Recipient Organization
UNIV OF HAWAII
3190 MAILE WAY
HONOLULU,HI 96822
Performing Department
(N/A)
Non Technical Summary
Our team recently created atool that provides robust soil health assessments to help land stewards work towards their sustainability goals and support healthy communities. However, our current suite of soil health indicators are costly and time-consuming to measure. While we also havenovel dataresources such as soil spectra and microbiomesat our fingertips, these data have yet to be integrated into our soil health database, predictions, or education. Therefore, wepropose to identify novel data resources that allow for more affordable, rapid, and comprehensive soil health testing in direct response to the Data Science for Food and Agriculture Systems call to "synthesize or analyze existing data and resources on soil health." We will first integrate these novel data streams into our current soil health database, and thentest whether these new data resources can help usaccurately predict soil health using machine and deep learning models.Finally, we willtrain undergraduate and graduate students through formalized data science internships, coursework, and assistantships. Through thisproject, we help to develop a deeper understanding of the relationship between the soilfingerprint (i.e., microbiome and spectroscopy) and its health status, an improved tool to effectively measure soil health, and the development of a skilled workforce. Ourcombined expertise creates a unique opportunity to leverage existing resources and make advancements in research and education that will benefit students, land stewards, the next generation of researchers, and the community at large.
Animal Health Component
0%
Research Effort Categories
Basic
34%
Applied
33%
Developmental
33%
Goals / Objectives
Our first goal is to harmonize existing data from several ongoing, interconnected research efforts in order to develop machine learning models that predict the health of a diversity of soil types-including but not limited to tropical and volcanic soils. We will first integrate three unique data streams into an innovative database, which includes conventional soil health indicators, mid-infrared spectroscopy, and soil microbiomes. We will then explore supervised machine learning techniques and deep learning models to predict soil health based on the high-dimensional data. These predictive models will become available and accessible through their integration into our existing user-based Hawaii Soil Health Webtool platform that supports monitoring efforts and adaptive management for the aggradation of soils and climate ready landscapes.Our second goal is to build up the capacity for place-appropriate data science. Given that community action is needed to protect, restore, and improve landscape health and resilience, we rely on the next generation of professionals to prioritize soil health through the implementation of evidence-based and equitable solutions. We will train undergraduate and graduate students through formalized internships, coursework, and assistantships in order to render the skills needed to monitor soil health and soil-related ecosystem services using advanced data science techniques. However, training and monitoring efforts must be culturally appropriate. Therefore, we will build in ethics training associated with indigenous ways of being into all student training activities to support the emerging place-appropriate data science practices. We propose three supporting objectives to help achieve these goals and outcomes:To Harmonize Soil Health Data Streams - Engineer existing data streams to facilitate artificial intelligence and deep learning.To Develop Deep Learning Models - Predict soil health scores using machine learning models that incorporate high-dimensional data for improved soil health assessments.To Build Pathways that Integrate Data Science into the Classroom and Beyond - Train undergraduate and graduate students in data science and data ethics through classroom instruction, internship opportunities, and assistantships.
Project Methods
A deeper understanding of the relationship between the soil fingerprint (microbiome and spectroscopy) and its health status will enable more routine and cost-effective soil health testingto help elucidatemanagement systems that enhance soil health, food production, and environmental outcomes. To achieve this, we will (i)apply our recently-released soil health scoring function to all incoming samples into the Hawaii Soil Health Webtool database to produce soil health scores and classes, (ii) apply mid-infrared spectra based machine learning algorithms (i.e., artificial neural networks, random forest and memory-based learning) to predict soil health indicators and soil health scores, (iii) utilize measures of microbiome community composition and topological characteristics of cross-domain networks to inform machine learning models, and (iv) to assess the ability of different machine learning algorithms to classify soil health based on these new high-resolution data streams. We will evaluate our progress by the achievement of specific milestones, including a soil health database with up-to-date soil health scores (year 1), a harmonized database with high resolution datastreams (i.e., spectroscopy and microbiome) (year 2-3),the evaluation of machine learning algorithms to predict soil health from multiple data streams(year 3-5), the matriculation of two graduate students (year 4-5), and the publication of 5 papers (year 3-5+).We also aim to help students build the skills neededto become competitive in the workforce by using novel methods to solve problems indata-driven agriculture and conservation.Students will also receiveexplicit training in data ethics to guide the application of machine learning and work towards ethical and equitable soil health assessments. Our methods will include formal classroom instruction, development of curriculum or innovative teaching methodologies, internships, and extension and outreach. Wewill evaluate educational activities and track metrics relevant to our objectives with IRB approval as needed. To assess performance, we will utilize a survey instrument developed by our research groupthat includes a set of questions assessing their interest and knowledge levels before and post-training, level of confidence in subject ability, and experience with course elements (e.g., group project). Specifically, the survey will address soil health tools and ethical concepts in data science, including soil health indicators, proxy measurements, database curation, machine learning, Native Hawaiian history, and data ethics. We will also seek student evaluations of education materials. For TPSS 333, we will ask students to evaluate our course through the Official University of Hawaii Course and Faculty Evaluation System (eCAFE), which is an entirely online evaluation system. For the internships, we will adapt and disseminate our current Google Form evaluation of our ongoing USDA REEU program. Finally, we will consider workforce placement of students involved in the project. We will consult two large potential local employers for their input on the quality of skills the students acquire as part of our advisory group (as part of ourManagement Plan). We will also work with our undergraduate program to track the placement of students enrolled in our program. We will apply these methods in Year 3-5 of the project.