Optimum Soybean Seeding Rates by Yield Environment in Southern Brazil. (1st November 2018)

• Record Type: Journal Article
• Authors: Corassa, Geomar M.; Amado, Telmo J. C.; Strieder, Mércio L.; Schwalbert, Rai; Pires, João L. F.; Carter, Paul R.; Ciampitti, Ignacio A.
• Is Part Of: Agronomy Journal. Volume 110:Number 6(2018)
• Page Start: 2430
• Page End: 2438
• Publication Date: 2018-11-01
• Subjects: Agronomy -- Periodicals; 630
• DOI: 10.2134/agronj2018.04.0239
• Languages: English
• ISSNs: 0002-1962

Abstract

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.