Subfield crop yields and temporal stability in thousands of US Midwest fields

Bernardo Maestrini, Bruno Basso*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Understanding subfield crop yields and temporal stability is critical to better manage crops. Several algorithms have proposed to study within-field temporal variability but they were mostly limited to few fields. In this study, a large dataset composed of 5520 yield maps from 768 fields provided by farmers was used to investigate the influence of subfield yield distribution skewness on temporal variability. The data are used to test two intuitive algorithms for mapping stability: one based on standard deviation and the second based on pixel ranking and percentiles. The analysis of yield monitor data indicates that yield distribution is asymmetric, and it tends to be negatively skewed (p < 0.05) for all of the four crops analyzed, meaning that low yielding areas are lower in frequency but cover a larger range of low values. The mean yield difference between the pixels classified as high-and-stable and the pixels classified as low-and-stable was 1.04 Mg ha−1 for maize, 0.39 Mg ha−1 for cotton, 0.34 Mg ha−1 for soybean, and 0.59 Mg ha−1 for wheat. The yield of the unstable zones was similar to the pixels classified as low-and-stable by the standard deviation algorithm, whereas the two-way outlier algorithm did not exhibit this bias. Furthermore, the increase in the number years of yield maps available induced a modest but significant increase in the certainty of stability classifications, and the proportion of unstable pixels increased with the precipitation heterogeneity between the years comprising the yield maps.

Original languageEnglish
JournalPrecision Agriculture
DOIs
Publication statusE-pub ahead of print - 8 May 2021

Keywords

  • Big-data
  • Standard deviation
  • Two-way outlier
  • Yield maps
  • Yield stability

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