The aspire of the study were to the effect of wheat crop row sowing on income of farmers, in Wayu Tuka Woreda East Wollaga Zone in Ethiopia. Wheat crop sowing is a highly valuable grain for Ethiopian people both in production and in consumption. The objective of the research was to describe the factors affecting adoption and intensity of wheat row sowing in the study area. The study was based on cross sectional research which was included both qualitative and quantitative research approach. The data were collected from total 135 respondents selected from three kebels of Wayu Tuka Woerda by using random sampling method. From the total 135 respondents 82 were wheat row sowing adopters while 53 were non wheat row sowing adopters. Both primary and secondary data used and analysed using descriptive statistics and logit model. The software used for data entry and analysis were STATA14.2. The results show that about 61% of the respondents are users of wheat row sowing whereas 39% can be classified as non-adopters of wheat row sowing. The empirical Results revealed that age, credit access and agricultural input use of household negatively influenced decision to adopt wheat row sowing while accesses to technology, total annual income, access to training and availability of labour force were positively influenced the decision to adopt wheat row sowing. Finally, wheat row sowing has significant impact on farmer’s income increment. It is better to encourage farmer households as they actively participant in wheat row sowing technology and support them by giving training, supplying agricultural inputs and adopting new technology for them with adequate skills for enhancing their annual income and development of the country economy.
| Published in | International Journal of Economy, Energy and Environment (Volume 7, Issue 6) |
| DOI | 10.11648/j.ijeee.20220706.11 |
| Page(s) | 125-138 |
| Creative Commons |
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), 2022. Published by Science Publishing Group |
Adoption of Wheat Row Sowing, Income of Farmers, Logit, Wayu Tuka Woreda
| [1] | Abebe and Workayehu, (2015) plant growth regulators were used Row sowing of wheat, rather than broadcasting method, improves production and productivity |
| [2] | Alston and Pardey (2014), In sub-Saharan Africa (SSA), productivity levels are low and growth rates have recently stagnated and World Bank, (2008). |
| [3] | Ehui and Pender (2005); Diao et al. (2008); Barrett et al. (2010); World Bank (2008); Kijima et al. (2011). underdeveloped agricultural research and extension systems. |
| [4] | Gollin et al. (2005); Doss2006; Lee et al. 2005; Erenstein et al. 2008). looking for other approaches to address low agricultural productivity in SSA |
| [5] | Vander casteelen et al., (2016). farmers grow wheat and it is the dominant cereal crop in over 30 of the 83 high-potential agricultural |
| [6] | Geremew et al., (2016). the latest farming technology aggressively promoted for adoption by smallholder farmers in Ethiopia |
| [7] | Kassa (2003); Tadesse and Kassa, 2004. In some cases, giving up or reducing the use of technologies has been reported |
| [8] | ATA, (2013b). As a result, average grain income increased from 12.6 to about 21 quintals/hektar or by 70%. |
| [9] | Behailu, (2014). Income of row sowing method reached about 24 quintals/hektar and the net revenue will 20% larger than the traditional broadcasting method |
| [10] | Ilahi (2000); Doss 2001; Lee (2005). This study expands what limit evidence there is on how new technologies affect labor usage. |
| [11] | Doss (2006). under which the profitability of a new technology can be assessed |
| [12] | BoARD, (2011). Uncovered seeds are also prone to erosion (water and wind) and bird attack |
| [13] | Minjara Wored. Yonas, B. and Behailu (2014) examine determinants of the adoption of row sowing on Wheat crop sowing farmer’s and improvement on the production of Wheat crop sowing]. |
| [14] | Mesafint, (2017) Determinants of adoption of Wheat crop sowing (Eragrostis tef) row sowing technology in Moretna Jiru Woreda, North Shoa Zone of Amhara Regional State |
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| [17] | Berhe et al. (2011); Bekabil et al., (2011); ATA, (2013) the amount of production is not as much as its area coverage and value. |
| [18] | ATA (2012a), Crop grain plant with space' commence with growing seedlings in a nursery school and sowing. |
| [19] | Tolesa. (2014), Conducted a study on the Socio-economic and Institutional Factors Limiting. |
| [20] | ATA, (2013a). As the Results of 2012 improved wheat Technologies Demonstration Trials Draft Report at Addis Ababa, Ethiopia. |
| [21] | Tsegaye and Bekele (2012) investigated impacts of adoption of improved wheat technologies on house hold’s food consumption in south eastern Ethiopia. |
| [22] | Gashaw et al. (2014) marketing assistance as full-package obtains higher wheat income as compared to non-users. |
| [23] | (Ejegayehu and Berhe, 2016) showed that Effect of wheat row sowing technology adoption on small farms income in Ofla Woreda, Ethiopia by using propensity score matching method |
