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Abstract Background RNA-seq is revolutionizing the way we study transcriptomes. mRNA can be surveyed without prior knowledge of gene transcripts. Alternative splicing of transcript isoforms and the identification of previously unknown exons are being reported. Initial reports of differences in exon usage, and splicing between samples as well as quantitative differences among samples are beginning to surface. Biological variation has been reported to be larger than technical variation. In addition, technical variation has been reported to be in line with expectations due to random sampling. However, strategies for dealing with technical variation will differ depending on the magnitude. The size of technical variance, and the role of sampling are examined in this manuscript. Results In this study three independent Solexa/Illumina experiments containing technical replicates are analyzed. When coverage is low, large disagreements between technical replicates are apparent. Exon detection between technical replicates is highly variable when the coverage is less than 5 reads per nucleotide and estimates of gene expression are more likely to disagree when coverage is low. Although large disagreements in the estimates of expression are observed at all levels of coverage. Conclusions Technical variability is too high to ignore. Technical variability results in inconsistent detection of exons at low levels of coverage. Further, the estimate of the relative abundance of a transcript can substantially disagree, even when coverage levels are high. This may be due to the low sampling fraction and if so, it will persist as an issue needing to be addressed in experimental design even as the next wave of technology produces larger numbers of reads. We provide practical recommendations for dealing with the technical variability, without dramatic cost increases.

Many National Statistical Offices are modernizing the systems and processes underpinning the production of official agricultural statistics. Moving data and processes to the cloud, collecting survey data via the web, automating editing and imputation, incorporating more administrative, remotely sensed and other non-survey data in the estimation process, and more flexible dissemination of information are only some of the areas of current efforts. Although specific modernization efforts have been described, less discussion has been focused on exactly what the future of official agricultural statistics will be. During the 9th International Conference on Agricultural Statistics, which was held May 17–19, 2023, at the World Bank in Washington DC USA, four statistical leaders with diverse perspectives envision the not-too-distant future of official agricultural statistics in 2040. [ABSTRACT FROM AUTHOR]

Many National Statistical Offices are modernizing the systems and processes underpinning the production of official agricultural statistics. Moving data and processes to the cloud, collecting survey data via the web, automating editing and imputation, incorporating more administrative, remotely sensed and other non-survey data in the estimation process, and more flexible dissemination of information are only some of the areas of current efforts. Although specific modernization efforts have been described, less discussion has been focused on exactly what the future of official agricultural statistics will be. During the 9th International Conference on Agricultural Statistics, which was held May 17–19, 2023, at the World Bank in Washington DC USA, four statistical leaders with diverse perspectives envision the not-too-distant future of official agricultural statistics in 2040. [ABSTRACT FROM AUTHOR]

As is the case for many National Statistics Institutes, the United States Department of Agriculture's (USDA's) National Agricultural Statistics Service (NASS) has observed dwindling survey response rates, and the requests for more information at finer temporal and spatial scales have led to increased response burdens. Non-survey data are becoming increasingly abundant and accessible. Consequently, NASS is exploring the potential to complete some or all of a survey record using non-survey data, which would reduce respondent burden and potentially lead to increased response rates. In this paper, the focus is on a large set of records associated with potential farms, which are operations with undetermined farm status (farm/non-farm) and are referred to here as operations with unknown status (OUS). Although they usually have some agriculture, most OUS records are eventually classified as non-farms. Those OUS that are classified as farms tend to have higher proportions of producers from under-represented groups compared to other records. Determining the probability that an OUS record is a farm is an important step in the imputation process. The OUS records that responded to the 2017 U.S. Census of Agriculture were used to develop models to predict farm status using multiple data sources. Evaluated models include bootstrap random forest (RF), logistic regression (LR), neural network (NN), and support vector machine (SVM). Although the SVM had the best outcomes for three of the five metrics, the sensitivity for identifying farms was the lowest (13.8%). The NN model had a sensitivity of 80.5%, which was substantially higher than the other models, and its specificity of 45.3% was the lowest of all models. Because sensitivity was the primary metric of interest and the NN performed reasonably well on the other metrics, the NN was selected as the preferred model. [ABSTRACT FROM AUTHOR]

