Most of the effort of crop breeding has focused on the expression of aboveground traits with the goals of increasing yield and disease resistance, decreasing height in grains, and improvement of nutritional qualities. The role of roots in supporting these goals has been largely ignored. With the increasing need to produce more food, feed, fiber, and fuel on less land and with fewer inputs, the next advance in plant breeding must include greater consideration of roots. Root traits are an untapped source of phenotypic variation that will prove essential for breeders working to increase yields and the provisioning of ecosystem services. Roots are dynamic, and their structure and the composition of metabolites introduced to the rhizosphere change as the plant develops and in response to environmental, biotic, and edaphic factors. The assessment of physical qualities of root system architecture will allow breeding for desired root placement in the soil profile, such as deeper roots in no-till production systems plagued with drought or shallow roots systems for accessing nutrients. Combining the assessment of physical characteristics with chemical traits, including enzymes and organic acid production, will provide a better understanding of biogeochemical mechanisms by which roots acquire resources. Lastly, information on the structural and elemental composition of the roots will help better predict root decomposition, their contribution to soil organic carbon pools, and the subsequent benefits provided to the following crop. Breeding can no longer continue with a narrow focus on aboveground traits, and breeding for belowground traits cannot only focus on root system architecture. Incorporation of root biogeochemical traits into breeding will permit the creation of germplasm with the required traits to meet production needs in a variety of soil types and projected climate scenarios.

Crop root segmentation models developed through deep learning have increased the throughput of in situ crop phenotyping studies. However, models trained to identify roots in one image dataset may not accurately identify roots in another dataset, especially when the new dataset contains known differences, called domain shifts. The objective of this study was to quantify how model performance changes when models are used to segment image datasets that contain domain shifts and evaluate approaches to reduce error associated with domain shifts. We collected maize root images at two growth stages (V7 and R2) in a field experiment and manually segmented images to measure total root length (TRL). We developed five segmentation models and evaluated each model's ability to handle a temporal (growth‐stage) domain shift. For the V7 growth stage, a growth‐stage‐specific model trained only on images captured at the V7 growth stage was best suited for measuring TRL. At the R2 growth stage, combining images from both growth stages into a single dataset to train a model resulted in the most accurate TRL measurements. We applied two of the field models to images from a greenhouse experiment to evaluate how model performance changed when exposed to a cross‐site domain shift. Field models were less accurate than models trained only on the greenhouse images even when crop growth stage was identical. Although models may perform well for one experiment, model error increases when applied to images from different experiments even when crop species, growth stage, and soil type are similar.

Hemp (Cannabis sativa L.) cultivation in the United States is reemerging following legalization in the 2018 Farm Bill. Cultivation of hemp for fiber, grain, and flowers has encountered many challenges, including stand establishment and unknowns regarding ecosystem services the crop may provide. Hemp proponents have touted hemp as being carbon neutral or negative, but there is limited research on its root system, the most direct pathway for carbon into soil. Therefore, we sought to measure root system architecture across a subset of commercially available cultivars and to measure aboveground traits to develop allometric relationships that could be used for carbon market estimates. Two monoecious (Fibror 79 and Futura 75/83) and two dioecious cultivars (New West Genetics 2730 and Yuma) were selected from the University of Kentucky's Hemp Trials in 2022 and 2023. Plants (n = 16 total per cultivar; eight per sex for dioecious cultivars) were randomly selected and excavated at early senescence. Plant height, stem basal diameter, aboveground biomass, and root biomass were quantified for development of allometric relationships. Root systems were imaged in PhotoCapture360 and analyzed for root traits, such as total length and average diameter, with GiARoots. Basal diameter was predictive of both aboveground and belowground biomass (r² = 0.69 and 0.77, respectively). Additionally, female and monoecious root systems had smaller diameters but were twice as large as male root systems in mass and surface area. Overall, this study developed predicative equations that may be useful in quantifying hemp's growth and carbon capture potential by flowering behavior.

Climate change will significantly impact the world’s ecosystems, in part by altering species interactions and ecological processes, such as herbivory and plant community dynamics, which may impact forage quality and ecosystem production. Yet relatively few field experimental manipulations assessing all of these parameters have been performed to date. To help fill this knowledge gap, we evaluated the effects of increased temperature (+3°C day and night, year-round) and precipitation (+30% of mean annual rainfall) on slug herbivory and abundance and plant community dynamics biweekly in a pasture located in central Kentucky, U.S.A. Warming increased slug abundance once during the winter, likely due to improving conditions for foraging, whereas warming reduced slug abundance at times in late spring, mid-summer, and early fall (from 62–95% reduction depending on month). We found that warming and increased precipitation did not significantly modify slug herbivory at our site, despite altering slug abundance and affecting plant community composition and forage quality. Climate change will alter seasonal patterns of slug abundance through both direct effects on slug biology and indirect effects mediated by changes in the plant community, suggesting that pasture management practices may have to adapt.