Soybean seed yield response to plant density by yield environment in north america

Inconsistent soybean [Glycine max (L.) Merr.] seed yield response to plant density has been previously reported. Moreover, recent economic and productive circumstances have caused interest in within-field variation of the agronomic optimal plant density (AOPD) for soybean. Thus, the objectives of this study were to: (i) determine the AOPD by yield environments (YE) and (ii) study variations in yield components (seed number and weight) related to the changes in seed yield response to plant density for soybean in North America. During 2013 and 2014, a total of 78 yield-to-plant density responses were evaluated in different regions of the United States and Canada. A soybean database evaluating multiple seeding rates ranging from 170,000 to 670,000 seeds ha–1 was collected, including final number of plants, seed yield, and its components (seed number and weight). The data was classified in YEs: Low (LYE, 4.3 Mg ha–1). The main outcomes were: (i) AOPD increased by 24% from HYE to LYE, (ii) per-plant yield increased due to a decrease in plant density: HYE > MYE > LYE, and (iii) per-plant yield was mainly driven by seed number across plant densities within a YE, but both yield components influenced per-plant yield across YEs. This study presents the first attempt to investigate the seed yieldto- plant density relationship via the understanding of plant establishment and yield components and by exploring the influence of weather variables defining soybean YEs.Fil: Carciochi, Walter Daniel. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Schwalbert, Rai. Kansas State University; Estados UnidosFil: Andrade, Fernando Héctor. Instituto Nacional de Tecnología Agropecuaria. Centro Regional Buenos Aires Sur. Estación Experimental Agropecuaria Balcarce. Agencia de Extensión Rural Balcarce; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Mar del Plata; ArgentinaFil: Corassa, Geomar M.. Kansas State University; Estados UnidosFil: Carter, Paul. Kansas State University; Estados UnidosFil: Gaspar, Adam P.. Kansas State University; Estados UnidosFil: Schmidt, John. Kansas State University; Estados UnidosFil: Ciampitti, Ignacio Antonio. Kansas State University; Estados Unido

Forecasting maize yield at field scale based on high-resolution satellite imagery

Estimating maize (Zea mays L.) yields at the field level is of great interest to farmers, service dealers, and policy-makers. The main objectives of this study were to: i) provide guidelines on data selection for building yield forecasting models using Sentinel-2 imagery; ii) compare different statistical techniques and vegetation indices (VIs) during model building; and iii) perform spatial and temporal validation to see if empirical models could be applied to other regions or when models' coefficients should be updated. Data analysis was divided into four steps: i) data acquisition and preparation; ii) selection of training data; iii) building of forecasting models; and iv) spatial and temporal validation. Analysis was performed using yield data collected from 19 maize fields located in Brazil (2016 and 2017) and in the United States (2016), and normalized vegetation indices (NDVI, green NDVI and red edge NDVI) derived from Sentinel-2. Main outcomes from this study were: i) data selection impacted yield forecast model and fields with narrow yield variability and/or with skewed data distribution should be avoided; ii) models considering spatial correlation of residuals outperformed Ordinary least squares (OLS) regression; iii) red edge NDVI was most frequently retained into the model compared with the other VIs; and iv) model prediction power was more sensitive to yield data frequency distribution than to the geographical distance or years. Thus, this study provided guidelines to build more accurate maize yield forecasting models, but also established limitations for up-scaling, from farm-level to county, district, and state-scales.Publicado originalmente en: Rai A. Schwalbert, Telmo J.C. Amado, Luciana Nieto, Sebastian Varela, Geomar M. Corassa, Tiago A.N. Horbe, Charles W. Rice, Nahuel R. Peralta, Ignacio A. Ciampitti. Forecasting maize yield at field scale based on high-resolution satellite imagery. Biosystem Engineering. 171: 179–192 DOI: https://doi.org/10.1016/j.biosystemseng.2018.04.020Sociedad Argentina de Informática e Investigación Operativ

Physiological quality of soybean seeds under different yield environments and plant density

ABSTRACT Yield potential of agricultural fields associated with plant spatial arrangement could determine the physiological quality of soybean (Glycine max L.) seeds. Thus, this study aimed to evaluate the physiological quality of soybean seeds from different yield environments and plant densities. Experiments were carried out in Boa Vista das Missões-RS, Brazil, during the 2014/2015 growing season. Yield environments were delineated by overlapping yield maps from the 2008, 2009/2010 and 2011/2012 growing seasons. The experimental design was a randomized complete block in a 2 x 5 factorial arrangement with two yield environments (low and high) and five plant densities, with four replicates. Two varieties were tested: Brasmax Ativa RR (10, 15, 20, 25 and 30 plants m-1) and Nidera 5909 RR (5, 10, 15, 20 and 25 plants m-1). After harvested, the seeds were analysed as following: first count index, germination, abnormal seedlings, dead seeds, electrical conductivity, accelerate aging test, root length, hypocotyl length and seedling length. The spatial variability of seed vigor in the production field could be reduced by adjusting plant density, but the adjustment should consider the variety. Harvest according to yield environment is a strategy to separate lots of seeds with higher vigor, originated from high-yield environments

White lupine yield under different sowing densities and row spacings

ABSTRACT The study aimed to evaluate different sowing densities and row spacings on grain yield and biomass in the white lupine crop, cv. ‘Comum’. The experimental design was a randomized block in a 4 x 4 factorial scheme, with four row spacings (20, 40, 60 and 80 cm) and four sowing densities in the row (10, 15, 20 and 25 plants m-1), with four replicates. The evaluated variables were: grain yield, hundred-grain weight, fresh and dry matter and the contents of nitrogen, phosphorus and potassium in the plant tissue. The highest grain yield was obtained with row spacing of 20 cm, regardless of plant density. The density of 25 plants m-1 and row spacing of 20 cm increased the fresh and dry matter yield. The adjustment of plant density and row spacing did not affect the content of nitrogen, phosphorus and potassium in plant tissue

Physiological quality of soybean seeds under different yield environments and plant density

<div><p>ABSTRACT Yield potential of agricultural fields associated with plant spatial arrangement could determine the physiological quality of soybean (Glycine max L.) seeds. Thus, this study aimed to evaluate the physiological quality of soybean seeds from different yield environments and plant densities. Experiments were carried out in Boa Vista das Missões-RS, Brazil, during the 2014/2015 growing season. Yield environments were delineated by overlapping yield maps from the 2008, 2009/2010 and 2011/2012 growing seasons. The experimental design was a randomized complete block in a 2 x 5 factorial arrangement with two yield environments (low and high) and five plant densities, with four replicates. Two varieties were tested: Brasmax Ativa RR (10, 15, 20, 25 and 30 plants m-1) and Nidera 5909 RR (5, 10, 15, 20 and 25 plants m-1). After harvested, the seeds were analysed as following: first count index, germination, abnormal seedlings, dead seeds, electrical conductivity, accelerate aging test, root length, hypocotyl length and seedling length. The spatial variability of seed vigor in the production field could be reduced by adjusting plant density, but the adjustment should consider the variety. Harvest according to yield environment is a strategy to separate lots of seeds with higher vigor, originated from high-yield environments.</p></div