The Puerto Rico Census of Agriculture (Census) is the leading source of statistics about the island's agricultural production. The Census is conducted every 5 years, in years ending in 2 and 7. However, the most recent Census was administered in 2018 due to delays caused by Hurricane Maria. Because the Census list frame is incomplete, a separate survey is conducted to inform measures of undercoverage, nonresponse, and misclassification, leading to adjusted weights. At the time of Hurricane Maria, the 2017 survey had already been conducted in preparation for the planned Census that year. In 2018, the survey was repeated using the same sample. Linking and analyzing the 2017 and 2018 survey data provide insights into the impacts of Hurricane Maria on the island's agriculture. Furthermore, the 2018 survey gives an opportunity to evaluate the 2018 Census results. Although the same sample was used in 2017 and 2018, automated record linkage methods are not suitable to link records from the two surveys. This paper discusses record linkage and estimation approaches used to determine the number of farms and land in farms in Puerto Rico before and after Hurricane Maria based on the two surveys and the Census of Agriculture. [ABSTRACT FROM AUTHOR]

The emerging sectors of agriculture, such as organics, urban, and local food, tend to be dominated by farms that are smaller, more transient, more diverse, and more dispersed than the traditional farms in the rural areas of the United States. As a consequence, a list frame of all farms within one of these sectors is difficult to construct and, even with the best of efforts, is incomplete. The United States Department of Agriculture's (USDA's) National Agricultural Statistics Service (NASS) maintains a list frame of all known and potential U.S. farms and uses this list frame as the sampling frame for most of its surveys. Traditionally, NASS has used its area frame to assess undercoverage. However, getting a good measure of the incompleteness of the NASS list frame using an area frame is cost prohibitive for farms in these emerging sectors that tend to be located within and near urban areas. In 2016, NASS conducted the Local Food Marketing Practices (LFMP) survey. Independent samples were drawn from (1) the NASS list frame and (2) a web-scraped list of local food farms. Using these two samples and capture–recapture methods, the total number and sales of local food operations at the United States, regional, and state levels were estimated. To our knowledge, the LFMP survey is the first survey in which a web-scraped list frame has been used to assess undercoverage in a capture–recapture setting to produce official statistics. In this article, the methods are presented, and the challenges encountered are reviewed. Best practices and open research questions for conducting surveys using web-scraped list frames and capture–recapture methods are discussed. [ABSTRACT FROM AUTHOR]

The USDA National Agricultural Statistics Service (NASS) and other federal statistical agencies have used probability-based surveys as the foundation for official statistics for over half a century. Non-survey data that can be used to improve the accuracy and precision of estimates such as administrative, remotely sensed, and retail data have become increasingly available. Both frequentist and Bayesian models are used to combine survey and non-survey data in a principled manner. NASS has recently adopted Bayesian subarea models for three of its national programs: farm labor, crop county estimates, and cash rent county estimates. Each program provides valuable estimates at multiple scales of geography. For each program, technical challenges had to be met and a strenuous review completed before models could be adopted as the foundation for official statistics. Moving models out of the research phase into production required major changes in the production process and a cultural shift. With the implemented models, NASS now has measures of uncertainty, transparency, and reproducibility of its official statistics. [ABSTRACT FROM AUTHOR]

The United States Department of Agriculture's (USDA's) National Agricultural Statistics Service (NASS) conducts the Farm Labor Survey to produce estimates of the number of workers, duration of the workweek, and wage rates for all agricultural workers. Traditionally, expert opinion is used to integrate auxiliary information, such as the previous year's estimates, with the survey's direct estimates. Alternatively, implementing small area models for integrating survey estimates with additional sources of information provides more reliable official estimates and valid measures of uncertainty for each type of estimate. In this paper, several hierarchical Bayesian subarea-level models are developed in support of different estimates of interest in the Farm Labor Survey. A 2020 case study illustrates the improvement of the direct survey estimates for areas with small sample sizes by using auxiliary information and borrowing information across areas and subareas. The resulting framework provides a complete set of coherent estimates for all required geographic levels. These methods were incorporated into the official Farm Labor publication for the first time in 2020. [ABSTRACT FROM AUTHOR]

