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Determinants of Adoption of Improved Sorghum Varieties by Small-Scale Farmers in Selected Districts of Western Oromia

Received: 7 November 2023     Accepted: 26 June 2024     Published: 29 July 2024
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Abstract

Achieving national food security and diversifying export earnings from agricultural products is one of the major challenges currently facing developing countries like Ethiopia. Ethiopia is the largest producer of sorghum in Africa, contributing to ensuring food security in the country. Despite the high production potential and the economic importance of the crop, the adoption, and dissemination of improved sorghum varieties are constrained by various factors. To this end, this study aimed to identify determinants of adoption of improved sorghum varieties in selected districts of Western Oromia Region, Ethiopia with the specific objectives of identifying factors affecting adoption and assessing factors hindering the production of improved sorghum varieties. The study was based on cross-sectional data from 154 randomly selected sorghum-producing farmers. Descriptive and econometric analyses were used to analyze data. The results show that about 14.94% and 85.06% were adopters and non-adopters of the crop respectively. Probit model results showed that education and extension service affected the probability of adoption of improved sorghum varieties positively and significantly while TLU affected it negatively and significantly. Untimely availability of improved seed, Price of seed, Quality of improved seed, unavailability of credit to buy seed, Untimely availability of fertilizer, High price of fertilizer, Access to market information, Low grain price, and Pests and disease are the major constraints that affect sorghum production in the study area. This study suggests the high importance of institutional and government support in education, Extension service, and improved cows than a large number of local breeds. Therefore, policy and development interventions should give emphasis on the improvement of such institutional support systems so as to achieve the adoption practice which increases the production and productivity of small-scale farmers.

Published in American Journal of Applied Scientific Research (Volume 10, Issue 3)
DOI 10.11648/j.ajasr.20241003.11
Page(s) 41-48
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This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Adoption, Constraint, Sorghum, Probit

