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Improving the CROPGRO Perennial Forage Model for simulating growth and biomass partitioning of guineagrass
Author(s) -
Brunetti Henrique B.,
Boote Kenneth J.,
Santos Patricia M.,
Pezzopane José R. M.,
Pedreira Carlos G. S.,
Lara Márcio A. S.,
Moreno Leonardo S. B.,
Hoogenboom Gerrit
Publication year - 2021
Publication title -
agronomy journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.752
H-Index - 131
eISSN - 1435-0645
pISSN - 0002-1962
DOI - 10.1002/agj2.20766
Subject(s) - forage , panicum , biomass (ecology) , agronomy , biomass partitioning , leaf area index , perennial plant , biology , pasture , phenology , specific leaf area , environmental science , photosynthesis , botany
Tropical forage grasses are used for several applications including grazing, silage, and biofuels; with harvesting at varying phenological stages. Mechanistic simulation models can be powerful tools to assist with planning and decision making of pasture utilization strategies. The objective of this study was to improve and evaluate the ability of the Cropping System Model‐CROPGRO‐Perennial Forage model (CSM‐CROPGRO‐PFM) to simulate growth and biomass partitioning of two guineagrass [ Panicum maximum Jacq. syn. Megathyrsus maximus (Jacq.) BK Simon & SWL Jacobs] cultivars, Tanzânia and Mombaça. Data from two experiments with contrasting harvest management and field conditions were used. Model parameters were modified, targeting improvement in d ‐statistic and root mean square error (RMSE) for aboveground, leaf, stem biomass, leaf area index (LAI), and leaf proportion of aboveground biomass. Major improvement in model performance was achieved by modifying the vegetative partitioning parameters between leaf and stem through increasing partitioning to leaf during early regrowth while increasing it to stem during late regrowth. Modifications were made to parameters affecting leaf and stem senescence, leaf photosynthesis, and leaf area expansion sensitivity to cool weather. The RMSE values decreased from 2,261 to 1,768 kg ha −1 for aboveground biomass, from 1,620 to 874 kg ha −1 for stem biomass, from 11.41 to 7.27% for leaf percentage, from 1.91 to 1.68 for LAI, but increased slightly for leaf biomass. The d ‐statistic computed over all these variables increased from .86 to .93. The improved model performance for both short and long harvest cycles will facilitate further applications for diverse forage crops utilization strategies.