Improving the CROPGRO Perennial Forage Model for simulating growth and biomass partitioning of guineagrass

Abstract

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.

Agrometeorological and Agronomic Characterization of Megathyrsus Grasses Cultivated in Tropical Humid and Semi-Arid Conditions: A Multivariate Approach

“…Not only agrometeorological conditions are important in understanding and predicting productive variables in pastures. Other factors such as management of mechanistic models that estimate biomass production, such as CROPGRO (Bosi et al, 2020b;Brunetti et al, 2021) and APSIM (Bosi et al, 2020a;Gomes et al, 2020), are also relevant.…”

Aspectos relacionados con la importancia del uso de modelos predictivos en la producción ovina. Revisión

“…The main purpose of these models is to explain how an element at a higher level behaves or responds to a range of elements at a lower level. This type of model can be better exemplified in the modelling of the herbage accumulation dynamics of a given forage plant (30) . In this case, the mechanistic model seeks to explain the sequence of actions of abiotic factors at the level of molecules, cells, tissues, organs, tillers, plants, clumps, and the forage canopy.…”