In-Season Estimation of Corn Yield Potential Using Proximal Sensing

Crop sensing is a promising approach for predicting corn (Zea mays L.) yield. Yield prediction is the first step in development of algorithms for sensor-based N management. Here, we evaluated the impact of (i) timing of sensing (growth stage), and (ii) method of reporting sensor data on estimations of corn grain and silage yield in New York. Sensor data were reported as the normalized difference vegetation index (NDVI), in-season estimated yield (INSEY) expressed as NDVI divided by days after planting (DAP; INSEYDAP), growing degree days (GGD; INSEYGGD), and the inverse simple ratio (ISR; [1–NDVI]/[1+NDVI]) divided by DAP (INSEYISR). To evaluate timing of sensing, corn of six fertility trials was scanned at every growth stage between V4 and V11. The replicated trials had up to six N rates (0, 56, 112, 168, 224, and 336 kg ha–¹). The V7 sensor and yield data from zero-N plots of nine additional on-farm trials (varying histories) were added to derive yield algorithms for New York. Drought at three sites in 2016 negatively impacted the accuracy of sensor-based grain yield estimates (R² 0.70 (grain) and >0.77 (silage). We conclude that INSEY data obtained at V7 can be used to accurately predict corn grain and silage yields in non-drought conditions in New York.

• Publication year

  2017

• Venue

  Agronomy Journal

• Publication date

  2017-07-01

• Fields of study

  Agricultural and Food Sciences, Mathematics

• Identifiers
  DOI 10.2134/AGRONJ2016.12.0732
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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