Coupling soil water processes and nitrogen cycle across spatial scales: Potentials, bottlenecks and solutions

Interactions among soil water processes and the nitrogen (N) cycle govern biological productivity and environmental outcomes in the earth’s critical zone. Soil water influences the N cycle in two distinct but interactive modes. First, the spatio-temporal variation of soil water content (SWC) controls redox coupling among oxidized and reduced compounds, and thus N mineralization, nitrification, and denitrification. Secondly, subsurface flow controls the movement of water and dissolved N. These two processes interact such that subsurface flow dynamics control the occurrence of relatively static, isolated soil solution environments that span a range of reduced to oxidized conditions. However, the soil water-N cycle is usually treated as a black box. Models focused on N cycling simplify soil water parameters, while models focused on soil water processes simplify N cycling parameters. In addition, effective ways to deal with upscaling are lacking. New techniques will allow comprehensive coupling of the soil water-N cycle across time and space: 1) using hydrogeophysical tools to detect soil water processes and then linked to electrochemical N sensors to reveal the soil N cycle, (2) upscaling small-scale observations and simulations by constructing functions between soil water-N cycle and ancillary soil, topography and vegetation variables in the hydropedological functional units, and (3) integrating soil hydrology models with N cycling models to minimize the over-simplification of N biogeochemistry and soil hydrology mechanisms in these models. These suggestions will enhance our understanding of soil water processes and the N cycle and improve modeling of N losses as important sources of greenhouse gas emission and water pollution

Why we do what we do: the views of bankers, insurers, and securities firms on specialization and diversification

Financial services industry

Economic Conditions And How It Effects Trade Between The U.S. And India

This paper examines the economic conditions that effect trade between the U.S. and India. Examining the existing economic conditions of both countries is important to determine the current and future trade issues that both countries may face. Since India’s formation in 1947 and up until 1991, India was a closed economy with strict controls and regulations regarding trade. After a severe economic crisis in 1991 India started opening its borders to foreign trade, embraced globalization and drastically liberalized its trade policy.  However, the exploding population in India is having strong economic and environmental implications for India and how it relates to the rest of the world.

Legacy effects of long-term nitrogen fertilizer application on the fate of nitrogen fertilizer inputs in continuous maize

Nitrogen fertilizer management can impact soil organic C (SOC) stocks in cereal-based cropping systems by regulating crop residue inputs and decomposition rates. However, the impact of long-term N fertilizer management, and associated changes in SOC quantity and quality, on the fate of N fertilizer inputs is uncertain. Using two 15-year N fertilizer rate experiments on continuous maize (Zea mays L.) in Iowa, which have generated gradients of SOC, we evaluated the legacy effects of N fertilizer inputs on the fate of added N. Across the historical N fertilizer rates, which ranged from 0 to 269 kg N ha−1 yr−1, we applied isotopically-labeled N fertilizer at the empirically-determined site-specific agronomic optimum rate (202 kg N ha−1 at the central location and 269 kg N ha−1 at the southern location) and measured fertilizer recovery in crop and soil pools, and, by difference, environmental losses. Crop fertilizer N recovery efficiency (NREcrop) at physiological maturity averaged 44% and 14% of applied N in central Iowa and southern Iowa, respectively (88 kg N ha−1 and 37 kg N ha−1, respectively). Despite these large differences in NREcrop, the response to historical N rate was remarkably similar across both locations: NREcrop was greatest at low and high historical N rates, and least at the intermediate rates. Decreasing NREcrop from low to intermediate historical N rates corresponded to a decline in early-season fertilizer N recovery in the relatively slow turnover topsoil mineral-associated organic matter pool (0–15 cm), while increasing NREcrop from intermediate to high historical N rates corresponded to an increase in early-season fertilizer N recovery in the relatively fast turnover topsoil particulate organic matter pool and an increase in crop yield potential. Despite the variation in NREcropalong the historical N rate gradient, we did not detect an effect of historical N rate on environmental losses during the growing season, which averaged 34% and 69% of fertilizer N inputs at the central and southern locations, respectively (69 kg N ha−1 and 185 kg N ha−1, respectively). Our results suggest that, while beneficial for SOC storage over the long term, fertilizing at the agronomic optimum N rate can lead to significant environmental N losses

Unlocking Complex Soil Systems as Carbon Sinks: Multi-pool Management as the Key

Much research focuses on increasing carbon storage in mineral-associated organic matter (MAOM), in which carbon may persist for centuries to millennia. However, MAOM-targeted management is insufficient because the formation pathways of persistent soil organic matter are diverse and vary with environmental conditions. Effective management must also consider particulate organic matter (POM). In many soils, there is potential for enlarging POM pools, POM can persist over long time scales, and POM can be a direct precursor of MAOM. We present a framework for context-dependent management strategies that recognizes soils as complex systems in which environmental conditions constrain POM and MAOM formation

Whole-profile soil organic matter content, composition, and stability under cropping systems that differ in belowground inputs

