The role of topography, soil, and remotely sensed vegetation condition towards predicting crop yield

Trenton E. Franz, Sayli Pokal, Justin P. Gibson, Yuzhen Zhou, Hamed Gholizadeh, Fatima Amor Tenorio, Daran Rudnick, Derek Heeren, Matthew McCabe, Matteo Ziliani, Zhenong Jin, Kaiyu Guan, Ming Pan, John Gates, Brian Wardlow

Research output: Contribution to journalArticlepeer-review

34 Scopus citations


Foreknowledge of the spatiotemporal drivers of crop yield would provide a valuable source of information to optimize on-farm inputs and maximize profitability. In recent years, an abundance of spatial data providing information on soils, topography, and vegetation condition have become available from both proximal and remote sensing platforms. Given the wide range of data costs (between USD $0−50/ha), it is important to understand where often limited financial resources should be directed to optimize field production. Two key questions arise. First, will these data actually aid in better fine-resolution yield prediction to help optimize crop management and farm economics? Second, what level of priority should stakeholders commit to in order to obtain these data? Before fully addressing these questions a remaining challenge is the complex nature of spatiotemporal yield variation. Here, a methodological framework is presented to separate the spatial and temporal components of crop yield variation at the subfield level. The framework can also be used to quantify the benefits of different data types on the predicted crop yield as well to better understand the connection of that data to underlying mechanisms controlling yield. Here, fine-resolution (10 m) datasets were assembled for eight 64 ha field sites, spanning a range of climatic, topographic, and soil conditions across Nebraska. Using Empirical Orthogonal Function (EOF) analysis, we found the first axis of variation contained 60–85 % of the explained variance from any particular field, thus greatly reducing the dimensionality of the problem. Using Multiple Linear Regression (MLR) and Random Forest (RF) approaches, we quantified that location within the field had the largest relative importance for modeling crop yield patterns. Secondary factors included a combination of vegetation condition, soil water content, and topography. With respect to predicting spatiotemporal crop yield patterns, we found the RF approach (prediction RMSE of 0.2−0.4 Mg/ha for maize) was superior to MLR (0.3−0.8 Mg/ha). While not directly comparable to MLR and RF the EOF approach had relatively low error (0.5–1.7 Mg/ha) and is intriguing as it requires few calibration parameters (2–6 used here) and utilizes the climate-based aridity index, allowing for pragmatic long-term predictions of subfield crop yield.
Original languageEnglish (US)
Pages (from-to)107788
JournalField Crops Research
StatePublished - Apr 27 2020

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: T.E.F. acknowledges the financial support of the USDA National Institute of Food and Agriculture, Hatch project #1009760 and project # 2019-67021-29312, as well as the Joint FAO/IAEA Programme of Nuclear Techniques in Food and Agriculture CRP D1.50.17. D.R. acknowledges the financial support of the USDA National Institute of Food and Agriculture, Hatch project #1015698. We would also like to thank Nathan Thorson of the Eastern Nebraska Research and Extension Center, the West Central Research and Extension Center, Paulman Farms, and Jacob Fritton of The Nature Conservancy for providing crop yield information, access to study sites, and liaison with private land owners. We also would like to thank Les Howard for providing the processed USGS DEM data, Yaping Cai for providing processed Landsat data, and Catherine Finkenbiner, William Avery and Matthew Russell for collecting hydrogeophysical surveys. M.F.M and M.Z. were supported by the King Abdullah University of Science and Technology.


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