The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform

Caterina Scoglio - One of the best experts on this subject based on the ideXlab platform.

  • estimation of swine movement network at farm level in the us from the Census of Agriculture data
    Scientific Reports, 2019
    Co-Authors: Sifat Afroj Moon, Tanvir Ferdousi, Adrian Self, Caterina Scoglio
    Abstract:

    Swine movement networks among farms/operations are an important source of information to understand and prevent the spread of diseases, nearly nonexistent in the United States. An understanding of the movement networks can help the policymakers in planning effective disease control measures. The objectives of this work are: (1) estimate swine movement probabilities at the county level from comprehensive anonymous inventory and sales data published by the United States Department of Agriculture - National Agriculture Statistics Service database, (2) develop a network based on those estimated probabilities, and (3) analyze that network using network science metrics. First, we use a probabilistic approach based on the maximum information entropy method to estimate the movement probabilities among different swine populations. Then, we create a swine movement network using the estimated probabilities for the counties of the central agricultural district of Iowa. The analysis of this network has found evidence of the small-world phenomenon. Our study suggests that the US swine industry may be vulnerable to infectious disease outbreaks because of the small-world structure of its movement network. Our system is easily adaptable to estimate movement networks for other sets of data, farm animal production systems, and geographic regions.

  • estimation of swine movement network at farm level in the us from the Census of Agriculture data
    bioRxiv, 2018
    Co-Authors: Sifat Afroj Moon, Tanvir Ferdousi, Adrian Self, Caterina Scoglio
    Abstract:

    Swine movement networks among farms/operations are an important source of information to understand and prevent the spread of diseases, nearly nonexistent in the United States. An understanding of the movement networks can help the policymakers in planning effective disease control measures. The objectives of this work are: 1) estimate swine movement probabilities at the county level from comprehensive anonymous inventory and sales data published by the United States Department of Agriculture - National Agriculture Statistics Service database, 2) develop a network based on those estimated probabilities, and 3) analyze that network using network science metrics. First, we use a probabilistic approach based on the maximum entropy method to estimate the movement probabilities among different swine populations. Then, we create a swine movement network using the estimated probabilities for the counties of the central agricultural district of Iowa. The analysis of this network has found evidence of small-world phenomenon. Our study suggests that the US swine industry may be vulnerable to infectious disease outbreaks because of the small-world structure of its movement network. Our system is easily adaptable to estimate movement networks for other sets of data, farm animal production systems, and geographic regions.

Sifat Afroj Moon - One of the best experts on this subject based on the ideXlab platform.

  • estimation of swine movement network at farm level in the us from the Census of Agriculture data
    Scientific Reports, 2019
    Co-Authors: Sifat Afroj Moon, Tanvir Ferdousi, Adrian Self, Caterina Scoglio
    Abstract:

    Swine movement networks among farms/operations are an important source of information to understand and prevent the spread of diseases, nearly nonexistent in the United States. An understanding of the movement networks can help the policymakers in planning effective disease control measures. The objectives of this work are: (1) estimate swine movement probabilities at the county level from comprehensive anonymous inventory and sales data published by the United States Department of Agriculture - National Agriculture Statistics Service database, (2) develop a network based on those estimated probabilities, and (3) analyze that network using network science metrics. First, we use a probabilistic approach based on the maximum information entropy method to estimate the movement probabilities among different swine populations. Then, we create a swine movement network using the estimated probabilities for the counties of the central agricultural district of Iowa. The analysis of this network has found evidence of the small-world phenomenon. Our study suggests that the US swine industry may be vulnerable to infectious disease outbreaks because of the small-world structure of its movement network. Our system is easily adaptable to estimate movement networks for other sets of data, farm animal production systems, and geographic regions.

  • estimation of swine movement network at farm level in the us from the Census of Agriculture data
    bioRxiv, 2018
    Co-Authors: Sifat Afroj Moon, Tanvir Ferdousi, Adrian Self, Caterina Scoglio
    Abstract:

    Swine movement networks among farms/operations are an important source of information to understand and prevent the spread of diseases, nearly nonexistent in the United States. An understanding of the movement networks can help the policymakers in planning effective disease control measures. The objectives of this work are: 1) estimate swine movement probabilities at the county level from comprehensive anonymous inventory and sales data published by the United States Department of Agriculture - National Agriculture Statistics Service database, 2) develop a network based on those estimated probabilities, and 3) analyze that network using network science metrics. First, we use a probabilistic approach based on the maximum entropy method to estimate the movement probabilities among different swine populations. Then, we create a swine movement network using the estimated probabilities for the counties of the central agricultural district of Iowa. The analysis of this network has found evidence of small-world phenomenon. Our study suggests that the US swine industry may be vulnerable to infectious disease outbreaks because of the small-world structure of its movement network. Our system is easily adaptable to estimate movement networks for other sets of data, farm animal production systems, and geographic regions.

