Dataset_For _P_Management_Paper. This dataset is associated with the following publication: Kamrath, B., and Y. Yuan. Effectiveness of Nutrient Management For Reducing Phosphorus Losses From Agricultural Areas.. Transactions of the ASABE. AMERICAN SOCIETY OF AGRICULTURAL AND BIOLOGICAL ENGINEERS, ST. JOSEPH, MI, USA, 1(2): 77-88, (2023).

Midwest and nitrate-N loss data for nutrient management. This dataset is associated with the following publication: Liu, W., Y. Yuan, and L. Koropeckyj-Cox. Effectiveness of Nutrient Management on Water Quality Improvement: A Synthesis on Nitrate-Nitrogen Loss from Subsurface Drainage. Transactions of the ASABE. AMERICAN SOCIETY OF AGRICULTURAL AND BIOLOGICAL ENGINEERS, ST. JOSEPH, MI, USA, 64(2): 675-689, (2021).

,The MANAGE (Measured Annual Nutrient loads from AGricultural Environments) database was developed to be a readily-accessible, easily-queried database of site characteristic and field-scale nutrient export data (Harmel et al., 2006). Initial funding for MANAGE was provided by USDA-ARS to support the USDA Conservation Effects Assessment Project (CEAP) and the Texas State Soil and Water Conservation Board as part of their mission to understand and mitigate agricultural impacts on water quality.,The original version of MANAGE, which drew heavily from an early 1980’s compilation of nutrient export data (Reckhow et al., 1980; Beaulac, 1980; Beaulac and Reckhow, 1982), created an electronic database with nutrient load data and corresponding site characteristics from 40 studies on agricultural (cultivated and pasture/range) land uses. The first revision in 2008 added N and P load data from 15 additional studies along with N and P runoff concentration data for all 55 studies (Harmel et al., 2008). The second revision in 2016 added 30 runoff studies from forested land uses, 91 drainage water quality studies from drained land, and 12 additional runoff studies from cultivated and pasture/range (Christianson and Harmel, 2015; Harmel et al., 2016). In this expansion, fertilizer application timing, crop yield, and N and P uptake data were added to facilitate analysis of 4R Nutrient Stewardship. The latest revision (Harmel et al., 2022) added 27 studies and Level II ecoregion delineations for each of the 94 studies such that data are now available from 11 of the 50 North American Level II ecoregions, representing the major U.S. agricultural regions.,With these updates, MANAGE contains data from a vast majority of published peer-reviewed N and P export studies on homogeneous cultivated, pasture/range, and forested land uses in the US under natural rainfall-runoff conditions, as well as artificially drained agricultural land. Thus MANAGE facilitates expanded spatial analyses and improved understanding of regional differences, management practice effectiveness, and impacts of land use conversions and management techniques, and it provides valuable data for modeling and decision-making related to agricultural runoff.,The Manage Database v5 04-04-2018 zip file resource superseded the previously available v4 and was added to this record on May 30, 2018.,Resource MANAGE Database v6 added Nov 17, 2022.,,

,Improving the quality of water discharged from agricultural watersheds requires comprehensive and adaptive approaches for planning and implementing conservation practices. These measures will need to consider landscape hydrology, distributions of soil types, land cover, and crop distributions in an integrated manner. The two most consistent challenges to these efforts will be consistency and reliability of data, and the capacity to translate conservation planning from watershed to farm and field scales. The translation of scale is required because, while conservation practices can be planned based on a watershed scale framework, they must be implemented by landowners in specific fields and riparian sites that are under private ownership. To support these goals, it has been necessary to develop planning approaches, high-resolution spatial datasets, and conservation practice assessment tools that will allow the agricultural and conservation communities to characterize and mitigate these challenges. The field boundary dataset represents a spatial framework for assembling and maintaining geospatial data to support conservation planning at the scale where conservation practices are implemented.,This field boundaries dataset has been assembled to support field-scale agricultural conservation planning using the USDA/ARS Agricultural Conservation Planning Framework (ACPF). The original data used to create this database are the pre-2008 Farm Bill FSA common land unit (CLU) datasets. A portion of metadata found herein pertains to the USDA FSA CLU. The remaining information has been developed to reflect the repurposing of the data in its aggregated form. It is important to note that all USDA programmatic and ownership information that was associated with the original data have been removed. Beyond that, these data has been extensively edited to reflect crop-specific land use and no longer reflects discrete ownership patterns.,,

