Topic Aggregation

Precision Agriculture

Indexed Papers
28
Recorded Hubs
1
Recorded funders
12
U.S. states + D.C.
16

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This is an automated aggregation of all research tagged "Precision Agriculture" across the OpenAgData network. It updates in real-time as new papers are published.

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Topic: Precision Agriculture

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Susan A. O’Shaughnessy, Paul D. Colaizzi, Craig W. Bednarz • Unknown Hub (2023)

Texas

Beatriz E. Bizzuti, Parker Williams, Marta M. Kohmann, Francisco J. Arriaga, Jessica L. Drewry, Dennis Hancock, Brian D. Luck • Unknown Hub (2026)

Wisconsin

Ana Morales-Ona, Katsutoshi Mizuta, James J. Camberato, Robert Nielsen, Yuxin Miao, Davide Cammarano, German Mandrini, Daniel J. Quinn • Unknown Hub (2026)

Indiana

Frank G. Dohleman, Ken Copenhaver, Clifford P. Haefke, Steffen Mueller • Unknown Hub (2026)

“County-level life cycle modeling shows Illinois maize has a carbon intensity 9%-12% below the US average of 251 g CO2e/kg, and maize grown within 56 km of ethanol plants is 14%-17% lower, driven by high yields and efficient fertilizer use. National average CI values understate the climate performance of high-yielding regions, and cover crops and conservation tillage shift scores differently from county to county.”

Reduced EmissionsIllinois

Boubacar Gano, Marie De Gracia Coquerel, Jocelyn Saxton, Nathaniel Eck, Kamaranga H. S. Peiris, Scott R. Bean, J.R. Stanton, N. A. Ahmed, Nadia Shakoor • Unknown Hub (2026)

“Machine learning models built from agronomic, drone, and environmental data predicted sorghum grain quality traits accurately enough to cut lab testing — crude protein R²=0.90, amylose R²=0.92-0.99, starch R²=0.80, with lysine-from-protein weakest at R²=0.64. Grain composition shifted measurably with year and management (precision N, cover crops, no-till), and leaf temperature and stomatal conductance had strong non-linear links to amylose.”

Food Quality ImprovementMissouri

Geng Bai, Yufeng Ge, David Scoby, Bryan Leavitt, Vincent Stoerger, Norbert Kirchgeßner, Suat Irmak, George L. Graef, James C. Schnable, Tala Awada • Nebraska Corn Board (2019)

“A University of Nebraska-built cable-and-sensor robot measures entire corn fields in fine detail, compressing years of hand data-collection in breeding and agronomy trials into a single season.”

Other / NeutralNebraska

Zhikai Liang, Piyush Pandey, Vincent Stoerger, Yuhang Xu, Yumou Qiu, Yufeng Ge, James C. Schnable • Nebraska Corn Board (2017)

“Open imaging datasets of widely-grown corn lines give breeders and data scientists a shared benchmark for building faster, cheaper crop-improvement tools.”

Other / NeutralNebraska

Saurav Das, Gladis Zinati, Sean Stokes, Dinesh Panday, Rachel Olson, Arash Ghalehgolabbehbahani, Andrew Smith, Michael Graham, Eric A. Carr • Unknown Hub (2026)

“Manure management in the United States must move from a disposal-based model to a circular resource framework that integrates nutrient recovery, emission mitigation, precision application, supportive policy, and site-specific decision-making.”

Nutrient ManagementPennsylvania

Jisung Geba Chang, Martha C. Anderson, Feng Gao, Andrew Russ, Haoteng Zhao, Richard Cirone, Yakov Pachepsky, David M. Johnson • Unknown Hub (2025)

“Mapping subsurface water pathways with ground-penetrating radar plus surface elevation explained corn and soybean yield variation far better than topography alone (error dropped from 23.7% to 15.3%, and to 10.0% when remote sensing was added). Even without costly subsurface surveys, adding surface flow accumulation cut error to 18.4%, making hydro-topographic zoning a practical basis for site-specific management.”

Improved YieldMaryland

Pablo Paccioretti, Laila Puntel, Mariano Córdoba, Taro Mieno, Richard Ferguson, Joe Luck, Laura Thompson, Guillermo Balboa • Unknown Hub (2025)

“Sensor-based N management cut nitrogen application by ~40 kg N/ha in corn with no yield loss across 57 Nebraska on-farm site-years (2015–2023). Benefits are greatest in moderate-to-high yield variability fields with coarser soils. Sensors showed no statistically significant gains in winter wheat. Profit probability exceeded 60% for corn across most price scenarios. Despite consistent evidence, only 11% of Nebraska producers currently use sensor-based approaches — indicating a technology transfer gap.”

Nutrient ManagementNebraska

Kabindra Adhikari, Douglas R. Smith, Chad Hajda, Tulsi P. Kharel • Unknown Hub (2023)

“Within-field yield stability and gross margin variations across corn fields and implications for precision conservation.”

Improved YieldTexas

Sudhanshu Panda, Daniel P. Ames, Suranjan Panigrahi • Unknown Hub (2010)

“Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques.”