The United States Department of Agriculture's National Agricultural Statistics Service (NASS) has the responsibility of quantifying the nation's agricultural production. Historically, it has focused on large production agriculture. With interest and activity increasing in urban areas, NASS has begun exploring how to better quantify urban agriculture. This segment of agriculture is particularly challenging to enumerate because the agricultural holdings tend to be small, diverse, widely dispersed, and more transient than the predominantly large farms in rural areas. In collaboration with the Multi-Agency Collaboration Environment (MACE), a new approach to list building was explored in a pilot study conducted in the City of Baltimore, Maryland. Using a big data approach, areas of potential agricultural activity were identified by gathering information (state and local permits, Facebook and twitter feeds, interest groups, etc.) via the web. A sample was drawn from the list, and an in-person survey was conducted to assess whether or not the identified areas had agricultural activity. The results of the pilot study are presented. Lessons learned from the study and next steps are discussed. [ABSTRACT FROM AUTHOR]

(1) Background: To demonstrate the potential effects of missing exposure data and model choice on public health conclusions concerning the impact of heat waves on heat-related morbidity. (2) Methods: Using four different methods to impute missing exposure data, four statistical models (case-crossover, time-series, zero-inflated, and truncated models) are compared. The methods are used to relate heat waves, based on heat index, and heat-related morbidities for Florida from 2005-2012. (3) Results: Truncated models using maximum daily heat index, imputed using spatio-temporal methods, provided the best model fit of regional and statewide heat-related morbidity, outperforming the commonly used case-crossover and time-series analysis methods. (4) Conclusions: The extent of missing exposure data, the method used to impute missing exposure data and the statistical model chosen can influence statistical inference. Further, using a statewide truncated negative binomial model, statistically significant associations between heat-related morbidity and regional heat index effects were identified. [ABSTRACT FROM AUTHOR]

The United States Department of Agriculture's National Agricultural Statistics Service (NASS) conducts the June Agricultural Survey (JAS) annually. Substantial misclassification occurs during the prescreening process and from field-estimating farm status for nonresponse and inaccessible records, resulting in a biased estimate of the number of US farms from the JAS. Here, the Annual Land Utilization Survey (ALUS) is proposed as a follow-on survey to the JAS to adjust the estimates of the number of US farms and other important variables. A three-phase survey design-based estimator is developed for the JAS-ALUS with nonresponse adjustment for the second phase (ALUS). A design-unbiased estimator of the variance is provided in explicit form. [ABSTRACT FROM AUTHOR]

Background: Using current climate models, regional-scale changes for Florida over the next 100 years are predicted to include warming over terrestrial areas and very likely increases in the number of high temperature extremes. No uniform definition of a heat wave exists. Most past research on heat waves has focused on evaluating the aftermath of known heat waves, with minimal consideration of missing exposure information. Objectives: To identify and discuss methods of handling and imputing missing weather data and how those methods can affect identified periods of extreme heat in Florida. Methods: In addition to ignoring missing data, temporal, spatial, and spatio-temporal models are described and utilized to impute missing historical weather data from 1973 to 2012 from 43 Florida weather monitors. Calculated thresholds are used to define periods of extreme heat across Florida. Results: Modeling of missing data and imputing missing values can affect the identified periods of extreme heat, through the missing data itself or through the computed thresholds. The differences observed are related to the amount of missingness during June, July, and August, the warmest months of the warm season (April through September). Conclusions: Missing data considerations are important when defining periods of extreme heat. Spatio-temporal methods are recommended for data imputation. A heat wave definition that incorporates information from all monitors is advised. [ABSTRACT FROM AUTHOR]