Purpose
Nitrous oxide (N
2
O) and methane (CH
4
) are some of the most important greenhouse gases in the atmosphere of the 21st century. Vegetated riparian buffers are primarily implemented for their ...water quality functions in agroecosystems. Their location in agricultural landscapes allows them to intercept and process pollutants from adjacent agricultural land. They recycle organic matter, which increases soil carbon (C), intercept nitrogen (N)-rich runoff from adjacent croplands, and are seasonally anoxic. Thus processes producing environmentally harmful gases including N
2
O and CH
4
are promoted. Against this context, the study quantified atmospheric losses between a cropland and vegetated riparian buffers that serve it.
Methods
Environmental variables and simultaneous N
2
O and CH
4
emissions were measured for a 6-month period in a replicated plot-scale facility comprising maize (
Zea mays
L.). A static chamber was used to measure gas emissions. The cropping was served by three vegetated riparian buffers, namely: (i) grass riparian buffer; (ii) willow riparian buffer and; (iii) woodland riparian buffer, which were compared with a no-buffer control.
Results
The no-buffer control generated the largest cumulative N
2
O emissions of 18.9 kg ha
− 1
(95% confidence interval: 0.5–63.6) whilst the maize crop upslope generated the largest cumulative CH
4
emissions (5.1 ± 0.88 kg ha
− 1
). Soil N
2
O and CH
4
-based global warming potential (GWP) were lower in the willow (1223.5 ± 362.0 and 134.7 ± 74.0 kg CO
2
-eq. ha
− 1
year
− 1
, respectively) and woodland (1771.3 ± 800.5 and 3.4 ± 35.9 kg CO
2
-eq. ha
− 1
year
− 1
, respectively) riparian buffers.
Conclusions
Our results suggest that in maize production and where no riparian buffer vegetation is introduced for water quality purposes (no buffer control), atmospheric CH
4
and N
2
O concerns may result.
Modeling of the 3D Tree Skeleton Using Real-World Data: A Survey Cardenas, Jose L.; Ogayar, Carlos J.; Feito, Francisco R. ...
IEEE transactions on visualization and computer graphics,
2023-Dec., 2023-12-00, 20231201, Letnik:
29, Številka:
12
Journal Article
Recenzirano
Tree modeling has been extensively studied in computer graphics. Recent advances in the development of high-resolution sensors and data processing techniques are extremely useful for collecting 3D ...datasets of real-world trees and generating increasingly plausible branching structures. The wide availability of versatile acquisition platforms allows us to capture multi-view images and scanned data that can be used for guided 3D tree modeling. In this paper, we carry out a comprehensive review of the state-of-the-art methods for the 3D modeling of botanical tree geometry by taking input data from real scenarios. A wide range of studies has been proposed following different approaches. The most relevant contributions are summarized and classified into three categories: (1) procedural reconstruction, (2) geometry-based extraction, and (3) image-based modeling. In addition, we describe other approaches focused on the reconstruction process by adding additional features to achieve a realistic appearance of the tree models. Thus, we provide an overview of the most effective procedures to assist researchers in the photorealistic modeling of trees in geometry and appearance. The article concludes with remarks and trends for promising research opportunities in 3D tree modeling using real-world data.
To advance sustainable and resilient agricultural management policies, especially during land use changes, it is imperative to monitor, report, and verify soil organic carbon (SOC) content rigorously ...to inform its stock. However, conventional methods often entail challenging, time-consuming, and costly direct soil measurements. Integrating data from long-term experiments (LTEs) with freely available remote sensing (RS) techniques presents exciting prospects for assessing SOC temporal and spatial change. The objective of this study was to develop a low-cost, field-based statistical model that could be used as a decision-making aid to understand the temporal and spatial variation of SOC content in temperate farmland under different land use and management. A ten-year dataset from the North Wyke Farm Platform, a 20-field, LTE system established in southwestern England in 2010, was used as a case study in conjunction with an RS dataset. Linear, additive and mixed regression models were compared for predicting SOC content based upon combinations of environmental variables that are freely accessible (termed open) and those that require direct measurement or farmer questionnaires (termed closed). These included an RS-derived Ecosystem Services Provision Index (ESPI), topography (slope, aspect), weather (temperature, precipitation), soil (soil units, total nitrogen TN, pH), and field management practices. Additive models (specifically Generalised Additive Models (GAMs)) were found to be the most effective at predicting space-time SOC variability. When the combined open and closed factors (excluding TN) were considered, significant predictors of SOC were: management related to ploughing being the most important predictor, soil unit (class), aspect, and temperature (GAM fit with a normalised RMSE = 9.1%, equivalent to 0.4% of SOC content). The relative strength of the best-fitting GAM with open data only, which included ESPI, aspect, and slope (normalised RMSE = 13.0%, equivalent to 0.6% of SOC content), suggested that this more practical and cost-effective model enables sufficiently accurate prediction of SOC.
