16 pages., via online journal., To date, little is known about how information flows within farmer groups and how extension interventions could be designed to deliver combined information on agriculture and nutrition. This study uses unique network data from 815 farm households in Kenya to investigate the structure and characteristics of agricultural and nutrition information networks within farmer groups. Dyadic regressions are used to analyze the factors influencing link formation for the exchange of agricultural and nutrition information. In addition, we apply fixed‐effects models to identify the characteristics of central persons driving information exchange in the two networks, as well as potentially isolated persons, who are excluded from information networks within their farmer groups. Our results show that nutrition information is exchanged within farmer groups, although to a limited extent, and mostly flows through the existing agricultural information links. Thus, diffusing nutrition information through agricultural extension systems may be a viable approach. Our findings further suggest that group leaders and persons living in central locations are important drivers in the diffusion of information in both networks and may thus serve as suitable entry points for nutrition‐sensitive extension programs. However, we also identify important heterogeneities in network characteristics. In particular, nutrition information is less often exchanged between men and women, and some group members are completely isolated from nutrition information exchange within their farmer groups. We derive recommendations on taking these differences in network structure and characteristics into account when designing nutrition‐sensitive extension programs.
AGRICOLA IND 92018417, The appropriate combination of extension-teaching methods for rapid farm-technology diffusion and sustained productivity growth in the World-Bank-Assisted Agricultural Development Project in rural Nigeria is examined. The multiple extension-teaching methods in the Ilorin and Oyo North Projects have led to self-defeating and counterproductive results. Using principal-components analysis, the ten extension-teaching methods (variables) are transformed into a linear equation by allocating relative weights to each variable. These weights (coefficients of the equation), which are reasonably unique to each variable, measure the relative importance of the variables and therefore facilitate their ranking in each of the project districts. The usefulness of the principal component model in the World-bank-assisted Agricultural Development Projects in particular, and the rural Nigerian agricultural industry in general, are briefly discussed. (original)
Yu Jin (author), Huffman, Wallace E. (author), and Department of Economics, Shanghai University of Finance and Economics
Department of Economics, Iowa State University
Format:
Journal article
Publication Date:
2016
Published:
Wiley Periodicals, Inc.
Location:
Agricultural Communications Documentation Center, Funk Library, University of Illinois Box: 16 Document Number: D10455
17 pages., Via online journal., This article provides new estimates of the marginal product of public agricultural research and extension on state agricultural productivity for the U.S., using updated data and definitions, and forecasts of future agricultural productivity growth by state. The underlying rationale for a number of important decisions that underlie the data used in cost‐return estimates for public agricultural research and extension are presented. The parameters of the state productivity model are estimated from a panel of contiguous U.S. 48 states from 1970 to 2004. Public research and extension are shown to be substitutes rather than complements. The econometric model of state agricultural TFP predicts growth rates of TFP for two‐thirds of states that is less than the past trend rate. The results and data indicate a real social rate of return to public investments in agricultural research of 67% and to agricultural extension of 100+%. The article concludes with guidance for TFP analyses in other countries.
