CoPIs: Scot E. Smith and K. Ramesh Reddy (University of Florida, Gainesville), Suhas P. Wani, International Crops Research Institute for the Semiarid Tropics (ICRISAT, India)
Collaborator: Balaji Venkataraman (Commonwealth of Learning, Vancouver, Canada)
This project aims to develop an inference engine to transform smallholder farm production systems for resource optimization by utilizing spectral technology and geospatial modeling. The intellectual merit focuses on fusing of innovative sensor technologies into a holistic engine aiming to enhance conservation of soil quality and optimize crop yield to support smallholder farmers. The overarching goal is to build a Geospatial Soil-Crop Inference Engine (GeoSCIE) that fuses ground-based and remotely-sensed spectral data with geo-referenced observations to derive critical metrics for crop and soil management. Several hypotheses will be tested in two smallholder farm settings in Karnataka and Andhra Pradesh, India, to provide a proof-of-concept for a novel multi-spectral, spatially-explicit engine that links research models to pixels of smallholder farms. The objectives are to (1) develop and validate quantitative models that relate analytically-derived measures of soil indicators including soil texture, soil organic matter, macronutrients (nitrogen and phosphorus), micronutrients (sulfur, boron, and zinc) and soil spectral data derived from diffuse reflectance spectroscopy (DRS) (visible/near-infrared and mid-infrared spectral ranges); (2) design and implement a three-tiered multi-spectral system using ground-based spectral reflectance measurements, aerial sensors, and satellite multispectral scanners for a sequence of cropping seasons that aims to identify the optimal footprint or instantaneous field of view (IFOV) required for accurate assessment of crop-specific properties and stressors in smallholder farm settings; (3a) fuse soil DRS and remote sensing data into a GeoSCIE and calibrate and validate the engine by estimating a suite of critical soil and crop/vegetation-specific properties; (3b) use a genetic algorithm to derive indices from fused spectral data to infer on soil quality, fertility, water deficiency, and crop stress; and (4) translate results from GeoSCIE into Reusable Learning Objects (RLOs) to provide training/learning material to smallholder farmers and streamline GeoSCIE-based management recommendations into the agricultural-oriented social network application and information system AGROPEDIA used by smallholder farmers in India.
The research is expected to have broad impact by integrating research with education and knowledge sharing activities. To foster advancing discovery and understanding while promoting teaching, training and learning the project will utilize the EcoLearnIT RLO System. RLOs are globally accessible and will not only reach smallholder farmers in the selected study areas in India, but can be also used for training by farmers elsewhere. Undergraduate students will be explicitly engaged in the educational component of this project. The aim is to create broad impact by engagement and networking activities which include integration of research findings and RLOs into short courses taught by project personnel at the UF-ICRISAT Education Center in India, which targets underrepresented groups in South and South-East Asia, and other developing countries in Africa. Research findings will be disseminated into a format easily understood by smallholder farmers using audio and video recordings and a text-based tool will be developed to disseminate GeoSCIE findings to smallholder farmers via AGROPEDIA which is ideally suited to broaden participation of smallholder farmers since it has been shown to be successful in linking farmers with researchers.
