Integrating agriculture and energy to assess GHG emissions reduction: a methodological approach

Agriculture is responsible for approximately 25% of anthropogenic global GHG emissions. This significant share highlights the fundamental importance of the agricultural sector in the global GHG emissions reduction challenge. This article develops and tests a methodology for the integration of agricultural and energy systems modelling. The goal of the research is to extend an energy systems modelling approach to agriculture in order to provide richer insights into the dynamics and interactions between the two (e.g. in competition for land-use). We build Agri-TIMES, an agricultural systems module using the TIMES energy systems modelling framework, to model the effect of livestock emissions and explore emissions reduction options. The research focuses on Ireland, which is an interesting test case for two reasons: first, agriculture currently accounts for about 30% of Ireland's GHG emissions, significantly higher than other industrialized countries yet comparable with global levels (here including emissions associated with other land-use change and forestation); second, Ireland is both a complete and reasonably sized agricultural system to act as a test case for this new approach. This article describes the methodology used, the data requirements, and technical assumptions made to facilitate the modelling. It also presents results to illustrate the approach and provide associated initial insights.

Policy relevance

Most of the policy focus with regard to climate mitigation targets has been on reducing energy-related CO₂ emissions, which is understandable as they represent by far the largest source of emissions. Non-energy-related GHG emissions – largely from agriculture, industrial processes, and waste – have received significantly less attention in policy discourse. Going forward, however, if significant cuts are made in energy-related CO₂ emissions, the role of non-energy-related GHG emissions will grow in importance. It is therefore crucial that climate mitigation analyses and strategies are not limited to the energy system. This article shows the value of using integrated energy and agriculture techno-economic modelling techniques to draw evidence for new comprehensive climate policy strategies able to discern between the full range of technical solutions available. It enables the production of economy-wide least-cost climate mitigation pathways.     

Trilobite Biostratigraphy of the lower Paleozoic (Cambrian–Ordovician) Joseon Supergroup, Taebaeksan Basin, Korea

In Korea,trilobites are among the most intensively studied fossil groups in the past century and provide invaluable information about lower Paleozoic stratigraphy,paleogeography,and tectonics of the Korean Peninsula. Trilobites occur in the lower Paleozoic Joseon Supergroup of the Taebaeksan Basin which was part of the Sino-Korean Craton in the Paleozoic. The Joseon Supergroup is divided into the Taebaek,Yeongwol,and Mungyeong groups. The Taebaek and Yeongwol groups are richly fossiliferous,while the Mungyeong Group is poorly fossiliferous. Contrasting trilobite faunal contents of the Taebaek and Yeongwol groups resulted in two separate biostratigraphic schemes for the Cambrian–Ordovician of the Taebaeksan Basin. A total of 22 biozones or fossiliferous horizons were recognized in the Taebaek Group; 19 zones were established in the Yeongwol Group; and four biozones were known from the Mungyeong Group. These trilobite biozones of the Taebaeksan Basin indicate the Joseon Supergroup ranges in age from the Cambrian Series 2 to Middle Ordovician and can be correlated well with the formations of North China,South China,and Australia.     

Platinum-group Mineral (PGM) and Base-metal Sulphide (BMS) Inclusions in Chromitites of the Zedang Ophiolite, Southern Tibet, China and their Petrogenetic Significance

Voluminous platinum-group mineral(PGM) inclusions including erlichmanite(Os,Ru)S_2, laurite(Ru,Os)S_2, and irarsite(Ir,Os,Ru,Rh)As S, as well as native osmium Os(Ir) and inclusions of base metal sulphides(BMS), including millerite(NiS), heazlewoodite(Ni_3S_2), covellite(CuS) and digenite(Cu_3S_2), accompanied by native iron, have been identified in chromitites of the Zedang ophiolite, Tibet. The PGMs occur as both inclusions in magnesiochromite grains and as small interstitial granules between them; most are less than 10 μm in size and vary in shape from euhedral to anhedral. They occur either as single or composite(biphase or polyphase) grains composed solely of PGM, or PGM associated with silicate grains. Os-, Ir-, and Ru-rich PGMs are the common species and Pt-, Pd-, and Rh-rich varieties have not been identified. Sulfur fugacity and temperature appear to be the main factors that controlled the PGE mineralogy during crystallization of the host chromitite in the upper mantle. If the activity of chalcogenides(such as S, and As) is low, PGE clusters will remain suspended in the silicate melt until they can coalesce to form alloys. Under appropriate conditions of ?S_2 and ?O_2, PGE alloys might react with the melt to form sulfides-sulfarsenides. Thus, we suggest that the Os, Ir and Ru metallic clusters and alloys in the Zedang chromitites crystallized first under high temperature and low ?S_2, followed by crystallization of sulphides of the laurite-erlichmanite, solid-solution series as the magma cooled and ?S_2 increased. The abundance of primary BMS in the chromitites suggests that ?S_2 reached relatively high values during the final stages of magnesiochromite crystallization. The diversity of the PGE minerals, in combination with differences in the petrological characteristics of the magnesiochromites, suggest different degrees of partial melting, perhaps at different depths in the mantle. The estimated parental magma composition suggests formation in a suprasubduction zone environment, perhaps in a forearc.     

A Spatial Data Infrastructure Approach for the Characterization of New Zealand's Groundwater Systems

The future information needs of stakeholders for hydrogeological and hydro‐climate data management and assessment in New Zealand may be met with an Open Geospatial Consortium (OGC) standards‐compliant publicly accessible web services framework which aims to provide integrated use of groundwater information and environmental observation data in general. The stages of the framework development described in this article are search and discovery as well as data collection and access with (meta)data services, which are developed in a community process. The concept and prototype implementation of OGC‐compliant web services for groundwater and hydro‐climate data include demonstration data services that present multiple distributed datasets of environmental observations. The results also iterate over the stakeholder community process and the refined profile of OGC services for environmental observation data sharing within the New Zealand Spatial Data Infrastructure (SDI) landscape, including datasets from the National Groundwater Monitoring Program and the New Zealand Climate Database along with datasets from affiliated regional councils at regional‐ and sub‐regional scales. With the definition of the New Zealand observation data profile we show that current state‐of‐the‐art standards do not necessarily need to be improved, but that the community has to agree upon how to use these standards in an iterative process.     

Estimating Population Attribute Values in a Table: “Get Me Started in” Iterative Proportional Fitting

Iterative proportional fitting (IPF) is a technique that can be used to adjust a distribution reported in one data set by totals reported in others. IPF is used to revise tables of data where the information is incomplete, inaccurate, outdated, or a sample. Although widely applied, the IPF methodology is rarely presented in a way that is accessible to nonexpert users. This article fills that gap through discussion of how to operationalize the method and argues that IPF is an accessible and transparent tool that can be applied to a range of data situations in population geography and demography. It offers three case study examples where IPF has been applied to geographical data problems; the data and algorithms are made available to users as supplementary material.