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Evaluating a cassava crop growth model by optimizing genotype-specifc parameters using multienvironment trial

Pamelas M Okoma1, Siraj Ismail Kayondo2, Ismail Y Rabbi2

  • 1Plant Breeding and Genetics Section, School of Integrative Plant Science, College of Agriculture and Life Sciences, Cornell University, Ithaca, NY, United States.

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PubMed
Summary

Calibrating crop growth models (CGM) for cassava breeding in Nigeria improved yield predictions. The model showed biases, underestimating yields in dry conditions and overestimating in wet conditions.

Keywords:
Nigeriacalibrationcassavacrop growth modelgeneral likelihood uncertainty estimation

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Area of Science:

  • Agricultural Science
  • Plant Breeding
  • Computational Biology

Background:

  • Cassava (Manihot esculenta) is vital for sub-Saharan African food security.
  • Crop growth models (CGMs) aid breeding by predicting performance across diverse environments and future climates.

Purpose of the Study:

  • To assess the feasibility of large-scale CGM calibration within a cassava breeding program.
  • To identify systematic biases in the CROPGRO-MANIHOT-Cassava model.

Main Methods:

  • Parameterized the CROPGRO-MANIHOT-Cassava model using data from 67 clones across eight Nigerian locations (2017-2020).
  • Employed trial-and-error adjustments and the General Likelihood Uncertainty Estimation (GLUE) method for calibration.
  • Evaluated model performance using Pearson correlation, root mean squared error (RMSE), and d statistics.

Main Results:

  • Post-calibration, correlation improved from -0.03 to +0.08, RMSE decreased from 21 t ha⁻¹ to 5 t ha⁻¹, and d increased from 0.23 to 0.44.
  • The model underestimated root yield in dry, hot environments.
  • The model overestimated root yield in wet, cool environments.

Conclusions:

  • CGM calibration can be integrated into routine cassava breeding data analysis.
  • Opportunities exist for refining the CROPGRO-MANIHOT-Cassava model to enhance prediction accuracy.