A descriptive study of dairy goat predation in Louisiana.
Questionnaires concerning herd size, housing and management, and previous instances of predation were mailed to 124 goat owners in Louisiana. Postcards were mailed over a period of one year on a monthly basis, to the 84 individuals who responded to this questionnaire. Owners were asked to indicate whether they had seen predators on their property and whether any goats were injured or killed by the predators. Upon receipt of any postcard with a "yes" answer to either of these questions, a telephone interview was conducted to determine the exact circumstances of predator experience. The interview format included time, physical circumstances, and detailed questions about the actions of the predator and the goats. In 85% of the cases, the predators sighted were reported as dogs, and most of these were thought to be pets, rather than wild. Instances of attacks occurred most frequently during periods of reduced light. No clear seasonal pattern of attacks emerged. Four management factors were associated with a reduction in number of attacks; penning the goats at night, use of a night light, proximity of the herd to an occupied residence, and presence of a dog.
Dogs Gone Wild: Feral Dog Damage in the United States
Feral dogs have been documented in all 50 states and estimates of damage in the U.S. from these animals amount to >$620 million annually. In Texas alone, it is estimated that over $5 million in damage to livestock annually can be attributed to feral dogs. We reviewed national statistics on feral dog damage reported to USDA, APHIS, Wildlife Services for a 10-year period from 1997 through 2006. Damage by feral dogs crossed multiple resource categories (e.g., agriculture, natural resources); some examples of damage include killing and affecting the behavior and habitat use of native wildlife; killing and maiming livestock; and their role as disease vectors to wildlife, domestic animals, and humans. We review the role of dog damage in the U.S., synthesize the amount of damage between resource categories (agriculture, human health and safety, disease, and natural resources), and report trends in dog damage during the 10-year period. Results showed an increase in dog damage across all resource categories indicating the importance of management.
Detection and Counting of On-Tree Citrus Fruit for Crop Yield Estimation
In this paper, we present a technique to estimate citrus fruit yield from the tree images. Manually counting the fruit for yield estimation for marketing and other managerial tasks is time consuming and requires human resources, which do not always come cheap. Different approaches have been used for the said purpose, yet separation of fruit from its background poses challenges, and renders the exercise inaccurate. In this paper, we use k-means segmentation for recognition of fruit, which segments the image accurately thus enabling more accurate yield estimation. We created a dataset containing 83 tree images with 4001 citrus fruits from three different fields. We are able to detect the on-tree fruits with an accuracy of 91.3%. In addition, we find a strong correlation between the manual and the automated fruit count by getting coefficients of determination R2 up to 0.99.