Pursuit of the triple bottom line of economic, community and ecological sustainability has increased the complexity of fishery management; fisheries assessments require new types of data and analysis to guide science-based policy in addition to traditional biological information and modeling. We introduce the Fishery Performance Indicators (FPIs), a broadly applicable and flexible tool for assessing performance in individual fisheries, and for establishing cross-sectional links between enabling conditions, management strategies and triple bottom line outcomes. Conceptually separating measures of performance, the FPIs use 68 individual outcome metrics—coded on a 1 to 5 scale based on expert assessment to facilitate application to data poor fisheries and sectors—that can be partitioned into sector-based or triple-bottom-line sustainability-based interpretative indicators. Variation among outcomes is explained with 54 similarly structured metrics of inputs, management approaches and enabling conditions. Using 61 initial fishery case studies drawn from industrial and developing countries around the world, we demonstrate the inferential importance of tracking economic and community outcomes, in addition to resource status.

Probit analysis identified factors that influence the adoption of precision farming technologies by Southeastern cotton farmers. Younger, more educated farmers who operated larger farms and were optimistic about the future of precision farming were most likely to adopt site-specific information technology. The probability of adopting variable-rate input application technology was higher for younger farmers who operated larger farms, owned more of the land they farmed, were more informed about the costs and benefits of precision farming, and were optimistic about the future of precision farming. Computer use was not important, possibly because custom hiring shifts the burden of computer use to agribusiness firms.

This research evaluated the factors that influenced cotton (Gossypium hirsutum L.) producers to adopt remote sensing for variable-rate application of inputs. A logit model estimated with data from a 2005 mail survey of cotton producers in 11 southern USA states was used to evaluate the adoption of remote sensing. The most frequently made management decisions using remote sensing were the application of plant growth regulators, the identification of drainage problems and the management of harvest aids. A producer who was younger, more highly educated and had a larger farm with irrigated cotton was more likely to adopt remote sensing. In addition, farmers who used portable computers in fields and produced their own map-based prescriptions had a greater probability of using remote sensing. The results suggest that value-added map-making services from imagery providers greatly increased the likelihood of a farmer being a user of remote sensing.

Many studies on the adoption of precision technologies have generally used logit models to explain the adoption behavior of individuals. This study investigates factors affecting the intensity of precision agriculture technologies adopted by cotton farmers. Particular attention is given to the role of spatial yield variability on the number of precision farming technologies adopted, using a count data estimation procedure and farm-level data. Results indicate that farmers with more within-field yield variability adopted a higher number of precision agriculture technologies. Younger and better educated producers and the number of precision agriculture technologies used were significantly correlated. Finally, farmers using computers for management decisions also adopted a higher number of precision agriculture technologies.

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