved Varieties of Wheat (Triticum aestivum), Teff (Eragrostis Teff), and Maize (Zea mays L.) in Central Ethiopia, Journal of Agricultural Extension. (2023) 27, no. 2, 1–14, https://doi.org/10.4314/jae.v27i2.1.
10.4314/jae.v27i2.1
Web of Science® Google Scholar
23 Workie D. M. and Tasew W., Adoption and Intensity use of Malt Barley Technology Package by Smallholder Farmers in Ethiopia: A Double Hurdle Model Approach, Heliyon. (2023) 9, no. 8, https://doi.org/10.1016/j.heliyon.2023.e18477.
10.1016/j.heliyon.2023.e18477
Google Scholar
24 Mume I. D., Mohammed J. H., and Ogeto M. A., Impact of Small-Scale Irrigation on the Livelihood and Resilience of Smallholder Farmers against Climate Change Stresses: Evidence from Kersa District, eastern Oromia, Ethiopia., Heliyon. (2023) 9, no. 8.
Google Scholar
25 Negera M., Alemu T., Hagos F., and Haileslassie A., Determinants of Adoption of Climate Smart Agricultural Practices among Farmers in Bale-Eco Region, Ethiopia, Heliyon. (2022) 8, no. 7, https://doi.org/10.1016/j.heliyon.2022.e09824.
10.1016/j.heliyon.2022.e09824
PubMed Google Scholar
26 Chekol F., Tsegaye A., Mazengia T., and Hiruy M., Small Scale Biogas Technology Adoption Behaviours of Rural Households and Its Effect on Major Crop Yields in East Gojjam Zone of Ethiopia: Propensity Score Matching Approach, Cogent Engineering. (2022) 9, no. 1, https://doi.org/10.1080/23311916.2022.2133343.
10.1080/23311916.2022.2133343
Google Scholar
27 Mulatu A., Marisennayya S., and Bojago E., Adoption of Modern Hive Beekeeping Technology: The Case of Kacha-Birra Woreda, Kembata Tembaro Zone, Southern Ethiopia, Advances in Agriculture. (2021) 2021, no. 1, 20, https://doi.org/10.1155/2021/4714020, 4714020.
10.1155/2021/4714020
Google Scholar
28 Milkias D. and Muleta G., Determinants of Adoption of Barley (HB1307) Variety in Western Showa Highlands: The Case of Elfeta District, International Journal of Economic and Business Management. (2021) 9, no. 3, 57–65.
Google Scholar
29 Massresha S. E., Lema T. Z., Neway M. M., and Degu W. A., Perception and Determinants of Agricultural Technology Adoption in North Shoa Zone, Amhara Regional State, Ethiopia, Cogent Economics & Finance. (2021) 9, no. 1, 1956https://doi.org/10.1080/23322039.2021.1956774.
10.1080/23322039.2021.1956774
Google Scholar
30 Mekuria Z. K., Amede A. K., and Mekonnen E. E, Adoption of Rainwater Harvesting and Its Impact on Smallholder Farmer Livelihoods in Kutaber District, South Wollo Zone, Ethiopia, Cogent Food & Agriculture. (2020) 6, no. 1, https://doi.org/10.1080/23311932.2020.1834910, 1834910.
10.1080/23311932.2020.1834910
Google Scholar
31 Milkias D., Factors Affecting High Yielding Teff Varieties Adoption Intensity by Small Holder Farmers in West Showa Zone, Ethiopia, International Journal of Economy, Energy and Environment. (2020) 5, no. 1, 6–13, https://doi.org/10.11648/j.ijeee.20200501.12.
10.11648/j.ijeee.20200501.12
Google Scholar
32 Teshome S. and Tegegne B., Determinants of Adoption of Improved Teff Varieties by Smallholder Farmers: The Case of Kobo District, North Wollo Zone, Amhara Region, Ethiopia, International Journal of Agricultural Economics. (2020) 5, no. 4, 114–122, https://doi.org/10.11648/j.ijae.20200504.14.
10.11648/j.ijae.20200504.14
Google Scholar
33 Fentie A. and Beyene A. D., Climate-Smart Agricultural Practices and Welfare of Rural Smallholders in Ethiopia: Does Planting Method Matter?, Land Use Policy. (2019) 85, 387–396, https://doi.org/10.1016/j.landusepol.2019.04.020, 2-s2.0-85064443901.
10.1016/j.landusepol.2019.04.020
Web of Science® Google Scholar
34 Feleke A., Regasa G., and Muche M., Factors Influencing Adoption of Improved Potato (Belete) Variety: Evidence from Ethiopian Smallholder Farmers, Journal of Agricultural Science. (2019) 30, 85–92.
Google Scholar
35 Sileshi M., Kadigi R., Mutabazi K., and Sieber S., Determinants for Adoption of Physical Soil and Water Conservation Measures by Smallholder Farmers in Ethiopia, International Soil and Water Conservation Research. (2019) 7, no. 4, 354–361, https://doi.org/10.1016/j.iswcr.2019.08.002, 2-s2.0-85070662387.
10.1016/j.iswcr.2019.08.002
Web of Science® Google Scholar
36 Woldie D. T., Analysis and Adoption of Coffee Technologies in Major Coffee Growing Areas: The Case of Wombera District, Metekel Zone, Ethiopia, International Journal of Scientific & Engineering Research. (2019) 10, no. 5, 2229–5518.
Google Scholar
37 Asfaw A., Simane B., Bantider A., and Hassen A., Determinants in the Adoption of Climate Change Adaptation Strategies: Evidence from Rainfed-Dependent Smallholder Farmers in North-Central Ethiopia (Woleka Sub-Basin), Environment, Development and Sustainability. (2019) 21, no. 5, 2535–2565, https://doi.org/10.1007/s10668-018-0150-y, 2-s2.0-85045055994.
