Dynamic modeling of nitrogen losses in river networks unravels the coupled effects of hydrological and biogeochemical processes

The importance of lotic systems as sinks for nitrogen inputs is well recognized. A fraction of nitrogen in streamflow is removed to the atmosphere via denitrification with the remainder exported in streamflow as nitrogen loads. At the watershed scale, there is a keen interest in understanding the factors that control the fate of nitrogen throughout the stream channel network, with particular attention to the processes that deliver large nitrogen loads to sensitive coastal ecosystems. We use a dynamic stream transport model to assess biogeochemical (nitrate loadings, concentration, temperature) and hydrological (discharge, depth, velocity) effects on reach-scale denitrification and nitrate removal in the river networks of two watersheds having widely differing levels of nitrate enrichment but nearly identical discharges. Stream denitrification is estimated by regression as a nonlinear function of nitrate concentration, streamflow, and temperature, using more than 300 published measurements from a variety of US streams. These relations are used in the stream transport model to characterize nitrate dynamics related to denitrification at a monthly time scale in the stream reaches of the two watersheds. Results indicate that the nitrate removal efficiency of streams, as measured by the percentage of the stream nitrate flux removed via denitrification per unit length of channel, is appreciably reduced during months with high discharge and nitrate flux and increases during months of low-discharge and flux. Biogeochemical factors, including land use, nitrate inputs, and stream concentrations, are a major control on reach-scale denitrification, evidenced by the disproportionately lower nitrate removal efficiency in streams of the highly nitrate-enriched watershed as compared with that in similarly sized streams in the less nitrate-enriched watershed. Sensitivity analyses reveal that these important biogeochemical factors and physical hydrological factors contribute nearly equally to seasonal and stream-size related variations in the percentage of the stream nitrate flux removed in each watershed

Effects of cover crops on multiple ecosystem services: Ten meta-analyses of data from arable farmland in California and the Mediterranean

Cover crops are considered to be beneficial for multiple ecosystem services, and they have been widely promoted through the Common Agricultural Policy (CAP) in the EU and Farm Bill Conservation Title Programs, such as the Environmental Quality Incentives Program (EQIP), in the USA. However, it can be difficult to decide whether the beneficial effects of cover crops on some ecosystem services are likely to outweigh their harmful effects on other services, and thus to decide whether they should be promoted by agricultural policy in specific situations. We used meta-analysis to quantify the effects of cover crops on five ecosystem services (food production, climate regulation, soil and water regulation, and weed control) in arable farmland in California and the Mediterranean, based on 326 experiments reported in 57 publications. In plots with cover crops, there as 13% less water, 9% more organic matter and 41% more microbial biomass in the soil, 27% fewer weeds, and 15% higher carbon dioxide emissions (but also more carbon stored in soil organic matter), compared to control plots with bare soils or winter fallows. Cash crop yields were 16% higher in plots that had legumes as cover crops (compared to controls) but 7% lower in plots that had non-legumes as cover crops. Soil nitrogen content was 41% lower, and nitrate leaching was 53% lower, in plots that had non-legume cover crops (compared to controls) but not significantly different in plots that had legumes. We did not find enough data to quantify the effects of cover crops on biodiversity conservation, pollination, or pest regulation. These gaps in the evidence need to be closed if cover crops continue to be widely promoted. We suggest that this novel combination of multiple meta-analyses for multiple ecosystem services could be used to support multi-criteria decision making about agri-environmental policy

Winter Rye Cover Crop Biomass Production, Degradation, and Nitrogen Recycling

Winter rye (Secale cereale L.) cover crop (RCC) use in corn (Zea mays L.) and soybean [Glycine max. (L.) Merr.] production can alter N dynamics compared to no RCC. The objectives of this study were to evaluate RCC biomass production (BP) and subsequent RCC degradation (BD) and N recycling in a no-till corn–soybean (CS) rotation. Aboveground RCC was sampled at spring termination for biomass dry matter (DM), C, and N. To evaluate BD and remaining C and N, RCC biomass was put into nylon mesh bags, placed on the soil surface, and collected multiple times over 105 d. Treatments included rye cover crop following soybean (RCC-FS) and corn (RCC-FC), and prior-year N applied to corn. Overall, the RCC BP and N was low due to low soil profile NO3–N. Across sites and years, the greatest BP was with RCC-FC that received 225 kg N ha–1 (1280 kg DM ha–1), with similar N uptake as with RCC-FS (27 kg N ha–1). The RCC biomass and N remaining decreased over time following an exponential decay. An average 62% biomass with RCC-FS and RCC-FC degraded after 105 d; however, N recycled was greater with RCC-FS than RCC-FC [22 (80%) vs. 14 (64%) kg N ha–1, respectively], and was influenced by the RCC C/N ratio. The RCC did not recycle an agronomically meaningful amount of N, which limited N that could potentially be supplied to corn. Rye cover crops can conserve soil N, and with improved management and growth, recycling of crop-available N should increase

