Tembo, Rachael (author) and Maumbe, Blessing M. (author)
Format:
Book chapter
Publication Date:
2010
Published:
South Africa
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Document Number: D02157
Notes:
Pages 19-42 in Blessing M. Maumbe (ed.), E-agriculture and e-government for global policy development: implications and future directions. Information Science Reference, Hershey, Pennsylvania. 321 pages.
10 pages, The main objectives of this research are to assess the educational and training requirements of farmers in Al-Ta’mim governorate in the field of using and maintaining the center pivot sprinkler irrigation systems and identifying the differences between these requirements according to some independent factors. The research sample consisted of 142 farmers representing 60% of the total number of farmers who were using these systems. The data was collected through a questionnaire form and utilizing the Borich equation. After testing the validity and reliability of the questionnaire, the data acquisition commenced through personal interview, the data then were analyzed by using analysis of variance and step-wise regression analysis. The results showed that 94% of farmers require medium to high levels of education and training and that there are significant differences in these requirements according to: educational level, number of months of utilizing the system, annual income, size of holding, and communication level with information sources. Yet, there are no significant differences according to readiness to change and social norms. It is also found that the (number of months of utilizing the system) is the most contributing factor in the interpretation of the variance of the educational and training requirements.
5 pages, We developed an Excel-based computational template Extension educators can use to assist clientele with scheduling irrigation for efficient use of water. With the template, the user applies the dual crop coefficient method to calculate evaporation and transpiration rates separately, with the result being more accurate soil water tracking as compared to what occurs when a single crop coefficient is used. Crop water needs can be conveniently calculated on the basis of soil characteristics, crop growth stages, and weather information. Application examples demonstrate that the amount and frequency of irrigation should be adjusted according to soil texture. The template and application examples are available to Extension professionals as electronic supplementary material.
12 pages, We use the 2013 cotton precision farming survey data to study the adoption of irrigation technologies by cotton farmers in 14 states of the United States. We find that farmers with a higher irrigated yield, and who are from the Southern Plains (Texas and Oklahoma), adopt water-efficient irrigation technologies, such as sub-surface drip and trickle irrigation technologies. There are 10 irrigation technologies that farmers can adopt for cotton production in these 14 cotton-growing states. The intensity of the irrigation technologies, as measured by the number of irrigation technologies adopted in cotton production, is affected by the irrigated cotton yield realized, land holding (total land owned), education, computer use, and the origin of the cotton farmer being from the Southern Plains. We use a multivariate fractional regression model to identify land allocation by the different irrigation technologies used. Our results indicate that significant variables affecting land allocation with different irrigation technologies are the age of the operator, the cover crop, the information sources used, the per acre irrigated yield, the education, and the cotton farmer being from the Southern Plains.
16 pages, The study examined the factors that drive decisions to adopt and use irrigation technologies among smallholder farmers in Machakos County, Kenya. Data were collected from a sample of 300 smallholder farmers. Cross-sectional survey design, a multistage sampling procedure and random sampling method were employed. Percentages, means and econometric analysis were used in data analysis. Results showed that, 31.7% of the respondents practiced irrigation. Sex of household head, education, farm size, off-farm income, credit accessed and access to extension services positively influenced adoption of irrigation technologies. Adoption intensity was positively influenced by gender, off-farm income, farming experience, primary occupation and extension services. As a result, it is suggested that while formulating development strategies and programs for smallholder farmers, agricultural extension organizations should give priority to these factors.
