Interference and threshold level of Sida rhombifolia in transgenic soybean cultivars

Abstract

This study aimed to assess the interference and threshold level (TL) of Sida rhombifolia, the arrowleaf sida, competing with different soybean cultivars. The treatments comprised different soybean cultivars (NS 6909, NA 5909, DM 5958, Brasmax ELITE, Brasmax LANÇA, and SYN 13561) and densities of arrowleaf sida plants per square meter (m-2) (0, 2, 3, 4, 9, 15, 16, 23, 22, and 58; 0, 2, 3, 3, 6, 6, 10, 11, 18, and 47; 0, 3, 4, 7, 8, 10, 11, 13, 15, and 24; 0, 1, 4, 6, 12, 18, 19, 31, 44, and 50; 0, 4, 5, 6, 9, 13, 17, 20, 20, and 47; 0, 2, 3, 5, 9, 11, 15, 18, 29, and 30, respectively) for each cultivar. Cultivars NS 6909, NA 5909, and Brasmax Lança were more competitive than DM 5958, Brasmax Elite, and SYN 13561. The TL values varied from 0.55 to 0.95 plants m-2 for cultivars NS 6909, NA 5909, and Brasmax Lança, which exhibited greater competitiveness with arrowleaf sida. The lowest values of TL varied from 0.26 to 0.61 plants m-2 for cultivars DM 5958, Brasmax Elite, and SYN 13561, which had less competitiveness with weed. Highlights The rectangular hyperbola model adequately estimates the grain yield losses of soybean cultivars. The density of arrowleaf sida plants was the variable that best fitted the rectangular hyperbola model. Soybean cultivars NS 6909 IPRO, NA 5909 RG, and Brasmax Lança IPRO are the most competitive in the presence of arrowleaf sida. The levels of economic damage decrease with the increase in grain yield, in the soybean price, with the reduction in the cost of controlling arrowleaf sida and the herbicide efficiency.

Interference and economic damage level of volunteer corn plants in soybeans

“…This indicates a good fit (R 2 ) of the data to the rectangular hyperbola model and low variability (MSR). The results are similar to those observed by Galon et al (2022), when evaluating competition and the EDL of guanxuma (Sida rhombifolia) infesting six soybean cultivars, found average R 2 values of over 0.57 for the same variables studied in this study and low MSR values.…”

“…Taking into account the average of the i parameter for the PD, GC, LA and DM variables, the cultivars Nidera 5909 RG, Syngenta 13561 IPRO and Brasmax Lança IPRO were characterized as the most competitive materials in the presence of volunteer corn. Studies indicate that soybean cultivars differ in their ability to compete in the presence of weeds (Forte et al, 2017;Souza et al, 2019;Galon et al, 2022). This is probably due to the (1)…”

“…In this research, the different densities of volunteer corn acted as replicates, providing the variance needed to carry out the statistical analysis using the non-linear rectangular hyperbola model proposed by Cousens (1985). This same methodology has been used in several other studies developed with a similar objective to the present study, to assess the competitive ability of cultivars and the economic damage level of weeds infesting crops (Agostinetto et al, 2010;Tavares et al, 2019;Brandler et al, 2021;Galon et al, 2022).…”

“…They compete with the crop of economic interest for water, light and nutrients (Petter et al, 2016;Aguiar et al, 2018). These plants come from grain losses at harvest called volunteer plants or tigueras (Piasecki et al, 2018;Galon et al, 2022). In addition to competing for resources in the environment, volunteer plants can be hosts to pests and diseases that affect crops of economic interest (Aguiar et al, 2018;Piasecki et al, 2018).…”

“…When they occur in high densities, it simplifies the decision for producers to adopt control measures (Agostinetto et al, 2010). However, when they appear in lower densities, adopting control measures becomes difficult, as farmers need to quantify the economic advantages associated with the cost of control (Tavares et al, 2019;Galon et al, 2022). Faced with this situation, it is necessary to implement management strategies that integrate technical knowledge and economic analysis, together with an understanding of the competitive relationship between crops and weeds (Jha et al, 2017).…”