Subsoils have been identified as a potential carbon sink because they typically have low soil organic carbon (SOC) concentrations and high SOC stability. One proposed strategy to increase SOC stocks is to enhance C inputs to the subsoil by increasing crop rotation diversity with deep-rooted perennial crops. Using three long-term field trials in Iowa (study durations of 60, 35, and 12 years), we examined the effects of contrasting cropping systems [maize (Zea mays L.)-soybean (Glycine max (L.) Merr) (= two-year system) vs. maize-soybean-oat (Avena sativa L.)/alfalfa (Medicago sativa L.)-alfalfa or maize-maize-oat/alfalfa-alfalfa (= four-year system)] on above- and below-ground C inputs, as well as the content, biochemical composition, and distribution of SOC among physical fractions differing in stability to 90 cm depth. Average annual total C inputs were similar in the two-year and four-year systems, but the proportion of C delivered belowground was 20–35 % greater in the four-year system. Despite the long duration of these studies, the effect of cropping system on SOC content to 90 cm was inconsistent across trials, ranging from −7 % to +16 % in the four-year relative to the two-year system. At the one site where SOC was significantly greater in the four-year system, the effect of cropping system on SOC content was observed in surface and subsoil layers rather than limited to the subsoil (i.e., below 30 cm). Cropping system had minimal effects on biochemical indicators of plant-derived organic matter or on the proportions of SOC in labile particulate organic matter versus stable mineral-associated organic matter. We conclude that adoption of cropping systems with enhanced belowground C inputs may increase total profile SOC, but the effect is minimal and inconsistent; furthermore, it has minor impact on the vertical distribution, biochemical composition, and stability of SOC in Mollisols of the Midwest U.S

Plant Litter Quality Affects the Accumulation Rate, Composition, and Stability of Mineral-associated Soil Organic Matter

Mineral-associated organic matter (MAOM) is a relatively large and stable fraction of soil organic matter (SOM). Plant litters with high rates of mineralization (high quality litters) are hypothesized to promote the accumulation of MAOM with greater efficiency than plant litters with low rates of mineralization (low-quality litters) because litters with high rates of mineralization maximize the synthesis of microbial products and most MAOM is microbial-derived. However, the effect of litter quality on MAOM is inconsistent. We conducted four repeated short-term incubations (46-d each) of four plant litters (alfalfa, oats, maize and soybean) in two low-carbon subsoils (sandy loam and silty loam) with and without nutrient addition. Our short-term incubations focused on the initial stage of litter decomposition during the time when litter quality has a measureable effect on mineralization rates. Plant litter quality had a much greater effect on litter-C mineralization rate and MAOM-C accumulation than did soil type or nutrient addition. Soils amended with high-quality oat and alfalfa litters had greater MAOM-C accumulation than soils amended with low-quality maize and soybean litters. However, soils amended with high-quality litters also had greater litter-C mineralization than soils amended with low-quality litters. As a result, the accumulation of MAOM-C per unit of litter-C mineralization was lower in soils amended with high-vs. low-quality litters (0.65 vs. 1.39 g MAOM-C accumulated g−1 C mineralized). Cellulose and hemicelluose indices of accumulated MAOM were greater for maize and soybean than oats and alfalfa, however, most carbohydrates in MAOM were plant-derived regardless of litter quality. At the end of the incubations, more of the accumulated MAOM-N was potentially mineralizable in soils amended with high quality litters. Nevertheless, most of the litter-C remained as residual litter; just 12% was mineralized to CO2 and 13% was transferred to MAOM. Our results demonstrate several unexpected effects of litter quality on MAOM stabilization including the direct stabilization of plant-derived carbohydrates

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

A Systems Modeling Approach to Forecast Corn Economic Optimum Nitrogen Rate

Historically crop models have been used to evaluate crop yield responses to nitrogen (N) rates after harvest when it is too late for the farmers to make in-season adjustments. We hypothesize that the use of a crop model as an in-season forecast tool will improve current N decision-making. To explore this, we used the Agricultural Production Systems sIMulator (APSIM) calibrated with long-term experimental data for central Iowa, USA (16-years in continuous corn and 15-years in soybean-corn rotation) combined with actual weather data up to a specific crop stage and historical weather data thereafter. The objectives were to: (1) evaluate the accuracy and uncertainty of corn yield and economic optimum N rate (EONR) predictions at four forecast times (planting time, 6th and 12th leaf, and silking phenological stages); (2) determine whether the use of analogous historical weather years based on precipitation and temperature patterns as opposed to using a 35-year dataset could improve the accuracy of the forecast; and (3) quantify the value added by the crop model in predicting annual EONR and yields using the site-mean EONR and the yield at the EONR to benchmark predicted values. Results indicated that the mean corn yield predictions at planting time (R2 = 0.77) using 35-years of historical weather was close to the observed and predicted yield at maturity (R2 = 0.81). Across all forecasting times, the EONR predictions were more accurate in corn-corn than soybean-corn rotation (relative root mean square error, RRMSE, of 25 vs. 45%, respectively). At planting time, the APSIM model predicted the direction of optimum N rates (above, below or at average site-mean EONR) in 62% of the cases examined (n = 31) with an average error range of ±38 kg N ha−1 (22% of the average N rate). Across all forecast times, prediction error of EONR was about three times higher than yield predictions. The use of the 35-year weather record was better than using selected historical weather years to forecast (RRMSE was on average 3%lower). Overall, the proposed approach of using the crop model as a forecasting tool could improve year-to-year predictability of corn yields and optimum N rates. Further improvements in modeling and set-up protocols are needed toward more accurate forecast, especially for extreme weather years with the most significant economic and environmental cost