1. Introduction
1.1. Background
Sorghum is the world’s fifth-largest and most important cereal grain crop after wheat, maize, rice, and barley . It is the second most important cereal crop after maize in Sub-Saharan Africa . It is used as human food, and it is a staple food for more than 100 million people in Eastern Africa . It is also used as animal feed and industrial raw material .
Ethiopia is the largest producer of sorghum in Africa after Nigeria and Sudan and the second after Sudan in the Common Market for Eastern and Southern Africa member countries . Sorghum is a major staple food crop that ranks second after maize in total production as well as the third-largest crop in productivity after wheat and maize and area coverage after tef and maize . The crop is one of the most widely grown cereal crops in a wide range of agro-ecologies between 400m and 2500m altitudes and a stable food crop for millions of poor Ethiopians whose food insecurity is rambling .
In the Oromia region, sorghum is the total production and area coverage from all regions of Ethiopia and the third most important crop in the region . Since sorghum is a staple crop of subsistence farmers, increasing productivity and production is often considered a means of improving the incomes and food security of poor farmers especially, in the East Wollega and West Shewa zones of Oromia . Sorghum research was conducted in the last five decades by different research centers including the Bako Agricultural Research Center with many success stories .
Productivity-improving crop technology can be an option for rural farmers to eliminate hunger and food insecurity by increasing production, reducing food prices, and making food more accessible to the poor. The use of high-yielding crop varieties facilitates the growth of agro-processing enterprises and non-farm sectors and stimulates the transition from low-productivity subsistence agriculture to a high-productivity agro-industrial economy . Further, developing and promoting the adoption of yield-increasing crop varieties in a sustainable manner helps improve the livelihood of rural farmers .
Clearly, the food security of the majority of rural farmers can be improved if the performance of the agricultural sector is enhanced. Improvement and diffusion of sorghum varieties have an invaluable role in reversing food insecurity. Due to this Chemeda, Lalo, and Gemedi sorghum varieties have been generated and promoted for many years in the study area by BARC. Despite such an intervention the adoption and intensity of adoption of improved sorghum varieties are not studied. Therefore, this study was proposed to identify the determinants of adoption and intensity of adoption of improved sorghum varieties to fill the existing knowledge gap.
1.2. Objectives of the Study
1. To identify factors affecting the adoption of improved sorghum varieties in the study areas.
2. To assess production constraints of improved sorghum varieties in the study areas.
2. Research Methodology
In this chapter, a brief description of the study areas, sampling methods and sample size, sources and types of data and data collection methods, methods of data analysis, and measurement & definitions of variables are presented.
2.1. Description of the Study Areas
This study was conducted in West Shewa, East Wollega, and Bonno Bedelle zones. From the West Shewa zone, Ilu Gelan and Dano districts are located at 200 km and 233 km, respectively to the west of Finfinne, the capital city of Ethiopia. The location of the Ilu Gelan district is 8059’51’’N latitude, 37019’49’’ E longitude, and 1812 meters above sea level attitude whereas the Dano location is 8040’ N latitude, 37020’ E longitude, and 1400 - 2500 m a.s.l. The annual rainfall of those districts is 1351 and 1400 - 1900 mm, respectively. In both districts, more than 90% of the population depends on agriculture for their livelihood with maize, tef, and sorghum leading crops. Agro-ecologically, both districts are characterized by highland (5%), midland (25%), and lowland (70%). In both districts, a mixed crop-livestock production system is the main agricultural practice performed by the majority of the farmers.
