Beginning Farmers and Ranchers

USDA defines beginning farmers and ranchers as those who have operated a farm or ranch for 10 years or less either as a sole operator or with others who have operated a farm or ranch for 10 years or less. Beginning farmers tend to be younger than established farmers and to operate smaller farms or ranches, some of which may provide no annual production. Beginning farmers often face obstacles getting started, including high startup costs and limited availability of land. USDA—through the Farm Service Agency and the Natural Resources Conservation Service—provides loans and conservation assistance to beginning farmers and ranchers. This report draws on data from annual surveys and the Census of Agriculture to provide policymakers with a better understanding of beginning farmers and ranchers, including how they contribute to U.S. agricultural production.agricultural production, Agricultural Resource Management Survey (ARMS), beginning farmers and ranchers, Census of Agriculture, farm assets, Food, Conservation, and Energy Act of 2008, Conservation Reserve Program, operator characteristics, Agricultural and Food Policy, Farm Management,

Management Strategies of Beginning Farmers and Ranchers

Farm Management,

Factors Driving Sow Breeding Operations to Become Large

This study examines the influences of economic and non-economic variables on the size of U.S. sow breeding operations using a probit model. Data from a national survey of U.S. hog operations identifying two different size categories were used in this study. Findings indicate that factors such as operations located in Delta States, climate controlled facilities, specialized operation, breeding practices, and risk attitudes toward investments influence decisions to establish breeding operations with 500 or more sows. Producers located in Iowa were more likely to choose breeding operations with 499 or less sows.Farm Management,

Factors Influencing Salaries of Agricultural Economics Professionals at Land Grant Institutions

Research in the mid 1900s suggested that salary gaps existed between men and women in academia. Though the research helped bring attention to salary gaps, less focus was on causes of salary differences. More recent research suggested differences in salaries were based on performance. A survey was sent to agricultural economics professionals at land grant intuitions to identify the factors that influence their salaries. Results of the ordered probit model suggest that seven variables can be used to explain salaries: having attained tenure, working at an 1862 institution, the amount of grant dollars, the number of journal articles, highest academic rank and the percentage of appointment that is in administration (positive influences) and importance of family time (negative influence). Other variables tested – gender, ethnicity and other preferences – were not found to influence salary levels.salary and performance, tracking survey, Institutional and Behavioral Economics, A11, A14,

Comparison of Factors Influencing Salaries of Agricultural Economics Professionals in Academic and Federal Employment

Results of two ordered probit models find differences in the major factors that influence salaries of agricultural economists employed in academia and in federal government. However some similarities were found; both sets of salaries were influenced by factors that measure job performance and neither set of salaries were significantly influenced by gender or ethnicity.Salary survey, job performance, Labor and Human Capital, Teaching/Communication/Extension/Profession,

Factors Influencing Salaries of Agricultural Economics Professionals in Federal Employment - Part I

Since the early 1900s much research has been conducted on salary gaps between men and women in different professions. While some of that work has focused on agricultural economics professionals, little research could be found specifically relating to agricultural economic professionals in federal employment. A survey was sent to known agricultural economics professionals within USDA. The data from this survey are being used for two purposes. The first is to determine whether differences exist between men and women (and between employees in different agencies) in factors that influence job choice and potential problems in the workplace. The second is to identify factors that influence salaries of men and women agricultural economics professionals in federal employment. This paper addresses the first purpose.salary and performance studies, Institutional and Behavioral Economics,

BMQ

BMQ: Boston Medical Quarterly was published from 1950-1966 by the Boston University School of Medicine and the Massachusetts Memorial Hospitals

Ct threshold values, a proxy for viral load in community SARS-CoV-2 cases, demonstrate wide variation across populations and over time.

BACKGROUND: Information on SARS-CoV-2 in representative community surveillance is limited, particularly cycle threshold (Ct) values (a proxy for viral load). METHODS: We included all positive nose and throat swabs 26 April 2020 to 13 March 2021 from the UK's national COVID-19 Infection Survey, tested by RT-PCR for the N, S, and ORF1ab genes. We investigated predictors of median Ct value using quantile regression. RESULTS: Of 3,312,159 nose and throat swabs, 27,902 (0.83%) were RT-PCR-positive, 10,317 (37%), 11,012 (40%), and 6550 (23%) for 3, 2, or 1 of the N, S, and ORF1ab genes, respectively, with median Ct = 29.2 (~215 copies/ml; IQR Ct = 21.9-32.8, 14-56,400 copies/ml). Independent predictors of lower Cts (i.e. higher viral load) included self-reported symptoms and more genes detected, with at most small effects of sex, ethnicity, and age. Single-gene positives almost invariably had Ct > 30, but Cts varied widely in triple-gene positives, including without symptoms. Population-level Cts changed over time, with declining Ct preceding increasing SARS-CoV-2 positivity. Of 6189 participants with IgG S-antibody tests post-first RT-PCR-positive, 4808 (78%) were ever antibody-positive; Cts were significantly higher in those remaining antibody negative. CONCLUSIONS: Marked variation in community SARS-CoV-2 Ct values suggests that they could be a useful epidemiological early-warning indicator. FUNDING: Department of Health and Social Care, National Institutes of Health Research, Huo Family Foundation, Medical Research Council UK; Wellcome Trust

