Climate Change and Management Impacts on Soybean N Fixation, Soil N Mineralization, N2O Emissions, and Seed Yield.

Limited knowledge about how nitrogen (N) dynamics are affected by climate change, weather variability, and crop management is a major barrier to improving the productivity and environmental performance of soybean-based cropping systems. To fill this knowledge gap, we created a systems understanding of agroecosystem N dynamics and quantified the impact of controllable (management) and uncontrollable (weather, climate) factors on N fluxes and soybean yields. We performed a simulation experiment across 10 soybean production environments in the United States using the Agricultural Production Systems sIMulator (APSIM) model and future climate projections from five global circulation models. Climate change (2020-2080) increased N mineralization (24%) and N2O emissions (19%) but decreased N fixation (32%), seed N (20%), and yields (19%). Soil and crop management practices altered N fluxes at a similar magnitude as climate change but in many different directions, revealing opportunities to improve soybean systems' performance. Among many practices explored, we identified two solutions with great potential: improved residue management (short-term) and water management (long-term). Inter-annual weather variability and management practices affected soybean yield less than N fluxes, which creates opportunities to manage N fluxes without compromising yields, especially in regions with adequate to excess soil moisture. This work provides actionable results (tradeoffs, synergies, directions) to inform decision-making for adapting crop management in a changing climate to improve soybean production systems.

Retrieving and processing agro-meteorological data from API-client sources using R software.

OBJECTIVES: The main purpose of this publication is to help users (students, researchers, farmers, advisors, etc.) of weather data with agronomic purposes (e.g. crop yield forecast) to retrieve and process gridded weather data from different Application Programming Interfaces (API client) sources using R software. DATA DESCRIPTION: This publication consists of a code-tutorial developed in R that is part of the data-curation process from numerous research projects carried out by the Ciampitti's Lab, Department of Agronomy, Kansas State University. We make use of three weather databases for which specific libraries were developed in R language: (i) DAYMET (Thornton et al. in https://daymet.ornl.gov/ , 2019; https://github.com/bluegreen-labs/daymetr ), (ii) NASA-POWER (Sparks in J Open Source Softw 3:1035, 2018; https://github.com/ropensci/nasapower ), and (iii) Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) (Funk et al. in Sci Data 2:150066, 2015; https://github.com/ropensci/chirps ). The databases offer different weather variables, and vary in terms of spatio-temporal coverage and resolution. The tutorial shows and explain how to retrieve weather data from multiple locations at once using latitude and longitude coordinates. Additionally, it offers the possibility to create relevant variables and summaries that are of agronomic interest such as Shannon Diversity Index (SDI) of precipitation, abundant and well distributed rainfall (AWDR), growing degree days (GDD), crop heat units (CHU), extreme precipitation (EPE) and temperature events (ETE), reference evapotranspiration (ET0), among others.

Vertical Canopy Profile and the Impact of Branches on Soybean Seed Composition.

Soybean [Glycine max (L.) Merr.] seeds are of global importance for human and animal nutrition due to their high protein and oil concentrations, and their complete amino acid (AA) and fatty acid (FA) profiles. However, a detailed description of seed composition at different canopy portions (i.e., main stem and branch nodes) is currently lacking in scientific literature. This study aims to (1) characterize seed yield and composition (protein, oil, AA, and FA) at the main stem (exploring a vertical canopy profile) and stem branches and (2) quantify the impact of canopy yield allocation on seed composition, focusing on branches as a potential contributor for higher yields. Four genotypes were field-grown during the 2018 and 2019 seasons, with seeds manually harvested from all the branches and three main stem segments (lower, middle, and upper). Seed samples were analyzed for seed yield (Mg/ha), seed size (mg/seed), protein and oil content (mg/seed) and their respective concentrations (g/kg), and AA and FA concentrations within protein and oil (g/100 g), herein called abundance. The upper main stem produced greater protein (25%) and oil (15%) content relative to the lower section; however, oil concentration increased from top to bottom while protein concentration followed opposite vertical gradient. Limiting AAs (lysine, cysteine, methionine, threonine, and tryptophan) were more abundant in the lower main stem, while the oleic/(linoleic + linolenic) ratio was greater in the upper segment. Overall, branches produced seeds with inferior nutritional quality than the main stem. However, the contribution of branches to yield (%) was positively related to limiting AA abundance and oil concentration across soybean genotypes. Future research studies should consider the morphological process of stem branching as a critical factor intimately involved with soybean seed composition across environments, genotypes, and management practices.

XPolaris: an R-package to retrieve United States soil data at 30-meter resolution.

OBJECTIVES: This data article aims to introduce the "XPolaris" R-package, designed to facilitate access to detailed soil data at any geographical location within the contiguous United States (CONUS). Without the need of advanced R-programming skills, XPolaris enables users to convert raster data from the POLARIS database into traditional spreadsheet format [i.e., Comma-Separated Values (CSV)] for further data analyses. DATA DESCRIPTION: The core of this publication is a code-tutorial envisioned to assist users in retrieving soil raster data within the CONUS. All data is sourced from the POLARIS database, a 30-m probabilistic map of soil series and different soil properties [Chaney et al. Geoderma 274:54, 2016, Chaney et al. Water Resour Res 55:2916, 2019]. POLARIS represents an optimization of the Soil Survey Geographic (SSURGO) database, circumventing issues of spatial disaggregation, harmonizing, and filling spatial gaps. POLARIS was constructed using a machine learning algorithm, the Disaggregation and Harmonisation of Soil Map Units Through Resampled Classification Trees (DSMART-HPC) [Odgers et al. Geoderma 214:91, 2014]. Although the data is easily accessible in a raster format, retrieving large amounts of data can be time-consuming or require advanced programming skills.

Revisiting Biological Nitrogen Fixation Dynamics in Soybeans.

Biological nitrogen (N) fixation is the most relevant process in soybeans (Glycine max L.) to satisfy plant N demand and sustain seed protein formation. Past studies describing N fixation for field-grown soybeans mainly focused on a single point time measurement (mainly toward the end of the season) and on the partial N budget (fixed-N minus seed N removal), overlooking the seasonal pattern of this process. Therefore, this study synthesized field datasets involving multiple temporal measurements during the crop growing season to characterize N fixation dynamics using both fixed-N (kg ha-1) and N derived from the atmosphere [Ndfa (%)] to define: (i) time to the maximum rate of N fixation (ß2), (ii) time to the maximum Ndfa (α2), and (iii) the cumulative fixed-N. The main outcomes of this study are that (1) the maximum rate of N fixation was around the beginning of pod formation (R3 stage), (2) time to the maximum Ndfa (%) was after full pod formation (R4), and (3) cumulative fixation was positively associated with the seasonal vapor-pressure deficit (VPD) and growth cycle length but negatively associated with soil clay content, and (4) time to the maximum N fixation rate (ß2) was positively impacted by season length and negatively impacted by high temperatures during vegetative growth (but positively for VPD, during the same period). Overall, variation in the timing of the maximum rate of N fixation occurred within a much narrower range of growth stages (R3) than the timing of the maximum Ndfa (%), which varied broadly from flowering (R1) to seed filing (R5-R6) depending on the evaluated studies. From a phenotyping standpoint, N fixation determinations after the R4 growth stage would most likely permit capturing both maximum fixed-N rate and maximum Ndfa (%). Further investigations that more closely screen the interplay between N fixation with soil-plant-environment factors should be pursued.

Environmental Factors Associated With Nitrogen Fixation Prediction in Soybean.

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.

Historical trend on seed amino acid concentration does not follow protein changes in soybeans.

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.