Estimating plant biomass in agroecosystems using a drop-plate meter.

Reason for doing the work: Plant biomass is a commonly used metric to assess agricultural health and productivity. Removing plant material is the most accurate method to estimate plant biomass, but this approach is time consuming, labor intensive, and destructive. Previous attempts to use indirect methods to estimate plant biomass have been limited in breadth and/or have added complexity in data collection and/or modeling. A cost-effective, quick, accurate, and easy to use and understand approach is desirable for use by scientists and growers. Objectives: An indirect method for estimating plant biomass using a drop-plate meter was explored for use in broad array of crop systems. Methods: Drop-plate data collected by more than 20 individuals from 16 crop types on 312 farms across 15 states were used to generate models to estimate plant biomass among and within crop types. Results: A linear model using data from all crop types explained approximately 67% of the variation in plant biomass overall. This model performed differently among crop types and stand heights, which was owed to differences among sample sizes and farming between annual and perennial systems. Comparatively, the model using the combined dataset explained more variance in biomass than models generated with commodity specific data, with the exception of wheat. Conclusions: The drop-plate approach described here was inexpensive, quick, simple, and easy to interpret, and the model generated was robust to error and accurate across multiple crop types. The methods met all expectations for a broad-use approach to estimating plant biomass and are recommended for use across all agroecosystems included in this study. While it may be useful in crops beyond those included, validation is suggested before application.

Moth community among apples during bloom in Northwest Arkansas: likely pollinators and activity periods.

Apples are a fruit crop of economic and nutritional importance that require cross-pollination primarily by insects for sustainable production. It was recently demonstrated that nocturnal pollinators can contribute as much to apple pollination as diurnal pollinators. However, information concerning nocturnal pollinator identity, activity periods, and community composition in apples is lacking, which limits research expansion. To address this knowledge gap, nocturnal moths in an apple orchard were surveyed during apple bloom from 2018 to 2020 using blacklight traps, with samples collected hourly to delineate moth activity. Observations during the same periods were made to identify moths visiting apple flowers, whose capture data were then compared to other captured moth species to provide useful information concerning community composition during apple bloom. Blacklight surveys yielded 1,087 moths representing at least 68 species from 12 families, wherein 15 species from five families were observed visiting apple flowers. Captured moths were most abundant and diverse in the first two hours after sunset. Most captured moth species did not visit flowers and are likely not associated with apple pollination. However, moth species that were observed visiting flowers were the most abundant overall and most diverse by hour in surveys. Data indicate a rich moth community present among apple orchards during bloom and identify likely moth pollinators of apples. Though more research is required to establish the precise relationships between moth pollination and apples, the information provided here allows for targeted efforts to do so.

Nocturnal Pollinators Significantly Contribute to Apple Production.

Agricultural dependency on insect-mediated pollination is increasing at the same time that pollinator populations are experiencing declines in diversity and abundance. Current pollinator research in agriculture focuses largely on diurnal pollinators, yet the evidence for pollination by moths and other nocturnal pollinators is growing. Apples are one of the most valuable and important fruits produced globally, and apple production is dependent on insect-mediated cross-pollination to generate a profitable crop. We examined contributions to apple production provided by nocturnal insects via an exclusion experiment. We compared the relative contributions to apple production provided by nocturnal and diurnal pollinators using fruit set, the likelihood of cluster pollination, and seed set. We found nocturnal pollinators capable of facilitating the production of as many apples at similar levels of pollination as diurnal pollinators. We further found evidence that nocturnal and diurnal pollinators pollinate synergistically, with pollination contributions being additive in one year of our study. Our research identifies significant contributions to apple production provided by nocturnal pollinators, which may interact with diurnal pollinators in ways that are currently unrecognized. Expansions of this research into additional pollinator-dependent crops and focused investigations on specific nocturnal insects will provide more accurate assessments of nocturnal-pollinator roles in agriculture and improve our overall understanding of pollination in agriculture.

Quantitative single-cell characterization of bacterial interactions reveals type VI secretion is a double-edged sword.

Interbacterial interaction pathways play an important role in defining the structure and complexity of bacterial associations. A quantitative description of such pathways offers promise for understanding the forces that contribute to community composition. We developed time-lapse fluorescence microscopy methods for quantitation of interbacterial interactions and applied these to the characterization of type VI secretion (T6S) in Pseudomonas aeruginosa. Our analyses allowed a direct determination of the efficiency of recipient cell lysis catalyzed by this intercellular toxin delivery pathway and provided evidence that its arsenal extends beyond known effector proteins. Measurement of T6S apparatus localization revealed correlated activation among neighboring cells, which, taken together with genetic data, implicate the elaboration of a functional T6S apparatus with a marked increase in susceptibility to intoxication. This possibility was supported by the identification of T6S-inactivating mutations in a genome-wide screen for resistance to T6S-mediated intoxication and by time-lapse fluorescence microscopy analyses showing a decreased lysis rate of recipient cells lacking T6S function. Our discoveries highlight the utility of single-cell approaches for measuring interbacterial phenomena and provide a foundation for studying the contribution of a widespread bacterial interaction pathway to community structure.