Global surgery, obstetric, and anaesthesia indicator definitions and reporting: An Utstein consensus report

Background Indicators to evaluate progress towards timely access to safe surgical, anaesthesia, and obstetric (SAO) care were proposed in 2015 by the Lancet Commission on Global Surgery. These aimed to capture access to surgery, surgical workforce, surgical volume, perioperative mortality rate, and catastrophic and impoverishing financial consequences of surgery. Despite being rapidly taken up by practitioners, data points from which to derive the indicators were not defined, limiting comparability across time or settings. We convened global experts to evaluate and explicitly define—for the first time—the indicators to improve comparability and support achievement of 2030 goals to improve access to safe affordable surgical and anaesthesia care globally. Methods and findings The Utstein process for developing and reporting guidelines through a consensus building process was followed. In-person discussions at a 2-day meeting were followed by an iterative process conducted by email and virtual group meetings until consensus was reached. The meeting was held between June 16 to 18, 2019; discussions continued until August 2020. Participants consisted of experts in surgery, anaesthesia, and obstetric care, data science, and health indicators from high-, middle-, and low-income countries. Considering each of the 6 indicators in turn, we refined overarching descriptions and agreed upon data points needed for construction of each indicator at current time (basic data points), and as each evolves over 2 to 5 (intermediate) and >5 year (full) time frames. We removed one of the original 6 indicators (one of 2 financial risk protection indicators was eliminated) and refined descriptions and defined data points required to construct the 5 remaining indicators: geospatial access, workforce, surgical volume, perioperative mortality, and catastrophic expenditure. A strength of the process was the number of people from global institutes and multilateral agencies involved in the collection and reporting of global health metrics; a limitation was the limited number of participants from low- or middle-income countries—who only made up 21% of the total attendees. Conclusions To track global progress towards timely access to quality SAO care, these indicators—at the basic level—should be implemented universally as soon as possible. Intermediate and full indicator sets should be achieved by all countries over time. Meanwhile, these evolutions can assist in the short term in developing national surgical plans and collecting more detailed data for research studies.publishedVersio

Community prevalence of SARS-CoV-2 in England from April to November, 2020: results from the ONS Coronavirus Infection Survey

Background: Decisions about the continued need for control measures to contain the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) rely on accurate and up-to-date information about the number of people testing positive for SARS-CoV-2 and risk factors for testing positive. Existing surveillance systems are generally not based on population samples and are not longitudinal in design. Methods: Samples were collected from individuals aged 2 years and older living in private households in England that were randomly selected from address lists and previous Office for National Statistics surveys in repeated cross-sectional household surveys with additional serial sampling and longitudinal follow-up. Participants completed a questionnaire and did nose and throat self-swabs. The percentage of individuals testing positive for SARS-CoV-2 RNA was estimated over time by use of dynamic multilevel regression and poststratification, to account for potential residual non-representativeness. Potential changes in risk factors for testing positive over time were also assessed. The study is registered with the ISRCTN Registry, ISRCTN21086382. Findings: Between April 26 and Nov 1, 2020, results were available from 1 191 170 samples from 280 327 individuals; 5231 samples were positive overall, from 3923 individuals. The percentage of people testing positive for SARS-CoV-2 changed substantially over time, with an initial decrease between April 26 and June 28, 2020, from 0·40% (95% credible interval 0·29–0·54) to 0·06% (0·04–0·07), followed by low levels during July and August, 2020, before substantial increases at the end of August, 2020, with percentages testing positive above 1% from the end of October, 2020. Having a patient-facing role and working outside your home were important risk factors for testing positive for SARS-CoV-2 at the end of the first wave (April 26 to June 28, 2020), but not in the second wave (from the end of August to Nov 1, 2020). Age (young adults, particularly those aged 17–24 years) was an important initial driver of increased positivity rates in the second wave. For example, the estimated percentage of individuals testing positive was more than six times higher in those aged 17–24 years than in those aged 70 years or older at the end of September, 2020. A substantial proportion of infections were in individuals not reporting symptoms around their positive test (45–68%, dependent on calendar time. Interpretation: Important risk factors for testing positive for SARS-CoV-2 varied substantially between the part of the first wave that was captured by the study (April to June, 2020) and the first part of the second wave of increased positivity rates (end of August to Nov 1, 2020), and a substantial proportion of infections were in individuals not reporting symptoms, indicating that continued monitoring for SARS-CoV-2 in the community will be important for managing the COVID-19 pandemic moving forwards. Funding: Department of Health and Social Care