No-till farming can improve soil health and reduce production costs, yet national adoption remains low, in part because producers face uncertainty about its long-term economic returns. Using more than 30 years of plot-level data from field experiments in Ohio and Michigan, we estimate site-specific effects of no-till on yields and input costs and calculate net profitability using partial budgeting. Results show that no-till consistently reduces operational costs across all sites. In Michigan, no-till also increases yields by 15.6 bu/ac for corn and 5.9 bu/ac for soybean. Estimated net return gains range from approximately $34 to $93 per acre for corn and $30 to $86 per acre for soybean. These results indicate that continuous no-till can generate positive long-run economic returns under the experimental conditions studied, although the magnitude varies across sites and crops.

Landscape pest management has traditionally relied on pesticides, but growing consumer interest in environmentally conscious practices is changing market expectations. A choice-based conjoint survey of 1,000 respondents evaluated willingness to pay for scouting-based integrated pest management (IPM) programs offered by landscape service providers. Attributes included spray reduction, biological controls, non-chemical controls, and price. Consumers were willing to pay ＄17.86 more per month for programs reducing sprays by at least four applications and up to ＄24.06 more when non-chemical controls were included. Results suggest scouting-based IPM services may represent a viable market opportunity for landscape service providers.

Extended radio sources present unique challenges for automated detection and classification in wide-field radio surveys. With current surveys such as the Evolutionary Map of the Universe (EMU), robust and scalable methods are essential to identify and catalogue these complex sources. We apply three automatic approaches to detect complex radio emission in EMU observations of the Galaxy And Mass Assembly (GAMA) 09 field (EMU-G09) in order to evaluate their relative strengths and limitations in preparation for large-scale application across future EMU data releases. These include DRAGNhunter, designed to detect likely DRAGNs (Double Radio sources associated with Active Galactic Nuclei) from a component catalogue; coarse-grained complexity, a metric designed to highlight regions of complex emission; and RG-CAT, a machine learning pipeline trained on radio sources identified in the EMU pilot survey. We find that together, the three methods recover nearly all extended sources in EMU-G09 but identify largely distinct, partially overlapping subsets, with only 375 sources identified by all finders. This demonstrates that a combination of complementary techniques will be required to achieve a complete census of extended radio sources in future large-scale surveys.