Challenges and opportunities for manureshed management across U.S. dairy systems: Case studies from four regions.

The manureshed represents cropland needed to safely assimilate manure nutrients from an animal feeding operation. Dairy manuresheds can be contained on-farm but may need to involve additional farms that can assimilate excess nutrients. We present case studies reviewing challenges and opportunities to manureshed management in four major dairy-producing states using available information on local manuresheds. Additionally, geographic information system software was used with data from regulated Minnesota dairies to assess cropland assimilative capacities and transport needs surrounding large dairies. Manureshed requirements vary across regions, but increased import of feed and soil phosphorus accumulation constrain on-farm manure utilization across the United States. In Minnesota, a growing proportion of Jersey cattle and differences in continuous corn (Zea mays L.) vs. corn-alfafa (Medicago sativa L.) rotations contribute to the amount of land needed to absorb dairy manure nutrients. Farm-gate budgets reveal that N-based manuresheds can be contained within Idaho dairies, but P-based manuresheds extend beyond the farm. In New Mexico, relocation of surplus manure nutrients off the farm is common via informal networks, but incentives to strengthen these networks could ensure sustainable manureshed management. Evaluation of manureshed requirements in Pennsylvania is often complicated by the need for additional nutrient management planning and greater understanding of nutrient balances on the preponderance of small dairies. Nutrient imbalances with highly concentrated dairy production often lead to the need for manure transport off-farm. However, advances in herd and cropland management offer opportunities to improve on-farm nutrient efficiencies, and emerging networks and technologies promise to facilitate manure export when needed.

Quadrat-based monitoring of desert grassland vegetation at the Jornada Experimental Range, New Mexico, 1915-2016.

The data set covers a 101-yr period (1915-2016) of quadrat-based plant sampling at the Jornada Experimental Range in southern New Mexico. At each sampling event, a pantograph was used to record the location and perimeter of living plants within permanent quadrats. Basal area was recorded for perennial grass species, canopy cover area was recorded for shrub species, and all other perennial species were recorded as point data. The data set includes 122 1 × 1 m permanent quadrats, although not all quadrats were sampled in each year of the study and there is a gap in monitoring from 1980 to 1995. These data provide a unique opportunity to investigate changes in the plant community over 100 yr of variation in precipitation and other environmental conditions. We provide the following data and data formats: (1) the digitized maps in shapefile format; (2) a data table containing coordinates (x, y) of perennial species within quadrats, including cover area for grasses and shrubs; (3) a data table of counts of annual plant individuals per quadrat; (4) a species list indicating growth form and habit of recorded species; (5) a table of dates when each quadrat was sampled; (6) a table of the pasture each quadrat was located within (note that pasture boundaries have changed over time); (7) a table of depth to petrocalcic layer measurements taken at quadrat locations; (8) a table of particle size analysis of soil samples taken at quadrat locations; (9) a table of topographic characteristics of quadrat locations (e.g., concave or convex topography). Pantograph sampling is currently conducted at 5-yr intervals by USDA-ARS staff, and new data will be added periodically to the EDI Data Portal Repository (see section V.E.2). This information is released under the Creative Commons license-Attribution-CC BY and the consumer of these data is required to cite it appropriately in any publication that results from its use.

Provoking a Cultural Shift in Data Quality.

Ecological studies require quality data to describe the nature of ecological processes and to advance understanding of ecosystem change. Increasing access to big data has magnified both the burden and the complexity of ensuring quality data. The costs of errors in ecology include low use of data, increased time spent cleaning data, and poor reproducibility that can result in a misunderstanding of ecosystem processes and dynamics, all of which can erode the efficacy of and trust in ecological research. Although conceptual and technological advances have improved ecological data access and management, a cultural shift is needed to embed data quality as a cultural practice. We present a comprehensive data quality framework to evoke this cultural shift. The data quality framework flexibly supports different collaboration models, supports all types of ecological data, and can be used to describe data quality within both short- and long-term ecological studies.