| [24] | Dasgupta (1989) indicate that, the decision to adopt an innovation is not normally a single instantaneous act, it involves as a process. |
| [25] | Tulema et al. (2008); Fufa et al. (2011) land is repeatedly ploughed before sowing to prepare the seedbed and control weeds, but this leads to increased erosion and. lower soil fertility |
| [26] | Tsegaye & Bekele (2012) investigated impacts of improved wheat technologies on house hold’s food consumption in south eastern Ethiopia. |
| [27] | Gashawetal., (2014); Sarah, 2014) wheat farmers in locate in four main wheat belt regions of Ethiopia: Amhara, Oromia, SNNPR, and Tigray |
| [28] | Ray (2001), Adoption does not inevitably follow the optional stages from consciousness to adoption; trial may not be always practiced by farmers to adopt new technology. |
| [29] | Berhane and Fufa et al. (2011). To improved wheat technologies has received limited international attention mainly because of the crop having only local importance |
| [30] | Legesse, (1998) Adoption of Wheat Row Sowing in Ethiopia, by applying logit model. |
| [31] | ATA Ethiopia (2009). ingredient in Ethiopian diets, but also an integral part of the national culture |
| [32] | Tsegaye and Bekele, (2012); Bolaetal. 2012; and Mamudu et al, 2012). Those results are consistent to the researches that had been done before |
| [33] | Ibid, (2010), The beneficiary tries to segregate the “cause” (independent variables) and scan whether it has any possessions on reliant variable wheat row sowing adoption. |
| [34] | IDRC (2003), According to this type of research design is use in descriptive research design and in determination of relationship of variables. |
| [35] | Ghauri & Grønhaug, (2010). Research design was use because of the limited time and finance in field work and the fact that it was deemed to be adequate for addressing the study objectives |
| [36] | Ejegayehu and Berhe, (2016) the conditions of areas where the technology is to be introduced showed that Effect of wheat row sowing technology adoption on small farms yield in Ethiopia |
| [37] | Watson Jeff, (2001) provides a simplify formula to calculate sample sizes. |
| [38] | Dawson, (2009). The accurate sample size in a study is reliant on the nature of the population and the function of the study |
| [39] | Babbie (2003), the most effective evaluation research is the one that combines qualitative and quantitative components. |
| [40] | Imbens (2000) and Lechner (2001) when leaving the binary treatment case the choice of multinomial logit is quite easier to analyze dichotomous variables |
| [41] | Assefa and Gezehegn, (2010) young farmers are more likely to adopt new technologies, because they may have more schooling than older farmers and have been exposed to new ideas and hence more risk takers. |
| [42] | Afework and Lemma, (2015), Sisay, (2016), Hassen et al, (2012) educational level of the household head has a positive effect on the status, intensity and speed of technology adoption |
| [43] | Aman and Tewdros, (2016) indicated that farm experience affect improved agricultural inputs |
| [44] | Almaz, (2008) Participation indifferent meetings and consequently have greater access to information. |
| [45] | Akpan et al, (2012) Income earning farm households are able to overcome the financial constraint with respect to technology adoption and purchase |
| [46] | Tiamiyu et al, (2014);-The credit availability positively affects the adoption of improved technologies. |
| [47] | Wuletaw and Daniel, (2015; Hadush, (2015). Farmers who participated on training, their probability of adopter and nan-adoption of new technologies increase. |
| [48] | Leuven and Sianesi (2003). The model is estimated with STATA software using the propensity score-matching algorithm developed. |
| [49] | Wang, P, (2013), Tilt and Gerkey (2016) Relocated people suffer from the loss of farmland, forestland, houses and other properties, which may then reduce their income. |
| [50] | McDonald et al. (2018) found impact wheat row could have positive impacts on maintaining and raising the income level of the farmer’s community. |
| [51] | Galipeau et al. (2013) compared the distinction between a adapter community and a non-adapter community in term of income and number of livestock |
| [52] | Rosenbuan, (2002);-If there are unobserved variables that affect assignment in to treatment and the outcome variable simultaneously a hidden bias might arise to which matching estimators are not robust |
| [53] | Hadush (2015) and Wuletaw and Daniel (2015) training affect use of wheat row sowing adoption. |
| [54] | Sall et al., (2002; Shiyani et al., (2002); Wabbi et al., (2006) the intensity or magnitude use of that technology, given that adoption has taken place. |
| [55] | Hassen, (2014), Empirical results revealed that frequency of contacts with extension agents has an influence on adoption of new technology. |
APA Style
