Optimum Soybean Seeding Rates by Yield Environment in Southern Brazil

Abstract

Core Ideas Soybean yield response to seeding rate was dependent on yield environment. Optimum seeding rate increased as yield environments were reduced. Seeding rate could be reduced by 18% for high‐yielding relative to low‐yielding environments, without penalizing yields. Planting date interacts with seed yield response to seeding rate, optimum seeding rates increase with late planting. For high‐yielding environment, late planting time decreased yields regardless of the seeding rate. Optimizing seed inputs while increasing farming profit is the main purpose of variable rate seeding (VRS) technology adoption. Previous studies in corn (Zea mays L.) suggested that optimal seeding rates increase as yield productivity level increased. For soybean [Glycine max (L.) Merr.], optimal yield‐to‐seeding rate by yield level has not been fully investigated, representing a scientific knowledge gap. Therefore, a dataset was collected from 109 replicated field trials from Southern Brazil (2180 experimental units) presenting the following objectives: (i) identify the optimum seeding rate at varying yield levels (herein termed as yield environments), and (ii) explore the contribution of management factors (i.e., seeding rate, planting date, row spacing, maturity groups, growing season, yield environment, and ecological region) on soybean seed yield. Hierarchical modeling and Bayesian statistical inference were used to predict optimum seeding rate at varying yield environments, while conditional inference tree analysis was explored to identify and rank factors contributing to yield variation. The main results were: (i) soybean seeding rate increased from high‐ to low‐yielding environments; (ii) seeding rate could be reduced by 18% in high‐yielding (>5 Mg ha−1) relative to the low‐yielding (<4 Mg ha−1) environments, without penalizing yields. For improving site‐specific soybean seeding rate prescriptions, future studies should focus on the physiological mechanisms underpinning yield formation and on understanding the main factors (soil × plant × weather) contributing to the differential optimum seeding rate response.

Minimum optimal seeding rate for indeterminate soybean cultivars grown in the tropics

“…Corassa et al. (2018) and Carciochi et al. (2019), studying SR reduction in different yield potential environments, also found that it is possible to reduce SR without damage to seed yield, especially in environments with high yield potential (up to 18% reduction compared to that in low‐yield environments).…”

“…For a detailed analysis of the response of each cultivar to SR, it was decided to unfold the results for SY, OY, and PY, even though there were no significant interactions among these factors. For these variables, a segmented regression (linear‐plateau) was chosen as a function of the biological behavior of soybean in response to the reduction in SR (Thompson et al., 2015, Corassa et al., 2018). For the other variables, linear or quadratic regression was adopted.…”

“…The main factors that motivate the reduction in soybean SR are: the increase in the costs of seed purchase and treatment and a positive relationship between lodging and SR (Gaspar et al., 2017; Grau, Oplinger, Adee, Hinkens, & Martinka, 1994). The minimum optimal seeding rate (MOSR, the smallest quantity of seeds necessary to achieve optimum yield) can vary as a function of environmental conditions (Carciochi et al., 2019; Corassa et al., 2018). In unfavorable environments, the MOSR tends to increase, since plants have lower phenotypic plasticity due to the scarcity of environmental resources.…”

Winter Wheat Yield Response to Plant Density as a Function of Yield Environment and Tillering Potential: A Review and Field Studies

“…In this line, recent studies in soybean [Glycine max(L.) Merr.] (Corassa et al, 2018;Carciochi et al, 2019), canola (Brassica napus L. "Canola") (Assefa et al, 2018b), and maize (Zea mays L.) (Assefa et al, 2016;Assefa et al, 2018a) classified the data on different YE levels based on its average yield and determined the AOPD at each YE. Therefore, as was observed for canola and soybean (e.g., crops that have compensation mechanisms comparable to wheat), the AOPD in wheat could change across YEs with a greater requirement of plants to attain the maximum yield at the low YE.…”

Defining optimal soybean seeding rates and associated risk across North America

“…Thus, there is clearly a wide range of agronomically and economically optimal seeding rates and plant stands driven by variation in seed costs, grain prices, seed treatment use, and most importantly, the productivity of the environment. Corassa, Amado, Schwalbert, Carter, and Ciampitti (2018) and Carciochi et al. (2019) demonstrated how the overall productivity of the environment affected the agronomically optimal seeding rate (AOSR) and plant density by segregating their data by yield level.…”