Cover crop root contributions to soil carbon in a no-till corn bioenergy cropping system

Crop residues are potential biofuel feedstocks, but residue removal may reduce soil carbon (C). The inclusion of a cover crop in a corn bioenergy system could provide additional biomass, mitigating the negative effects of residue removal by adding to stable soil C pools. In a no-till continuous corn bioenergy system in the northern US Corn Belt, we used 13CO2 pulse labeling to trace plant C from a winter rye (Secale cereale) cover crop into different soil C pools for 2 years following rye cover crop termination. Corn stover left as residue (30% of total stover) contributed 66, corn roots 57, rye shoots 61, rye roots 50, and rye rhizodeposits 25 g C m−2 to soil. Five months following cover crop termination, belowground cover crop inputs were three times more likely to remain in soil C pools than were aboveground inputs, and much of the root-derived C was in mineral-associated soil fractions. After 2 years, both above- and belowground inputs had declined substantially, indicating that the majority of both root and shoot inputs are eventually mineralized. Our results underscore the importance of cover crop roots vs. shoots and the importance of cover crop rhizodeposition (33% of total belowground cover crop C inputs) as a source of soil C. However, the eventual loss of most cover crop C from these soils indicates that cover crops will likely need to be included every year in rotations to accumulate soil C

Influence of root and leaf traits on the uptake of nutrients in cover crops

Aims: Cover crops play an important role in soil fertility as they can accumulate large amounts of nutrients. This study aimed at understanding the nutrient uptake capacity of a wide range of cover crops and at assessing the relevance of acquisition strategies. Methods: A field experiment was conducted to characterize 20 species in terms of leaf and root traits. Plant traits were related to nutrient concentration and shoot biomass production with a redundancy analysis. Acquisition strategies were identified using a cluster analysis. Results: Root systems varied greatly among cover crop species. Five nutrient acquisition strategies were delineated. Significant amounts of nutrients (about 120 kg ha−1 of nitrogen, 30 kg ha−1 of phosphorus and 190 kg ha−1 of potassium) were accumulated by the species in a short period. Nutrient acquisition strategies related to high accumulations of nutrients consisted in either high shoot biomass and root mass and dense tissues, or high nutrient concentrations and root length densities. Species with high root length densities showed lower C/N ratios. Conclusions: The same amounts of nutrients were accumulated by groups with different acquisition strategies. However, their nutrient concentrations offer different perspectives in terms of nutrient release for the subsequent crop and nutrient cycling improvement

A model-data comparison of gross primary productivity: Results from the North American Carbon Program site synthesis

Accurately simulating gross primary productivity (GPP) in terrestrial ecosystem models is critical because errors in simulated GPP propagate through the model to introduce additional errors in simulated biomass and other fluxes. We evaluated simulated, daily average GPP from 26 models against estimated GPP at 39 eddy covariance flux tower sites across the United States and Canada. None of the models in this study match estimated GPP within observed uncertainty. On average, models overestimate GPP in winter, spring, and fall, and underestimate GPP in summer. Models overpredicted GPP under dry conditions and for temperatures below 0°C. Improvements in simulated soil moisture and ecosystem response to drought or humidity stress will improve simulated GPP under dry conditions. Adding a low-temperature response to shut down GPP for temperatures below 0°C will reduce the positive bias in winter, spring, and fall and improve simulated phenology. The negative bias in summer and poor overall performance resulted from mismatches between simulated and observed light use efficiency (LUE). Improving simulated GPP requires better leaf-to-canopy scaling and better values of model parameters that control the maximum potential GPP, such as ε[subscript max] (LUE), V[subscript cmax] (unstressed Rubisco catalytic capacity) or J[subscript max] (the maximum electron transport rate)