Agricultural Economics (Amsterdam, Netherlands), GIS-derived measures of location and space have increasingly been used in models of land use and ecology. However, they have made few inroads into the literature on technology adoption in developing countries, which continues to rely mainly on survey-derived information. Location, with all its dimensions of market access, demographics and agro-climate, nevertheless remains key to understanding potential for technology use. The measures of location typically used in the adoption literature, such as locational dummy variables that proxy a range of locational factors, now appear relatively crude given the increased availability of more explicit GIS-derived measures. This paper attempts to demonstrate the usefulness of integrating GIS-measures into analysis of technology uptake, for better differentiating and understanding locational effects. A set of GIS-derived measures of market access and agro-climate are included in a standard household model of technology uptake, applied to smallholder dairy farms in Kenya, using a sample of 3330 geo-referenced farm households. The three technologies examined are keeping of dairy cattle, planting of specialised fodder, and use of concentrate feed. Logit estimations are conducted that significantly differentiate effects of individual household characteristics from those related to location. The predicted values of the locational variables are then used to make spatial predictions of technology potential. Comparisons are made with estimations based only on survey data, which demonstrate that while overall explanatory power may not improve with GIS-derived variables, the latter yield more practical interpretations, which is further demonstrated through predictions of technology uptake change with a shift in infrastructure policy. Although requiring large geo-referenced data sets and high resolution GIS layers, the methodology demonstrates the potential to better unravel the multiple effects of location on farmer decisions on technology and land use.
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.
The potential uses of on-farm computers in management and the problems in these uses are analyzed. The analysis is based on a study of present uses of on-farm computers in Sweden. The results are compared with experiences from other countries. On-farm computer owners use almost the same management methods as before the computer investment. The main difference is that they used to hire service organizations to do some of the management tasks and now they are doing it by themselves with the aid of the computer. Thus, the on-farm computer owners have to have the same knowledge level as the service agents and advisers. The use of on-farm computers has so far affected the processing and storage of data for farm management purposes. A potential next step is communication of data from external computer systems at suppliers, customers, advisers and other farmers as well as automated data capture within the farm. One hindrance for this development is the lack of standardization of data and concept definitions. If this potential was realized the marginal costs of data and information would decrease. It would be profitable to use more information in the farm management, i.e. to develop the farm management functions. When farmers develop their management methods they will need still more knowledge. Service agents and advisers would have to change from doing management tasks for farmers to teaching farmers how to do these tasks and supporting farmers in the interpretation and analysis of information.
