Integrating Social and Biophysical Indicators of Nutrient Reduction Progress in Iowa Watershed Projects
Because there has been limited empirical research on the factors that influence the progress or effectiveness of watershed projects that aim to reduce nutrient loss, this study will address the research question: On a watershed project scale, what are the primary indicators for predicting nutrient reduction effectiveness? These indicators may fall into a variety of operational categories, including availability of funding and financial incentives, participation by diverse stakeholder groups, extent of outreach work among farmers and the wider community, and the extent of conservation practice use.
This study will explore the factors that impact nutrient reduction in watershed project areas at the scale of hydrologic unit code-12 (HUC12) watersheds. The proposed research will accomplish the following outcomes:
- Inventory and compile publicly available data sources measuring watershed project efforts and outcomes;
- Inventory and compile relevant social (e.g. survey research) and biophysical (e.g. land use) data;
- Build datasets for analysis;
- Assess relationships between the identified indicators and modeled nutrient reduction progress on a watershed project scale. In the case that indicators are identified as significant predictors of nutrient reduction progress, indicators will be ranked in order of effect size.
This research will examine watershed projects that were funded through the Iowa Water Quality Initiative, known as the WQI. A subset of key watershed projects will be selected, based on data availability and quality. Additional watershed projects will be incorporated into the research project depending on investigators’ capacity to process the data sources associated with these watershed areas.
A subset of about 20 variables are expected for use in statistical analyses and models, which represent potential indicators of watershed project effectiveness and can be extracted from social and biophysical data sources. Additional data sources may also be identified in the early phases of the project. Modeling will be performed to examine potential contributing effects of financial, social and environmental factors on changes in “watershed performance,” based on nutrient loss reductions. In addition, multi-level modeling or spatial analyses may be performed to assess relationships between nutrient reduction progress of watershed projects and their respective, larger HUC8 watersheds.
Upon finalizing the project, the investigators will share information from this project through development of a technical report, submission to one or more scientific journals, a presentation to a future Watershed Academy, and other outreach and extension-related activities.
The full team met three times virtually. Sub-teams met about five other times. Having completed most of the data processing), spring and summer 2021 are on target and will be focused on analysis. A general conceptual framework was established to analyze the effects of administrative and social factors on conservation adoption and nutrient reduction in the eight watershed project study areas. See the attached graphic.
Extensive time and effort focused on quality control (QC) of data sources remaining after June 30, 2020. Researchers completed processing all the identified data sources. The biophysical variables (i.e. conservation practices, land use, and agronomic data) were aggregated at the HUC12 scale or watershed project scale, except for the BMP Mapping Project data on structural conservation practices, which is nearly complete.
Preliminary annual nitrogen load estimates for each study area from 2010 to 2018 are also calculated. Statistics team members are now working to understand the relationships between farmer survey data and nitrogen load outcomes. Work continues to estimate annual phosphorus loads. Researchers met with ISU colleagues familiar with available sediment/phosphorus models and determined that an estimate of annual sediment loss will be more appropriate than an estimate of annual phosphorus loss, due to challenges with geospatial soils data at the HUC12 scale.
To capture administrative and outreach activities occurring in the study areas, the written quarterly reports from watershed coordinators from 2013-2018 were compiled and tabulated. A coding structure for these written documents was established, so that administrative activities are defined as partnership-building, mass media outreach, one-on-one outreach, among others.
- The team met five times in-person and virtually. PhD student Jiaming Qiu, statistics, began working on the project in January.
- 18 data sources have been identified as having potential inputs of value for this multidisciplinary project that include include administrative, biophysical, social-psychological and financial factors. A subset of variables was prioritized for Year 1 and the rest for Year 2, if needed. This strategy enables our team to progress with the research scope using the most robust/valuable data sources. Data sources prioritized for Year 1 include: IDALS watershed project reports, partner organization outreach events, USDA-NASS cropland data layer, BMP Mapping Project, NRS farmer survey, and federal and state cost-share.
- Extensive effort has focused on quality control (QC) of the Year 1 data sources. The QC looked different for each data source. Overall, we have had conversations with IDALS and BMP personnel, populated spreadsheets from text non-standard documents, categorized data to allow for future aggregation necessary for statistical analyses, edited and populated location data, standardized nomenclature and abbreviations across sources, and determined outliers.
- Data sources have been mapped, along with corresponding data variables. There has been on-going refinement research questions based on scope and quality of data.
- Researchers met with Kay Stefanik, Iowa Nutrient Research Center, to discuss the nitrogen model she has been working on and how we can utilize it to derive estimated loads from our priority watersheds. For nitrogen and phosphorus, we need to establish protocols for calculating load to have a consistent output indicator.
- Social data has been isolated from the Iowa Nutrient Reduction Strategy Farmer Survey to reflect respondents that are located within the study areas for this project. Since January, this process has focused primarily on processing the data and resolving some inconsistencies with geolocation data. Preliminary analyses have estimated the variation in social factors (i.e. attitudes, concerns and behaviors related to water quality and conservation) within the study areas. This survey will be the primary data source for social factors, to complement other data sources that describe administrative and biophysical factors.
The team met three times in-person and plans to meet regularly in 2020 to:
- Develop internal team website in Google Cloud and email list-serve.
- Build out the team to include staff and students. A team member was added through a PSCA (contract) for data ingestion and standardization efforts. An undergraduate student was hired for the fall semester, and a statistics graduate student will begin spring semester.
- Assign priority and secondary status to HUC8 watersheds to prioritize data acquisition, translation, and QC. Begin working on priority watersheds; 8 priority watersheds and 8 secondary.
- Develop project maps for use in presentations, reports, etc.
- Map out social and biophysical data sources in terms of location, type, availability and status. At this point, there are 18 data sources identified, although a few are optional based on their quality and completeness.
- Obtain annual progress reports for priority watersheds from IDALS. Reviewed variability across watersheds in reporting structure, consistency of variables reported. Determined how to structure data into spreadsheets for analyses.
- Interpreted, translated and standardized unstructured data from IDALS reports into structured dataset for two watersheds to-date: Boone and Floyd River. Constructed a data dictionary for this data source to be merged into master data dictionary.
- Extracted BMP data for all watersheds from BMP Mapping Project for conservation practices mapped such as terraces, wetlands, grassed waterways, etc. Constructed a data dictionary for this data source to be merged into master data dictionary.
- Begin developing short, medium and long-term research questions.