From the East Wollega zone, Bonaya Boshe and Wayu Tuka districts which are located at 307 and 323 km, respectively to the west of Finfinne are used. The location of Bonaya Boshe district is 8054’45’’N latitude, 37002’16’’ E longitude, and 1613-1641 meters above sea level attitude whereas Wayu Tuka location is 8056’N and 9007’N latitude, 36032’ E and 36048’ E longitude and 1300-3140 m a.s.l attitude. The annual rainfall of those districts is 1000-1200 and 99.9 – 4026 mm, respectively. In both districts, more than 90% of the population depends on agriculture for their livelihood with maize, tef, and sorghum leading crops. In both districts, a mixed crop-livestock production system is the main agricultural practice performed by the majority of the farmers.
From Bunno Bedelle zone, Chewaqa district which is located 390 km to the west of Finfinne is used. The area lies within altitude ranges of 900 - 1400 m a.s.l. The annual rainfall of this district ranges from 1000 - 1200 mm. In the Chewaqa district, again more than 90% of the population depends on agriculture for their livelihood with maize, rice, sorghum, and sesame leading crops.
2.2. Sampling Methods and Sample Size
A three-stage sampling technique was employed to select sample respondents. In the first stage, districts were purposively selected for this study, due to the fact that improved sorghum technology is widely popularized by Bako Agricultural Research Center (BARC). In the second stage, Kebeles were randomly selected. Households in districts were stratified into producers and non-producers and representative samples were selected from each producer of sorghum.
The sample size was determined by using a formula developed by Cochran’s sample size formula for categorical data.
Where n is the sample size for the study, is for the selected alpha level of 0.025 in each trial =1.96, p is the number of adopters q is the number of non-adopters and e is the precision level.
2.3. Sources and Types of Data and Data Collection Methods
Both primary and secondary data were used. For this study, primary data were collected on a one-to-one interview basis using a structured survey questionnaire pre-tested and administered by well-trained and experienced enumerators who have knowledge of the farming system and the local language. During the personal interview information on sorghum varieties grown, key socio-economic elements (including age, gender, education level, family size, membership in farmers’ organizations, consumption expenditures, distance of a residence from input and output markets, and extension offices, and institutional and other relevant) factors were collected. The secondary data source includes books, journals, and other published and unpublished documents from Bako Agricultural Research Center, Zonal and District Agricultural offices, the internet, and other related sources to supplement primary data.
2.4. Methods of Data Analysis
In this study, both descriptive statistics and econometric models were used to analyze the data.
2.4.1. Descriptive Statistics
Descriptive statistics such as mean, standard deviation, frequency distribution, and percentages were used to clearly show sample unit characteristics. A chi-square test and an independent sample t-test were also used to compare adopters and non-adopters in terms of explanatory variables.
2.4.2. Econometric Model
The Probit econometric model was applied to analyze explanatory variables of as shown in equation (1).
The probit model can be specified as shown below:
Yi = F(Xiβ) + εi(1)
Yi =1, if adopted0, otherwise(2)
where, ε~N (0,1); β = maximum likelihood; ε = error term; X = set of independent variables included in the model. Since estimates of the probit model provide only the direction of effects, the marginal effects are usually calculated to interpret the actual change in the probability of independent variables.
Marginal effects = βi ϕ(z)(3)
where, βi = coefficients of the variables; ϕ(z) = cumulative normal distribution value associated with the mean dependent variable from the probit estimation.
2.5. Measurement and Definitions of Variables for Adoption
2.5.1. Dependent Variables
Adoption Decision
The dependent variable for the probit model takes a dichotomous value depending on the farmers’ decision either to adopt (one) or not to adopt (zero) the improved Sorghum varieties production.
Adopters are farmers who use the Lalo, Chemeda, and Gemedi Sorghum varieties. Non-adopters are farmers who didn’t use at least one variety.
Table 1. Summary of independent variables, their definitions, and expected effect.

