Soil depth and geographic distance modulate bacterial β-diversity in deep soil profiles throughout the U.S. Corn Belt

Understanding how microbial communities are shaped across spatial dimensions is of fundamental importance in microbial ecology. However, most studies on soil biogeography have focused on the topsoil microbiome, while the factors driving the subsoil microbiome distribution are largely unknown. Here we used 16S rRNA amplicon sequencing to analyse the factors underlying the bacterial β-diversity along vertical (0–240 cm of soil depth) and horizontal spatial dimensions (~500,000 km2) in the U.S. Corn Belt. With these data we tested whether the horizontal or vertical spatial variation had stronger impacts on the taxonomic (Bray-Curtis) and phylogenetic (weighted Unifrac) β-diversity. Additionally, we assessed whether the distance-decay (horizontal dimension) was greater in the topsoil (0–30 cm) or subsoil (in each 30 cm layer from 30–240 cm) using Mantel tests. The influence of geographic distance versus edaphic variables on the bacterial communities from the different soil layers was also compared. Results indicated that the phylogenetic β-diversity was impacted more by soil depth, while the taxonomic β-diversity changed more between geographic locations. The distance-decay was lower in the topsoil than in all subsoil layers analysed. Moreover, some subsoil layers were influenced more by geographic distance than any edaphic variable, including pH. Although different factors affected the topsoil and subsoil biogeography, niche-based models explained the community assembly of all soil layers. This comprehensive study contributed to elucidating important aspects of soil bacterial biogeography including the major impact of soil depth on the phylogenetic β-diversity, and the greater influence of geographic distance on subsoil than on topsoil bacterial communities in agroecosystems

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts; (2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end‐of‐season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50–64%. Model initial conditions and management information accounted for one‐fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2 = 0.88), root depth (R2 = 0.83), biomass production (R2 = 0.93), grain yield (R2 = 0.90), plant N uptake (R2 = 0.87), soil moisture (R2 = 0.42), soil temperature (R2 = 0.93), soil nitrate (R2 = 0.77), and water table depth (R2 = 0.41). We concluded that model set‐up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment

Assessing temporal and spatial variability in annual nitrate loads, yields, and flow weighted concentration across Iowa

The second-largest zone of coastal hypoxia in the world is found on the northern Gulf of Mexico continental shelf adjacent to the outflows of the Mississippi and Atchafalaya Rivers. Studies performed in the late 90s indicated that hypoxic conditions likely began to appear around the turn of the last century and became more severe since the 1950s as the nitrate flux from the Mississippi River to the Gulf of Mexico tripled. Although the hypoxia formation in the Gulf of Mexico is predominantly driven by increased riverine nitrogen (N) export from the Mississippi-Atchafalaya River basin, it remains unclear how hydroclimate extremes affect downstream N loads. We analyzed the flow-weighted nitrate-N concentration (FWNC; mg NO3--N L-1), load (kg NO3--N y-1), and yields (kg NO3--N ha-1 maize and soybeans cropland y-1) for 44 watersheds in Iowa, USA over the last two decades and estimated the probability of measuring non-weather related 41% reductions in nitrate losses. Our objectives were: 1) to identify the magnitude and spatial variability of three nitrate levels, across 44 watersheds during the period from 2001 to 2018; 2) measure the probability of measuring a 41% real reduction; and 3) explain which main factors are explaining these reductions, and the time-periods needed to achieve these goals. We found that reductions in FWNC over 15 years resulting from changes in land use and management exceeded 59% in all watersheds. In contrast, over the same timeframe, the mean probability across all watersheds of measuring 41% reductions in load and yield from changes in land use and management were only 49%, and 47%. Weather, land-uses, and soils explained 90% of the cross-watershed variability in FWNC. Overall, this study brings new data and analysis to assist decision-making in monitoring nitrogen in Iowa.</p

Assessing temporal and spatial variability in annual nitrate loads, yields, and flow weighted concentration across Iowa

