Defining optimal soybean seeding rates and associated risk across North America

Soybean [Glycine max (L.) Merr.] seeding rate research across North America is typically conducted in small geo-political regions where environmental effects on the seeding rate × yield relationship are minimized. Data from 211 individual field studies (∼21,000 data points, 2007–2017) were combined from across North America ranging in yield from 1,000– 7,500 kg ha−1. Cluster analysis was used to stratify each individual field study into similar environmental (soil × climate) clusters and into high (HYL), medium (MYL), and low (LYL) yield levels. Agronomically optimal seeding rates (AOSR) were calculated and Monte Carlo risk analysis was implemented. Within the two northern most clusters the AOSR was higher in the LYL followed by the MYL and then HYL. Within the farthest south cluster, a relatively small (±15,000 seeds ha−1) change in seeding rate from the MYL was required to reach the AOSR of the LYL and HYL, respectively. The increase in seeding rate to reach the LYL AOSR was relatively greater (5x) than the decrease to reach the HYL AOSR within the northern most cluster. Regardless, seeding rates below the AOSR presented substantial risk and potential yield loss, while seeding rates above provided slight risk reduction and yield increases. Specific to LYLs and MYLs, establishing and maintaining an adequate plant stand until harvest maximized yield regardless of the seeding rate, while maximizing seed number was important with lower seeding rates. These findings will help growers manage their soybean seed investment by adjusting seeding rates based upon the productivity of the environment.Fil: Gaspar, Adam P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mourtzinis, Spyridon. University of Wisconsin; Estados UnidosFil: Kyle, Don. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Galdi, Eric. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Lindsey, Laura E.. Ohio State University; Estados UnidosFil: Hamman, William P.. Ohio State University; Estados UnidosFil: Matcham, Emma G. University of Wisconsin; Estados UnidosFil: Kandel, Hans J.. North Dakota State University; Estados UnidosFil: Schmitz, Peder. North Dakota State University; Estados UnidosFil: Stanley, Jordan D.. North Dakota State University; Estados UnidosFil: Schmidt, John P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mueller, Daren S.. University of Iowa; Estados UnidosFil: Nafziger, Emerson D.. University of Illinois; Estados UnidosFil: Ross, Jeremy. University of Arkansas for Medical Sciences; Estados UnidosFil: Carter, Paul R.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Varenhorst, Adam J.. University of South Dakota; Estados UnidosFil: Wise, Kiersten A.. University of Kentucky; Estados UnidosFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Carciochi, Walter Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Kansas State University; Estados UnidosFil: Chilvers, Martin I.. Michigan State University; Estados UnidosFil: Hauswedell, Brady. University of South Dakota; Estados UnidosFil: Tenuta, Albert U.. University of Guelph; CanadáFil: Conley, Shawn P.. University of Wisconsin; Estados Unido

Soybean Seeding Rate and Row Spacing Effects on Plant Establishment and Yield

North Dakota soybean [Glycine max (L.) Merrill] management varies across the state, resulting in yield differences. Eight soybean seeding rates (starting at 197600 and increasing by 49400 live seed ha-1 increments) and row spacing (30 and 61 cm) were evaluated in 15 North Dakota environments in 2017-2018 to determine plant densities, seed yield, and plant loss, which were compared with soybean producer field data. Planting 30 cm row spacing yielded 183 kg ha-1 greater than 61 cm row spacing. On farm, maximum yields occurred at 414000 live seed ha-1 and final plant densities of 352000 plants ha-1. In research plots, 494000 live seed ha-1 had the highest yield. On farm, 8.9% plant loss occurred after plant establishment while research data observed 6.9% plant loss. North Dakota soybean producers should use narrow row spacing, use final plant density to estimate yields, and 444600 live seed ha-1 provided the highest net revenue.North Central Soybean Research ProgramNorth Dakota Soybean Counci

Strategies for Improving Wheat and Soybean Production Systems in North Dakota

Planting date (PD), seeding rate (SR), genotype, and row spacing (RS) influence hard red spring wheat (HRSW, Triticum aestivum L. emend. Thell.) and soybean [Glycine max (L.) Merr.] yield. Evaluating HRSW economic optimum seeding rates (EOSR) is needed as modern hybrids may improve performance and have different SR requirements than cultivars. Two cultivars and five hybrids were evaluated in five North Dakota environments at two PDs and five SRs ranging from 2.22-5.19 million live seeds ha-1 in 2019-2020. Planting date, SR, and genotypes have unique yield responses across environments. Hybrid yield was the most associated with kernels spike-1 (r=0.17 to 0.43). The best hybrid yielded greater than cultivars in three environments. The EOSR ranged from 4.08-4.15 and 3.67-3.85 million seeds ha-1 for cultivars and hybrids, respectively. Hybrids are economical if seed prices are within $0.18 kg-1 of cultivars. In soybean, individual and synergistic effects of PD, SR, genotype relative maturity (RM), and RS on seed yield and agronomic characteristics, and how well canopy measurements can predict seed yield in North Dakota were investigated. Early and late PD, early and late RM, and two SRs (457 000 and 408 000 seed ha-1) were evaluated in 14 environments and two RS (30.5 and 61 cm) were included in four environments in 2019-2020. Individual factors resulted in 245 and 189 kg ha-1 more yield for early PD and late RM, respectively. The improved treatment of early PD, late RM, and high SR factors had 16$140 ha-1 more partial profit greater than the control. When including RS, 30.5 cm RS had 7% more yield than 61 cm RS. Adding 30.5 cm RS to the improved treatment in four environments resulted in 26% yield and $291 ha-1 more partial net profit compared to the control. A normalized difference vegetative index (NDVI) at R5 was the single best yield predictor, and stepwise regression using canopy measurements explained 69% of yield variation. North Dakota farmers are recommended to combine early PDs, late RM cultivars, 457 000 seed ha-1 SR, and 30.5 cm RS to improve soybean yield and profit compared to current management trends