Dependent variables 1. Adoption of improved sorghum variety

Dummy (1. Yes 0. No)

Independent Variables

Definitions of variables

Unit of measurement

Expected sign

Age

Age of household head

Years

+/-

Sex

Sex of the household head

1. Male 0. Female

+

Family size

Number of persons per household

No

+

Marital statues

Marital status of household heads

0. Married; 2. Widowed; 1. Divorced; 3. Single

+/-

Education

Formal education level of household head

Grade attended

+

Non-farm

Income from non-farm activities

1. Yes 0. No

+

Farming experience

Sorghum farming experience of the household head

Years

+

Off-farm

Income from off-farm activities

1. Yes 0. No

+

Livestock

Number of livestock owned

TLU

+

Distance to a market center

Distance of farmers' house from a nearby market

Hour

-

Credit

Use of credit for framing

1= Yes 0=No

+

Farm size

Total land holding size of the household head

Hectares

+

Extension

Participation of extension service

1=Yes 0=No

+

3. Results and Discussion
3.1. Descriptive Results
The sample size handled during the survey was 154. Out of the total interviewed sorghum producers 129 (83.77%) were male-headed and the remaining 25 (16.23%) were female-headed. The chi-square test of sex distribution, marital status, and credit access between the adopters and non-adopters was found to be insignificant while the chi-square test of access to extension between the adopters and non-adopters was found to be significant at a 5% level of significance.
Table 2. 𝑥2-test for binary independent variables.

Variable

Non-Adopters (N=131)

Adopters (N=23)

Total (N=154)

No

%

No

%

𝑥2-test

No

(%)

Sex

Male

110

83.97

19

82.61

0.0266

129

83.77

Female

21

16.03

4

16.39

25

16.23

Marital status

Married

124

94.66

22

95.65

0.6297

146

94.80

Widowed

4

3.05

1

4.35

5

3.25

Single

3

2.29

0

0.00

3

1.95

Credit

Yes

113

86.26

18

78.26

0.9853

131

85.07

No

18

13.74

5

21.74

23

14.93

Extension

Yes

96

73.28

20

86.96

30.9682**

116

75.32

No

35

26.72

3

13.04

38

24.68

The adopters' average age, Sorghum production experience, and education level were 42.52 years, 15.35 years, and 4.05 grades respectively, and it is about 42.88 years, 17.92 years, and 2.54 grades respectively for non-adopters. The t-test of age, sorghum production experience, TLU, Off-farm income, non-farm income, family size, and distance to market between adopters and non-adopters was found to be insignificant. That means there is no statistical mean difference between adopters and non-adopters in terms of the variables. The t-test of education level between adopters and non-adopters was found to be significant at a 1% level of significance. That means there is a statistical mean difference between adopters and non-adopters in terms of education level.
Table 3. t-test for continuous independent variables.

