High Yielding Soybean: Genetic Gain and Nitrogen Limitation

The United States and Argentina account for more than 50% of the global soybean production. Closing yield gaps (actual on-farm yield vs. genetic yield potential) would require an improvement in the use of the available resources. Overall, 50-60% of soybean nitrogen (N) demand is usually met by the biological nitrogen fixation (BNF) process. A scientific knowledge gap still exists related to the ability of the BNF process to satisfy soybean N demand at varying yield levels. The overall objective of this project is to study the contribution of N via utilization of varying N strategies under historical and modern soybean genotypes. Two field experiments were conducted during the 2016-2017 growing seasons: Rossville, KS (US) and Oliveros, Santa Fe (ARG). This report focuses on the 2016 results. Twenty-one historical and modern soybean genotypes were utilized with release decades between 1980s and 2010s. All were inoculated and tested under three N management strategies: S1, non-N applied; S2, all N provided by fertilizer; and S3, late-N applied. The genetic improvement of soybean yield from the 1980s to 2010s was an overall increase of 30%, averaging results from US and ARG. Seed N content (N exported in seed) followed a similar trend for yield, while N concen­tration in seed was decreased as yields increased. Regarding N management for geno­types from all release decades, S2 (all N provided by fertilizer) generated up to a 20% increase in yields in the US and 5% in ARG. These results suggest that high yielding soybeans could be limited by N under specific growing conditions to express the yield potential

EVALUATING A HYBRID SOIL TEMPERATURE MODEL IN A CORN-SOYBEAN AGROECOSYSTEM AND A TALLGRASS PRAIRIE IN THE GREAT PLAINS

Simulation models of soil-related biological processes usually require soil temperature data. Frequently these soil temperatures are simulated, and the soil temperature algorithms cannot be more complicated than the original process model. This situation has led to the use of semi-empirical-type relationships in these process models. The objective of this study was to evaluate a hybrid soil temperature model, which combines empirical and mechanistic approaches, in an agroecosystem and a tallgrass prairie in the Great Plains. The original hybrid soil temperature model was developed and verified for a temperate forest system. This model simulated soil temperatures on a daily basis from meteorological inputs (maximum and minimum air temperatures) and soil and plant properties. This model was modified using different extinction coefficients for the plant canopy and ground litter. The agroecosystem consisted of a no-till rotation system of corn (Zea mays L.) and soybeans (Glycine max [L.] Merr.). Soil temperatures were measured at different depths in multiple years (three years and two-and-a-half years in the agroecosystem and tallgrass prairie, respectively). In the agroecosystem, the root mean square error of the modified model simulation varied from 1.41º to 2.05ºC for the four depths (0.1, 0.2, 0.3, and 0.5 m). The mean absolute error varied from 1.06º to 1.53ºC. The root mean square error and mean absolute error of the modified model were about 0.1º–0.3ºC less than the original model at the 0.2–0.5 m depths. For the tallgrass prairie, the mean absolute errors of the simulated soil temperatures were slightly greater than the agroecosystem, varying from 1.48º to 1.7ºC for all years and from 1.09º to 1.37ºC during the active growing seasons for all years

