The talk with present the key results of the IPCC Working Group III 5th assessment report. Concluding four years of intense scientific collaboration by hundreds of authors from around the world, the report responds to the request of the world's governments for a comprehensive, objective and policy neutral assessment of the current scientific knowledge on mitigating climate change. The report has been extensively reviewed by experts and governments to ensure quality and comprehensiveness.

Climate change 2014 - Mitigation of climate change

As global land surface temperature continues to rise and heatwave events increase in frequency, duration, and/or intensity, our key food and fuel cropping systems will likely face increased heat-related stress. A large volume of literature exists on exploring measured and modelled impacts of rising temperature on crop photosynthesis, from enzymatic responses within the leaf up to larger ecosystem-scale responses that reflect seasonal and interannual crop responses to heat. This review discusses (i) how crop photosynthesis changes with temperature at the enzymatic scale within the leaf; (ii) how stomata and plant transport systems are affected by temperature; (iii) what features make a plant susceptible or tolerant to elevated temperature and heat stress; and (iv) how these temperature and heat effects compound at the ecosystem scale to affect crop yields. Throughout the review, we identify current advancements and future research trajectories that are needed to make our cropping systems more resilient to rising temperature and heat stress, which are both projected to occur due to current global fossil fuel emissions.

The effect of increasing temperature on crop photosynthesis: from enzymes to ecosystems.

Cellulosic biofuels are intended to improve future energy and climate security. Nitrogen (N) fertilizer is commonly recommended to stimulate yields but can increase losses of the greenhouse gas nitrous oxide (N2O) and other forms of reactive N, including nitrate. We measured soil N2O emissions and nitrate leaching along a switchgrass (Panicum virgatum) high resolution N-fertilizer gradient for three years post-establishment. Results revealed an exponential increase in annual N2O emissions that each year became stronger (R 2 > 0.9, P < 0.001) and deviated further from the fixed percentage assumed for IPCC Tier 1 emission factors. Concomitantly, switchgrass yields became less responsive each year to N fertilizer. Nitrate leaching (and calculated indirect N2O emissions) also increased exponentially in response to N inputs, but neither methane (CH4) uptake nor soil organic carbon changed detectably. Overall, N fertilizer inputs at rates greater than crop need curtailed the climate benefit of ethanol production almost two-fold, from a maximum mitigation capacity of −5.71 ± 0.22 Mg CO2e ha−1 yr−1 in switchgrass fertilized at 56 kg N ha−1 to only −2.97 ± 0.18 Mg CO2e ha−1 yr−1 in switchgrass fertilized at 196 kg N ha−1. Minimizing N fertilizer use will be an important strategy for fully realizing the climate benefits of cellulosic biofuel production.

Data from: Nitrogen fertilization challenges the climate benefit of cellulosic biofuels

21st‐century modeling of greenhouse gas (GHG) emissions from bioenergy crops is necessary to quantify the extent to which bioenergy production can mitigate climate change. For over 30 years, the Century‐based biogeochemical models have provided the preeminent framework for belowground carbon and nitrogen cycling in ecosystem and earth system models. While monthly Century and the daily time‐step version of Century (DayCent) have advanced our ability to predict the sustainability of bioenergy crop production, new advances in feedstock generation, and our empirical understanding of sources and sinks of GHGs in soils call for a re‐visitation of DayCent's core model structures. Here, we evaluate current challenges with modeling soil carbon dynamics, trace gas fluxes, and drought and age‐related impacts on bioenergy crop productivity. We propose coupling a microbial process‐based soil organic carbon and nitrogen model with DayCent to improve soil carbon dynamics. We describe recent improvements to DayCent for simulating unique plant structural and physiological attributes of perennial bioenergy grasses. Finally, we propose a method for using machine learning to identify key parameters for simulating N2O emissions. Our efforts are focused on meeting the needs for modeling bioenergy crops; however, many updates reviewed and suggested to DayCent will be broadly applicable to other systems.

21st-century biogeochemical modeling: Challenges for Century-based models and where do we go from here?

Responses of soil mineral-associated and particulate organic carbon to carbon input: A meta-analysis.

