In this exploratory analysis, we investigate the genesis and the evolution of local food-purchasing networks created and operated by consumers. In details, we describe how collecting and sharing information about food-products can become a central activity for some consumers’ communities and how these communities are starting to play an active role in the food supply chain. We define this community-based food-purchasing model as collaborative food network (CFN), and we analytically describe its characteristics and differences with respect to the traditional and industrialized agrifood supply chain models. A collaborative food network community in Italy, known as GAS (“Gruppi di Acquisto Solidale” – “Solidarity Purchasing Groups”), is introduced as an example of our analytical model. We will use this empirical example to present the strengths and weaknesses of the CFN model.

This study describes the development of a web-based system that collects all data generated in the research conducted to set pre-harvest residue limits (PHRLs) for agricultural product safety control. These data, including concentrations of pesticide residues, limit of detection, limit of quantitation, recoveries, weather charts, and growth rates, are incorporated into a database, a regression analysis of the data is performed using statistical techniques, and the PHRL for an agricultural product is automatically computed. The development and establishment of this system increased the efficiency and improved the reliability of the research in this area by standardizing the data and maintaining its accuracy without temporal or spatial limitations. The system permits automatic computation of the PHRL and a quick review of the goodness of fit of the regression model. By building and analyzing a database, it also allows data accumulated over the last 10 years to be utilized.

The development of informatization impacts all sectors, including agriculture. Agricultural informatization builds the knowledge linkage structures of agricultural innovation systems globally. This study investigated the global knowledge linkage structures in agricultural innovation pertinent to information technology (IT) for agricultural research and development (R&D) investments and activities. We explored the longitudinal trend of systemness within the networked research relationships in the triple helix (TH) of the university, industry and government (UIG). We collected data from publications in the Science Citation Index (SCI), the Social Sciences Citation Index (SSCI), and the Arts and Humanities Citation Index (A&HCI) to analyze the TH network dynamics. We also performed a scientometrics analysis to quantitatively identify the knowledge and insights of global agricultural innovation structures. These results could be informative for individual countries. Our findings reveal that the global knowledge linkage structures in the agricultural sector that are pertinent to IT fluctuate widely and fail to increase the capacity of agricultural innovation research due to a neglect of the network effects of the TH dynamics of UIG.

The dissemination process of agricultural research and development (R&D) results has somewhat different characteristics from that of typical R&D results. However, these characteristics are not adequately considered on the basis of an examination of the current performance system, the resulting management plans, and strategies for the application and dissemination of the results of agricultural R&D in Korea. The performance evaluation indicator exposed the problem of the inadequate consideration of the characteristics of each of these areas, particularly the lack of unified R&D-related institutions and the inadequacy of the system to monitor outcomes. To address these shortcomings in the agricultural R&D programs in Korea, the policies pertaining to agricultural R&D performance, results management, and dissemination in the U.S. and Japan were examined. Based on these investigations, we proposed strategies to improve the agricultural R&D policies in Korea.

This study examines the effects of lightemitting diode (LED) light and temperature on lettuce growth. For plant growth, we used an LED bar composed of red, white and blue LEDs (4:1:2). Six types of cultivation equipment were used to measure the temperature. To compare their effects, the heights of the lettuces and the water temperatures were measured. The results demonstrated that the lettuce growth was optimal at 25ºC.