Nitrogen uptake, fixation and response to fertilizer N in soybeans: A review

Although relationships among soybean (Glycine max [L.] Merr) seed yield, nitrogen (N) uptake, biological N2 fixation (BNF), and response to N fertilization have received considerable coverage in the scientific literature, a comprehensive summary and interpretation of these interactions with specific emphasis on high yield environments is lacking. Six hundred and thirty-seven data sets (site–year–treatment combinations) were analyzed from field studies that had examined these variables and had been published in refereed journals from 1966 to 2006. A mean linear increase of 0.013 Mg soybean seed yield per kg increase in N accumulation in above-ground biomass was evident in these data. The lower (maximum N accumulation) and upper (maximum N dilution) boundaries for this relationship had slopes of 0.0064 and 0.0188 Mg grain kg−1 N, respectively. On an average, 50–60% of soybean N demand was met by biological N2 fixation. In most situations the amount of N fixed was not sufficient to replace N export from the field in harvested seed. The partial N balance (fixed N in above-ground biomass − N in seeds) was negative in 80% of all data sets, with a mean net soil N mining of −40 kg N ha−1. However, when an average estimated below-ground N contribution of 24% of total plant N was included, the average N balance was close to neutral (−4 kg N ha−1). The gap between crop N uptake and N supplied by BNF tended to increase at higher seed yields for which the associated crop N demand is higher. Soybean yield was more likely to respond to N fertilization in high-yield (\u3e4.5 Mg ha-1) environments. A negative exponential relationship was observed between N fertilizer rate and N2 fixation when N was applied on the surface or incorporated in the topmost soil layers. Deep placement of slow-release fertilizer below the nodulation zone, or late N applications during reproductive stages, may be promising alternatives for achieving a yield response to N fertilization in high-yielding environments. The results from many N fertilization studies are often confounded by insufficiently optimized BNF or other management factors that may have precluded achieving BNF-mediated yields near the yield potential ceiling. More studies will be needed to fully understand the extent to which the N requirements of soybean grown at potential yields levels can be met by optimizing BNF alone as opposed to supplementing BNF with applied N. Such optimization will require evaluating new inoculant technologies, greater temporal precision in crop and soil management, and most importantly, detailed measurements of the contributions of soil N, BNF, and the efficiency of fertilizer N uptake throughout the crop cycle. Such information is required to develop more reliable guidelines for managing both BNF and fertilizer N in high-yielding environments, and also to improve soybean simulation models

Soybean: Genetic Gain × Fertilizer Nitrogen Interaction

The United States (US) and Argentina (ARG) account for more than 50% of the global soybean production. Soybean yields are determined by the genotype, environment, and management practices (G × E × M) interaction. Overall, 50-60% of soybean nitrogen (N) demand is usually met by the biological nitrogen fixation (BNF) process. An unanswered scientific question concerns the ability of BNF process to satisfy soybean N demand at varying yield levels. The overall objective of this project was to study the contribution of N via utilization of different N strategies, evaluating soybean genotypes released in different eras. Four field experiments were conducted during the 2016 season: Ottawa (east central Kansas, US), Ashland Bottoms (central Kansas, US), Rossville (central Kansas, US), and Oliveros (Santa Fe province, Argentina). A wide variety of historical and modern soybean genotypes were used (from the 1980s, 1990s, 2000s and 2010s release decades) in the US and ARG, all tested under three N management strategies (S1: non-N applied but inoculated, S2: all N provided by fertilizer, and S3: late-N applied) and all seeds inoculated. At Ottawa, the study was planted in an area without previous soybean history with yields ranging from 21 to 30 bu/a. Modern genotype (2010) increased yields by 15% relative to the other varieties. As related to the N management approach, higher yields occurred when the N nutrition was based on S2 (overall 10% increase). At Ashland Bottoms, yields ranged from 47 to 65 bu/a, and the 1990s variety out-yielded the rest of the varieties by 13%. There was not statistical significance for N management at this location. At Rossville, yields ranged from 37 to 85 bu/a, with higher yields observed for the modern genotype (released after 2010). Regarding N strategies, S2 increased yields by 18% compared to S1. At ARG, yield ranged from 40 to 74 bu/a, with modern soybean varieties (released after 2010) yielding 34% greater than the rest of the varieties. Nitrogen application S2 increased yields by 5% when compared to the S1 strategy. Relative to yield potential, yield levels in Argentina were similar to those in central Kansas (Ashland Bottoms and Rossville)

Ponderación de la información generada en la Estación Experimental Agropecuaria Oliveros del INTA (INTA EEA Oliveros) mediante el proceso analítico jerárquico

