740 research outputs found
Greenhouse gas emissions from solid and liquid organic fertilizers applied to lettuce
Improper application of nitrogen (N) fertilizer and environmental factors can cause the loss of nitrous oxide (N2O) to the environment. Different types of fertilizers with different C/N ratios may have different effects on the environment. The focus of this study was to evaluate the effects of environmental factors and four organic fertilizers (feather meal, blood meal, fish emulsion, and cyano-fertilizer) applied at different rates (0, 28, 56, and 112 kg N ha−1) on N2O emissions and to track CO2 emissions from a lettuce field (Lactuca sativa L.). The study was conducted in 2013 and 2014 and compared preplant-applied solid fertilizers (feather meal and blood meal) and multiple applications of liquid fertilizers (fish emulsion and cyano-fertilizer). Three days a week, N2O and CO2 emissions were measured twice per day in 2013 and once per day in 2014 using a closed-static chamber, and gas samples were analyzed by gas chromatography. Preplant-applied solid fertilizers significantly increased cumulative N2O emissions as compared with control, but multiple applications of liquid fertilizers did not. Emission factors for N2O ranged from 0 to 0.1% for multiple applications of liquid fertilizers and 0.6 to 11% for preplant-applied solid fertilizers, which could be overestimated due to chamber placement over fertilizer bands. In 2014, solid fertilizers with higher C/N ratios (3.3–3.5) resulted in higher CO2 emissions than liquid fertilizers (C/N ratio, 0.9–1.5). Therefore, organic farmers should consider the use of multiple applications of liquid fertilizers as a means to reduce soil greenhouse gas emissions while maintaining high yields
Recommended from our members
The importance of management information and soil moisture representation for simulating tillage effects on N2O emissions in LPJmL5.0-tillage
No-tillage is often suggested as a strategy to reduce greenhouse gas emissions. Modeling tillage effects on nitrous oxide (N2O) emissions is challenging and subject to great uncertainties as the processes producing the emissions are complex and strongly nonlinear. Previous findings have shown deviations between the LPJmL5.0-tillage model (LPJmL: Lund–Potsdam–Jena managed Land) and results from meta-analysis on global estimates of tillage effects on N2O emissions. Here we tested LPJmL5.0-tillage at four different experimental sites across Europe and the USA to verify whether deviations in N2O emissions under different tillage regimes result from a lack of detailed information on agricultural management, the representation of soil water dynamics or both. Model results were compared to observational data and outputs from field-scale DayCent model simulations. DayCent has been successfully applied for the simulation of N2O emissions and provides a richer database for comparison than noncontinuous measurements at experimental sites. We found that adding information on agricultural management improved the simulation of tillage effects on N2O emissions in LPJmL. We also found that LPJmL overestimated N2O emissions and the effects of no-tillage on N2O emissions, whereas DayCent tended to underestimate the emissions of no-tillage treatments. LPJmL showed a general bias to overestimate soil moisture content. Modifications of hydraulic properties in LPJmL in order to match properties assumed in DayCent, as well as of the parameters related to residue cover, improved the overall simulation of soil water and N2O emissions simulated under tillage and no-tillage separately. However, the effects of no-tillage (shifting from tillage to no-tillage) did not improve. Advancing the current state of information on agricultural management and improvements in soil moisture highlights the potential to improve LPJmL5.0-tillage and global estimates of tillage effects on N2O emissions
Approaches and concepts of modelling denitrification: increased process understanding using observational data can reduce uncertainties
Denitrification is a key but poorly quantified component of the Ncycle. Because it is difficult to measure the gaseous (NO, NO, N)and soluble (NO) components of denitrification with sufficientintensity, models of varying scope and complexity have beendeveloped and applied to estimate how vegetation cover, landmanagement and environmental factors such as soil type andweather interact to control these variables. In this paper we assessthe strengths and limitations of different modeling approaches,highlight major uncertainties, and suggest how differentobservational methods and process-based understanding can becombined to better quantify N cycling. Representation of howbiogeochemical (e.g. org. C., pH) and physical (e.g. soil structure)factors influence denitrification rates and product ratios combinedwith ensemble approaches may increase accuracy withoutrequiring additional site level model inputs
Understanding the DayCent model: Calibration, sensitivity, and identifiability through inverse modeling