Variable

Non-Adopter (N=131)

Adopters (N=23)

mean

Std

Mean

Std

t-test

Age

42.88

1.07

42.52

1.78

0.1337

Sorghum Production experience

17.92

0.88

15.35

1.89

1.1508

Education level

4.05

2.78

2.54

2.86

-2.95***

TLU

6.535802

.388999

5.46913

.9541606

1.0554

Off-farm income

1915.73

536.06

2336.96

1576.88

-0.2929

Non-farm income

745.04

227.61

739.13

486.85

0.0102

Family size

6.56

0.26

6.43

0.46

0.1882

Distance to market

30.21

1.48

26.96

4.70

0.8060

3.2. Econometric Results
In this sub-section, the results of the Probit regression model are presented and discussed and the major constraints of sorghum production are explained.
Table 4. Determinants of adoption of improved sorghum varieties.

Variables

Probit regression

Coef.

Std. Err.

Marginal Effect

Sex

0.121

0.076

0.026

Age

0.013

0.003

0.003

Education

0.157***

0.013

0.0270

Sorghum production Experience

-0.024

0.004

-0.005

Family size

0.053

0.012

0.011

Distance market

-0.004

0.002

-0.001

Marital Status

-0.037

0.056

-0.008

TLU

-0.058*

0.007

-0.012

Off-farm income

-6.29e-06

.000

-1.34e-06

Non-farm income

-0.001

.0001

-3.46e-06

Credit

-0.471

0.101

-0.119

Extension

0.693*

0.050

0.120

constant

-1.526*

sigma

Number of obs =154 Prob > chi2 = 0.06064

LR chi2(12) =10.11 Log likelihood = -57.98

Pseudo R2 =0.0780

The adoption decision of farm households is influenced by different socioeconomic, technical, and institutional factors. Different variables are important across different spaces and over time in explaining the adoption of technologies by farmers. Based on theoretical models and empirical evidence, many factors are hypothesized to influence the adoption of improved sorghum varieties.
The 3 explanatory variables that have been found to significantly influence the decision by the sample farm households with regard to whether or not to adopt improved Sorghum varieties are interpreted and discussed below.
Education: The education level of the household head, which is one of the important indicators of human capital, has a positive and significant effect on the adoption of improved sorghum varieties at a 1% level of significance, implying that the likelihood of adoption increases with farmer's formal education level. On average, each additional year of education of the household head increases the probability that a farmer adopts improved sorghum varieties by 2.7%. This is consistent with the research results of .
Livestock (TLU): Livestock holding negatively and significantly related to the adoption of improved sorghum varieties at a 10% level of significance, implying that farmers with more livestock holding are more unlikely to devote a significant amount of land to improved sorghum varieties than those households with less livestock holding. A household with large livestock holdings allocates more land for grazing than for sorghum production. A one-unit increase in livestock holding (TLU) decreases the adoption of improved sorghum varieties by 1.2%.
Extension: Extension was positively related to the adoption of improved sorghum varieties at a 10% level of significance. The result of probit regression indicates that as compared to farmers who got extension service on improved sorghum varieties, those farmers who did not get an extension on sorghum their probability of adoption of improved sorghum varieties decreased by 12%.
3.3. Major Sorghum Production Constraints
Major sorghum production constraints are presented in Table 5 below. The first major constraint of sorghum production was the high price of fertilizers (86.36%). This implies that the government should give due attention to the availability and price of the sorghum. Pests and disease, Low grain prices, and Access to market information are Constraints ranked high in importance by the farmers.
Table 5. Major sorghum production constraints of sample households.