The second-largest zone of coastal hypoxia in the world is found on the northern Gulf of Mexico continental shelf adjacent to the outflows of the Mississippi and Atchafalaya Rivers. Studies performed in the late 90s indicated that hypoxic conditions likely began to appear around the turn of the last century and became more severe since the 1950s as the nitrate flux from the Mississippi River to the Gulf of Mexico tripled. Although the hypoxia formation in the Gulf of Mexico is predominantly driven by increased riverine nitrogen (N) export from the Mississippi-Atchafalaya River basin, it remains unclear how hydroclimate extremes affect downstream N loads. We analyzed the flow-weighted nitrate-N concentration (FWNC; mg NO3--N L-1), load (kg NO3--N y-1), and yields (kg NO3--N ha-1 maize and soybeans cropland y-1) for 44 watersheds in Iowa, USA over the last two decades and estimated the probability of measuring non-weather related 41% reductions in nitrate losses. Our objectives were: 1) to identify the magnitude and spatial variability of three nitrate levels, across 44 watersheds during the period from 2001 to 2018; 2) measure the probability of measuring a 41% real reduction; and 3) explain which main factors are explaining these reductions, and the time-periods needed to achieve these goals. We found that reductions in FWNC over 15 years resulting from changes in land use and management exceeded 59% in all watersheds. In contrast, over the same timeframe, the mean probability across all watersheds of measuring 41% reductions in load and yield from changes in land use and management were only 49%, and 47%. Weather, land-uses, and soils explained 90% of the cross-watershed variability in FWNC. Overall, this study brings new data and analysis to assist decision-making in monitoring nitrogen in Iowa

Nitrate losses across 29 Iowa watersheds: Measuring long-term trends in the context of interannual variability

In the U.S. Corn Belt, annual croplands are the primary source of nitrate loading to waterways. Long periods of fallow cause most nitrate loss, but there is extreme interannual variability in the magnitude of nitrate loss due to weather. Using mean annual (2001–2018) flow-weighted nitrate-N concentration (FWNC; mg NO3––N L–1), load (kg NO3––N), and yield (kg NO3––N ha–1 cropland) for 29 watersheds, our objectives were (a) to quantify the magnitude and interannual variability of 5-yr moving average FWNC, load, and yield; (2) to estimate the probability of measuring 41% reductions in nitrate loss after isolating the effect of weather on nitrate loss by quantifying the interannual variability of nitrate loss in watersheds where there was no trend in 5-yr moving average nitrate loss (Iowa targets a 41% nitrate loss reduction from croplands); and (c) to identify factors that, in the absence of long-term trends in nitrate loss, best explain the interannual variability in nitrate loss. Averaged across all watersheds, the mean probability of measuring a statistically significant 41% reduction in FWNC across 15 yr, should it occur, was 96%. However, the probabilities of measuring 41% reductions in nitrate load and yield were only 44 and 32%. Across watersheds, soil organic matter, tile drainage, interannual variability of precipitation, and watershed area accounted for interannual variability in these nitrate loss indices. Our results have important implications for setting realistic timelines to measure nitrate loss reductions against the background of interannual weather variation and can help to target monitoring intensity across diverse watersheds.This article is published as Danalatos, Gerasimos, Calvin Wolter, Sotirios Archontoulis, and Mike Castellano. Nitrate losses across 29 Iowa watersheds: Measuring long‐term trends in the context of interannual variability. 2022. doi:10.1002/jeq2.20349. Posted with permission. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes

Soil depth and geographic distance modulate bacterial β-diversity in deep soil profiles throughout the U.S. Corn Belt

