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The project objectives are to: <OL> <LI> Develop in-field mobile applications for watershed delineation, field characterization, watershed characterization, water quality evaluation, and BMP evaluation. <LI> Evaluate the in-field water quality mobile applications to assure these deliverables meet priority needs and are user-centered with effective interfaces and required accuracy. <LI> Disseminate these mobile water quality applications to a wide audience of technical service providers, field agents, researchers, students, and soil and water agency professionals through a new open-source Water Management App Center. </ol> The primary project deliverables are the apps and their dissemination for widespread use. We anticipate technical service providers and agency field staff will eventually be using these apps for improved decision making regarding conservation and water quality at the field and farm level which subsequently contribute to improvements at the watershed scale. The model developers have invested significant resources over the past decades to raise the existing knowledge base; the work of this project will facilitate the implementation of that knowledge - and knowledge to come - long beyond the project duration. <P> Development, outreach, and instruction in this area will surely continue. The apps will add significant value to the models and databases which should increase the likelihood of additional funding for modeling, apps, and outreach.
Non-Technical Summary: <BR>Water quality problems resulting from agricultural practices are usually solved by implementing best management practices (BMPs) that address the source, transport, or fate of pollutants before they reach a stream. Complex models have greatly improved the evaluation, prioritization, and design of these BMPs to improve water management and water quality in recent years. Many of these complex models, now available as web-based tools, allow many farm technical advisors who are not necessarily expert modelers to use them effectively. However, these tools require at least an internet connection and web browser, and are typically run on a desktop computer, meaning that farm advisors generally do not have user-friendly access to these tools while they are in the field with farmers and landowners discussing opportunities for BMPs. As a result, the process of evaluating, developing, and communicating preliminary designs typically requires three distinct steps: (1) a field visit to visualize and discuss a potential BMP, (2) evaluation of the BMP feasibility and effectiveness on a computer in the office, and (3) return visit to communicate alternative solutions. This significantly extends the decision-making cycle, with 10 to 15 days typically required between data collection, solution generation, and communication back to the decision maker(s). Our goal is to improve the efficiency and quality of the decision making process by developing tools that access existing on-line databases and complex models through mobile computing technology in the field. The resulting in-field decision capability will shorten the decision cycle and thus streamline recommendations as well as actions. These developed tools would be used by agency field agents, crop consultants, and technical service providers to collect specific farm and field information and generate feasible management options which preserve water quality. They will also be available to researchers and students who will soon be the specialists across the country and also farmers themselves who may have a particular interest in water management. We propose to create these tools as open-source applications ("apps") for common smartphone and tablet operating systems in order to invite community involvement throughout the development and support lifecycles. <P> Approach: <BR> Each of the apps will require specific input data to function properly. This may include publicly available datasets such as elevation and soil survey data, as well as locally available data entered directly by users of the software. In addition, external, non-public data sources such as precision agriculture records from farmers may be available in a wide variety of formats. To support this disparate collection of data, we will develop strategies for merging duplicate sets of data based on a confidence metric. Design criteria for all apps Minimal data entry: Any data that can be taken automatically from existing sources must be utilized seamlessly. In addition, any information entered manually by a user must be reusable in any other app, and exportable to general formats for archival and sharing. Defaults for many inputs likely apply within a region; capability to save these will be incorporated. Private synchronization: The only way to ensure that all apps can share data is to provide a secure, network-based data location. To improve the chances of adoption by users, these locations should be able to be distributed and owned by the user, for example as a set of web- based (cloud) file services with a set of shared files. Minimal data requirements: Data entered by a user previously or available locally should be used first. Data available over the network should be used next provided that bandwidth limitations are adequate. If needed data are not available in any of these places, the application should not fail, but rather have a reduced set of functionality. Secure data/Sharing: The apps should provide a secure means of data sharing. Off-line capabilities: In order for a field representative to guarantee that the apps will work when meeting with a land owner on site, the apps will cache all possible pieces of information locally on the mobile device in preparation for the meeting. Architecture Considerations The architecture of each application will combine server-side computation with local device processing and display. Each application will require its own unique blend of tradeoffs between on-device processing and storage limitations, and available network bandwidth. Some pre-processing for localized use may also reduce time lags in the field and thus improve usability. We will focus on two mobile platforms: Apple's iPhone and Android, each with its own set of development challenges. The operating system on Apple's iPhone, known as iOS, requires the use of the Objective-C language. Android, the current market favorite smartphone operating system, is open-source and requires the use of the Java language and the open-source integrated development environment known as Eclipse. Additionally, given the requirement of minimal data entry combined with mobile access and potential secure data sharing, data structures and storage formats will be designed and developed that are flexible enough to withstand intermittent and missing data while also supporting fast searching and retrieval of data from cloud-based file systems which will likely serve as the storage point for each user's data.