Constraints (n=154)

N

%

Rank

1. Untimely availability of improved seed

106

68.83

5

2. Price of seed

66

42.86

8

3. Quality of improved seed

80

51.95

7

4. Unavailability of credit to buy seed

57

37.03

9

5. Untimely availability of fertilizer

94

61.04

6

6. High price of fertilizer

133

86.36

1

7. Access to market information

117

75.97

4

8. Low grain price

125

81.17

3

9. Pests and disease

129

83.77

2

4. Conclusion and Recommendations
4.1. Conclusion
The activity was initiated with the objective of identifying factors affecting the adoption and assessing production constraints of improved sorghum varieties in the selected districts of western Oromia.
A three-stage sampling technique was employed to select sample respondents using Cochran’s (1977) sample size formula for categorical data. Both descriptive statistics and econometric (probit) models were employed to identify factors affecting the adoption of improved sorghum varieties.
The sample size handled during the survey was 154. Out of the total interviewed sorghum producers, 129 (83.77%) were male-headed and 825 (16.23%) were female-headed households.
Descriptive statistics such as mean, standard deviation, and percentages were used. The chi-square test and t-test were also used to compare adopters and non-adopters regarding explanatory variables.
The econometric results showed that education level, TLU, and Extension services affected the probability of adoption of the improved sorghum varieties at 1%, 10%, and 10% levels respectively. In contrast, Untimely availability of improved seed, Price of seed, Quality of enhanced seed, unavailability of credit to buy seed, Untimely availability of fertilizer, High price of fertilizer, Access to market information, Low grain price, and Pests and disease are the major constraints that affect sorghum production in the study area.
4.2. Recommendations
On the basis of the results of this study, the following recommendations are suggested to be considered in the future intervention strategies which are aimed at promotion of improved sorghum varieties.
Education has a significant positive impact on the adoption of improved sorghum varieties. Hence, strengthening adequate and effective basic educational opportunities for rural farming households in general and to the study areas in particular is required. In this regard, the federal and regional governments need to strengthen the existing provision of formal and informal education by facilitating all necessary materials.
The size of livestock owned has a significant negative impact on the adoption of improved sorghum varieties. The government should Strengthen the farmers by providing high-yielding crossbreeds over a large number of local breeds, improving health services, better livestock feed (forage), and disseminating artificial insemination in the areas to improve the adoption of improved sorghum varieties.
Extension Service was found to be positively and significantly influencing the adoption of the improved sorghum varieties. So, Development agents should give intensive extension services and education that improve the adoption of improved sorghum varieties by farm households.
The government should give attention to the major constraints of sorghum production in the study area.
Author Contributions
Galmesa Abebe is the sole author. The author read and approved the final manuscript.
Conflicts of Interest
The authors declare no conflicts of interest.
References
[1] Afework Hagos and Lemma Zemedu. 2015. Determinants of improved rice varieties Adoption in Fogera District of Ethiopia. Science, Technology and Arts Research Journal, 4(1): 221-228.
[2] Amelework, B. A., Shimelis, H. A., Tongoona, P., Mengistu, F., Laing, M. D. and Ayele. D. G. (2016). Sorghum Production Systems and Constraints, and Coping Strategies under Drought-Prone Agro-Ecologies of Ethiopia. South African J. Pla. & Soil; 33 (3): 207–217.
[3] Asfaw, A. (2007). The Role of Introduced Sorghum and Millets in Ethiopian Agriculture. SAT eJournal, 3 (1). J R N Taylor UD: overview: the importance of sorghum in Africa. Department of food science, university of Pretoria, Pretoria 0002, South Africa.
[4] Asfaw S, Shiferaw B, Simtowe F, Lipper L. 2012. Impact of modern agricultural technologies on smallholder welfare: Evidence from Tanzania and Ethiopia. Food Policy, 37(3): 283– 295.
[5] Central Statistics Agency for Ethiopia (CSA). 2018. Report on Agricultural Sample Survey of Area and Production of Major Crops 2017/2018. Addis Ababa; Vol. 1.
[6] Central Statistics Agency for Ethiopia (CSA). 2017. Report on Agricultural Sample Survey of Area and Production of Major Crops 2016/2017. Addis Ababa; Vol. 1.
[7] Cochran, W. G. 1977. Sampling techniques (3rd ed.). New York: John Wiley & Sons.
[8] Ethiopian Institute of Agricultural Research (EIAR). 2014. Ethiopian strategy for sorghum 2014-2024: 1-10.
[9] FAOSTAT. (2019). Database of agricultural production. FAO Statistical Databases (FAOSTAT).
[10] Gudu, S., Ouma, E. O., Onkware, A. O., Too, E. J., Were, B. A., Ochuodho, J. O., Othieno, C. O., Okalebo, J. R. and Agalo, J. (2013). Preliminary Participatory On-farm Sorghum Variety Selection for Tolerance to drought, Soil Acidity and Striga in Western Kenya. Maina Moi University, Kenya First Bio-Innovate Regional Scientific Conference United Nations Conference Centre (UNCC-ECA) Addis Ababa, Ethiopia.
[11] Hassen Beshir, Bezabih Emana, Belay Kassa and Jema Haji. 2012. Determinants of chemical fertilizer technology adoption in North Eastern highlands of Ethiopia: the double hurdle approach. Journal of Research in Economics and International Finance, 1(2): 39-49.
[12] Just R E, Zilberman D. 1988. The effects of agricultural development policies on income distribution and technological change in agriculture. J Dev Econ, 28(2): 193–216.
[13] Moti Jaleta, Chilot Yirga, Menale Kassie, Groote. H. D. and Bekele Shiferaw. 2013. Knowledge, adoption and use intensity of improved maize technologies in Ethiopia. Invited paper presented at the 4th International Conference of the African Association of Agricultural Economists, September 22-25, 2013. Hammamet, Tunisia.
[14] Mutisya, J. (2004). Starch branching enzymes and their genes in sorghum. PhD thesis, Swedish University of Agricultural Sciences, Uppsala p 24.
[15] Sisay Debebe Kaba. 2016. Agricultural technology adoption, crop diversification and efficiency of maize-dominated smallholder farming system in Jimma Zone, South- -Western Ethiopia. PhD Dissertation, Haramaya University, Haramaya, Ethiopia.
[16] USAID. 2010. Staple food value chain analysis. Country Report, Ethiopia Van Beuningen LT, Busch RH (1997). Genetic diversity among North American spring wheat cultivars: III. Cluster analysis based on quantitative morphological traits. Crop Sci 37: 981-988.
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    Abebe, G. (2024). Determinants of Adoption of Improved Sorghum Varieties by Small-Scale Farmers in Selected Districts of Western Oromia. American Journal of Applied Scientific Research, 10(3), 41-48. https://doi.org/10.11648/j.ajasr.20241003.11