•Ecosystem Services Provision Index as C inputs proxy estimated by remote sensing.•Testing statistical models to predict SOC changes using open and closed data.•Ploughing emerged as crucial using closed predictors of SOC when TN was excluded.•Satellite imagery enables effective modelling of SOC without field data collection.•SOC accurately predicted by Ecosystem Services Provision Index, aspect, slope.
•We present surface fluxes and subsurface CO2, CH4 and N2O from a grazed UK peatland.•Cattle urine increased N2O fluxes due to high NO3− in surface soil layers.•Addition of cattle urine increased ...subsurface CH4 and N2O concentrations.•N2O production at depth continued long after emissions were detected at the surface.•Urine addition had little long-term impact on CO2 production or emission.
Grazing systems represent a substantial percentage of the global anthropogenic flux of nitrous oxide (N2O) as a result of nitrogen addition to the soil. The pool of available carbon that is added to the soil from livestock excreta also provides substrate for the production of carbon dioxide (CO2) and methane (CH4) by soil microorganisms. A study into the production and emission of CO2, CH4 and N2O from cattle urine amended pasture was carried out on the Somerset Levels and Moors, UK over a three-month period. Urine-amended plots (50gNm−2) were compared to control plots to which only water (12mgNm−2) was applied. CO2 emission peaked at 5200mgCO2m−2d−1 directly after application. CH4 flux decreased to −2000μgCH4m−2d−1 two days after application; however, net CH4 flux was positive from urine treated plots and negative from control plots. N2O emission peaked at 88mgN2Om−2d−1 12 days after application. Subsurface CH4 and N2O concentrations were higher in the urine treated plots than the controls. There was no effect of treatment on subsurface CO2 concentrations. Subsurface N2O peaked at 500ppm 12 days after and 1200ppm 56 days after application. Subsurface NO3− concentration peaked at approximately 300mgNkg dry soil−1 12 days after application. Results indicate that denitrification is the key driver for N2O release in peatlands and that this production is strongly related to rainfall events and water-table movement. N2O production at depth continued long after emissions were detected at the surface. Further understanding of the interaction between subsurface gas concentrations, surface emissions and soil hydrological conditions is required to successfully predict greenhouse gas production and emission.
KEY MESSAGE : Agrobacterium tumefaciens strains differ not only in their ability to transform tomato Micro-Tom, but also in the number of transgene copies that the strains integrate in the genome. ...The transformation efficiency of tomato (Solanum lycopersicum L.) cv. Micro-Tom with Agrobacterium tumefaciens strains AGL1, EHA105, GV3101, and MP90, harboring the plasmid pBI121 was compared. The presence of the nptII and/or uidA transgenes in regenerated T₀ plants was determined by PCR, Southern blotting, and/or GUS histochemical analyses. In addition, a rapid and reliable duplex, qPCR TaqMan assay was standardized to estimate transgene copy number. The highest transformation rate (65 %) was obtained with the Agrobacterium strain GV3101, followed by EHA105 (40 %), AGL1 (35 %), and MP90 (15 %). The mortality rate of cotyledons due to Agrobacterium overgrowth was the lowest with the strain GV3101. The Agrobacterium strain EHA105 was more efficient than GV3101 in the transfer of single T-DNA insertions of nptII and uidA transgenes into the tomato genome. Even though Agrobacterium strain MP90 had the lowest transformation rate of 15 %, the qPCR analysis showed that the strain MP90 was the most efficient in the transfer of single transgene insertions, and none of the transgenic plants produced with this strain had more than two insertion events in their genome. The combination of higher transformation efficiency and fewer transgene insertions in plants transformed using EHA105 makes this Agrobacterium strain optimal for functional genomics and biotechnological applications in tomato.
