Background: Respondent-driven sampling is used worldwide to estimate the population prevalence of characteristics, such as HIV/AIDS and associated risk factors in hard-to-reach populations. Estimating the total size of these populations is of great interest to national and international organizations; however, reliable measures of population size often do not exist. Methods: Successive sampling-population size estimation (SS-PSE) along with network size imputation allows population size estimates to be made without relying on separate studies or additional data (as in network scale-up, multiplier, and capture–recapture methods), which may be biased. Results: Ten population size estimates were calculated for people who inject drugs, female sex workers, men who have sex with other men, and migrants from sub-Saharan Africa in six different cities in Morocco. SS-PSE estimates fell within or very close to the likely values provided by experts and the estimates from previous studies using other methods. Conclusions: SS-PSE is an effective method for estimating the size of hard-to-reach populations that leverages important information within respondent-driven sampling studies. The addition of a network size imputation method helps to smooth network sizes allowing for more accurate results. However, caution should be used particularly when there is reason to believe that clustered subgroups may exist within the population of interest or when the sample size is small in relation to the population.

Growers of high-end ‘Pinot noir’ wine grapes ( Vitis vinifera L.) commonly reduce yield by cluster thinning with the goal of increasing fruit quality; however, there are no objectively defined yield targets to achieve optimum fruit composition. Canopy leaf area relative to fruit yield can affect total soluble solids (TSS), and recommendations have been established for warm wine grape production regions. However, the relationship between leaf area and photoassimilation differs among climates and training systems. Leaf area to yield (LA:Y) ratios developed in warm, arid regions may not be suitable for cool, wet regions such as western Oregon. A 3-year field study was conducted to elucidate relationships between canopy to yield ratios and berry composition for ‘Pinot noir’. Vegetative growth and fruit yield were manipulated through competitive cover cropping and cluster thinning. Growth was manipulated in three ways: perennial red fescue ( Festuca rubra L.) was grown in 1) both (Grass), 2) one (Alternate), or 3) neither (Tilled) of the alleyways flanking the vine row. Within each vineyard floor treatment, fruit clusters were thinned to one per shoot (Half Crop) or vines were left unthinned (Full Crop). Floor management influenced both canopy size and yield because of altered vine nitrogen (N) status. Effects of crop load on berry components were not always consistent between the crop load metrics used [yield to pruning weight (Y:PW) ratio or LA:Y]. In 2 years, TSS reached a maximum at similar LA:Y; however, this did not necessarily produce optimum TSS. Yield had the greatest influence on pH and total anthocyanins (ACY) in the highest yielding, coolest year. Crop load metrics were not reliable predictors of TSS because of the dominant effect of seasonal variation. Relationships between canopy to yield metrics and other berry components were partially explained by tissue N, photosynthetic photon flux ( PPF ) through the cluster zone, and/or yield. Cluster thinning to adjust yields may not alter source to sink relationships or canopy to yield ratios enough to overcome ripening limitations in cool climates. Only one wine vintage had sensory differences with Alternate-Half Crop and Alternate-Full Crop wines ranked high quality and Tilled-Half Crop and Tilled-Full Crop wines ranked low quality by both consumer and winemaker panels. Therefore, cluster thinning may have limited impact on wine sensory properties.

Successive sampling (SS)-population size estimation (PSE) is a technique used to estimate the sizes of hidden populations using data collected in respondent-driven sampling (RDS) surveys. We assess past estimations and use new data from an RDS survey to calculate a new PSE. In 2012, 852 adult women in South Kivu Province, Democratic Republic of Congo, who self-identified as survivors of sexual violence, resulting in a pregnancy, since the start of the war (in 1996) were sampled using RDS. We used imputed visibility, enrollment order, and prior estimates for PSE using SS-PSE in RDS Analyst. Prior estimates varied between Congolese local experts and researchers. We calculated the PSE of women with a sexual violence-related pregnancy in South Kivu using researchers' priors to be approximately 17,400. SS-PSE is an effective method for estimating the population sizes of hidden populations, useful for providing evidence for services and resource allocation. SS-PSE is beneficial because population sizes can be calculated after conducting the survey and do not rely on separate studies or additional data (as in network scale-up, multiplier, and capture-recapture methods).

No-till or direct seeding can be described as seeding directly into the crop stubble from the previous season without use of tillage. A reduction in tillage can result in many benefits, including increased soil organic matter, increased water holding capacity, and reduced fuel costs. However, the effect of no-till and reduced tillage on crop root disease profiles is poorly understood. To study the effect of tillage on disease dynamics, soil samples were collected from commercial wheat fields representing a wide range of tillage strategies in fall 2016 and fall 2017. Because precipitation might affect soilborne diseases, wheat fields located across a diverse gradient of precipitation zones of the dryland Pacific Northwest were selected. Fusarium spp., Pythium spp., and Rhizoctonia spp. were quantified from soil samples using soil dilution plating and quantitative PCR (qPCR) assays. Results of dilution plating showed that the colony counts of Fusarium, Pythium, and Rhizoctonia at the genus level were negatively associated with tillage. However, the same patterns were not observed when specific causal agents of Fusarium, Pythium, and Rhizoctonia that are known to be pathogenic on wheat were quantified with qPCR. Furthermore, precipitation affected the population density of some fungal pathogens (F. culmorum, P. ultimum, and R. solani AG 8). Within the scope of inference of this study, results of this study indicate that the benefits of adopting reduced tillage likely outweigh potential risk for increased root disease.

Background: Estimates of the sizes of hidden populations, including female sex workers (FSW), men who have sex with men (MSM), and people who inject drugs (PWID), are essential for understanding the magnitude of vulnerabilities, health care needs, risk behaviors, and HIV and other infections. Objective: This article advances the successive sampling-population size estimation (SS-PSE) method by examining the performance of a modification allowing visibility to be jointly modeled with population size in the context of 15 datasets. Datasets are from respondent-driven sampling (RDS) surveys of FSW, MSM, and PWID from three cities in Armenia. We compare and evaluate the accuracy of our imputed visibility population size estimates to those found for the same populations through other unpublished methods. We then suggest questions that are useful for eliciting information needed to compute SS-PSE and provide guidelines and caveats to improve the implementation of SS-PSE for real data. Methods: SS-PSE approximates the RDS sampling mechanism via the successive sampling model and uses the order of selection of the sample to provide information on the distribution of network sizes over the population members. We incorporate visibility imputation, a measure of a person’s propensity to participate in the study, given that inclusion probabilities for RDS are unknown and social network sizes, often used as a proxy for inclusion probability, are subject to measurement errors from self-reported study data. Results: FSW in Yerevan (2012, 2016) and Vanadzor (2016) as well as PWID in Yerevan (2014), Gyumri (2016), and Vanadzor (2016) had great fits with prior estimations. The MSM populations in all three cities had inconsistencies with expert prior values. The maximum low prior value was larger than the minimum high prior value, making a great fit impossible. One possible explanation is the inclusion of transgender individuals in the MSM populations during these studies. There could be differences between what experts perceive as the size of the population, based on who is an eligible member of that population, and what members of the population perceive. There could also be inconsistencies among different study participants, as some may include transgender individuals in their accounting of personal network size, while others may not. Because of these difficulties, the transgender population was split apart from the MSM population for the 2018 study. Conclusions: Prior estimations from expert opinions may not always be accurate. RDS surveys should be assessed to ensure that they have met all of the assumptions, that variables have reached convergence, and that the network structure of the population does not have bottlenecks. We recommend that SS-PSE be used in conjunction with other population size estimations commonly used in RDS, as well as results of other years of SS-PSE, to ensure generation of the most accurate size estimation.