Improved YieldNorth Dakota

Lakesh K. Sharma, Honggang Bu, Anne Denton, David W. Franzen • Unknown Hub (2015)

“Across 15 North Dakota nitrogen-rate sites, red and red edge NDVI predicted corn yield equally well at V6, but at the later V12 stage red edge NDVI outperformed red NDVI because red readings saturate once the canopy closes. For growers using GreenSeeker or Crop Circle sensors to guide in-season N, red edge is the better choice for late-season sidedress decisions.”

Nutrient ManagementNorth Dakota

Morgen W. V. Burke, Brad Rundquist, Haochi Zheng • Unknown Hub (2019)

“Detection of Shelterbelt Density Change Using Historic APFO and NAIP Aerial Imagery.”

Erosion ReductionNorth Dakota

Andrew Molthan, Jordan R. Bell, Tony Cole, Jason Burks • Unknown Hub (2014)

“Satellite-based identification of tornado damage tracks from the 27 April 2011 severe weather outbreak.”

Reduced EmissionsAlabama

Jeanette K. Howard, Matt Merrifield • Unknown Hub (2010)

“Mapping Groundwater Dependent Ecosystems in California.”

Using existing data to locate groundwater-dependent ecosystems.California

David J. Muth, Douglas S. McCorkle, Joshua Koch, Kenneth M. Bryden • Unknown Hub (2012)

“Modeling sustainable agricultural residue removal at the subfield scale.”

Improved YieldIowa

Anthony J. Tesoriero, Jo Ann M. Gronberg, Paul F. Juckem, Matthew P. Miller, Brian Austin • Unknown Hub (2017)

“Predicting redoxsensitive contaminant concentrations in groundwater using random forest classification.”

Application of machine learning techniques to existing compliance monitoring data offers an opportunity to broa.Wisconsin

“Mapping Large-Area Landscape Suitability for Honey Bees to Assess the Influence of Land-Use Change on Sustainability of National Pollination Services.”

Pollinator SupportNorth Dakota

Deepthi Kolady, Evert Van der Sluis, Md Mahi Uddin, Allen P. Deutz • Unknown Hub (2020)

South Dakota

“A modified surface energy balance algorithm for land (MSEBAL) based on a trapezoidal framework.”

Pollinator SupportIowa

Rahul Raman, Haly L. Neely, Nithya Rajan, Jeffrey Siegfried, Amir M. H. Ibrahim, Curtis B. Adams, Robert G. Hardin • Unknown Hub (2026)

“UAS VIs are robust to soil variability when canopy cover >~30%. NDVI can be biased high by wet soil or compost at ~30–35% cover. PVI showed the lowest sensitivity to soil background and canopy-cover changes.”

Other / NeutralTexas

Kenny Paul, Mirella Sorrentino, Luigi Lucini, Youssef Rouphael, Mariateresa Cardarelli, Paolo Bonini, Hélène Reynaud, Renaud Canaguier, Martin Trtílek, Klará Panzarová, Giuseppe Colla • Unknown Hub (2019)

“Foliar sprays of eight plant-derived protein hydrolysates on tomato generally increased digital biomass, with two products (A and I) significantly improving relative growth rate and overall growth performance, though chlorophyll fluorescence showed no photosynthetic differences. Metabolomics indicated the biostimulants worked by reprogramming ethylene-precursor, polyamine, and ROS-signaling pathways, and the phenotyping-plus-metabolomics combination proved an efficient way to screen new biostimulant candidates.”

Improved YieldMississippi

Steven W. Evans, Gustavious P. Williams, Norman L. Jones, Daniel P. Ames, E. James Nelson • Unknown Hub (2020)

“Exploiting Earth Observation Data to Impute Groundwater Level Measurements with an Extreme Learning Machine.”

A method to impute missing data at single wells.Utah

Ayman Nassar, Alfonso F. Torres‐Rua, William P. Kustas, Héctor Nieto, Mac McKee, Lawrence E. Hipps, David King Stevens, Joseph G. Alfieri, John H. Prueger, María Mar Alsina, L. McKee, Calvin Coopmans, Luis Sánchez, Nick Dokoozlian • Unknown Hub (2020)

“The Two-Source Energy Balance with a dual temperature (TSEB2T) model was used to estimate evapotranspiration and identify the impact of spatial domain scale under different vine phenological conditions.”

Water Use EfficiencyCalifornia

Earl D. Vories, Susan A. O’Shaughnessy, Kenneth A. Sudduth, Steven R. Evett, Manuel A. Andrade, Scott T. Drummond • Unknown Hub (2020)

“A study was conducted at Portageville, MO, USA, in 2016 and 2017 with the objective to compare yield and irrigation water use efficiency among three water-management treatments for cotton.”

Improved YieldMissouri

Ashley E. Larsen, Frederik Noack, L. Claire Powers • Unknown Hub (2024)

“Spillover effects of organic agriculture on pesticide use on nearby fields.”

Pest & Disease ManagementCalifornia