As part of a UK government funded research project to update the UK N2O inventory methodology, a systematic review of published nitrous oxide (N2O) emission factors was carried out of non-UK ...research, for future comparison and synthesis with the UK measurement based evidence base. The aim of the study is to assess how the UK IPCC default emission factor for N2O emissions derived from synthetic or organic fertiliser inputs (EF1) compares to international values reported in published literature. The availability of data for comparing and/or refining the UK IPCC default value and the possibility of analysing sufficient auxiliary data to propose a Tier 2 EF1 reporting strategy is evaluated.
The review demonstrated a lack of consistency in reporting error bounds for fertiliser-derived EFs and N2O flux data with 8% and 44% of publications reporting EF and N2O flux error bounds respectively. There was also poor description of environmental (climate and soil) and experimental design auxiliary data. This is likely to be due to differences in study objectives, however potential improvements to soil parameter reporting are proposed. The review demonstrates that emission factors for agricultural-derived N2O emissions ranged −0.34% to 37% showing high variation compared to the UK Tier 1 IPCC EF1 default values of 1.25% (IPCC 1996) and 1% (IPPC 2006). However, the majority (83%) of EFs reported for UK-relevant soils fell within the UK IPCC EF1 uncertainty range of 0.03% to 3%.
Residual maximum likelihood (REML) analysis of the data collated in the review showed that the type and rate of fertiliser N applied and soil type were significant factors influencing EFs reported. Country of emission, the length of the measurement period, the number of splits, the crop type, pH and SOC did not have a significant impact on N2O emissions. A subset of publications where sufficient data was reported for meta-analysis to be conducted was identified. Meta-analysis of effect sizes of 41 treatments demonstrated that the application of fertiliser has a significant effect on N2O emissions in comparison to control plots and that emission factors were significantly different to zero. However no significant relationships between the quantity of fertiliser applied and the effect size of the amount of N2O emitted from fertilised plots compared to control plots were found. Annual addition of fertiliser of 35 to 557kgN/ha gave a mean increase in emissions of 2.02±0.28g N2O/ha/day compared to control treatments (p<0.01). Emission factors were significantly different from zero, with a mean emission factor estimated directly from the meta analysis of 0.17±0.02%. This is lower than the IPCC 2006 Tier 1 EF1 value of 1% but falling within the uncertainty bound for the IPCC 2006 Tier 1 EF1 (0.03% to 3%).
As only a small number of papers were viable for meta analysis to be conducted due to lack of reporting of the key controlling factors, the estimates of EF in this paper cannot include the true variability under conditions similar to the UK. Review-derived EFs of 0.34% to 37% and mean EF from meta-analysis of 0.17±0.02% highlight variability in reporting EFs depending on the method applied and sample size. A protocol of systematic reporting of N2O emissions and key auxiliary parameters in publications across disciplines is proposed. If adopted this would strengthen the community to inform IPCC Tier 2 reporting development and reduce the uncertainty surrounding reported UK N2O emissions.
•EFs for agricultural-derived N2O emissions ranged − 0.34% to 37%.•Majority (83%) of EFs fell within the UK IPCC EF uncertainty range of 0.03% to 3%.•REML shows a significant effect of N-application rate and soil type on EFs.•Meta-analysis shows a significant effect of N application rate on N2O emissions.•Lack of consistency in data reported in the literature.
