Importance of genetic parameters and uncertainty of MANIHOT, a new mechanistic cassava simulation model

• Leidy Patricia Moreno-Cadena ^[1] ; Gerrit Hoogenboom ^[2] ; Myles James Fisher ^[1] ; Julian Ramirez-Villegas ^[1] ; Steven Dean Prager ^[1] ; Luis Augusto Becerra Lopez-Lavalle ^[1] ; Pieter Pypers ^[3] ; Maria Sara Mejia de Tafur ^[4] ; Daniel Wallach ^[5] ; Rafael Muñoz-Carpena ^[2] ; Senthold Asseng ^[2]
  1. [1] International Center for Tropical Agriculture

     International Center for Tropical Agriculture

     Kenia

     
  2. [2] University of Florida

     University of Florida

     Estados Unidos

     
  3. [3] International Institute of Tropical Agriculture
  4. [4] Universidad Nacional UN–Palmira, Colombia
  5. [5] Agrosystèmes et développement territorial

  Mostrar afiliaciones +
• Localización: European journal of agronomy: the official journal of the European Society for Agronomy, ISSN 1161-0301, Vol. 115, Nº. 4, 2020, págs. 156-170
• Idioma: inglés
• Texto completo no disponible (Saber más ...)
• Resumen

  • We identified the most sensitive genotype-specific parameters (GSPs) and their contribution to the uncertainty of the MANIHOT simulation model. We applied a global sensitivity and uncertainty analysis (GSUA) of the GSPs to the simulation outputs for the cassava development, growth, and yield in contrasting environments. We compared enhanced Sampling for Uniformity, a qualitative screening method new to crop simulation modeling, and Sobol, a quantitative, variance-based method. About 80% of the GSPs contributed to most of the variation in maximum leaf area index (LAI), yield, and aboveground biomass at harvest. Relative importance of the GSPs varied between warm and cool temperatures but did not differ between rainfed and no water limitation conditions. Interactions between GSPs explained 20% of the variance in simulated outputs. Overall, the most important GSPs were individual node weight, radiation use efficiency, and maximum individual leaf area. Base temperature for leaf development was more important for cool compared to warm temperatures. Parameter uncertainty had a substantial impact on model predictions in MANIHOT simulations, with the uncertainty 2–5 times larger for warm compared to cool temperatures. Identification of important GSPs provides an objective way to determine the processes of a simulation model that are critical versus those that have little relevance