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Core Ideas Corn yield response to plant density and N rate were dependent on yield environment.Agronomic optimal plant density and N rate were positively correlated to yield level.Yield to density within a yield environment was independent on year, country, and hybrid.Similarity in yield frequency data distributions lead to similar yield–factor responses. Understanding the relationship of corn (Zea mays L.) yield responses to plant density and nitrogen (N) fertilization is critical to production decisions. The main objectives of this study were to (i) evaluate yield responses to plant density and fertilizer N rate at varying yields adjusting models considering a spatial component, (ii) perform a validation for the fitted models with an independent dataset, and (iii) identify key statistical parameters for the yield data distribution governing response models. Analyses were conducted with information from seven fields with 21 studies (one study per yield environment, with three environments per field) conducted from 2009 to 2017 in southern Brazil with geospatial data collected to evaluate yield response to plant density and fertilizer N rates (28911 data points) and one additional database with 12 field studies conducted from 2012 to 2015 in the US Midwest (1773 data points). Databases were divided into training and validation datasets. Field experiments evaluating both plant density and N rate were selected as training dataset. Key research findings were (i) yield–factor response models were dependent on yield environment and within a yield environment those models remained constant regardless the year, country, and hybrid for all evaluated fields, (ii) statistical models considering spatial correlation of the random errors outperformed those considering errors independent and identically distributed and, (iii) yield distribution with comparable 50% interquartile range and mode portrayed similar yield–factor relationship. In summary, fitting spatial yield–density models considering yield data distribution is critical to upscale site‐specific models to larger spatial domains.

Successive swine effluent applications can substantially increase the transfer of phosphorus (P) forms in runoff. The aim of this study was to evaluate P accumulation in the soil and transfer of P forms in surface runoff from a Hapludalf soil under no-tillage subjected to successive swine effluent applications. This research was carried out in the Agricultural Engineering Department of the Federal University of Santa Maria, Brazil, from 2004 to 2007, on a Typic Hapludalf soil. Swine effluent rates of 0, 20, 40, and 80 m3 ha(-1) were broadcast over the soil surface prior to sowing of different species in a crop rotation. Soil samples were collected in stratified layers, and the levels of available P were determined. Samples of water runoff from the soil surface were collected throughout the period, and the available, soluble, particulate, and total P were measured. Successive swine effluent applications led to increases in P availability, especially in the soil surface, and P migration through the soil profile. Transfer of P forms was closely associated with runoff, which is directly related to rainfall volume. Swine effluent applications also reduced surface runoff. These results show that in areas with successive swine effluent applications, practices that promote higher water infiltration into the soil are required, e.g., crop rotation and no-tillage system.

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