En el siglo XXI, el subsistema de conocimiento científico-tecnológico se convirtió en el principal componente del capital cultural de un país viabilizando el desarrollo socioeconómico y la potencialidad de los países desarrollados. El Instituto Nacional de Tecnología Agropecuaria (INTA) es un organismo de ciencia y tecnología de Argentina entre cuyos objetivos está la generación y transferencia de conocimiento científico al sector agropecuario. Dicha información se genera en estaciones experimentales agropecuarias e institutos de investigación que lo integran y que abordan los problemas del sector en distintas regiones el país. Los sectores destinados a la investigación, experimentación y transferencia del conocimiento tienen como producto las publicaciones científicas, manuales, informes técnicos, cursos y talleres entre otros. Los objetivos de este estudio fueron: 1) clasificar la formas de presentación del conocimiento producido en la EEA Oliveros, así como seleccionar y definir los criterios y alternativas requeridos en la ponderación de este y 2) aplicar la ponderación a la producción de conocimientos de la EEA Oliveros para el año 2014. Para clasificar, agrupar y ponderar el producto de la investigación/extensión generada por el INTA Oliveros se reunió un equipo de investigadores y extensionistas de la institución. El Proceso Analítico Jerárquico (AHP) fue utilizado para el presente estudio. Este permite, a partir de valoraciones preasignadas, priorizar un conjunto de elementos, según los juicios, y preferencias de los individuos del equipo, adoptando un valor consensuado. Los resultados mostraron que los productos generados como información original (IO=revistas con y sin referato, así como presentación a congresos) representaron el 60.8% de la producción de conocimiento total; la información elaborada (IE=libros, capítulos libros, manuales y producción audiovisual) representó el 10.8% y la transferencia de conocimiento (TC=cursos, jornadas y disertaciones), el 28.4%. La metodología empleada resultó útil para ponderar la generación de conocimiento. Estos resultados pueden ser utilizados para comparar la producción de conocimiento entre períodos, entre unidades, consensuando los criterios con un equipo representativo de las estas, e incluso para ser utilizadas como insumo en análisis o evaluaciones de otras temáticas. De todas maneras, es aconsejable revisar y adecuar periódicamente los criterios y alternativas seleccionadas para acompañar los posibles cambios en enfoques y desarrollos que ocurren en investigación, según los períodos que se van atravesando.The scientific and technological knowledge subsystem becomes one of the main components of a country cultural capital, fostering its economic development. The National Institute of Agricultural Technology (INTA) is a science and technology body in Argentina whose objectives include the generation and transfer of scientific knowledge to the agricultural sector through experimental stations and research institutes located in different regions of the country. Research, experimentation and knowledge transfer produce scientific publications, manuals, technical reports, courses, congress presentations and workshops. The objectives of this study were: 1) to classify the way of featuring knowledge production in the EEA Oliveros, as well as to select and define the criteria and alternatives used for weighting (without seeking to qualify it) the original and elaborate knowledge produced, and 2) to apply the weighting to the EEA Oliveros’ knowledge production for the year 2014. A team of researchers and extensionists was gathered to classify, group and weight the research/extension produced by the EEA Oliveros. The Analytic Hierarchy Process (AHP) method was used for this study. It allows to prioritize a set of elements based on an established valuation scale, according to the team individuals’ judge, preferences and agreement. The results showed that the products generated as original information (OI=journals with and without peer review and congress presentation) accounted for 60.8% of total knowledge production; elaborated information (EI=books, book’s chapters, manuals and audiovisual production) 10.8% and the knowledge transfer (KT=courses, seminars and lectures) 28.4%.The methodology was useful to weigh the generation of knowledge. These results can be used to compare the production of knowledge between periods, between INTA units by the consensus of the criteria used, and even it can be used as input for analysis or evaluation of other issues. However, it is desirable to periodically review and adjust the selected criteria and alternatives to join the possible research changes along the periods that go through on approaches and developments.EEA OliverosFil: Rotolo, Gloria Claudia. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Milo Vaccaro, Marcelo. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Hoyos Mallqui, M. Pontificia Universidad Católica Argentina. Campus Rosario. Facultad de Química e Ingeniería; ArgentinaFil: Bacigaluppo, Silvina. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Salvagiotti, Fernando. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; ArgentinaFil: Castellarin, Julio Manuel. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Oliveros; Argentin