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Yield estimation is an essential preharvest practice among most large-scale farming companies, since it enables the predetermination of essential logistics to be allocated (i.e., transportation means, supplies, labor force, among others). An overestimation may thus incur further costs, whereas an underestimation entails potential crop waste. More interestingly, an accurate yield estimation enables stakeholders to better place themselves in the market. Yet, computer-aided precision farming is set to play a pivotal role in this respect. Kiwifruit represents a major produce in several countries (e.g., Italy, China, New and Zealand). However, up to date, the relevant literature remains short of a complete as well as automatic system for kiwifruit yield estimation. In this paper, we present a fully automatic and noninvasive computer vision system for kiwifruit yield estimation across a given orchard. It consists mainly of an optical sensor mounted on a minitractor that surveys the orchard of interest at a low pace. Afterwards, the acquired images are fed to a pipeline that incorporates image preprocessing, stitching, and fruit counting stages and outputs an estimated fruit count and yield estimation. Experimental results conducted on two large kiwifruit orchards confirm a high plausibility (i.e., errors of 6% and 15%) of the proposed system. The proposed yield estimation solution has been in commercial use for about 2 years. With respect to the traditional manual yield estimation carried out by kiwifruit companies, it was demonstrated to save a significant amount of time and cut down on estimation errors, especially when speaking of large-scale farming.

In 2014, 116 Guelph residents were surveyed to investigate attitudes about community cats and preferences for population management. There are an estimated 29 579 owned cats in Guelph, an estimated 40% of residents allow outdoor access to their cat(s), and 8054 households (15%) feed community cats. Participants reported more concern with community cat welfare than nuisance behavior. There were high levels of support for responsible pet ownership education (90%), accessible or low-cost spay/neuter (86%), and trap-neuter-return (78%), and low levels of support for inaction (4%) and euthanasia (20%). Respondents who did not own cats or who considered cats as "pests" or a "non-native, invasive species" were more supportive of euthanasia. Results suggest that Guelph residents want to see action taken to address community cat population concerns, and that proactive management tools such as education, accessible or low-cost spay/neuter, and trap-neuter-return would be well supported and less divisive than euthanasia.

Implementation of cat population management strategies that are effective and supported by the community requires knowledge of local contexts, public values, and preferences. From 2014 to 2015 the Guelph Cat Population Taskforce surveyed Guelph, Ontario residents to investigate attitudes and values concerning community cats and preferences for cat population management. Responses from 449 individuals were received through a combination of paper and online surveys. The results of a qualitative analysis of 7 open-ended survey questions are reported herein. Results indicate that community cat issues are largely attributed to perceptions of irresponsible pet ownership practices, highlighting the opportunity for public education. Participants identified the whole community as responsible for addressing the problem, suggesting the value of collaborative management approaches. This analysis also illuminated opportunities for combining well-supported strategies such as accessible or low-cost spay/neuter and trap-neuter-return (TNR) to implement a multifaceted approach. Overall, Guelph residents who responded to the survey gave a high priority to proactive, humane management of community cats.