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.

Quantifying maize root response to nitrogen (N) fertilizer, soil texture, and weather is crucial to understand complex soil-root-plant processes. We performed a 2-year x 4 locations (sand content range: 5-95%) x N treatments (range: 0 to 336 kg N ha- 1) field experiment in Iowa, U.S. to (1) determine the response of root traits to N fertilizer, and (2) develop generalized functions to aid understanding and prediction of root mass and root to shoot (R:S) ratio. Deep root samples (0-210 cm, increments of 30 cm) were collected using the soil core approach at early to middle grain fill period and quantified root mass, length, and N and C concentrations. In addition, yield and shoot biomass was measured. Root traits and yield had different responses to N fertilizer input. Root mass was maximized at 168 kg N ha- 1; zero and excessive N fertilization decreased root mass by 33 and 17 %, respectively. Nitrogen fertilizer significantly affected root traits only in the top 30 cm soil layer. Soil texture affected root traits in a dry year (root mass was positively associated with silt and clay), but not in a wet year, suggesting that soil moisture overwhelms the effect of texture. The combined data (N rates x locations x years) revealed a negative relationship between R:S ratio and yield. This resulted in a new set of equations (e.g., upper bound R:S = e(-1.5 - 0.04*yield)) that can replace the constant R:S approach used in the literature. Yield, which is commonly measured, integrates the effects of environment, management, and genetic variation; hence the proposed equations can be widely applied. This study provides evidence that different plant traits are maximized at different levels of mineral N nutrition. Results can enhance biophysical models and prediction of R: S ratio.

Root traits are important to crop functioning, yet there is little information about how root traits vary with shoot traits. Using a standardized protocol, we collected 160 soil cores (0-210 cm) across 10 locations, three years and multiple cropping systems (crops x management practices) in Iowa, USA. Maximum root biomass ranged from 1.2 to 2.8 Mg ha(-1) in maize and 0.86 to 1.93 Mg ha(-1) in soybean. The root:shoot (R:S) ratio ranged from 0.04 to 0.13 in maize and 0.09 to 0.26 in soybean. Maize produced 27 % more root biomass, 20 % longer roots, with 35 % higher carbon to nitrogen (C:N) ratio than soybean. In contrast, soybean had a 47 % greater R:S ratio than maize. The maize R:S ratio values were substantially lower than literature values, possibly due to differences in measurement methodologies, genotypes, and environment. In particular, we sampled at plant maturity rather than crop harvest to minimize the effect of senescence on measurements of shoots and roots. Maximum shoot biomass explained 70 % of the variation in root biomass, and the R:S ratio was positively correlated with the root C:N measured in both crops. Easily-measured environmental variables including temperature and precipitation were weakly associated with root traits. These results begin to fill an important knowledge gap that will enable better estimates of belowground net primary productivity and soil organic matter dynamics. Ultimately, the ability to explain variation in root mass production can be used to improve C and N budgets and modeling studies from crop to regional scales.

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 4-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 ~ 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 to 64%. Model initial conditions and management information accounted for 1⁄4 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. Disciplines Agriculture | Agronomy and Crop Sciences | Bioresource and Agricultural Engineering | Soil Science | Statistical Models Comments This is a manuscript of an article 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. Authors Sotirios V. Archontoulis, Michael J. Castellano, Mark A. Licht, Virginia Nichols, Mitch Baum, Isaiah Huber, Rafael Martinez-Feria, Laila Puntel, Raziel A. Ordonez, Javed Iqbal, Emily E. Wright, Ranae N. Dietzel, Matthew Helmers, Andy VanLoocke, Matt Liebman, Jerry L. Hatfield, Daryl Herzmann, S. Carolina Córdova, Patrick Edmonds, Kaitlin Togliatti, Ashlyn Kessler, Gerasimos Danalatos, Heather Pasley, Carl Pederson, and Kendall R. Lamkey This article is available at Iowa State University Digital Repository: https://lib.dr.iastate.edu/agron_pubs/623 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process, which may lead to differences between this version and the Version of Record. Please cite this article as doi:

The determination of how microbial community structure changes within the soil profile, will be beneficial to understanding the long-term health of agricultural soil ecosystems and will provide a first step towards elucidating how deep soil microbial communities contribute to carbon sequestration. This study aimed to investigate the differences in the microbial community abundance, composition and diversity throughout from the surface layers down to deep soils in corn and soybean fields in Iowa, USA. We used 16S rRNA amplicon sequencing of soil samples to characterize the change in microbial community structure. Our results revealed decreased richness and diversity in bacterial community structure with increasing soil depth. We also observed distinct distribution patterns of bacterial community composition along soil profiles. Soil and root data at different depths enabled us to demonstrate that the soil organic matter, soil bulk density and plant water availability were all significant factors in explaining the variation in soil microbial community composition. Our findings provide valuable insights in the changes in microbial community structure to depths of 180 cm in one of the most productive agricultural regions in the world. This knowledge will be important for future management and productivity of agroecosystems in the face of increasing demand for food and climate change.