Adrian Self - One of the best experts on this subject based on the ideXlab platform.

  • estimation of swine movement network at farm level in the us from the Census of Agriculture data
    Scientific Reports, 2019
    Co-Authors: Sifat Afroj Moon, Tanvir Ferdousi, Adrian Self, Caterina Scoglio
    Abstract:

    Swine movement networks among farms/operations are an important source of information to understand and prevent the spread of diseases, nearly nonexistent in the United States. An understanding of the movement networks can help the policymakers in planning effective disease control measures. The objectives of this work are: (1) estimate swine movement probabilities at the county level from comprehensive anonymous inventory and sales data published by the United States Department of Agriculture - National Agriculture Statistics Service database, (2) develop a network based on those estimated probabilities, and (3) analyze that network using network science metrics. First, we use a probabilistic approach based on the maximum information entropy method to estimate the movement probabilities among different swine populations. Then, we create a swine movement network using the estimated probabilities for the counties of the central agricultural district of Iowa. The analysis of this network has found evidence of the small-world phenomenon. Our study suggests that the US swine industry may be vulnerable to infectious disease outbreaks because of the small-world structure of its movement network. Our system is easily adaptable to estimate movement networks for other sets of data, farm animal production systems, and geographic regions.

  • estimation of swine movement network at farm level in the us from the Census of Agriculture data
    bioRxiv, 2018
    Co-Authors: Sifat Afroj Moon, Tanvir Ferdousi, Adrian Self, Caterina Scoglio
    Abstract:

    Swine movement networks among farms/operations are an important source of information to understand and prevent the spread of diseases, nearly nonexistent in the United States. An understanding of the movement networks can help the policymakers in planning effective disease control measures. The objectives of this work are: 1) estimate swine movement probabilities at the county level from comprehensive anonymous inventory and sales data published by the United States Department of Agriculture - National Agriculture Statistics Service database, 2) develop a network based on those estimated probabilities, and 3) analyze that network using network science metrics. First, we use a probabilistic approach based on the maximum entropy method to estimate the movement probabilities among different swine populations. Then, we create a swine movement network using the estimated probabilities for the counties of the central agricultural district of Iowa. The analysis of this network has found evidence of small-world phenomenon. Our study suggests that the US swine industry may be vulnerable to infectious disease outbreaks because of the small-world structure of its movement network. Our system is easily adaptable to estimate movement networks for other sets of data, farm animal production systems, and geographic regions.

Tanvir Ferdousi - One of the best experts on this subject based on the ideXlab platform.

  • estimation of swine movement network at farm level in the us from the Census of Agriculture data
    Scientific Reports, 2019
    Co-Authors: Sifat Afroj Moon, Tanvir Ferdousi, Adrian Self, Caterina Scoglio
    Abstract:

    Swine movement networks among farms/operations are an important source of information to understand and prevent the spread of diseases, nearly nonexistent in the United States. An understanding of the movement networks can help the policymakers in planning effective disease control measures. The objectives of this work are: (1) estimate swine movement probabilities at the county level from comprehensive anonymous inventory and sales data published by the United States Department of Agriculture - National Agriculture Statistics Service database, (2) develop a network based on those estimated probabilities, and (3) analyze that network using network science metrics. First, we use a probabilistic approach based on the maximum information entropy method to estimate the movement probabilities among different swine populations. Then, we create a swine movement network using the estimated probabilities for the counties of the central agricultural district of Iowa. The analysis of this network has found evidence of the small-world phenomenon. Our study suggests that the US swine industry may be vulnerable to infectious disease outbreaks because of the small-world structure of its movement network. Our system is easily adaptable to estimate movement networks for other sets of data, farm animal production systems, and geographic regions.