Lelisa Mamo Abdisa. (2022). The Effect of Wheat Crop Row Sowing on Income of Farmers, in Wayu Tuka Woreda East Wollaga Zone, Oromia Regional State, Ethiopia. International Journal of Economy, Energy and Environment, 7(6), 125-138. https://doi.org/10.11648/j.ijeee.20220706.11
ACS Style
Lelisa Mamo Abdisa. The Effect of Wheat Crop Row Sowing on Income of Farmers, in Wayu Tuka Woreda East Wollaga Zone, Oromia Regional State, Ethiopia. Int. J. Econ. Energy Environ. 2022, 7(6), 125-138. doi: 10.11648/j.ijeee.20220706.11
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Download@article{10.11648/j.ijeee.20220706.11,
author = {Lelisa Mamo Abdisa},
title = {The Effect of Wheat Crop Row Sowing on Income of Farmers, in Wayu Tuka Woreda East Wollaga Zone, Oromia Regional State, Ethiopia},
journal = {International Journal of Economy, Energy and Environment},
volume = {7},
number = {6},
pages = {125-138},
doi = {10.11648/j.ijeee.20220706.11},
url = {https://doi.org/10.11648/j.ijeee.20220706.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijeee.20220706.11},
abstract = {The aspire of the study were to the effect of wheat crop row sowing on income of farmers, in Wayu Tuka Woreda East Wollaga Zone in Ethiopia. Wheat crop sowing is a highly valuable grain for Ethiopian people both in production and in consumption. The objective of the research was to describe the factors affecting adoption and intensity of wheat row sowing in the study area. The study was based on cross sectional research which was included both qualitative and quantitative research approach. The data were collected from total 135 respondents selected from three kebels of Wayu Tuka Woerda by using random sampling method. From the total 135 respondents 82 were wheat row sowing adopters while 53 were non wheat row sowing adopters. Both primary and secondary data used and analysed using descriptive statistics and logit model. The software used for data entry and analysis were STATA14.2. The results show that about 61% of the respondents are users of wheat row sowing whereas 39% can be classified as non-adopters of wheat row sowing. The empirical Results revealed that age, credit access and agricultural input use of household negatively influenced decision to adopt wheat row sowing while accesses to technology, total annual income, access to training and availability of labour force were positively influenced the decision to adopt wheat row sowing. Finally, wheat row sowing has significant impact on farmer’s income increment. It is better to encourage farmer households as they actively participant in wheat row sowing technology and support them by giving training, supplying agricultural inputs and adopting new technology for them with adequate skills for enhancing their annual income and development of the country economy.},
year = {2022}
}
TY - JOUR T1 - The Effect of Wheat Crop Row Sowing on Income of Farmers, in Wayu Tuka Woreda East Wollaga Zone, Oromia Regional State, Ethiopia AU - Lelisa Mamo Abdisa Y1 - 2022/11/16 PY - 2022 N1 - https://doi.org/10.11648/j.ijeee.20220706.11 DO - 10.11648/j.ijeee.20220706.11 T2 - International Journal of Economy, Energy and Environment JF - International Journal of Economy, Energy and Environment JO - International Journal of Economy, Energy and Environment SP - 125 EP - 138 PB - Science Publishing Group SN - 2575-5021 UR - https://doi.org/10.11648/j.ijeee.20220706.11 AB - The aspire of the study were to the effect of wheat crop row sowing on income of farmers, in Wayu Tuka Woreda East Wollaga Zone in Ethiopia. Wheat crop sowing is a highly valuable grain for Ethiopian people both in production and in consumption. The objective of the research was to describe the factors affecting adoption and intensity of wheat row sowing in the study area. The study was based on cross sectional research which was included both qualitative and quantitative research approach. The data were collected from total 135 respondents selected from three kebels of Wayu Tuka Woerda by using random sampling method. From the total 135 respondents 82 were wheat row sowing adopters while 53 were non wheat row sowing adopters. Both primary and secondary data used and analysed using descriptive statistics and logit model. The software used for data entry and analysis were STATA14.2. The results show that about 61% of the respondents are users of wheat row sowing whereas 39% can be classified as non-adopters of wheat row sowing. The empirical Results revealed that age, credit access and agricultural input use of household negatively influenced decision to adopt wheat row sowing while accesses to technology, total annual income, access to training and availability of labour force were positively influenced the decision to adopt wheat row sowing. Finally, wheat row sowing has significant impact on farmer’s income increment. It is better to encourage farmer households as they actively participant in wheat row sowing technology and support them by giving training, supplying agricultural inputs and adopting new technology for them with adequate skills for enhancing their annual income and development of the country economy. VL - 7 IS - 6 ER -
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