Using Canopy Measurements to Predict Soybean Seed Yield

Predicting soybean [Glycine max (L.) Merr.] seed yield is of interest for crop producers to make important agronomic and economic decisions. Evaluating the soybean canopy across a range of common agronomic practices, using canopy measurements, provides a large inference for soybean producers. The individual and synergistic relationships between fractional green canopy cover (FGCC), photosynthetically active radiation (PAR) interception, and a normalized difference vegetative index (NDVI) measurements taken throughout the growing season to predict soybean seed yield in North Dakota, USA, were investigated in 12 environments. Canopy measurements were evaluated across early and late planting dates, 407,000 and 457,000 seeds ha−1 seeding rates, 0.5 and 0.8 relative maturities, and 30.5 and 61 cm row spacings. The single best yield predictor was an NDVI measurement at R5 (beginning of seed development) with a coefficient of determination of 0.65 followed by an FGCC measurement at R5 (R2 = 0.52). Stepwise and Lasso multiple regression methods were used to select the best prediction models using the canopy measurements explaining 69% and 67% of the variation in yield, respectively. Including plant density, which can be easily measured by a producer, with an individual canopy measurement did not improve the explanation in yield. Using FGCC to estimate yield across the growing season explained a range of 49% to 56% of yield variation, and a single FGCC measurement at R5 (R2 = 0.52) being the most efficient and practical method for a soybean producer to estimate yield

Defining optimal soybean seeding rates and associated risk across North America

Soybean [Glycine max (L.) Merr.] seeding rate research across North America is typically conducted in small geo-political regions where environmental effects on the seeding rate × yield relationship are minimized. Data from 211 individual field studies (∼21,000 data points, 2007–2017) were combined from across North America ranging in yield from 1,000– 7,500 kg ha−1. Cluster analysis was used to stratify each individual field study into similar environmental (soil × climate) clusters and into high (HYL), medium (MYL), and low (LYL) yield levels. Agronomically optimal seeding rates (AOSR) were calculated and Monte Carlo risk analysis was implemented. Within the two northern most clusters the AOSR was higher in the LYL followed by the MYL and then HYL. Within the farthest south cluster, a relatively small (±15,000 seeds ha−1) change in seeding rate from the MYL was required to reach the AOSR of the LYL and HYL, respectively. The increase in seeding rate to reach the LYL AOSR was relatively greater (5x) than the decrease to reach the HYL AOSR within the northern most cluster. Regardless, seeding rates below the AOSR presented substantial risk and potential yield loss, while seeding rates above provided slight risk reduction and yield increases. Specific to LYLs and MYLs, establishing and maintaining an adequate plant stand until harvest maximized yield regardless of the seeding rate, while maximizing seed number was important with lower seeding rates. These findings will help growers manage their soybean seed investment by adjusting seeding rates based upon the productivity of the environment.Fil: Gaspar, Adam P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mourtzinis, Spyridon. University of Wisconsin; Estados UnidosFil: Kyle, Don. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Galdi, Eric. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Lindsey, Laura E.. Ohio State University; Estados UnidosFil: Hamman, William P.. Ohio State University; Estados UnidosFil: Matcham, Emma G. University of Wisconsin; Estados UnidosFil: Kandel, Hans J.. North Dakota State University; Estados UnidosFil: Schmitz, Peder. North Dakota State University; Estados UnidosFil: Stanley, Jordan D.. North Dakota State University; Estados UnidosFil: Schmidt, John P.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Mueller, Daren S.. University of Iowa; Estados UnidosFil: Nafziger, Emerson D.. University of Illinois; Estados UnidosFil: Ross, Jeremy. University of Arkansas for Medical Sciences; Estados UnidosFil: Carter, Paul R.. Dow Agrosciences Argentina Sociedad de Responsabilidad Limitada.; ArgentinaFil: Varenhorst, Adam J.. University of South Dakota; Estados UnidosFil: Wise, Kiersten A.. University of Kentucky; Estados UnidosFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados UnidosFil: Carciochi, Walter Daniel. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; Argentina. Kansas State University; Estados UnidosFil: Chilvers, Martin I.. Michigan State University; Estados UnidosFil: Hauswedell, Brady. University of South Dakota; Estados UnidosFil: Tenuta, Albert U.. University of Guelph; CanadáFil: Conley, Shawn P.. University of Wisconsin; Estados Unido