There is only limited knowledge of the presence and incidence of viruses in peas within the United Kingdom, therefore high-throughput sequencing (HTS) in combination with a bulk sampling strategy and targeted testing was used to determine the virome in cultivated pea crops. Bulks of 120 leaves collected from twenty fields from around the UK were initially tested by HTS, and presence and incidence of virus was then determined using specific real-time reverse-transcription PCR assays by testing smaller mixed-bulk size samples. This study presents the first finding of turnip yellows virus (TuYV) in peas in the UK and the first finding of soybean dwarf virus (SbDV) in the UK. While TuYV was not previously known to be present in UK peas, it was found in 13 of the 20 sites tested and was present at incidences up to 100%. Pea enation mosaic virus-1, pea enation mosaic virus-2, pea seed-borne mosaic virus, bean yellow mosaic virus, pea enation mosaic virus satellite RNA and turnip yellows virus associated RNA were also identified by HTS. Additionally, a subset of bulked samples were re-sequenced at greater depth to ascertain whether the relatively low depth of sequencing had missed any infections. In each case the same viruses were identified as had been identified using the lower sequencing depth. Sequencing of an isolate of pea seed-borne mosaic virus from 2007 also revealed the presence of TuYV and SbDV, showing that both viruses have been present in the UK for at least a decade, and represents the earliest whole genome of SbDV from Europe. This study demonstrates the potential of HTS to be used as a surveillance tool, or for crop-specific field survey, using a bulk sampling strategy combined with HTS and targeted diagnostics to indicate both presence and incidence of viruses in a crop.

Field beans (Vicia faba L.) are the most extensively grown grain legume in the UK but their contribution to farming and food systems could be improved if their yields were enhanced. Average on-farm bean yields have varied between 3 and 4 t ha À1 for four decades but with much variation between individual crops. A "Bean Yield Enhancement Network" (Bean YEN) was initiated in 2019, supported by industry sponsors, to promote crop monitoring, sampling, and sharing of data between farms, thus learning about key yield-affecting factors. Bean YEN continues, gathering new data annually; data from crops harvested in 2019 to 2021 are reported here. For each crop entered, data were collated on agronomy, soil, and weather, samples were analysed for height, nutrient content, yield components, and seed quality, and accurate yields were recorded. A localised biophysical yield potential (Y bp ) was also estimated based on the best (repeatedly observed) resource capture and conversion coefficients and harvest index for beans, after accounting for costs of nitrogen (N) fixation. Over the three seasons, yields were collated from 26 winter bean and 63 spring bean crops, all well dispersed across the British Isles, with sufficient supporting information to make 87 estimates of Y bp . Average winter bean yields were 5.1 t ha À1 (range 1-8 t ha À1 ) and spring bean yields were 4.9 t ha À1 (range 1-7 t ha À1 ), respectively 38 and 43% of Y bp (13.7 & 11.2 t ha À1 ); yield shortfalls from Y bp averaged 7.2 t ha À1 (range 2.4-12.6 t ha À1 ). Yields correlated positively with plant height, thousand seed weight, total biomass shoot À1 , seeds pod À1 , harvest index and total straw biomass (t ha À1 ) in both winter and spring crops. In spring crops, the number of pods shoot À1 was also positively correlated with yield. Correlations suggested that growers could enhance yields by favouring an ideotype with deep roots, tall, multi-noded stems, and prolonged canopy survival. This ideotype will be subject to modification and improvement after data are collected through future seasons of Bean YEN. The accumulating Bean YEN dataset is enabling benchmarks to be derived for crop attributes that should guide growers in their quest for sustained yield enhancements.

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Developing and implementing landscape-scale management strategies capable of balancing the need for restoring natural fire regimes and promoting ecosystem resiliency for future climate change remains an urgent need globally. To help guide development of a landscape management strategy capable of meeting multiple objectives, five alternative landscape management scenarios for reducing the risk of uncharacteristically severe wildfire using thinning or managed and prescribed wildfire were developed by local forest managers in the Lake Tahoe region of the Sierra Nevada mountains. Effects of each scenario on forest structure, composition, and wildfire behavior were simulated over a 100-year period using the dynamic landscape simulation model LANDIS-II. We developed empirical territory occurrence models for three old-forest-associated predators, using 22 California Spotted Owl, 28 Northern Goshawk, and 16 female Pacific marten territories and presence-only modeling to evaluate the effects of each management scenario. The recruitment of more old-forest habitat across the simulated landscape was a more significant factor than any differences in the management scenarios, resulting in increases in the numbers of territories for all three predators, regardless of scenario. Increases in the numbers of territories were slowed in Scenario 4, which had the greatest amount of thinning, but the positive trend continued decades beyond when the other scenarios began to show a decline in territory numbers from severe wildfire. However, increases in the numbers of territories over time were slowed and overall were the lowest for the two old-forest predators that were most sensitive to the amount of old forest at the territory scale in the scenario with the greatest pace and scale of treatments. This suggests a trade-off between slowing the increase in the numbers of territories in the short term from forest growth by using fuels treatments with increased pace and scale to create forest structure that is less susceptible to severe wildfire in the long term, and that these management scenarios may need to be re-evaluated in 50 years before committing to continuing management efforts that may ultimately become detrimental for old-forest predators within 100 years.

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