Understanding how microbial communities are shaped across spatial dimensions is of fundamental importance in microbial ecology. However, most studies on soil biogeography have focused on the topsoil microbiome, while the factors driving the subsoil microbiome distribution are largely unknown. Here we used 16S rRNA amplicon sequencing to analyse the factors underlying the bacterial β-diversity along vertical (0–240 cm of soil depth) and horizontal spatial dimensions (~500,000 km2) in the U.S. Corn Belt. With these data we tested whether the horizontal or vertical spatial variation had stronger impacts on the taxonomic (Bray-Curtis) and phylogenetic (weighted Unifrac) β-diversity. Additionally, we assessed whether the distance-decay (horizontal dimension) was greater in the topsoil (0–30 cm) or subsoil (in each 30 cm layer from 30–240 cm) using Mantel tests. The influence of geographic distance versus edaphic variables on the bacterial communities from the different soil layers was also compared. Results indicated that the phylogenetic β-diversity was impacted more by soil depth, while the taxonomic β-diversity changed more between geographic locations. The distance-decay was lower in the topsoil than in all subsoil layers analysed. Moreover, some subsoil layers were influenced more by geographic distance than any edaphic variable, including pH. Although different factors affected the topsoil and subsoil biogeography, niche-based models explained the community assembly of all soil layers. This comprehensive study contributed to elucidating important aspects of soil bacterial biogeography including the major impact of soil depth on the phylogenetic β-diversity, and the greater influence of geographic distance on subsoil than on topsoil bacterial communities in agroecosystems.This article is published as Lopes, Lucas Dantas, Stephanie L. Futrell, Emily E. Wright, Gerasimos J. Danalatos, Michael J. Castellano, Tony J. Vyn, Sotirios V. Archontoulis, and Daniel P. Schachtman. "Soil depth and geographic distance modulate bacterial β‐diversity in deep soil profiles throughout the US Corn Belt." Molecular Ecology (2023). doi:10.1111/mec.16945. Posted with permission.This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes

Chemical composition and antimicrobial activity of the essential oil of Abies cephalonica Loudon from Mount Ainos (Kefalonia, Greece)

Abies cephalonica Loudon consists one of the three fir species occurring in Greece. The aim of this study was to evaluate the chemical composition of the essential oil (EO) from needles of A. cephalonica from Mt. Aenos (Kefalonia Island, Greece) and to investigate its antimicrobial activity. The EO was obtained by hydrodistillation and 68 constituents corresponding to 99.4% of the EO were identified by using GC-MS analysis. The major chemical category of the volatile compounds were the monoterpene hydrocarbons (63.9%), followed by oxygenated monoterpenes (20.2%). In particular, the main components were beta-pinene (26.9%), bornyl acetate (12.5%), alpha-pinene (10.1%), camphene (9.2%), limonene (8.1%), gamma-eudesmol (8.1%) and beta-phellandrene (5.8%). To the best of our knowledge, the present study is the first research, reporting the phytochemical profile and the antimicrobial potential of the EO of A. cephalonica from this distinct geographic area

Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt

We used the Agricultural Production Systems sIMulator (APSIM) to predict and explain maize and soybean yields, phenology, and soil water and nitrogen (N) dynamics during the growing season in Iowa, USA. Historical, current and forecasted weather data were used to drive simulations, which were released in public four weeks after planting. In this paper, we (1) describe the methodology used to perform forecasts; (2) evaluate model prediction accuracy against data collected from 10 locations over four years; and (3) identify inputs that are key in forecasting yields and soil N dynamics. We found that the predicted median yield at planting was a very good indicator of end‐of‐season yields (relative root mean square error [RRMSE] of ∼20%). For reference, the prediction at maturity, when all the weather was known, had a RRMSE of 14%. The good prediction at planting time was explained by the existence of shallow water tables, which decreased model sensitivity to unknown summer precipitation by 50–64%. Model initial conditions and management information accounted for one‐fourth of the variation in maize yield. End of season model evaluations indicated that the model simulated well crop phenology (R2 = 0.88), root depth (R2 = 0.83), biomass production (R2 = 0.93), grain yield (R2 = 0.90), plant N uptake (R2 = 0.87), soil moisture (R2 = 0.42), soil temperature (R2 = 0.93), soil nitrate (R2 = 0.77), and water table depth (R2 = 0.41). We concluded that model set‐up by the user (e.g. inclusion of water table), initial conditions, and early season measurements are very important for accurate predictions of soil water, N and crop yields in this environment.This article is published as Archontoulis, Sotirios V., Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez‐Feria et al. "Predicting crop yields and soil‐plant nitrogen dynamics in the US Corn Belt." Crop Science (2020). doi: 10.1002/csc2.20039.</p