Competição de plantas daninhas na cultura da soja cultivada em sucessão ao milho com uso do sensoriamento remoto proximal

Abstract

Soybeans and corn are vital crops in the global agricultural sector, with Brazil being the largest producer of soybeans and the third largest of corn. These crops are often cultivated in succession due to the country's favorable climatic conditions. However, this practice leads to intensified use of herbicides with similar mechanisms of action, creating selection pressure and promoting the emergence of resistant biotypes. In this context, the establishment of vegetation cover has emerged as a promising alternative for weed management. Furthermore, proximal remote sensing (PRS) has proven to be an effective tool for decision-making, offering detailed insights into the variability of vegetation and agricultural crop areas. This study aimed to analyze weed competition in soybean crops following corn cultivation and various cover crops, employing phytosociological indices and PRS techniques. The experiment was conducted in the experimental area of the Federal Technological University of Paraná (UTFPR), at the Santa Helena campus. A randomized block design was employed, featuring six treatments and four replications. The treatments (T) evaluated were as follows: T1 - corn straw with Saflufenacil; T2 - corn straw with Diclosulan + Halauxifen-methyl; T3 - wheat straw; T4 - black oat straw; T5 - brachiaria straw; and T6 - control (uncultivated area). The vegetation indices analyzed included the NDVI (Normalized Difference Vegetation Index) and NDRE (Normalized Difference Red Edge Index) at various stages of soybean development. A total of 15 weed species, representing eight botanical families, were identified, with black tar (Bidens pilosa), ragweed (Commelina benghalensis), bitter grass (Digitaria insularis), and white poaia (Richardia brasiliensis) being the most prevalent. The impact of weeds on productivity was notably greater in the treatments involving corn straw, attributed to its rapid decomposition, which facilitated the emergence of weed species. The treatments that yielded the highest soybean productivity were black oat straw (T4), brachiaria (T5) and wheat (T3). The lowest yields were observed in treatments T2 (corn + Diclosulan + Halauxifen-methyl), T1 (corn + Saflufenacil), and T6 (no-tillage control). In terms of weed control, the most effective treatments were T5 (brachiaria), T4 (black oats), and T6 (control). The herbicides applied alone did not demonstrate significant benefits over the other treatments, reinforcing the critical role of plant cover. According to the linear regression model, the treatments yielding the highest coefficients of determination (R²) for NDVI and NDRE were soybeans following black oats and corn treated with a herbicide containing two active ingredients. These results underscore the importance of utilizing cover crops for effective weed management and validate the suitability of SRP in assessing competition between crops and weed species. A combination of chemical management and conservation strategies is vital for fostering more sustainable agricultural practices