FieldDoc Program Manager Tools: Metrics and Practices
I’ve been working for about a decade and then some, trying to convey to modelers, policymakers, and foundations the value of quantifying restoration efforts to the scale of the management practice (BMP). While it seems deceivingly simple to establish workflows and processes around a BMP data pipeline, you’d be blown away at the bottlenecks we face as practitioners and technologists in creating machine readable data on restoration actions at the scale of the landowner. Competing platforms, differing reporting standards, and juxtaposing models make the seemingly simple task of sharing progress on BMP implementation a data management nightmare.
To provide some insight and context, let’s first let’s talk about the telephone game. This is the construct where all players form a circle and attempt to pass one message to one another in the chain. As the message is told from one informant to the next the information shifts and changes. By the time one gets all the way around the circle, it’s likely the underlying theme of the message is totally different. We face this challenge both vertically and horizontally with our efforts to quantify BMPs. For example, United States Department of Agriculture (USDA), maintains arguably the most comprehensive standardized list of agricultural practices we as farmers and practitioners know about. This list was created to establish a common language or lexicon when planning, tracking, and accounting for restoration work funded through local conservation district offices. For those versed in the language and the eligible programs, it is a relatively small lift to report practices from field to server. But let’s say your environmental restoration effort hinges on BMP implementation in other sectors such as stormwater and there are requirements to match your internal BMP names and definitions across agencies, this becomes a much taller order.
Many decision makers at the top of regional restoration efforts such as the Chesapeake and Delaware, are busy establishing funding vehicles to support water quality improvement projects and meet implementation goals. They rely on modelers, programmers, data scientists, and local practitioners to build the infrastructure and data pipelines required to validate that BMPs get installed and are operational. Believe it or not prior to v.1 of FieldDoc and other more rudimentary systems, the best we could do is tell you where the check for restoration was mailed, how much it was for, and who received it. But as one can imagine, this level of tracking is far from acceptable when over 10 billion dollars in state and federal funds have been spent since 2015 on Bay restoration. Building consistency and easing the challenges of BMP reporting is why the Commons has now released massive updates to FieldDoc’s program manager tools.
Contrary to FieldDoc, traditional, metric tracking systems requires funders and program managers to foot the bill for costly software development to extend their grant tracking software and account for measurable impact related to restoration. These restoration projects are highly complex for a few reasons:
- Place matters and geo-spatially explicit datas model are imperative
- Practitioners could be working across many landowner parcels and employing a variety of unique management practices
- Each management practice has a unique means for documenting implementation and modeling improvement to water quality
- Each program investing in, or administering restoration efforts, has a slightly differing nomenclature and definition for a given management practice and the implementation goals they seek to measure.
FieldDoc and the system’s new manager tools solve this challenge by enabling program administrators to quickly organize their restoration portfolios into a simple list of metrics and practices. A practice or BMP, typically consists of the action or specific type of work occurring on the landscape. Program administrators in FieldDoc can now upload their own list of BMP names and definitions for their practitioners, landowners, or grantees to track progress against. Practices can be added or edited one at a time or for entities such as NRCS that have an established list of explicit practices and definitions, changes can be included as a batch upload in the system using FieldDoc’s import engine. This is all easily achieved in house without calling the software developer to request modifications to the platform’s underlying data or executing a costly re-deployment of the system.
In FieldDoc we lean on metrics as a means to track implementation of one or many practices. For example if I’m working on Riparian Forest Buffer on a farm in Pennsylvania, the practice name is “Riparian Forest Buffer.” A metric for said Riparian Forest Buffer might be “Acres of Riparian Forest Buffer Installed”. Similar to managing practices in FieldDoc, program managers can create any type of metrics they wish to track in the system through individual editing and batch upload. What’s more is that these metrics can be linked to practices so that anytime a practitioner, landowner, or grantee adds a practice for tracking, the appropriate metrics come along for reporting.
Last but certainly not least, metrics come in two flavors. The first is a manual metric where users simply fill in the value that pertains to the work completed and the second is an automated or modeled metric. Modeled metrics provide the necessary architecture making it easier report on more complex outcomes associated with a restoration project. If we go back to our forest buffer example above, a user may also wish to know what nutrient and sediment loads that buffer is reducing per year to the edge of stream. Calculating the pounds of nitrogen, phosphorus, and sediment reduced on an annual basis can be a challenging proposition for those who are not well versed in environmental modeling.
Luckily FieldDoc’s powerful modeling engine allows users to automate more complex metrics such as nitrogen, phosphorus, and sediment as well as many others including rapid watershed delineation and fast zonal statistics. (Eager to learn more about these modeling methods? We’ll be publishing a post next month on how we develop these models and integrate them into FieldDoc, so stay tuned).
FieldDoc’s approach to restoration tracking is demonstrating the potential to standardize restoration efforts across many programs, metrics, and practices using a single platform for data entry. We don’t believe in a multi-tenant approach because it’s cumbersome and requires too many resources to scale and maintain. In the coming months we aim to position FieldDoc to be able track or be configured (not rebuilt) to support nearly any restoration tracking program that can be conceived. Stay tuned because models and dashboard analytics are up next and if you have any questions about our development or how you can leverage FieldDoc, please be sure to reach out
