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Many agricultural management tactics, such as reduced tillage, aim to promote biodiversity and ecosystem services. Responses to such tactics can be context dependent, however, and differentially impact (i) functional groups of service-providing organisms and (ii) crop yields.In canola (Brassica napusLinnaeus,B. rapaL) crop fields we assessed how soil tillage and landscape context affected arthropod biodiversity and crop yield. We assessed effects of full (multiple tillage passes that leave soil surface bare), intermediate (tilled once and some stubble remains), or no (seed planted directly into last year’s stubble) tillage on functional groups with unique diets and reproductive strategies: (i) herbivores, (ii) kleptoparasites, (iii) parasitoids, (iv) pollinators, and (v) predators.Effects of tillage and landscape context on arthropod abundance and diversity varied across functional groups. Pollinators responded strongest to tillage, benefitting from intermediate tillage. Predators and herbivores responded strongly to landscape context, as both were more abundant in landscapes with more semi-natural habitat. Our results suggest natural history differences among functional groups mediate effects of landscape context on biodiversity. However, variation in arthropod communities had little effect on canola crop yield.Policy implications:The effects of soil management practices on aboveground arthropods are complex, and practices thought to increase some aspects of agricultural sustainability may not be beneficial in other contexts. Identifying practices such as intermediate tillage that may increase soil quality and arthropod diversity is key to designing agricultural ecosystems that will effectively benefit both biodiversity and human wellbeing.

Floral plantings are promoted to foster ecological intensification of agriculture through provisioning of ecosystem services. However, a comprehensive assessment of the effectiveness of different floral plantings, their characteristics and consequences for crop yield is lacking. Here we quantified the impacts of flower strips and hedgerows on pest control (18 studies) and pollination services (17 studies) in adjacent crops in North America, Europe and New Zealand. Flower strips, but not hedgerows, enhanced pest control services in adjacent fields by 16% on average. However, effects on crop pollination and yield were more variable. Our synthesis identifies several important drivers of variability in effectiveness of plantings: pollination services declined exponentially with distance from plantings, and perennial and older flower strips with higher flowering plant diversity enhanced pollination more effectively. These findings provide promising pathways to optimise floral plantings to more effectively contribute to ecosystem service delivery and ecological intensification of agriculture in the future., Our quantitative synthesis of the impacts of floral plantings on pest control, crop pollination and yield reveals that flower strips, but not hedgerows, enhanced pest control services in adjacent fields by 16% on average, while effects on crop pollination and yield were more variable. Our synthesis identifies several important drivers of this variability in effectiveness of plantings: pollination services declined exponentially with distance from plantings, and perennial and older flower strips with higher flowering plant diversity enhanced pollination more effectively, with important implications for the the design and implementation of these measures to effectively promote ecological intensification of agriculture in the future

Qualitative data can be gathered from an array of rich sources of research information. One of the popular ways to collect this data is by interviewing a range of experts on the topic, followed by transcription, resulting in a database of written documents, often supplemented by other documented data that informs the topic. Thematic or Content Analysis can then be used to explore the data and identify themes of meaning that enlighten the research topic, with the themes being gathered into nodes. The researcher now has an array of nodes, which needs to be organised into a coherent model, and more importantly, one that represents the views of the research informants. To do this with some degree of rigour, the researcher needs some way of ranking the nodes in terms of their relative importance. The node ranking can be based on experience, or on the literature, but neither of these approaches looks to the data itself. If the database contains new or unexpected knowledge, neither experience nor the literature will guide us to it, and vital new insights may easily be missed. The framework outlined in this paper aims to provide a sound first‑cut analysis of the data, based on the evidence in the research interviews themselves. Clearly the literature and research experience have an important role to play in shaping the results of any research. However this paper argues that one should proceed only after the data itself has been offered "the first chance to speak".The node classification matrix detailed here, identifies distinct node categories, each ranging in significance and with particular characteristics that reveal key aspects of the informants' views. In this way the researcher can use the nodes to reveal the voice of the experts, and build a scientifically rigorous set of results from a qualitative database.