Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions

Systematic analysis of site-specific yield distributions resulting from nitrogen management and... At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque–Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha−1), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of −1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha−1) of the distribution. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Precision Agriculture Springer Journals

Systematic analysis of site-specific yield distributions resulting from nitrogen management and climatic variability interactions

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Publisher
Springer US
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Life Sciences; Agriculture; Soil Science & Conservation; Remote Sensing/Photogrammetry; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences; Atmospheric Sciences
ISSN
1385-2256
eISSN
1573-1618
D.O.I.
10.1007/s11119-014-9380-7
Publisher site
See Article on Publisher Site

Abstract

At the plot level, crop simulation models such as STICS have the potential to evaluate risk associated with management practices. In nitrogen (N) management, however, the decision-making process is complex because the decision has to be taken without any knowledge of future weather conditions. The objective of this paper is to present a general methodology for assessing yield variability linked to climatic uncertainty and variable N rate strategies. The STICS model was coupled with the LARS-Weather Generator. The Pearson system and coefficients were used to characterise the shape of yield distribution. Alternatives to classical statistical tests were proposed for assessing the normality of distributions and conducting comparisons (namely, the Jarque–Bera and Wilcoxon tests, respectively). Finally, the focus was put on the probability risk assessment, which remains a key point within the decision process. The simulation results showed that, based on current N application practice among Belgian farmers (60-60-60 kgN ha−1), yield distribution was very highly significantly non-normal, with the highest degree of asymmetry characterised by a skewness value of −1.02. They showed that this strategy gave the greatest probability (60 %) of achieving yields that were superior to the mean (10.5 t ha−1) of the distribution.

Journal

Precision AgricultureSpringer Journals

Published: Oct 1, 2014

References

  • Grain Yield Mapping: Yield Sensing, Yield Reconstruction, and Errors
    Arslan, S; Colvin, T
  • Impact of compost, manure and inorganic fertilizer on nitrate leaching and yield for a 6-year maize–alfalfa rotation in Michigan
    Basso, B; Ritchie, JT
  • Long-term nitrogen dynamics in various catch crop scenarios: Test and simulations with STICS model in a temperate climate
    Constantin, J; Beaudoin, N; Launay, M; Duval, J; Mary, B
  • Cumulative effects of catch crops on nitrogen uptake, leaching and net mineralization
    Constantin, J; Beaudoin, N; Laurent, F; Cohan, J-P; Duyme, F; Mary, B

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