AbstractThe ability of biogeochemical ecosystem models to represent agro-ecosystems depends on their correct integration with field observations. We report simultaneous calibration of 67 DayCent model parameters using multiple observation types through inverse modeling using the PEST parameter estimation software. Parameter estimation reduced the total sum of weighted squared residuals by 56% and improved model fit to crop productivity, soil carbon, volumetric soil water content, soil temperature, N2O, and soil NO3− compared to the default simulation. Inverse modeling substantially reduced predictive model error relative to the default model for all model predictions, except for soil NO3− and NH4+. Post-processing analyses provided insights into parameter–observation relationships based on parameter correlations, sensitivity and identifiability. Inverse modeling tools are shown to be a powerful way to systematize and accelerate the process of biogeochemical model interrogation, improving our understanding of model function and the underlying ecosystem biogeochemical processes that they represent
What does it take to detect a change in soil carbon stock? A regional comparison of minimum detectable difference and experiment duration in the north central United States
Variability in soil organic carbon (SOC) results from natural and human processes interacting across time and space, and leads to large variation in the minimum difference in SOC that can be detected with a particular experimental design. Here we report a unique comparison of minimum detectable differences (MDDs) in SOC, and the estimated times required to observe those MDDs across the north central United States, calculated for the two most common SOC experiments: (1) a comparison between two treatments, e.g., moldboard plow (MP) and no-tillage (NT), using a randomized complete block design experiment; and (2) a comparison of changes in SOC over time for a particular treatment, e.g., NT, using a randomized complete block design experiment with time as an additional factor. We estimated the duration of the two experiment types required to achieve MDD through simulation of SOC dynamics. Data for the study came from 13 experimental sites located in Iowa, Illinois, Ohio, Michigan, Wisconsin, Missouri, and Minnesota. Soil organic carbon, bulk density, and texture were measured at four soil depths. Minimum detectable differences were calculated with probability of Type I error of 0.05 and probability of Type II error of 0.15. The MDDs in SOC were highly variable across the region and increased with soil depth. At 0 to 10 cm (0 to 3.9 in) soil depth, MDDs with five replications ranged from 1.04 g C kg−1 (0.017 oz C lb−1; 6%) to 7.15 g C kg−1 (0.114 oz C lb−1; 31%) for comparison of two treatments; and from 0.46 g C kg−1 (0.007 oz C lb−1; 3%) to 3.12 g C kg−1 (0.050 oz C lb−1; 13%) for SOC change over time. Large differences were also predicted in the experiment duration required to detect a difference in SOC between MP and NT (from 8 to \u3e100 years with five replications), or a change in SOC over time under NT management (from 11 to 71 years with five replications). At most locations, the time required to detect a change in SOC under NT was shorter than the time required to detect a difference between MP and NT. Minimum detectable difference and experiment duration decreased with the number of replications and were correlated with SOC variability and soil texture of the experimental sites, i.e., they tended to be lower in fine textured soils. Experiment duration was also reduced by increased crop productivity and the amount of residue left on the soil. The relationships and methods described here enable the design of experiments with high power of detecting differences and changes in SOC and enhance our understanding of how management practices influence SOC storage
Chapter 15. COMET2.0-Decision Support System for Agricultural Greenhouse Gas Accounting
Improved agricultural practices have a significant potential to mitigate greenhouse gas (GHG) emissions. A key issue for implementing mitigation options is quantifying emissions practically and cost effectively. Web-based systems using process-based models provide a promising approach.
COMET2.0 is a further development of the web-based COMET-VR system with an expanded set of crop management systems, inclusion of orchard and vineyards, new agroforestry options, and a nitrous oxide (N2O) emissions estimator, using the Century and DAYCENT dynamic ecosystem models.
Compared to empirical emission factor models, COMET2.0 accounted for more of the between site variation in soil C changes following no-till adoption in Corn Belt and Great Plains experiment sites. Predicted N2O emission rates, as a function of application rate, timing (spring vs. fall), and use of nitrification inhibitors, were consistent with observations in the literature. Carbon dynamics for orchard and agroforestry compared well with field measurements but limited availability of data poses a challenge for a fuller validation of these systems.