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    Abebe, G. Determinants of Adoption of Improved Sorghum Varieties by Small-Scale Farmers in Selected Districts of Western Oromia. Am. J. Appl. Sci. Res. 2024, 10(3), 41-48. doi: 10.11648/j.ajasr.20241003.11

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    Abebe G. Determinants of Adoption of Improved Sorghum Varieties by Small-Scale Farmers in Selected Districts of Western Oromia. Am J Appl Sci Res. 2024;10(3):41-48. doi: 10.11648/j.ajasr.20241003.11

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  • @article{10.11648/j.ajasr.20241003.11,
      author = {Galmesa Abebe},
      title = {Determinants of Adoption of Improved Sorghum Varieties by Small-Scale Farmers in Selected Districts of Western Oromia
    },
      journal = {American Journal of Applied Scientific Research},
      volume = {10},
      number = {3},
      pages = {41-48},
      doi = {10.11648/j.ajasr.20241003.11},
      url = {https://doi.org/10.11648/j.ajasr.20241003.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajasr.20241003.11},
      abstract = {Achieving national food security and diversifying export earnings from agricultural products is one of the major challenges currently facing developing countries like Ethiopia. Ethiopia is the largest producer of sorghum in Africa, contributing to ensuring food security in the country. Despite the high production potential and the economic importance of the crop, the adoption, and dissemination of improved sorghum varieties are constrained by various factors. To this end, this study aimed to identify determinants of adoption of improved sorghum varieties in selected districts of Western Oromia Region, Ethiopia with the specific objectives of identifying factors affecting adoption and assessing factors hindering the production of improved sorghum varieties. The study was based on cross-sectional data from 154 randomly selected sorghum-producing farmers. Descriptive and econometric analyses were used to analyze data. The results show that about 14.94% and 85.06% were adopters and non-adopters of the crop respectively. Probit model results showed that education and extension service affected the probability of adoption of improved sorghum varieties positively and significantly while TLU affected it negatively and significantly. Untimely availability of improved seed, Price of seed, Quality of improved seed, unavailability of credit to buy seed, Untimely availability of fertilizer, High price of fertilizer, Access to market information, Low grain price, and Pests and disease are the major constraints that affect sorghum production in the study area. This study suggests the high importance of institutional and government support in education, Extension service, and improved cows than a large number of local breeds. Therefore, policy and development interventions should give emphasis on the improvement of such institutional support systems so as to achieve the adoption practice which increases the production and productivity of small-scale farmers.
    },
     year = {2024}
    }
    

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  • TY  - JOUR
    T1  - Determinants of Adoption of Improved Sorghum Varieties by Small-Scale Farmers in Selected Districts of Western Oromia
    
    AU  - Galmesa Abebe
    Y1  - 2024/07/29
    PY  - 2024
    N1  - https://doi.org/10.11648/j.ajasr.20241003.11
    DO  - 10.11648/j.ajasr.20241003.11
    T2  - American Journal of Applied Scientific Research
    JF  - American Journal of Applied Scientific Research
    JO  - American Journal of Applied Scientific Research
    SP  - 41
    EP  - 48
    PB  - Science Publishing Group
    SN  - 2471-9730
    UR  - https://doi.org/10.11648/j.ajasr.20241003.11
    AB  - Achieving national food security and diversifying export earnings from agricultural products is one of the major challenges currently facing developing countries like Ethiopia. Ethiopia is the largest producer of sorghum in Africa, contributing to ensuring food security in the country. Despite the high production potential and the economic importance of the crop, the adoption, and dissemination of improved sorghum varieties are constrained by various factors. To this end, this study aimed to identify determinants of adoption of improved sorghum varieties in selected districts of Western Oromia Region, Ethiopia with the specific objectives of identifying factors affecting adoption and assessing factors hindering the production of improved sorghum varieties. The study was based on cross-sectional data from 154 randomly selected sorghum-producing farmers. Descriptive and econometric analyses were used to analyze data. The results show that about 14.94% and 85.06% were adopters and non-adopters of the crop respectively. Probit model results showed that education and extension service affected the probability of adoption of improved sorghum varieties positively and significantly while TLU affected it negatively and significantly. Untimely availability of improved seed, Price of seed, Quality of improved seed, unavailability of credit to buy seed, Untimely availability of fertilizer, High price of fertilizer, Access to market information, Low grain price, and Pests and disease are the major constraints that affect sorghum production in the study area. This study suggests the high importance of institutional and government support in education, Extension service, and improved cows than a large number of local breeds. Therefore, policy and development interventions should give emphasis on the improvement of such institutional support systems so as to achieve the adoption practice which increases the production and productivity of small-scale farmers.
    
    VL  - 10
    IS  - 3
    ER  - 

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Author Information
  • Bako Agricultural Research Center, Bako, Ethiopia

  • Abstract
  • Keywords
  • Document Sections

    1. 1. Introduction
    2. 2. Research Methodology
    3. 3. Results and Discussion
    4. 4. Conclusion and Recommendations
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  • Author Contributions
  • Conflicts of Interest
  • References
  • Cite This Article
  • Author Information