Author
Listed:
- Godfrey C Onuwa
- Sunday S Mailumo
- Adeshola Olatunde Adepoju
Abstract
This study aims to critically bring to the fore appropriate soybean production technologies that boost the level of farm productivity. Multistage sampling techniques were used in selecting respondents for this study. Primary data was collected using structured questionnaires. Descriptive statistics and Multinomial Logit regression model were the analytical techniques employed. The results indicated that most (35%) were within the age bracket of 21-30 years; 39.7% had farming experience of 1-5 years. Most (73.3%) had extension contact; most (75%) were married, and most (63.3%) were male. Furthermore, most (55%) had farm size of ≤1.9 hectares; most (38.3%) had household size of 11-30 people. Also, planting on ridges (80%), use of viable seeds (79.2%) and recommended harvesting time (50.0%); were the prevalent soybean production technologies adopted in the study area. In addition, the coefficient of multiple determinations (R2) was 0.7831 suggesting that 78% of the variation in the soybean farmer’s adoption decision was accounted for by the variables in the regression model. The remaining 22% is attributable to omitted variables and the stochastic error term. Furthermore, the most significant constraints of adoption of soybean production technologies were; high cost of technology (68.3%), lack of technical expertise (50.8%), inadequate capital (40.8%), and poor market linkages (40.0%). Thus, this study revealed that socioeconomic variables affected farmer’s adoption decisions. Moreover, technology adoption was relatively low with consequent declining farm productivity. However, improved extension service, subsidized and improved access and/ or supply of inputs, credit and market linkages are strongly recommended.
Suggested Citation
Godfrey C Onuwa & Sunday S Mailumo & Adeshola Olatunde Adepoju, 2021.
"Boosting Farm Productivity through Intensification of Soybean Production Technology,"
International Journal of Sustainable Agricultural Research, Conscientia Beam, vol. 8(1), pages 61-70.
Handle:
RePEc:pkp:ijosar:v:8:y:2021:i:1:p:61-70:id:320
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