Use of soil and other environmental data to recommend customized agronomic programs

A method, using a computer system, for recommending an agronomic program for a specific geographic site for the purpose of promoting soil carbon sequestration at the specific geographic site, the method comprising:

• obtaining a Training Data Set comprising DNA sequencing data derived from each of a plurality of soil, plant, water and/or air samples taken from a plurality of geographic sites, wherein said DNA sequencing data provides relative frequencies of taxonomic-indicating and/or function-indicating microbial gene markers in the each of the plurality of soil, plant, water and/or air samples taken from the plurality of geographic sites;
• inputting the Training Data Set into an Algorithm Development (AD) module, wherein the AD module comprises an Unsupervised Machine Learning Classifier (UMLC);
• developing, using the UMLC, a classification model that assigns microbiome classifications to the each of the plurality of soil, plant, water and/or air samples based upon similarities between relative frequencies of the taxonomic-indicating microbial gene markers and/or the function-indicating microbial gene markers in the each of the plurality of soil, plant, water and/or air samples;
• inputting environmental training data and microbial test training data from the each of the plurality of soil, plant, water and/or air samples into the AD module;
• developing, using a Supervised Machine Learning Classifier (SMLC), a predictive classifier model based on relationships between the assigned microbiome classifications, the environmental training data, and the microbial test training data from the each of the plurality of soil, plant, water and/or air samples;
• inputting, using the SMLC, site-specific prediction data collected from the specific geographic site into the predictive classifier model to predict microbiome classifications for soil, plant, water and/or air located at the specific geographic site, wherein the site-specific prediction data is site-specific environmental prediction data and site-specific microbial test prediction data from the specific geographic site;
• correlating, using a Recommendation Engine, the predicted microbiome classifications for the specific geographic site with a recommended agronomic program, wherein the Recommendation Engine uses a Supervised Machine Learning Predictor to identify statistically impactful relationships between the predicted microbiome classifications, the site-specific prediction data, and soil carbon sequestration to identify agricultural activities and/or agricultural products that have a statistically relevant impact on promoting soil carbon sequestration at the specific geographic site, and wherein the recommended agronomic program comprises a list and/or schedule of the agricultural activities identified and/or one or more customized microbial soil treatment compositions to apply at the specific geographic site to promote soil carbon sequestration;
• implementing the recommended agronomic program at the specific geographic site;
• and
• conducting field trials and experimentation at the specific geographic site to determine an impact of the recommended agronomic program on the promoting soil carbon sequestration.