A meta-analysis of hairy vetch as a previous cover crop for maize.

Background: The use of hairy vetch (Vicia villosa Roth.) as cover crop is increasing worldwide. Hairy vetch can contribute as a nitrogen (N) source with potential to impact subsequent high N demanding cereals such as maize (Zea mays L.). Contrasting literature results emphasize the need for a global synthesis analysis to quantify changes in maize yield after hairy vetch. Objectives: A meta-analysis was conducted to i) quantify maize yield response to hairy vetch as previous crop, ii) explore hairy vetch influence on fertilized and non-N fertilized maize yields, and iii) assess the tillage and environment factors on maize yield response to hairy vetch. Methods: The global systematic search yielded 23 publications selected by the following criteria, i) hairy vetch dry matter at the end of the season, ii) maize grain yield, and iii) experimental design with (Mzhv) and without (Mzcontrol) hairy vetch treatments. Information such as N fertilization for maize, N accumulation in hairy vetch, organic matter, and tillage before maize sowing were recorded. Hairy vetch effects (effect size) were expressed as a ratio (percentage of grain yield variation in Mzhv/Mzcontrol). Results: Under non-N fertilization (n = 9), results revealed hairy vetch had mostly a positive effect, ranging from 13 to 45% (n = 6). In contrast, N-fertilized maize (n = 20) showed a high chance of neutral effects (n = 12), moderate probability of positive yield impact (7 to 38%, n = 6), and a low likelihood of negative effects (-32 and -17%, n = 2). Notably, maize yields improved by 21-25% when the N accumulation in hairy vetch ranged from 95 to 150 kg ha-1 and N rate from 0 to 120 kg ha-1. Non-N-fertilized maize exhibited a 14% increase in response in no-till systems and a 31% increase with conventional tillage. Conclusion: This study summarizes potential benefits of hairy vetch preceding maize. Yet, the heterogeneous outcomes deserve further exploration in terms of environment and management factors.

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.

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.

Subsoil-potassium depletion accounts for the nutrient budget in high-potassium agricultural soils.

Continuous potassium (K) removal without replenishment is progressively mining Argentinean soils. Our goals were to evaluate the sensitivity of soil-K to K budgets, quantify soil-K changes over time along the soil profile, and identify soil variables that regulate soil-K depletion. Four on-farm trials under two crop rotations including maize, wheat and soybean were evaluated. Three treatments were compared: (1) control (no fertilizer applied); (2) application of nitrogen, phosphorus, and sulfur fertilizers -NPS-; and (3) pristine condition. After nine years, crops removed from 258 to 556 kg K ha-1. Only two sites showed a decline in the exchangeable-K levels at 0-20 cm but unrelated to K budget. Topsoil exchangeable-K levels under agriculture resulted 48% lower than their pristine conditions, although still above response levels. Both soil exchangeable-K and slowly-exchangeable K vertical distribution patterns (0-100 cm) displayed substantial depletion relative to pristine conditions, mainly concentrated at subsoil (20-100 cm), with 55-83% for exchangeable-K, and 74-95% for slowly-exchangeable-K. Higher pristine levels of exchangeable-K and slowly-exchangeable-K and lower clay and silt contents resulted in higher soil-K depletion. Soil K management guidelines should consider both topsoil and subsoil nutrient status and variables related to soil K buffer capacity.

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.