Productivity of soybean in management zones with application of different sowing densities

Abstract

The present study aimed to assess the efficiency of sowing at variable rates for soybean cultivation in two management zones (MZs) which were defined based on stable attributes and correlated with productivity using the Fuzzy C-means clustering algorithm and the kriging interpolation.Seeding was carried out in the 2015/2016 and 2017/2018 crops with a variation of 20% of seeds and crop row spacing of 0.70m. In each MZ, 8 plots with higher and lower seed density were established. Productivity was measured using a harvest monitor connected to a harvester. Data were filtered and submitted to descriptive analysis. Productivity maps were generated using the inverse square distance interpolation for each seeding density. In the MZ with the highest productive potential (MZ 1), the productivity was 3.39 and 3.18t ha-1, and in the MZ with the lowest productive potential (MZ 2) the productivity was 3.30 and 3.11t ha-1 for the years 2016 and 2018, respectively. Interpolation estimated higher productivity with the application of 15 plants m-1. Based on the economic analysis, it is suggested in this study the application of 214,000 plants ha-1 in both MZs.

Soybean Performance and Canonic Inter-Relations Towards Spacing Between Sowing Lines

“…Positive correlations of yield components as a function of morphological components were also reported by Santos et al (2018), as well as, the line spacing of 0.70 m, the density of 15 plants m -1 provided the highest yields in soybean crop (CAMICIA et al, 2018).…”

“…Cruz et al (2016) report the sowing of soybeans with double lines causes alteration of the plant architecture, reducing mainly its height. According to Camicia et al (2018), the modification of spacing in cultivars with greater plasticity, may not influence the final yield of the crop, when the spacing is greater than recommended. The use of different sowing densities and spacing are important practices that can provide better use of the resources available for growth and grain yield (SILVA et al, 2015;SANTOS et al, 2018).…”

Delineation of management zones in integrated crop–livestock systems

“…Monocultures such as soybean [ Glycine max (L.) Merr. ], corn ( Zea mays L.), and coffee ( Coffea arabica L.) (Camicia et al., 2018; Rodrigues & Corá, 2015; Rodrigues et al., 2013) have been the most prominent crops using this approach worldwide, but the spatio‐temporal interactions between multiple crops and the effects of cattle behavior in the livestock phase have been rarely analyzed from the point of view of PA in general. Recently, McEntee et al.…”

“…Monocultures such as soybean [Glycine max (L.) Merr. ], corn (Zea mays L.), and coffee (Coffea arabica L.) (Camicia et al, 2018;Rodrigues & Corá, 2015;Rodrigues et al, 2013) have been the most prominent crops using this approach worldwide, but the spatio-temporal interactions between multiple crops and the effects of cattle behavior in the livestock phase have been rarely analyzed from the point of view of PA in general. Recently, McEntee et al (2020) stated that modeling this system is a complex task, but possible to be performed through stable and unstable regions in terms of soil fertility and crop yield by using vegetation indices, georeferenced soil data, and soil apparent electrical conductivity (ECa).…”

Towards a new approach to estimate soybean yield at the field level

“…In view of the economic importance of soybean crop, several studies have been conducted to improve soybean crop production applying different management practices and goals: genetic traits (Todeschini et al, 2019), seed composition (Assefa et al, 2019) seed population (Camicia et al, 2018), planting date (Mourtzinis et al, 2017) and seed treatment (Kandel et al, 2016). Despite their different goals, all the cited studies evaluated crop yield as a comparison factor, highlighting the importance of yield in all the cases.…”