Socio-economic Determinants of Sugarcane-Soybean Intercropping among Smallholder Farmers in Awendo Sub-County, Kenya

Abstract

Global climate change and decreases in available land are significant challenges humans currently face. Alternative management approaches for sugarcane fields have great potential to help mitigate these problems in Kenya. Intercropping as a crop diversification strategy is a crucial coping mechanism for agriculture's income, production, and marketing risks. The main purpose of this study was to analyze the determinants of sugarcane-soybean intercropping among sugarcane farmers to inform policymakers about policy adjustment. The study used primary data collected from 246 households using a structured questionnaire. Data were analyzed using descriptive statistics and a logistic regression model. Results showed that 63% of the respondents had adopted sugarcane monocropping compared to only 37 % who had adopted sugarcane-soybean intercropping. Specifically, the study found that sugarcane farming experience (p =<0.10), production acreage (p =<0.10), land ownership (p =<0.10), and divorced as marital status ((p =<0.05) had negative and significant effects on sugarcane – soybean intercropping, while farmers' age (p =<0.10) and widowed as a marital status (p =<0.01) depicted a positive and significant association with sugarcane – soybean intercropping. From the findings, lack of credit for farm operations and the high cost of farm inputs also emerged as barriers to the adoption of sugarcane intercropping systems. Based on the results, the study suggests the need for government to promote the development of agricultural policy that supports the shift from non-diversification to crop diversification through developing guaranteed access to inputs and subsidies on farming input resources with priority given to smallholder farmers.

Trade-Off between Land Use Pattern and Technical Efficiency Performance: Evidence from Arable Crop Farming in Tunisia

“…In addition, the results revealed a significant effect of family size on farm productivity. Consistent with previous findings [50,51], larger households tended to be more efficient, indicating a rationale use of an available family workforce for the production activities throughout the growing season. Our results further support earlier research showing male farmers are more efficient than female farmers [51][52][53].…”

“…Following existing studies on efficiency, the DEA efficiency scores can be expressed as a function of key socioeconomic characteristics of farms and farmers. Z 1 represents the farm manager's age as a proxy to farming experience and expected to have a positive impact on efficiency [33,50,51]. Z 2 identifies the gender of farmers to capture the difference in farm management between men and women [10,[51][52][53], taking a value of one if the farmer is male and zero otherwise.…”

“…Further, the framer's education level is included in Z 3 and defined as a dummy variable that equals to one if the farmer has completed at least an elementary degree and zero otherwise. We expect that better educated farmers tend to be more technically efficient [33,50,51]. We also considered the agricultural household size reflected in Z 4, which could have positive impact on efficiency since it ensures a continuous availability of the workforce for different framing activities [43,[50][51][52][53].…”

“…We expect that better educated farmers tend to be more technically efficient [33,50,51]. We also considered the agricultural household size reflected in Z 4, which could have positive impact on efficiency since it ensures a continuous availability of the workforce for different framing activities [43,[50][51][52][53]. Moreover, the model includes the crop diversification index (SID) (Z 5 ), our key variable in this study, to investigate its effect on efficiency.…”