Nitrogen and phosphorus losses from agricultural areas have impacted the water quality of downstream rivers, lakes, and oceans. As a result, investment in the adoption of agricultural best management practices (BMPs) has grown but assessments of their effectiveness at large spatial scales have been sparse. This study applies regional Spatially Referenced Regression On Watershed-attributes (SPARROW) models developed for the Midwest, Northeast, and Southeast regions of the United States to quantify regional effects of BMPs on nutrient losses from agricultural lands. These models were used because they account for specific BMPs in the prediction of instream nutrient loads. This data release accompanies the journal article "Quantifying regional effects of best management practices on nutrient losses from agricultural lands" (https:// doi:10.5066/pending), and it contains the input and output data for the modeling scenarios that were evaluated relative to the 2012 regional SPARROW models.

Nitrogen and phosphorus losses from agricultural areas have impacted the water quality of downstream rivers, lakes, and oceans. As a result, investment in the adoption of agricultural best management practices (BMPs) has grown but assessments of their effectiveness at large spatial scales have been sparse. This study applies regional Spatially Referenced Regression On Watershed-attributes (SPARROW) models developed for the Midwest, Northeast, and Southeast regions of the United States to quantify regional effects of BMPs on nutrient losses from agricultural lands. These models were used because they account for specific BMPs in the prediction of instream nutrient loads. This data release accompanies the journal article "Quantifying regional effects of best management practices on nutrient losses from agricultural lands" (https:// doi:10.5066/pending), and it contains the input and output data for the modeling scenarios that were evaluated relative to the 2012 regional SPARROW models.

,Improving the quality of water discharged from agricultural watersheds requires comprehensive and adaptive approaches for planning and implementing conservation practices. These measures will need to consider landscape hydrology, distributions of soil types, land cover, and crop distributions in an integrated manner. The two most consistent challenges to these efforts will be consistency and reliability of data, and the capacity to translate conservation planning from watershed to farm and field scales. The translation of scale is required because, while conservation practices can be planned based on a watershed scale framework, they must be implemented by landowners in specific fields and riparian sites that are under private ownership. To support these goals, it has been necessary to develop planning approaches, high-resolution spatial datasets, and conservation practice assessment tools that will allow the agricultural and conservation communities to characterize and mitigate these challenges. The field boundary dataset represents a spatial framework for assembling and maintaining geospatial data to support conservation planning at the scale where conservation practices are implemented.,This field boundaries dataset has been assembled to support field-scale agricultural conservation planning using the USDA/ARS Agricultural Conservation Planning Framework (ACPF). The original data used to create this database are the pre-2008 Farm Bill FSA common land unit (CLU) datasets. A portion of metadata found herein pertains to the USDA FSA CLU. The remaining information has been developed to reflect the repurposing of the data in its aggregated form. It is important to note that all USDA programmatic and ownership information that was associated with the original data have been removed. Beyond that, these data has been extensively edited to reflect crop-specific land use consistent with 2009 land cover as derived from 2009 NASS Crop Data Layer datasets and 2009 aerial photography, and no longer reflects discrete ownership patterns.,The ACPF field boundaries feature class incorporates two additional resources that form the Upper Mississippi River Basin (UMRB) ACPF Land Use database. The UMRB ACPF Fields Crop History table holds the dominant land use class, derived from the NASS CDL, for individual fields from 2010 to 2020. The UMRB ACPF Land Use table hold summary land use information for individual fields for 2015 to 2020 including an assigned General Land Use (GenLU) that represent the cropping system over that period. In lieu of a data dictionary for these resources, each dataset has a FGDC-compliant metadata file using the North American ISO 19115-2003 profile in .xml format.,For more information about this dataset contact David E. James at davide.james@usda.gov or dejames@iastate.edu,,