Adoption of Chemical Fertilizer for Land Management in the North-West Zone of Nigeria

Abstract

The study aimed at: (i)estimating the rates of adoption and application of inorganic fertilizer; and (ii)determining the farmer-and farm-characteristics conditioning the adoption of inorganic fertilizer in two selected States in the northwest zone of Nigeria, namely, Kano and Katsina States. A two-stage sampling procedure was adopted in the survey, First, two Agricultural Development Programme (ADP) zones were purposively sampled in each State on the basis of relative climate. In Kano State, Rano and Danbatta zones were chosen, while in Katsina State, Funtua and Ajiwa zones were selected. Second, 60 farmers were randomly selected from each of the four ADP zones giving a total sample size of 240. The results of the survey showed that the mean rates of adoption of inorganic fertilizer were 85.85% for Kano, 98.35% for Katsina, and 92.10% for both States. These rates of adoption were illustrative of a long history of exposure to fertilizers. The mean rates of application of inorganic fertilizers of 41.49kg ha-1 for Kano, 67.24kg ha-1 for Katsina and 54.36 kg ha-1 for both States were, however, short of the recommended nutrient levels for the staple food crops grown in the two States. The result of the analysis of the socioeconomic factors conditioning adoption of inorganic fertilizer showed that all the factors included in our model influenced the adoption of inorganic fertilizer, but there were locational differences in the significance of these factors. Recommendations made included: the complementary applications of inorganic and organic fertilizers; the adoption of integrated soil fertility management practices; training extension educators and other technical assistants to understand the factors conditioning adoption for more effective targeting and delivery of programmes; the education of the rural populace, encouraging membership of farmers' associations; the expansion of farm sizes; improved access to production credit; and the strengthening of existing extension systems.

Economic impact and determinants of adoption of improved maize production technologies

“…The adoption index ranges from 0 to 1 depending upon the farmer's degree of the technology adoption. The overall adoption indices of all the farmerswere categorised into four distinct categories (non-adopters, low adopters, medium adopters and high adopters) following Maiangwa et al (2007). The adoption score 0 point implies the non-adoption of the improved maize production package.…”

Smallholder farmers’ perception on tomato (solanum lycopersicum) seedling technologies in the north west zone of Nigeria

“…These States are considered representative in terms of biophysical characteristics and high agricultural production activities. In addition, these States have a high agricultural production potential, they were selected based on their high concentration on tomato production in the region with Kano being the most successful in Nigeria (Maiangwa et al, 2010). Multistage sampling techniques was used for the study.…”

Farmer Field Fora and adoption of yam integrated pest and disease management technologies in Northern Ghana

“…Younger farmers have been found to be more innovative and willing to bear risk, as opposed to older farmers (Asiabaka and Owens, 2002). However, Asante et al (2012), Wiredu et al (2011), andMaiangwa et al (2007) found a positive relationship between age and adoption. It is important that projects like the IPDM while targeting both older and young farmers, give priority to younger farmers because of their flexibility and willingness to try new things.…”

“…The argument is that larger households often have a lot of mouths to feed, to the extent that they do not have enough money to invest in technology adoption, as opposed to smaller families. In addition, farmers with relatively large family sizes attach greater importance to other non-farm activities than farm activities such as the adoption of IPDM technologies (Maiangwa et al, 2007;Amaza et al, 2008). On the contrary, Baffoe et al (2013), found a positive association between household size and adoption, arguing that larger families have greater labour force to be able to engage in the adoption of technologies.…”