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Data-Driven Approach for Estimating Agricultural Activity in Texas

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan www.asabe.org

Citation:  2026 ASABE Annual International Meeting  2601385.(doi:10.13031/aim.202601385)
Authors:   Genaro Demuner Molina, Rodolfo Souza, Madhusudan Venugopal, Robert Glen Hardin, Santosh Palmate
Keywords:   Agricultural machinery, Data acquisition, Electric weed control, Emission inventory, Fuel consumption, Texas agriculture

Abstract. Energy consumption and emissions from agricultural equipment represent a significant information gap in understanding Texas agriculture's environmental impact. This study presents a data-driven framework for estimating agricultural machinery activity and associated emissions, representing the first systematic statewide initiative integrating expertise in agricultural, environmental, and transportation disciplines. OBD Mini LoggerTM units were installed on five pieces of John Deere equipment: an 8R-310 tractor (used with a diske harrow, vertical tillage and transporting for harvest), a 5130M tractor equipped with Zasso model THOR for electric weed control, a 7300 Forage Harvester for sorghum harvest, a 155 tractor for weed control, and an S780 Combine for corn harvest. The loggers continuously recorded fuel consumption, traveled distance, engine performance, operational hours, and geospatial coordinates in compliance with SAE J1979 standards. Collected datasets were used to characterize operational efficiency, spatial coverage, and temporal activity patterns. Emissions were calculated using EPA-provided formulas. These findings highlighted significant variability in fuel efficiency across machinery types and emphasized the importance of activity-based data collection for emission inventory development. Carbon emissions varied by equipment and operation: JD S780 Combine (30.988 tons CO₂), JD 8R-310 (8.469 tons CO₂), JD 7300 Forage Harvester (4.018 tons CO₂), JD 155 (1.414 tons CO₂), and JD 5130M (0.001 tons CO₂). This approach establishes an initial methodology facilitating Texas agricultural emissions inventory, enabling sustainable management, performance assessment, and energy optimization within the agricultural sector.

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