Soybean [Glycine max (L.) Merr.] is the most important oilseed crop for animal industry due to its high protein concentration and high relative abundance of essential and non-essential amino acids (AAs). However, the selection for high-yielding genotypes has reduced seed protein concentration over time, and little is known about its impact on AAs. The aim of this research was to determine the genetic shifts of seed composition for 18 AAs in 13 soybean genotypes released between 1980 and 2014. Additionally, we tested the effect of nitrogen (N) fertilization on protein and AAs trends. Soybean genotypes were grown in field conditions during two seasons under a control (0 N) and a N-fertilized treatment receiving 670 kg N ha⁻¹. Seed yield increased 50% and protein decreased 1.2% comparing the oldest and newest genotypes. The application of N fertilizer did not significantly affect protein and AAs concentrations. Leucine, proline, cysteine, and tryptophan concentrations were not influenced by genotype. The other AAs concentrations showed linear rates of decrease over time ranging from − 0.021 to − 0.001 g kg⁻¹ year⁻¹. The shifts of 11 AAs (some essentials such as lysine, tryptophan, and threonine) displayed a relative-to-protein increasing concentration. These results provide a quantitative assessment of the trade-off between yield improvement and seed AAs concentrations and will enable future genetic yield gain without overlooking seed nutritional value.

Biological nitrogen (N)-fixation is the most important source of N for soybean [Glycine max (L.) Merr.], with considerable implications for sustainable intensification. Therefore, this study aimed to investigate the relevance of environmental factors driving N-fixation and to develop predictive models defining the role of N-fixation for improved productivity and increased seed protein concentration. Using the elastic net regularization of multiple linear regression, we analyzed 40 environmental factors related to weather, soil, and crop management. We selected the most important factors associated with the relative abundance of ureides (RAU) as an indicator of the fraction of N derived from N-fixation. The most relevant RAU predictors were N fertilization, atmospheric vapor pressure deficit (VPD) and precipitation during early reproductive growth (R1–R4 stages), sowing date, drought stress during seed filling (R5–R6), soil cation exchange capacity (CEC), and soil sulfate concentration before sowing. Soybean N-fixation ranged from 60 to 98% across locations and years (n = 95). The predictive model for RAU showed relative mean square error (RRMSE) of 4.5% and an R² value of 0.69, estimated via cross-validation. In addition, we built similar predictive models of yield and seed protein to assess the association of RAU and these plant traits. The variable RAU was selected as a covariable for the models predicting yield and seed protein, but with a small magnitude relative to the sowing date for yield or soil sulfate for protein. The early-reproductive period VPD affected all independent variables, namely RAU, yield, and seed protein. The elastic net algorithm successfully depicted some otherwise challenging empirical relationships to assess with bivariate associations in observational data. This approach provides inference about environmental variables while predicting N-fixation. The outcomes of this study will provide a foundation for improving the understanding of N-fixation within the context of sustainable intensification of soybean production.

The corn biomass and nutrient dynamics may be altered when it is intercropped with Brachiaria (syn. Urochloa spp.). The present study aimed to investigate the dynamics of biomass, nitrogen (N), phosphorus (P) and potassium (K) for farming systems that produce corn intercropped with Brachiaria species. Field experiments were performed during the season and off-season, in a split-plot design. The main plots were composed of Brachiaria species (B. brizantha,B. ruziziensis and B. Convert) intercropped with corn, in addition to corn monocropping. The subplots consisted of three forage sampling periods, ranging from 0 to 60 days after the corn harvest. The intercropping arrangements did not affect the corn grain yield, nutrient accumulation and partitioning, relatively to the corn monocropping. After the grain harvest, B. brizantha achieved the greater biomass accumulation rate in both the season (69 kg ha^-1 day^-1) and off-season (17 kg ha^-1 day^-1). The nutrient accumulation ranged widely between the Brachiaria species and planting seasons: 0.2-1.2 kg ha^-1 day^-1 for N; 0.01-0.07 kg ha^-1 day^-1 for P; and 0.13-0.8 kg ha^-1 day^-1 for K. However, the greatest nutrient accumulation was found for B. brizantha, followed by B. ruziziensis and then B. Convert. In the short-term, corn intercropped with Brachiaria in the season showed the largest effect on the nutrient cycling and biomass yield. The intercropping between corn and B. brizantha in the season was the best way to enhance the biomass yield and the N, P and K cycling.

On‐farm research has emerged in recent years as a unique approach to involve farmers and other agricultural stakeholders as active participants in knowledge development and as an effective method of technology and innovation transfer across farms. This study assessed the perspective and knowledge of US farmers regarding on‐farm research via the implementation of a 24‐question survey distributed across most of the US Midwest and South‐Central regions. We found that farmers generally are willing to engage with universities to conduct on‐farm research and were 40% more likely to adopt practices supported by on‐farm research findings than research not conducted on‐farm. Notably, a shift toward conservation practices was made, with cover crops and no‐till at the forefront. Insights of this nature have implications for fostering collaborations, addressing constraints, and maximizing the impact of on‐farm research, offering guidance for sustainable agriculture progress in the United States and beyond. Results from this research survey could be used to initiate much‐needed policies to promote on‐farm research. Further, information on the benefits and drawbacks of on‐farm research could be used in the development of studies that benefit both farmers and researchers.