Spatial variability of crop responses to agronomic inputs in on-farm precision experimentation

R. G. Trevisan, D. S. Bullock, N. F. Martin

Research output: Contribution to journalArticlepeer-review


Within-field variability of crop yield levels has been extensively investigated, but the spatial variability of crop yield responses to agronomic treatments is less understood. On-farm precision experimentation (OFPE) can be a valuable tool for the estimation of in-field variation of optimal input rates and thus improve agronomic decisions. Therefore, the objectives of this study were to investigate the spatial variability of optimal input rates in OFPE and the potential economic benefit of site-specific input management. Mixed geographically weighted regression (GWR) models were used to estimate local yield response functions. The methodology was applied to investigate the spatial variability in corn response to nitrogen and seed rates in four cornfields in Illinois, USA. The results showed that spatial heterogeneity of model parameters was significant in all four fields evaluated. On average, the RMSE of the fitted yield decreased from 1.2 Mg ha−1 in the non-spatial global model to 0.7 Mg ha−1 in the GWR model, and the r-squared increased from 10 to 68%. The average potential gain of using optimized uniform rates of seed and nitrogen was US$ 65.00 ha−1, while the added potential gain of the site-specific application was US$ 58.00 ha−1. The combination of OFPE and GWR proved to be an effective tool for testing precision agriculture’s central hypothesis of whether optimal input application rates display adequate spatial variability to justify the costs of the variable rate technology itself. The reported results encourage more research on response-based input management recommendations instead of the still widespread focus on yield-based algorithms.

Original languageEnglish (US)
Pages (from-to)342-363
Number of pages22
JournalPrecision Agriculture
Issue number2
StatePublished - Apr 2021


  • Geographically weighted regression
  • Nitrogen
  • Seed
  • Variable rate application
  • Yield response functions
  • Zea mays L

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)


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