NSF SCC-IRG Track 1: Co-Producing Community - An integrated approach to building smart and connected nutrient management communities in the US Corn Belt

Enabling farmers to manage N and P with greater precision is needed to increase farmer profitability and decrease off-farm losses of nutrients, which can compromise water resources. The objective of this project is to develop science-driven recommendations on N and P management that can be tailored to different farmers’ needs, focusing on the heart of the US Corn Belt: Illinois. This work has three objectives: (1) identify major constraints on how Illinois farmers manage N and P; (2) determine how much N and P are stocked in soils across a diversity of Illinois farms (through soil sampling and the use of soil sensors and satellite observations), and how this soil nutrient capital contributes to crop growth in order to model field-specific fertilizer needs; and (3) develop smart and connected technology solutions that enable constrained farmers to join a Nutrient Management Community (NuMC). 

DOE ARPA-E SMARTFARM: The “System of Systems” Solutions for Commercial Field-Level Quantification of Soil Organic Carbon and Nitrous Oxide Emission for Scalable Applications (SYMFONI)

Accurate and rapid field-level quantification of carbon intensity at a regional scale is critical to facilitate adoption of new technologies to increase the bioeconomy’s feedstock productivity and reduce its carbon footprint. This project will develop a commercial solution, SYMFONI, to estimate soil organic carbon and the dynamics of nitrous oxide emissions at an individual field level. The solution can be scaled up to perform per-field estimates for an entire region. SYMFONI is a “system of systems” solution that integrates airborne-satellite remote sensing, process-based modeling, deep learning, atmospheric inversion, field-level sensing, and high-performance computing.

NSF SitS: Spatial and Temporal Patterns of Soil N and P Cycles Quantified by a Sensor-Model Fusion Framework: Implications for Sustainable Nutrient Management

High crop productivity in the Midwestern US was achieved by artificially draining wetlands and applying millions of tons of nitrogen (N) and phosphorous (P) fertilizers. However, 40-80% of these N and P nutrient inputs are lost from soils and become pollutants in water bodies and the atmosphere. This project will integrate recent advances in nanotechnology, sensing technology, and machine learning to enable new methods for measuring and managing N and P in croplands to reduce losses to the environment. The outcomes of this project can be used directly by farmers to better manage field application of N and P fertilizers and by local/federal governments and other organizations to pinpoint pollution hotspots and develop strategies for nutrient reduction.

NASA LCLUC: Evaluating land use change and livelihood responses to large investments for high-value agriculture: managing risks in the era of the Green Morocco Plan

This project evaluates the social and environmental consequences of large-scale agricultural
investments, focused on (1) the transition from cereals to perennial crops in the drought-prone
Mediterranean country of Morocco and (2) the possibility to use remotely-sensed indicators of
environmental stress at the basis of responsible, adaptive relief financing. 

USAID SIIL Digital and Geospatial tools Consortium: Building a new era of Predictive Agricultural Innovation to improve the livelihood of smallholder farmers

This newly restructured consortium will focus on developing develop foundational information to support the development of digital support tools that can guide decision-makers and producers to take action towards improving food security, human nutrition, and risk management and resiliency of smallholder farming systems today and in the future. Ultimately, this project will provide new information that enhances long-term strategic and short-term adaptive management of smallholder farming systems and to increase capacity for Extension practitioners and government officials to utilize the outcomes obtained on this project to improve resiliency at the farming system scale.

TechnoServe: Mapping Cashew Plantations in Benin

Methods for mapping tree crops or plantations are limited in their spatiotemporal resolution and scalability. Spectral information (e.g. NDVI) alone is often not sufficient to distinguish perennial crops like fruits, nuts and vineyards from other types of vegetation. This project will use variants of deep neural network and very-high-resolution satellite imagery to map cashew plantations (area and counts). Multiple attention-based neural networks will be explored to predict cashew yield and farmers’ practices.

PepsiCo: Develop mechanistic models and key remote sensing features for Opt-Oat: integrated research of crop modeling, remote sensing, and field zoning


NSF STTR Phase I: A Novel Approach to Manage Nitrogen Fertilizer for Potato Production using Remote Sensing

Over-application of nitrogen fertilizer contributes to groundwater contamination via nitrate-nitrogen leaching, and puts a substantial financial burden on rural municipalities and private well owners who are required to install and pay for treatment of their drinking water. An estimated 1% of global energy consumption is attributed to the production of synthetic nitrogen fertilizer. Producers operate under tight margins and face pressure to maximize crop yields to remain profitable and sustain their business. The proposed technology aims to optimize nitrogen application and minimize the susceptibility of loss to the environment while accounting for the year to year weather variability that poses the largest production challenge. The technology not only determines the optimum nitrogen rate for achieving maximum profit, but it also provides transparency in nitrogen management and can serve as a means for demonstrating compliance with incentive or regulatory programs.