100 years of data sovereignty: Cooperative data governance and innovation in <scp>US</scp> dairy

Hutchins, Jared; Hueth, Brent

doi:10.1002/aepp.13339

Cited by 7 publications

(6 citation statements)

References 29 publications

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“…Farm data networks could bridge this gap by allowing farmer‐members to share data collected from different soil types and climates. Organizations like the National Cooperative Dairy Herd Improvement Program (NCDHIP) offer a model of how this can be done successfully (Hutchins & Hueth, 2022).…”

Section: Discussionmentioning

confidence: 99%

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The economics of property rights in digital farming data: Implications for farmland markets

DeLay

Boehlje

Ferrell

2023

Applied Eco Perspectives Pol

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The emergence of precision farming technologies has increased the amount and detail of farming data collected by producers. Data increases farm profitability by complementing digitally connected equipment and improving on‐farm decision making. The value generated by farm data may be capitalized into the underlying farmland asset, potentially raising sale and rental prices. However, the absence of clear property rights over farmland and farm operating practice data limits the ability to capture this value. We explore the issue of farm data through a property rights and transaction costs lens, and propose a conceptual framework of farm data valuation to identify conditions under which landowners and tenant‐operators can engage in mutually beneficial negotiation over farm data. We conclude that establishing property rights to farm data within the farmland lease can facilitate welfare‐improving exchange, allowing farm data records to be allocated to their highest valued use.

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Section: Discussionmentioning

confidence: 99%

“…This work complements two other papers in this featured series. In the first, Hutchins and Hueth (2022) draw lessons from a unique data sharing community known as the National Cooperative Dairy Herd Improvement Program (NCDHIP). The success of the NCDHIP can inform best practices around data governance in agriculture.…”

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confidence: 99%

The economics of property rights in digital farming data: Implications for farmland markets

DeLay

Boehlje

Ferrell

2023

Applied Eco Perspectives Pol

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“…Fuente: Elaboración propia con base a los resultados obtenidos en Scopus Estados Unidos se ubica en el segundo lugar con 147 publicaciones. Su investigación se ha centrado en estrategias para que las grandes tecnologías lleguen a los pequeños agricultores, en impulsar una soberanía de datos agraria para la oportuna gobernanza e innovación y en desarrollar tecnologías robóticas para el control de malezas y la óptima identificación de enfermedades en los cultivos (Gao et al, 2023;Faxon, 2023;Hutchins & Hueth, 2023). Seguidamente, se encuentra India y la Federación de Rusia con 130 y 109 publicaciones, respectivamente.…”

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Tecnologías emergentes para el agro y su aplicación en Colombia

Ovalle Másmela

2023

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La agricultura juega un rol vital en el desarrollo de la sociedad al ser un componente clave en el sector económico para el crecimiento del producto interno bruto de la mayoría de los países (Abioye et al., 2020). Su participación en la productividad económica mundial es del 6,4%, posicionándose en nueve países como el sector dominante (Pathan et al., 2020). La agricultura es la base para la seguridad alimentaria, aliviando el hambre de las comunidades en general al garantizar un suministro de alimentos sostenibles, inocuos y nutritivos (Nicholson et al 2020). Al mismo tiempo, la agricultura es un factor determinante para la salud del planeta, ya que influye directamente en la fertilidad del suelo, en las emisiones de los gases efecto invernadero, en la deforestación y en la pérdida de biodiversidad (Clark et al 2022). Esto ha conllevado al desarrollo y la innovación tecnológica en el sector que han permitido modernizar las cadenas productivas agropecuarias (da Silveira et al 2021). Diversos países en el mundo, que son potencia agrícola, como Estados Unidos, China y Brasil, han enfocado sus esfuerzos e inversiones en las tecnologías digitales emergentes para que apoyen a sus unidades de producción agropecuaria (Saiz-Rubio & Rovira-Más, 2020). En Colombia, la agricultura representa uno de los principales sectores de la economía; no obstante, el uso agrícola del suelo es tan sólo del 13,5% de 39,2 millones de hectáreas con potencial agrícola, donde menos del 10% de las unidades de producción agropecuaria establecidas cuenta con algún tipo de activo de tecnologías de información y comunicación (dispositivos móviles, computadores, tabletas, geo localizadores), y solo el 1,7% tiene acceso a Internet (Rico, 2022; DANE, 2019).

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“…A recent pilot project in the U.S. has demonstrated the willingness of swine farms to share AMU data (Davies and Singer, 2020). A century-old National Cooperative Dairy Herd Improvement Program (NCDHIP) provides a roadmap on how to develop a system for cooperative data governance and sharing of AMU and AMR data in the dairy sector (Hutchins and Hueth, 2023).…”

Section: Evaluated Amu Indicators Are Characterized By the Accuracy V...mentioning

confidence: 99%

Standardization and evaluation of indicators for quantifying antimicrobial use on U.S. dairy farms

et al. 2023

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Antimicrobial resistance (AMR) is a global One Health threat. A portion of AMR development can be attributed to antimicrobial use (AMU) in animals, including dairy cattle. Quantifying AMU on U.S. dairy farms is necessary to inform antimicrobial stewardship strategies and help evaluate the relationship between AMU and AMR. Many AMU indicators have been proposed for quantifying AMU in dairy cattle. However, these indicators are difficult to interpret and compare because they differ in the type of data used, the calculation approach, and the definitions of variables and parameters used in the calculation. Therefore, we selected 16 indicators (count-based, mass-based, and dose-based) applicable for quantifying AMU on U.S. dairy farms. We systematized the indicators by standardizing their variables and parameters to improve their interchangeability, interpretation, and comparability. We scored indicators against six data-driven criteria (assessing their accuracy, data and effort needs, and level of privacy concern) and five stewardship-driven criteria (assessing their ability to capture trends and inform antimicrobial stewardship). The derived standardized indicators will aid farmers and veterinarians in selecting suitable indicators based on data availability and stewardship needs on a farm. The comparison of indicators revealed a trade-off requiring farmers to balance the granularity of data necessary for an accurate indicator and effort to collect the data, and a trade-off relevant to farmers interested in data sharing to inform stewardship because more accurate indicators are typically based on more sensitive information. Indicators with better accuracy tended to score better in stewardship criteria. Overall, two dose-based indicators, estimating the number of treatments and administered doses, scored best in accuracy and stewardship. Conversely, two count-based indicators, estimating the length of AMU, and a mass-based indicator, estimating the mass of administered antimicrobials, performed best in the effort and privacy criteria. These findings are expected to benefit One Health by aiding the uptake of farm-level AMU indicators by U.S. dairy farms.

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100 years of data sovereignty: Cooperative data governance and innovation in US dairy

Cited by 7 publications

References 29 publications

The economics of property rights in digital farming data: Implications for farmland markets

The economics of property rights in digital farming data: Implications for farmland markets

Tecnologías emergentes para el agro y su aplicación en Colombia

Standardization and evaluation of indicators for quantifying antimicrobial use on U.S. dairy farms

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