Root distributions determine crop nutrient access and soil carbon input patterns. To date, root distribution data are rare but needed to improve knowledge and prediction of cropping system sustainability. In this study, we sought to (i) quantify variation in maize (Zea mays) and soybean (Glycine max) roots by depth and environment across Iowa, USA and (ii) identify environmental factors explaining the most variation. Over three years we collected soil cores from 0 to 210 cm in 16 maize and 12 soybean field experiments at grain filling. Root mass, length, carbon (C) and nitrogen (N) were determined at 30 cm increments, coupled with crop, soil, management, and weather-related measurements. Percentage of root mass located in the top 30 cm varied from 52 to 94% in maize and 54–84% in soybean. Variation in maize root distributions was strongly associated with depth to water tables, variation in soybean with soil physical attributes. Root C:N ratios were highly variable with no depth-pattern, averaging 20 and 30 for soybean and maize, respectively. In both crops, specific root lengths increased with depth to 60 cm, and thereafter remained constant. Field studies of roots should consider depth to water tables and soil moisture measurements, as they influence vertical root distributions.

Root mass and length attributes are difficult to obtain in the field and currently there is uniformity among literature studies in estimating the effect of sampling position error. With the objectives of 1) quantifying the sampling position error in calculating weighted average root values per unit area and 2) developing an algorithm to minimize root position sampling error so that existing data in the literature can be used in future studies, we collected and analyzed root mass and length data across four sampling positions (0, 12, 24 and 36 cm distance from the plant row; row-to-row spacing 76 cm) from two maize and two soybean fields in central Iowa, USA. In-row sampling position (i.e., 0 cm from the plant row) over-estimated root mass and length by 66% and 46% for maize and soybean, while cores taken in the middle of plant rows (i.e., 36 cm from the plant row) underestimated root mass and length by 34% and 23% for maize and soybean. As sampling distance from the plant row increased from 0 to 36 cm, maize root mass declined four times faster than soybean, while root length declined at almost the same rate between crops. Sampling 10 cm from the plant row provided the closest estimate to the weighted average value in both crops. We developed a new algorithm that predicts weighted average root attributes values with a R-2 of 0.93 for mass and a R-2 of 0.70 for length. The algorithm requires two user inputs (the measured root attribute value and the distance from the plant row). The new algorithm was tested across diverse environments, cultivars, and management practices and proven accurate for subsequent use (R-2 = 0.70 and R-2 = 0.87 for mass and length). This study provides guidance to strategically sample roots in future row crop research and an algorithm to eliminate sampling position bias in existing data.

Quantitative measurements of root traits can improve our understanding of how crops respond to soil and weather conditions, but such data are rare. Our objective was to quantify maximum root depth and root front velocity (RFV) for maize (Zea mays) and soybean (Glycine max) crops across a range of growing conditions in the Midwest USA. Two sets of root measurements were taken every 10-15 days: in the crop row (in-row) and between two crop rows (center-row) across six Iowa sites having different management practices such as planting dates and drainage systems, totaling 20 replicated experimental treatments. Temporal root data were best described by linear segmental functions. Maize RFV was 0.62 +/- 0.2 cm d(-1) until the 5th leaf stage when it increased to 3.12 +/- 0.03 an d(-1) until maximum depth occurred at the 18th leaf stage (860 degrees Cd after planting). Similar to maize, soybean RFV was 1.19 +/- 0.4 cm d(-1) until the 3rd node when it increased to 3.31 +/- 0.5 cm d(-1) until maximum root depth occurred at the 13th node (813.6 degrees C d after planting). The maximum root depth was similar between crops (P > 0.05) and ranged from 120 to 157 cm across 18 experimental treatments, and 89-90 cm in two experimental treatments. Root depth did not exceed the average water table (two weeks prior to start grain filling) and there was a significant relationship between maximum root depth and water table depth (R-2 = 0.61; P = 0.001). Current models of root dynamics rely on temperature as the main control on root growth; our results provide strong support for this relationship (R-2 > 0.76; P < 0.001), but suggest that water table depth should also be considered, particularly in conditions such as the Midwest USA where excess water routinely limits crop production. These results can assist crop model calibration and improvements as well as agronomic assessments and plant breeding efforts in this region.