  • estimation of swine movement network at farm level in the us from the Census of Agriculture data
    bioRxiv, 2018
    Co-Authors: Sifat Afroj Moon, Tanvir Ferdousi, Adrian Self, Caterina Scoglio
    Abstract:

    Swine movement networks among farms/operations are an important source of information to understand and prevent the spread of diseases, nearly nonexistent in the United States. An understanding of the movement networks can help the policymakers in planning effective disease control measures. The objectives of this work are: 1) estimate swine movement probabilities at the county level from comprehensive anonymous inventory and sales data published by the United States Department of Agriculture - National Agriculture Statistics Service database, 2) develop a network based on those estimated probabilities, and 3) analyze that network using network science metrics. First, we use a probabilistic approach based on the maximum entropy method to estimate the movement probabilities among different swine populations. Then, we create a swine movement network using the estimated probabilities for the counties of the central agricultural district of Iowa. The analysis of this network has found evidence of small-world phenomenon. Our study suggests that the US swine industry may be vulnerable to infectious disease outbreaks because of the small-world structure of its movement network. Our system is easily adaptable to estimate movement networks for other sets of data, farm animal production systems, and geographic regions.

Todd Kuethe - One of the best experts on this subject based on the ideXlab platform.

  • Trends in Farm Size and Potential Demand for Farm Management Association Services
    Journal of American Society of Farm Managers and Rural Appraisers, 2015
    Co-Authors: Nicholas D. Paulson, Todd Kuethe
    Abstract:

    According to the 2012 Census of Agriculture, the number of farm operations in the United States has declined by more than 4.3 percent since 2007. This supports the common notion that US farms are consolidating and increasing in size. A key issue facing the professional farm management industry is how the changing structure of US Agriculture might impact future demand for their services. This article uses farm-level data from Illinois and from various years of the US Census of Agriculture to look at this issue more closely, focusing on the services offered by farm management associations. Farm types or sizes which are more likely to be members of a management association are identified, and trends in the number of these farm operations are examined at regional and national scales. The data suggests that the number of farms across the US who are more likely to ,demand services from farm management associations is likely increasing, but these changes are highly regional in nature.

  • Highlights of the 2012 Census of Agriculture: Legal Structure of Illinois Farms
    farmdoc daily, 2014
    Co-Authors: Todd Kuethe
    Abstract:

    Throughout the United States and around the globe, consumers are increasingly concerned with how their food is produced. These concerns range from healthfulness and food safety to the environmental impact of various production methods and the treatment of animals and farm labor. One concern that is often expressed by those both inside and outside of the agricultural community is the belief in the “loss of the family farm” or “the rise in corporate farming.”

  • Highlights of the 2012 Census of Agriculture: A Closer Look at Farm Size
    farmdoc daily, 2014
    Co-Authors: Todd Kuethe
    Abstract:

    As discussed previously, Illinois was home to 75,087 farms in 2012 (farmdoc daily, May 9, 2014), and the average farm size, as measured by acreage operated, increased by 3.2% from the 2007 Census of Agriculture (farmdoc daily, July 16, 2014). This article revisits the distribution of farm size, but highlights two important findings. One, the majority of Illinois’ small farms are dedicated to livestock production, and, two, the largest farms, in terms of acreage operated, account for a significant majority of the State’s value of agricultural production.

  • Highlights of the 2012 Census of Agriculture: Distribution of Farm Size
    farmdoc daily, 2014
    Co-Authors: Todd Kuethe
    Abstract:

    There are two conventional ways of measuring farm size: (1) number of acres operated and (2) volume of sales. Between the 2007 and 2012 Censuses of Agriculture, the size of the average Illinois farm increased by 3.2% to 359 acres, and the average market value of agricultural products sold per farm increased by 32% to $228,895. However, given the size and diversity of the Illinois farm population, the average farm size may mask some important changes occurring across the distribution.

  • Highlights of the 2012 Census of Agriculture: Farmland Acreage
    farmdoc daily, 2014
    Co-Authors: Todd Kuethe
    Abstract:

    The Census defines a farm as any place from which $1,000 or more of agricultural products were produced and sold, or normally would have been sold, during the reference year, and this definition has been used since 1974. The amount of land in farms in the United States declined between the 2007 and 2012 Censuses from 922 million acres to 915 million acres. This represents a less than one percent decline, the third smallest decline between Censuses since 1950 (source). The states with the largest decline in land devoted to agricultural production include Kentucky (6.7%), Alaska (5.4%), Georgia (5.2%), Mississippi (4.6%), and Wisconsin (4.1%). Given the returns to agricultural production between 2007 and 2012, it is perhaps not surprising that 19 states reported an increase in farmland between the two Censuses.