RESPONSE OF Amaranthus viridis PLANT FUNCTIONAL TRAITS TO NPK 12:12:17 AND NPK 15:15:15 FERTILIZERS - Pei Sin Tong; Ker Ning Chew; Hoe Yin Yik; Jin Zhe Tan (BIOTROPIA, 2024)
A paradigm shift from the prevailing reliance on chemical methods to alternative weed-control approaches is necessary to achieve sustainable weed management. However, the understanding of weed biology explaining “how” and “why” remains insufficient in facilitating this shift. This study employed a trait-based approach — examined the number of leaves, number of inflorescences, and height — to investigate the growth and developmental patterns of Amaranthus viridis, a weed species in the tropics, in response to NPK fertilization. The experiments were carried out in three sets of weeds — wild population (untreated and not transplanted; n = 6), NPK 15:15:15 (transplanted and fertilized with NPK 15:15:15 from March 2020 to September 2020; n = 30), and NPK 12:12:17 (transplanted and fertilized with NPK 12:12:17 from May 2021 to September 2021). The NPK treatment sets comprised five treatments, including one untreated control, with six replications for each treatment. Pearson’s correlation coefficient (r) and linear regression (R2) in three models were estimated using leaves, inflorescences and height as dependent and independent variables. In Model 1, the number of leaves was the dependent variable and plant height was the independent variable; Model 2 included the number of inflorescences as the dependent variable and the number of leaves as the independent variable, whereas the number of inflorescences as the dependent variable and number of leaves and height as the independent variables were used in Model 3. All models exhibited a significantly positive correlation and R2 (p < 0.01). Specifically, Model 3, examining the interactions of inflorescence with leaf numbers and plant height, demonstrated higher values for both r and R2. In conclusion, this study reveals the distinct patterns of functional traits in A. viridis in response to fertilizers and within wild populations, providing predictive models applicable to diverse data types, with implications for understanding inherent growth and responses of weed species for sustainable weed management practices, particularly in collaboration with smallholder farmers.