In the age of biofuel innovation, bioenergy crop sustainability assessment has determined how candidate systems alter the carbon (C) and nitrogen (N) cycle. These research efforts revealed how perennial crops, such as switchgrass, increase belowground soil organic carbon (SOC) and lose less N than annual crops, like maize. As demand for bioenergy increases, land managers will need to choose whether to invest in food or fuel cropping systems. However, little research has focused on the C and N cycle impacts of reverting purpose‐grown perennial bioenergy crops back to annual cropping systems. We investigated this knowledge gap by measuring C and N pools and fluxes over 2 years following reversion of a mature switchgrass stand to an annual maize rotation. The most striking treatment difference was in ecosystem respiration (ER), with the maize‐converted treatment showing the highest respiration flux of 2,073.63 (± 367.20) g C m−2 year−1 compared to the switchgrass 1,412.70 (± 28.72) g C m−2 year−1 and maize‐control treatments 1,699.16 (± 234.79) g C m−2 year−1. This difference was likely driven by increased heterotrophic respiration of belowground switchgrass necromass in the maize‐converted treatment. Predictions from the DayCent model showed it would take approximately 5 years for SOC dynamics in the converted treatment to return to conditions of the maize‐control treatment. N losses were highest from the maize‐converted treatment when compared to undisturbed switchgrass and maize‐control, particularly during the first conversion year. These results show substantial C and N losses occur within the first 2 years after reversion of switchgrass to maize. Given farmers are likely to rotate between perennial and annual crops in the future to meet market demands, our results indicate that improvements to the land conversion approach are needed to preserve SOC built up by perennial crops to maintain the long‐term ecological sustainability of bioenergy cropping systems.

The carbon and nitrogen cycle impacts of reverting perennial bioenergy switchgrass to an annual maize crop rotation.

Background Assessments of the soil microbiome provide valuable insight to ecosystem function due to the integral role microorganisms play in biogeochemical cycling of carbon and nutrients. For example, treatment effects on nitrogen cycling functional groups are often presented alongside one another to demonstrate how agricultural management practices affect various nitrogen cycling processes. However, the functional groups commonly evaluated in nitrogen cycling microbiome studies range from phylogenetically narrow (e.g., N-fixation, nitrification) to broad [e.g., denitrification, dissimilatory nitrate reduction to ammonium (DNRA)]. The bioinformatics methods used in such studies were developed for 16S rRNA gene sequence data, and how these tools perform across functional genes of different phylogenetic diversity has not been established. For example, an OTU clustering method that can accurately characterize sequences harboring comparatively little diversity may not accurately resolve the diversity within a gene comprised of a large number of clades. This study uses two nitrogen cycling genes, nifH, a gene which segregates into only three distinct clades, and nrfA, a gene which is comprised of at least eighteen clades, to investigate differences which may arise when using heuristic OTU clustering (abundance-based greedy clustering, AGC) vs. true hierarchical OTU clustering (Matthews Correlation Coefficient optimizing algorithm, Opti-MCC). Detection of treatment differences for each gene were evaluated to demonstrate how conclusions drawn from a given dataset may differ depending on clustering method used. Results The heuristic and hierarchical methods performed comparably for the more conserved gene, nifH. The hierarchical method outperformed the heuristic method for the more diverse gene, nrfA; this included both the ability to detect treatment differences using PERMANOVA, as well as higher resolution in taxonomic classification. The difference in performance between the two methods may be traced to the AGC method’s preferential assignment of sequences to the most abundant OTUs: when analysis was limited to only the largest 100 OTUs, results from the AGC-assembled OTU table more closely resembled those of the Opti-MCC OTU table. Additionally, both AGC and Opti-MCC OTU tables detected comparable treatment differences using the rank-based ANOSIM test. This demonstrates that treatment differences were preserved using both clustering methods but were structured differently within the OTU tables produced using each method. Conclusion For questions which can be answered using tests agnostic to clustering method (e.g., ANOSIM), or for genes of relatively low phylogenetic diversity (e.g., nifH), most upstream processing methods should lead to similar conclusions from downstream analyses. For studies involving more diverse genes, however, care should be exercised to choose methods that ensure accurate clustering for all genes. This will mitigate the risk of introducing Type II errors by allowing for detection of comparable treatment differences for all genes assessed, rather than disproportionately detecting treatment differences in only low-diversity genes.

https://www.frontiersin.org/articles/10.3389/fmicb.2022.730340/pdf

Nitrogen Cycling Microbial Diversity and Operational Taxonomic Unit Clustering: When to Prioritize Accuracy Over Speed

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The carbon and nitrogen cycle impacts of reverting perennial bioenergy switchgrass to an annual maize crop rotation.

Nitrogen Cycling Microbial Diversity and Operational Taxonomic Unit Clustering: When to Prioritize Accuracy Over Speed