10.1007/s10668-018-0150-y
Web of Science® Google Scholar
38 Milkias D. and Abdulahi A., Determinants of Agricultural Technology Adoption: The Case of Improved Highland Maize Varieties in Toke Kutaye District, Oromia Regional State, Ethiopia, Journal of Investment and Management. (2018) 7, no. 4, 125–132, https://doi.org/10.11648/j.jim.20180704.13.
10.11648/j.jim.20180704.13
Google Scholar
39 Gebresilassie L. and Bekele A., Factors Determining Allocation of Land for Improved Wheat Variety by Smallholder Farmers of Northern Ethiopia, Journal of Development and Agricultural Economics. (2015) 7, no. 3, 105–112, https://doi.org/10.5897/JDAE2014.0621.
10.5897/JDAE2014.0621
Google Scholar
40 Hailu B. K., Abrha B. K., and Weldegiorgis K. A., Adoption and Impact of Agricultural Technologies on Farm Income: Evidence from Southern Tigray, Northern Ethiopia, International Journal of Food and Agricultural Economics (IJFAEC). (2014) 2, no. 4, 91–106.
Google Scholar
41 GitHub-mqRusers, GitHub-mMqRusers/2020_05_Meta-aAnalysis_ GitHub-mqRusers/2020_05_Meta-analysis_ Shinichi-Nakagawa, Workshop on Performing Meta-aAnalyses in R Using the Metafor Package and Other Tips From Shinichi. See “Extra Info” for Recording and Links, 2020.
Google Scholar
42 Higgins J. and Thompson S., Presenting Random Effects Meta-Analyses: Where Are We Going Wrong?, 9th International Cochrane Colloquium, 2001, Lyon, France.
10.1186/2048-4623-1-S3-OP001
Google Scholar
43 Higgins J. P. T., Thompson S. G., Deeks J. J., and Altman D. G., Measuring Inconsistency in Meta-Analyses, British Medical Journal. (2003) 327, no. 7414, 557–560, https://doi.org/10.1136/bmj.327.7414.557.
10.1136/bmj.327.7414.557
PubMed Google Scholar
44 Girma Y., Credit Access and Agricultural Technology Adoption Nexus in Ethiopia: A Systematic Review and Meta-Analysis, Journal of Agriculture and Food Research. (2022) 10, https://doi.org/10.1016/j.jafr.2022.100362.
10.1016/j.jafr.2022.100362
Google Scholar
45 CSA (Central Statistical Agency), CSA (Central Statistical Agency), Agricultural Sample Survey 2016/2017:Report on Livestock and Livestock Characteristics, 2017, 2, Addis Ababa, Ethiopia, 9–12.
Google Scholar
46 Daniel T. and Getaneh S., Determinates of Employment Generation through Urban Agriculture: The Case of Bishoftu Area of Oromia Region, Ethiopia, International Journal of Asian and African Studies. (2016) 26, 49–55, [Online]. Available: https://api.semanticscholar.org/CorpusID:56027774.
Google Scholar
47 Zegeye M. B., Fikire A. H., and Meshesha G. B., Determinants of Multiple Agricultural Technology Adoption: Evidence from Rural Amhara Region, Ethiopia, Cogent Economics & Finance. (2022) 10, no. 1, https://doi.org/10.1080/23322039.2022.2058189.
10.1080/23322039.2022.2058189
Google Scholar
48 Swanepoel J. W., Van Niekerk J. A., and Tirivanhu P., Analysing the Contribution of Urban Agriculture towards Urban Household Food Security in Informal Settlement Areas, Development Southern Africa. (2021) 38, no. 5, 785–798, https://doi.org/10.1080/0376835X.2021.1920888.
10.1080/0376835X.2021.1920888
Google Scholar
49 Zemarku Z., Senapathy M., and Bojago E., Determinants of Adoption of Improved Dairy Technologies: The Case of Offa Woreda, Wolaita Zone, Southern Ethiopia, Advances in Agriculture. (2022) 2022, 19, https://doi.org/10.1155/2022/3947794, 3947794.
10.1155/2022/3947794
Google Scholar
50 Bonabana-Wabbi J., Assessing Factors Affecting Adoption of Agricultural Technologies: The Case of Integrated Pest Management (IPM) in Kumi District, Eastern Uganda (Doctoral dissertation, Virginia Tech), 2002.
Google Scholar
51 Abera N. T. and Girma G., Impact of Modern Beehive Technology Adoption on Household Income: Evidence from North Shewa Zone, Oromia National Regional State, Ethiopia, Turkish Journal of Agriculture-Food Science and Technology. (2023) 11, no. 10, 1871–1877, https://doi.org/10.24925/turjaf.v11i10.1871-1877.6140.
10.24925/turjaf.v11i10.1871-1877.6140
Google Scholar
52 Peters J., Sutton A., Jones D., Abrams K., and Rushton L., Comparison of Two Methods to Detect Publication Bias in Meta-Analysis, Journal of the American Medical Association. (2006) 295, no. 6, 676–680, https://doi.org/10.1001/jama.295.6.676, 2-s2.0-32144440794.
10.1001/jama.295.6.676
CAS PubMed Web of Science® Google Scholar
53 Egger M., Smith M., and Minder C., Bias in Meta-Analysis Detected by a Simple, Graphical Test, British Medical Journal. (1997) 315, no. 7109, 629–634, https://doi.org/10.1136/bmj.315.7109.629.
10.1136/bmj.315.7109.629
CAS PubMed Web of Science® Google Scholar

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The Effect of Age on Agricultural Technology Adoption by Smallholder Farmers in Ethiopia: A Systematic Review and Meta-Analysis

Abstract

1. Introduction