Chemical composition, secondary and primary metabolites of Tithonia diversifolia related to climate

R.S. Herrera, D.M. Verdecia, J.L. Ramírez

Abstract


To establish the relationship between chemical composition,
secondary metabolites, primary metabolites and digestibility of
organic matter, dry matter, protein, NDF and ADF of Tithonia
diversifolia and climatic factors, the correlation study was carried out.
The indicators were: A) chemical composition: ADF, NDF, lignin,
cellulose, hemicellulose, cellular content, OM, ash, N, Si, Ca, P and
Mg; B) secondary metabolites: total tannins, total phenols, total
condensed tannins, fiber-bound condensed tannins, free condensed
tannins, alkaloids, saponins, triterpenes, steroids and flavonoids, and
C) primary metabolites: glucose, fructose, sucrose, N, verbascose,
stachyose and raffinose. Correlation coefficients (r) between climatic
factors and chemical composition were variable and the highest
values were obtained for cellulose, total rainfall and number of days
with rain (-0.69 and -0.73, respectively). For secondary metabolites,
total condensed tannins and free condensed tannins stood out with
temperatures and indicators of rain (values of r between 0.62 and
0.85) and for the primary metabolites, verbascose and stachyose were
better with temperatures (r higher than 0.78). In vitro digestibility
of DM correlated (r between -0.63 and -0.91) with temperatures,
rainfall and their distribution. Similar performance was registered
for digestibility of organic matter and the r varied between -0.69
and -0.90. Digestibilities of CP, ADF and NDF only correlated with
rainfall and their distribution (r between -0.74 and -0.87). Data
showed the specific and variable effect of climate elements in the
studied indicators, which is useful for the efficient management of
this plant under climate change conditions. It is recommended to
consider these results to counteract the negative effect that secondary
metabolites of Tithonia diversifolia can cause in ruminant feeding
systems, apply mathematical modeling to predict the performance
of these substances with climatic factors and other aspects of this
plant management, as well as extend this type of research to other
plants of interest to livestock.

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