Grasslands cover around 25% of the global ice-free land surface, they are used predominantly for forage and livestock production and are considered to contribute significantly to soil carbon (C) ...sequestration. Recent investigations into using ‘nature-based solutions’ to limit warming to <2 °C suggest up to 25% of GHG mitigation might be achieved through changes to grassland management. In this study we evaluate pasture management interventions at the Rothamsted Research North Wyke Farm Platform, under commercial farming conditions, over two years and consider their impacts on net CO2 exchange. We investigate if our permanent pasture system (PP) is, in the short-term, a net sink for CO2 and whether reseeding this with deep-rooting, high-sugar grass (HS) or a mix of high-sugar grass and clover (HSC) might increase the net removal of atmospheric CO2. In general CO2 fluxes were less variable in 2018 than in 2017 while overall we found that net CO2 fluxes for the PP treatment changed from a sink in 2017 (−5.40 t CO2 ha−1 y−1) to a source in 2018 (6.17 t CO2 ha−1 y−1), resulting in an overall small source of 0.76 t CO2 ha−1 over the two years for this treatment. HS showed a similar trend, changing from a net sink in 2017 (−4.82 t CO2 ha−1 y−1) to a net source in 2018 (3.91 t CO2 ha−1 y−1) whilst the HSC field was a net source in both years (3.92 and 4.10 t CO2 ha−1 y−1, respectively). These results suggested that pasture type has an influence in the atmospheric CO2 balance and our regression modelling supported this conclusion, with pasture type and time of the year (and their interaction) being significant factors in predicting fluxes.
Display omitted
•Pasture type influenced the atmospheric CO2 balance in a temperate climate.•Permanent (PP) and high sugar grass (HS) pastures had similar yearly net CO2 fluxes.•PP and HS pastures were a sink for atmospheric CO2 in 2017 and a source in 2018.•A pasture of high sugar grass and clover (HSC) acted as a net source of CO2.•Night-time CO2 emissions were influenced by season and pasture type.
Agriculture significantly contributes to global greenhouse gas (GHG) emissions and there is a need to develop effective mitigation strategies. The efficacy of methods to reduce GHG fluxes from ...agricultural soils can be affected by a range of interacting management and environmental factors. Uniquely, we used the Taguchi experimental design methodology to rank the relative importance of six factors known to affect the emission of GHG from soil: nitrate (NO₃ ⁻) addition, carbon quality (labile and non‐labile C), soil temperature, water‐filled pore space (WFPS) and extent of soil compaction. Grassland soil was incubated in jars where selected factors, considered at two or three amounts within the experimental range, were combined in an orthogonal array to determine the importance and interactions between factors with a L₁₆ design, comprising 16 experimental units. Within this L₁₆ design, 216 combinations of the full factorial experimental design were represented. Headspace nitrous oxide (N₂O), methane (CH₄) and carbon dioxide (CO₂) concentrations were measured and used to calculate fluxes. Results found for the relative influence of factors (WFPS and NO₃ ⁻ addition were the main factors affecting N₂O fluxes, whilst glucose, NO₃ ⁻ and soil temperature were the main factors affecting CO₂ and CH₄ fluxes) were consistent with those already well documented. Interactions between factors were also studied and results showed that factors with little individual influence became more influential in combination. The proposed methodology offers new possibilities for GHG researchers to study interactions between influential factors and address the optimized sets of conditions to reduce GHG emissions in agro‐ecosystems, while reducing the number of experimental units required compared with conventional experimental procedures that adjust one variable at a time.
Nitrous oxide emissions (N₂O) from agricultural land are spatially and temporally variable. Most emission measurements are made with small (≪ 1 m² area) static chambers. We used N₂O chamber data ...collected from multiple field experiments across different geo‐climatic zones in the UK and from a range of nitrogen treatments to quantify uncertainties associated with flux measurements. Data were analysed to assess the spatial variability of fluxes, the degree of linearity of headspace N₂O accumulation and the robustness of using ambient air N₂O concentrations as a surrogate for sampling immediately after closure (T₀). Data showed differences of up to more than 50‐fold between the maximum and minimum N₂O flux from five chambers within one plot on a single sampling occasion, and that reliability of flux measurements increased with greater numbers of chambers. In more than 90% of the 1970 cases where linearity of headspace N₂O accumulation was measured (with four or more sampling points), linear accumulation was observed; however, where non‐linear accumulation was seen this could result in a 26% under‐estimate of the flux. Statistical analysis demonstrated that the use of ambient air as a surrogate for T₀ headspace samples did not result in any consistent bias in calculated fluxes. Spatial variability has the potential to result in erroneous flux estimates if not taken into account, and generally introduces a far larger uncertainty into the calculated flux (commonly orders of magnitude more) than any uncertainties introduced through reduced headspace sampling or assumption of linearity of headspace accumulation. Hence, when deploying finite resources, maximizing chamber numbers should be given priority over maximizing the number of headspace samplings per enclosure period.
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