Spatial Yield Modeling

Spatial Yield Modeling is a predictive modeling framework that integrates geospatial data and environmental covariates to estimate agricultural yield variability across heterogeneous landscapes, distinguishing itself from non-spatial approaches by explicitly accounting for spatial autocorrelation and location-specific biophysical conditions.

267

Publications

12.7K

Citations

996

Authors

412

Institutions

Publications per year

2017–2026

89

Authors

996

  1. CN
    Claas Nendel

    Leibniz Centre for Agricultural Landscape Research, Germany

    6 publications · 236 citations

    6

    h-index

  2. JL
    James Lowenberg‐DeBoer

    Purdue University West Lafayette, United States

    6 publications · 206 citations

    6

    h-index

  3. JO
    Joel O. Paz

    Mississippi State University, United States

    5 publications · 160 citations

    5

    h-index

  4. RB
    Rodolfo Bongiovanni

    National Agricultural Technology Institute, Argentina

    5 publications · 309 citations

    5

    h-index

  5. WD
    William D. Batchelor

    Auburn University, United States

    5 publications · 160 citations

    5

    h-index

1–5 of 996

Venues

  1. Agronomy Journal

    15 publications · 481 citations

    13

    h-index

  2. Precision Agriculture

    10 publications · 368 citations

    10

    h-index

  3. GCB Bioenergy

    9 publications · 485 citations

    9

    h-index

  4. Agricultural and Forest Meteorology

    8 publications · 320 citations

    8

    h-index

  5. Agricultural Economics

    8 publications · 350 citations

    8

    h-index

1–5