MEETING PRIVATE GRADES AND STANDARDS IN TRANSITION AGRICULTURE: EXPERIENCES FROM THE ARMENIAN DAIRY INDUSTRY

One of the main trends emerging from the agroindustrialization process is the rise of 'grades and standards' (G&S) in food products. G&S were initially developed by the public sector to reduce transaction costs and ensure product quality and safety but have become a strategic instrument of competition in differentiated product markets (Reardon et al, 2001). Firms are using grades and standards to protect and develop brands in the international marketplace and in some cases to fill in for missing public standards. While producers in developed countries have the resources to meet these requirements, in developing countries these changes have tended to exclude small firms and farmers from participating in market growth, because of the implied investment requirements (Reardon et al, 2001). This is leading to already disadvantaged farmers in these countries being forced to produce basic subsistence food crops and become further excluded from the opportunity to join the global food industry. While past research has evaluated the effects and trends of G&S (Reardon, et al, 2001; Farina & Reardon, 2000; Reardon & Farina, 2002) the organizational structure to enable small farmers to meet these requirements has largely been overlooked. In this paper we use a theoretical contract enforcement framework to argue that private enforcement capital developed through the facilitation of an external aid agency can be an effective means for creating credible and sustainable relationships capable of meeting G&S. We draw upon theory from Cocks and Gow (2002), Oliver and Gow (2002) and Gow et al. (2000) to argue that in situations characterized by high discount rates and low reputation or trust levels (such as transition agriculture) that the use of a third party external enforcement agent can be used to provide the necessary linkage between the parties to facilitate transactions. Through the facilitation role of the external agency, private enforcement capital is developed between the firm and the farmers, opening the path for a sustainable mutually beneficial relationship. Empirical evidence is provided by the case of the United States Department of Agriculture Marketing Assistance Project (USDA MAP) in Armenia and its role in establishing farmer owned milk marketing cooperatives. By acting as an external facilitator in the initial establishment and ongoing development of milk supply cooperatives the USDA MAP has provided a solution to the dual market failure problems of reliable supply of the consistent quality of milk required by processors while enabling farmers access to markets and ensuring timely payment and therefore enabling farmers and firms to credibly contract for the collective marketing of their milk. Through the establishment of a unique and flexibly designed combination of leadership development, training in governance, financial management, dairy management, and quality improvement programs, the USDA MAP has assisted the groups in expanding the self enforcing range in such a manner that the cooperative should be capable of sustaining long term credible exchange relationships once the external agency withdraws. This is important as aid programs have often failed at ensuring sustainability once external management and financial support is removed. Data for this paper was collected through a series of semi-structured interview with USDA MAP staff, dairy processing firm managers, cooperative managers, and cooperative presidents during the fall of 2002, and over a two week period in March, 2003.Livestock Production/Industries,

MEETING PRIVATE GRADES AND STANDARDS IN TRANSITION AGRICULTURE: EXPERIENCES FROM THE ARMENIAN DAIRY INDUSTRY

Livestock Production/Industries,

Attacker-Induced Traffic Flow Instability in a Stream of Automated Vehicles

Highway systems world wide continue to see an ever increased number of vehicles and subsequently a rise in congested traffic. This results in longer commute times, wasted energy as vehicles idle in stop and go traffic, and increases the risk of accidents. In short, increased congestion costs time and money. These issues have prompted much research into Automated Highway Systems (AHS). In AHS vehicles using computer algorithms can safely travel at much smaller inter-vehicle distances than human drivers are capable of. This increases the capacity of existing highway systems. Sensors aboard each vehicle make this possible by monitoring their surroundings. Vehicles equipped with Adaptive Cruise Control (ACC) are capable of this type of close proximity travel. ACC packages are becoming common as a standard package on many mid-priced vehicles. Another form of automation, Cooperative Adaptive Cruise Control (CACC), which utilizes wireless communication between vehicles, has been proposed and will likely become available within the next couple decades. CACC allows each vehicle to communicate their intended speed or position changes to surrounding vehicles, further decreasing the possibility of collisions. These automation methods are proposed to reduce driver stress, increase highway throughput, and decrease accident rates. However, the fact that vehicles are being automated creates new opportunities for malicious individuals to wreak havoc on society. This research investigates the possibility that some vehicles on the highway might be under the control of malicious individuals who have modified their automated control systems to negatively affect vehicles around them. These malicious actors might also exploit the wireless communication of CACC vehicles and hack their control algorithms, causing them to become unstable. These hacked vehicles could become passive participants in the attack unbeknownst to the driver of the vehicle. The result of such attacks could be congested traffic, rapid changes in acceleration causing drivers discomfort, or multi-vehicle collisions. Such attacks could effectively negate the benefits of implementing AHS. The goal of this work is to bring to light possible weaknesses in the proposed systems so they can be rectified before becoming an issue to the public at large

Automatic selection of preferred images

Image-hosting services and camera apps can select images for highlighting or as the best shot from a burst of shots based on criteria such as lighting conditions, blur (or lack thereof), whether people in the image are smiling, whether their eyes are open, etc. However, the selection of images is not based on user preferences, e.g., that indicate whether a user likes or dislikes an image. This disclosure describes techniques that enable a user to specify examples of favorite images. With user permission, examples provided by the user are used to surface similar images, e.g., images that match the user’s preferences

Automatic removal of unwanted objects from images

It is sometimes necessary to remove a person or other object from an image. Per current techniques, the removal of a person from an image, e.g., via manual cropping, produces a crude result, e.g., an image with discontinuities, an image with visible cropping boundaries, etc. Some image management and photo sharing applications/ services include features that enable users to remove specific persons from being categorized or surfaced in images; however, such solutions simply de-lists photos with the specified persons. This disclosure describes techniques that enable a user to remove specified individuals or other objects from images. The portion of the image from which the objects are removed are replaced with content generated by applying machine-learning models to other portions, e.g., background, of the image. With user permission, the techniques also automatically insert a descriptor in the image that indicates that image has been thus modified