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Environment and crop management can play an important role in determining upland cotton (Gossypium hirsutumL.) fiber quality. An important quality parameter is fiber micronaire, which is an indirect measure of fiber linear density (fineness) and maturity, and it is affected by crop supply and partitioning of assimilates to cotton fruit. High micronaire occurs when there is an excess of assimilates because of good growing conditions and fruit number is low. Conversely low micronaire occurs when growing conditions are poor and fruit number is high. Little research has established the degree of impact of these variables in combination influencing micronaire. Two field experiments were conducted to generate variability in micronaire by changing planting time, cultivars, canopy size, and fruit load (boll number). From these experiments, a significant relationship (r2= 0.79) to predict micronaire was generated using temperature during crop fiber filling, leaf area index (LAI) at the start of crop fiber thickening, and the mass of individual bolls at harvest. Subsequent experiments in different seasons (included variations in planting time, fruit load, and water deficit during fiber thickening) successfully validated the relationship. Reasonable success was attained in predicting fiber linear density and maturity ratio independently and then used to calculate estimates of micronaire. Prediction of these quality attributes may offer more detailed information on the effects of environment and management influencing fiber quality affecting yarn quality. Overall, the ability to predict micronaire (and potentially its components) could be used to refine management decisions to improve fiber quality management prior to or at harvest time. Crop fiber micronaire is primarily influenced by temperature during boll filling period.Final average individual boll mass and leaf area index at flowering moderates crop micronaire.New functions have been successfully developed to predict micronaire at crop maturity.

The boll cutting technique allows cotton producers to gauge the maturity of cotton bolls to help in their decision-making in the final stages of management of their crop leading up to harvest. The aim of this work was to assess the feasibility of using portable near infrared technology as a non-destructive rapid replacement for the manual boll cutting technique. Near infrared spectra (702–1100 nm) were collected from bolls using a Felix F-750 Produce Quality Meter. Spectra were successfully calibrated with two levels of boll maturity (immature and mature) using a partial least squares model for a population of 201 bolls. Independent validation of the model using separate boll populations predicted boll maturity correctly with an average accuracy of 96%. Calibrations that used spectra collected from bolls under field conditions performed similarly to calibrations that used spectra collected from the same bolls under controlled laboratory conditions. There was no preferred spectral sampling location on the bolls that resulted in better calibration performance. Calibration performance did not improve when spectral mathematical pre-treatments were used.

In the western United States, wolverines (Gulo gulo) typically occupy high‐elevation habitats. Because wolverine populations occur in vast, remote areas across multiple states, biologists have an imperfect understanding of this species' current distribution and population status. The historical extirpation of the wolverine, a subsequent period of recovery, and the lack of a coordinated monitoring program in the western United States to determine their current distribution further complicate understanding of their population status. We sought to define the limits to the current distribution, identify potential gaps in distribution, and provide a baseline dataset for future monitoring and analysis of factors contributing to changes in distribution of wolverines across 4 western states. We used remotely triggered camera stations and hair snares to detect wolverines across randomly selected 15‐km × 15‐km cells in Idaho, Montana, Washington, and Wyoming, USA, during winters 2016 and 2017. We used spatial occupancy models to examine patterns in wolverine distribution. We also examined the influence of proportion of the cell containing predicted wolverine habitat, human‐modified land, and green vegetation, and area of the cluster of contiguous sampling cells. We sampled 183 (28.9%) of 633 cells that comprised a suspected wolverine range in these 4 states and we detected wolverines in 59 (32.2%) of these 183 sampled cells. We estimated that 268 cells (42.3%; 95% CI = 182–347) of the 633 cells were used by wolverines. Proportion of the cell containing modeled wolverine habitat was weakly positively correlated with wolverine occupancy, but no other covariates examined were correlated with wolverine occupancy. Occupancy rates (ψ) were highest in the Northern Continental Divide Ecosystem (ψ range = 0.8–1), intermediate in the Cascades and Central Mountains of Idaho (ψ range = 0.4–0.6), and lower in the Greater Yellowstone Ecosystem (ψ range = 0.1–0.3). We provide baseline data for future surveys of wolverine along with a design and protocol to conduct those surveys. © 2020 The Authors. The Journal of Wildlife Managementpublished by Wiley Periodicals, Inc. on behalf of The Wildlife Society. Wolverines occupy most of the predicted habitat in Idaho, Montana, Washington, and Wyoming; occupancy probability is highest in the Northern Continental Divide Ecosystem and declines to the south and west. We provide a monitoring design for wolverines across the region.