Advantages of a practiced-based approach, using dynamic process-based models include integration of interacting processes and local conditions for more accurate and complete GHG accounting. Web-based systems, designed for non-experts, allow land managers and others to evaluate trade-offs and select mitigation options for their particular conditions. Experimental networks such as GRACEnet will play an important role in improving decision support tools for implementation of agricultural GHG mitigation.Peer Reviewe
Assessing precipitation, evapotranspiration, and NDVI as controls of U.S. Great Plains plant production
Productivity throughout the North American Great Plains grasslands is generally considered to be water limited, with the strength of this limitation increasing as precipitation decreases. We hypothesize that cumulative actual evapotranspiration water loss (AET) from April to July is the precipitation-related variable most correlated to aboveground net primary production (ANPP) in the U.S. Great Plains (GP). We tested this by evaluating the relationship of ANPP to AET, precipitation, and plant transpiration (Tr). We used multi-year ANPP data from five sites ranging from semiarid grasslands in Colorado and Wyoming to mesic grasslands in Nebraska and Kansas, mean annual NRCS ANPP, and satellite-derived normalized difference vegetation index (NDVI) data. Results from the five sites showed that cumulative April-to-July AET, precipitation, and Tr were well correlated (R2: 0.54–0.70) to annual changes in ANPP for all but the wettest site. AET and Tr were better correlated to annual changes in ANPP compared to precipitation for the drier sites, and precipitation in August and September had little impact on productivity in drier sites. April-to-July cumulative precipitation was best correlated (R2 = 0.63) with interannual variability in ANPP in the most mesic site, while AET and Tr were poorly correlated with ANPP at this site. Cumulative growing season (May-to-September) NDVI (iNDVI) was strongly correlated with annual ANPP at the five sites (R2 = 0.90). Using iNDVI as a surrogate for ANPP, we found that county-level cumulative April–July AET was more strongly correlated to ANPP than precipitation for more than 80% of the GP counties, with precipitation tending to perform better in the eastern more mesic portion of the GP. Including the ratio of AET to potential evapotranspiration (PET) improved the correlation of AET to both iNDVI and mean county-level NRCS ANPP. Accounting for how different precipitation-related variables control ANPP (AET in drier portion, precipitation in wetter portion) provides opportunity to develop spatially explicit forecasting of ANPP across the GP for enhancing decision-making by land managers and use of grassland ANPP for biofuels
Review of research to inform California's climate scoping plan: Agriculture and working lands
Agriculture in California contributes 8% of the state's greenhouse gas (GHG) emissions. To inform the state's policy and program strategy to meet climate targets, we review recent research on practices that can reduce emissions, sequester carbon and provide other co-benefits to producers and the environment across agriculture and rangeland systems. Importantly, the research reviewed here was conducted in California and addresses practices in our specific agricultural, socioeconomic and biophysical environment. Farmland conversion and the dairy and intensive livestock sector are the largest contributors to GHG emissions and offer the greatest opportunities for avoided emissions. We also identify a range of other opportunities including soil and nutrient management, integrated and diversified farming systems, rangeland management, and biomass-based energy generation. Additional research to replicate and quantify the emissions reduction or carbon sequestration potential of these practices will strengthen the evidence base for California climate policy
Weakened growth of cropland‐N2O emissions in China associated with nationwide policy interventions
This study was supported by the National Natural Science Foundation of China (41671464; 7181101181), the National Key Research and Development Program of China (2016YFD0800501; 2018YFC0213304), 111 Project (B14001), the GCP-INI Global N2O Budget and the INMS Asia Demo Activities. The input of P.S. contributes to the UK-China Virtual Joint Centre on Nitrogen ìN-Circleî funded by the Newton Fund via UK BBSRC/NERC (BB/N013484/1). We acknowledged Eric Ceschia, Kristiina Regina, Dario Papale, and the NANORP for sharing a part of observation data.Peer reviewedPostprin
Horizontal DNA transfer mechanisms of bacteria as weapons of intragenomic conflict
Horizontal DNA transfer (HDT) is a pervasive mechanism of diversification in many microbial species, but its primary evolutionary role remains controversial. Much recent research has emphasised the adaptive benefit of acquiring novel DNA, but here we argue instead that intragenomic conflict provides a coherent framework for understanding the evolutionary origins of HDT. To test this hypothesis, we developed a mathematical model of a clonally descended bacterial population undergoing HDT through transmission of mobile genetic elements (MGEs) and genetic transformation. Including the known bias of transformation toward the acquisition of shorter alleles into the model suggested it could be an effective means of counteracting the spread of MGEs. Both constitutive and transient competence for transformation were found to provide an effective defence against parasitic MGEs; transient competence could also be effective at permitting the selective spread of MGEs conferring a benefit on their host bacterium. The coordination of transient competence with cell-cell killing, observed in multiple species, was found to result in synergistic blocking of MGE transmission through releasing genomic DNA for homologous recombination while simultaneously reducing horizontal MGE spread by lowering the local cell density. To evaluate the feasibility of the functions suggested by the modelling analysis, we analysed genomic data from longitudinal sampling of individuals carrying Streptococcus pneumoniae. This revealed the frequent within-host coexistence of clonally descended cells that differed in their MGE infection status, a necessary condition for the proposed mechanism to operate. Additionally, we found multiple examples of MGEs inhibiting transformation through integrative disruption of genes encoding the competence machinery across many species, providing evidence of an ongoing "arms race." Reduced rates of transformation have also been observed in cells infected by MGEs that reduce the concentration of extracellular DNA through secretion of DNases. Simulations predicted that either mechanism of limiting transformation would benefit individual MGEs, but also that this tactic's effectiveness was limited by competition with other MGEs coinfecting the same cell. A further observed behaviour we hypothesised to reduce elimination by transformation was MGE activation when cells become competent. Our model predicted that this response was effective at counteracting transformation independently of competing MGEs. Therefore, this framework is able to explain both common properties of MGEs, and the seemingly paradoxical bacterial behaviours of transformation and cell-cell killing within clonally related populations, as the consequences of intragenomic conflict between self-replicating chromosomes and parasitic MGEs. The antagonistic nature of the different mechanisms of HDT over short timescales means their contribution to bacterial evolution is likely to be substantially greater than previously appreciated
- …