The 2012 Census of Agriculture: A Capture–Recapture Analysis

Young, Linda J.; Lamas, Andrea C.; Abreu, Denise A.

doi:10.1007/s13253-017-0303-8

Cited by 11 publications

(12 citation statements)

References 16 publications

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“…According to the 2017 Census of Agriculture, an estimated 11% of Maryland farmers engage in direct market sales (USDA NASS, 2019b). However, this figure could likely be larger because smaller peri-urban and urban farms are disproportionately not captured by the Census of Agriculture (Rogus & Dimitri, 2015;Young et al, 2017;Young et al, 2018). Research on direct marketing is important because, compared to traditional marketing channels, it is associated with higher business survival rates among small and beginning farmers (Low et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

An examination of adaptations of direct marketing channels and practices by Maryland fruit and vegetable farmers during the COVID-19 pandemic

Bachman

Lupolt

Strauss

et al. 2021

J. Agric. Food Syst. Community Dev.

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This study explores the impacts of the COVID-19 pandemic and the Maryland stay-at-home order on fruit and vegetable farmers in Maryland. Focusing on farms’ direct-to-consumer marketing channels, we aim to characterize the diversity of farm responses and identify practices that facilitated adaptation. This research is grounded in the socio-ecological systems framework, which emphasizes the interconnection between social and ecological systems and characterizes the dual-driving forces that impact food producers and their livelihood. The study team conducted interviews with 20 Maryland farm owners/managers who grow and sell produce. The semistructured interviews included questions relating to production practices, sales and marketing, and resilience. The interviewer followed up with probes to understand the dimensions of response diversity and adaptive capacity. Interviews were transcribed verbatim, and responses were analyzed using the framework approach. In the context of a global pandemic, community supported agriculture (CSA), farmers markets, and pick-your-own channels provided a high degree of stability and financial security. No farmer reported relying solely on intermediated markets (e.g., restaurants, grocery stores, institutions). Distribution channels that incorporated an online marketplace offering prepacked pre-orders were a notable strength of highly adaptive Maryland produce farmers. Farmers reported that expanding established CSAs was an important method for reallocating produce originally intended to be sold to reduced/terminated marketing channels. Common challenges among farmers included increased administrative workload, concerns associated with raising food prices during a crisis, and environmental concerns about the use of additional packaging. We describe a range of adaptive behaviors that aided farmers in withstanding shocks.

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Section: Introductionmentioning

confidence: 99%

An examination of adaptations of direct marketing channels and practices by Maryland fruit and vegetable farmers during the COVID-19 pandemic

Bachman

Lupolt

Strauss

et al. 2021

J. Agric. Food Syst. Community Dev.

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“…Thus, capture-recapture and other methods that are able to account for population units not included on any list need to be considered. As an example, for the 2012 US Census of Agriculture, capture-recapture was used to adjust for undercoverage, nonresponse, and misclassification (Lohr 2010;and Young et al 2012). Independent samples were drawn from the Census mail list (a subset of the NASS list frame) and the NASS area frame, and a 12.3% adjustment in the number of farms was made for undercoverage of the Census mail list.…”

Section: Discussionmentioning

confidence: 99%

Exploring a Big Data Approach to Building a List Frame for Urban Agriculture: A Pilot Study in the City of Baltimore

Young

Hyman

Rater

2018

Journal of Official Statistics

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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.

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“…First, the modeling approach by Patyk et al relies on data from the Census of Agriculture (CoA). The CoA, however, is taken only once every 5 years, masks results in small counties, and is based principally on a survey that is affected by non-response and unknown farm locations (26). We demonstrate that our approach identifies significant blind spots in the CoA, identifying counties with zero reported CAFOs that, in fact, have large numbers of CAFOs.…”

Section: Introductionmentioning

confidence: 97%

Mapping industrial poultry operations at scale with deep learning and aerial imagery

Robinson¹,

Chugg²,

Anderson³

et al. 2021

Preprint

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Concentrated Animal Feeding Operations (CAFOs) pose serious risks to air, water, and public health, but have proven to be challenging to regulate. The U.S. Government Accountability Office notes that a basic challenge is the lack of comprehensive location information on CAFOs. We use the USDA's National Agricultural Imagery Program (NAIP) 1m/pixel aerial imagery to detect poultry CAFOs across the continental United States. We train convolutional neural network (CNN) models to identify individual poultry barns and apply the best performing model to over 42 TB of imagery to create the first national, open-source dataset of poultry CAFOs. We validate the model predictions against held-out validation set on poultry CAFO facility locations from 10 hand-labeled counties in California and demonstrate that this approach has significant potential to fill gaps in environmental monitoring.

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The 2012 Census of Agriculture: A Capture–Recapture Analysis

Cited by 11 publications

References 16 publications

An examination of adaptations of direct marketing channels and practices by Maryland fruit and vegetable farmers during the COVID-19 pandemic

An examination of adaptations of direct marketing channels and practices by Maryland fruit and vegetable farmers during the COVID-19 pandemic

Exploring a Big Data Approach to Building a List Frame for Urban Agriculture: A Pilot Study in the City of Baltimore

Mapping industrial poultry operations at scale with deep learning and aerial imagery

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