IPCC Research Articles

Development of National Emission Factor for Rice Production System in Malaysia: A Step Toward Achieving Sustainable Development Goals

Objective: Currently, methane (CH4) emissions from rice cultivation in Malaysia are calculated using the regional’s methane emission factor (EF) of 1.60 kg CH₄ ha⁻¹ day⁻¹, as Malaysia has not yet developed a national emission factor. The objective of this study is to generate a country-specific EF of CH4 emission from rice cultivation in Malaysian rice fields. The EF generated would then be used in future emission estimates and the country’s greenhouse gas (GHG) inventory reports to the United Nations Framework Convention on Climate Change (UNFCCC). Theoretical Framework: The establishment of a national GHG emission factor for rice cultivation in Malaysia is critical for accurate GHG inventory reporting and effective climate change mitigation. This study utilizes the Intergovernmental Panel on Climate Change (IPCC) guidelines to derive a country-specific emission factor, enhancing Malaysia’s compliance with international climate obligations and promoting sustainable agricultural practices. Method: A new national EF was developed from eleven rice planting seasons in rice granary areas (IADA Pulau Pinang, IADA Barat Laut Selangor, MADA and KADA) and non-granary area (Sik, Kedah). The EF was calculated from methane emissions from the new data sets, published journals and unpublished data from MARDI. For the new data set, methane gas (CH4) was measured using a static chamber method (Minamikawa et al., 2015). Sampling of GHG from the gas chamber is carried out in the field every 2 weeks and gas was analysed by a GC System Agilent 7890A gas chromatography. The daily methane flux and methane emission follow the methods by Habib et al. (2007), Fauzi et al. (2023) and the Guidelines for National Greenhouse Gas Inventories for Cropland (IPCC, 2006a). Results and Discussion: The national EF calculated from CH4 gas emissions over eleven rice planting seasons in Malaysia between 2012-2024 was 1.80 kg CH4 ha-1 day-1, which is higher than the current regional EF used (1.60 kg CH4 ha-1 day-1). However, the value is within the range of the default value of 1.30 kg CH4 ha-1 day-1 by the Intergovernmental Panel on Climate Change (IPCC) 2006 and 2.00 kg CH4 ha-1 day-1 (IPCC, 1996). Research Implications: The use of this new EF resulted in 12.49% increase in the national GHG inventory from the rice cultivation sub-sector as compared to using the current regional EF. This initiative is part of a comprehensive plan to enhance and strengthen GHG inventory reporting for Malaysia's agriculture sector, aiming to meet IPCC requirements and progress to Tier 2 status.

The indirect global warming potential of methane oxidation in the IPCC’s sixth assessment report

Abstract Greenhouse-gas emission (GHG) metric values for methane in the sixth assessment report by the Intergovernmental Panel on Climate Change (IPCC) include the indirect effect associated to the oxidation of methane to CO2. An analysis of the figures provided by the IPCC reveals they assume that in average 75% of methane is ultimately oxidized to CO2, while the remaining 25% is converted to intermediate degradation products, most notably formaldehyde, which are removed from the atmosphere via wet and dry deposition and treated by the IPCC as a potential carbon sink in terrestrial and aquatic ecosystems. In this short article we present a critique to this assumption, based on existing knowledge about the environmental fate of methane’s degradation products. We conclude that any assumption other than full degradation of methane to CO2 in a rather short time frame is questionable, whereby the default CO2 yield from this oxidation, as far as GHG metrics are concerned, should be 100%. We re-calculate values for the global warming potential (GWP) metric in accordance with our findings, resulting in an increase in GWP100 from 29.8 and 27.0 to 30.40 and 27.65 kg CO2-equivalents/kg fossil and biogenic methane, respectively. Although we only present the implications in terms GWP, our proposal is conceptually valid for other GHG metrics as well.

Production losses in Russian agriculture: Impact on food security and environmental sustainability

This paper provides a quantitative assessment of the impact of losses on specific aspects of food security and environmental sustainability. The lack of full information on the benefits of food loss reduction destimulates developing appropriate effective measures at the public level and in the private sector. The study covers losses generated at the initial stages of the food supply chain, including agricultural production and primary processing. Methodological assessments are based on works of foreign authors, recommendations of the Food and Agriculture Organization of the United Nations (FAO), the Intergovernmental Panel on Climate Change (IPCC) and domestic methodological developments in this area. The results indicate that at the primary stages of the food supply chain, food losses generate a relatively small amount of greenhouse gas emissions, so their reduction in order to lower the carbon footprint is not as effective as at the stage of retail and household consumption. However, preventing losses in agriculture contributes to the creation of additional volumes of food, which can be directed to the consumption of vulnerable groups of population. At the same time, the consequences of loss reduction vary greatly depending on the type of product

Barley vulnerability to climate change: perspectives for cultivation in South America.

Barley (Hordeum vulgare) is a globally significant cereal crop, widely used in both food production and brewing. However, it is particularly vulnerable to climate change, especially extreme temperature fluctuations, which can severely reduce yields. To address this challenge, a detailed climate zoning study was conducted to assess the suitability of barley production areas across South America, considering both current conditions and future climate scenarios from the Intergovernmental Panel on Climate Change (IPCC). The study utilized historical climate data along with projections from the CMIP6 IPSL-CM6A-LR model for the period 2021-2100. Several indices, such as evapotranspiration, were calculated, and factors like soil composition and topography were integrated into the classification of regions based on their agricultural potential. Critical variables in this assessment included temperature, precipitation, and water or thermal excess. The results showed that 6.59% of South America's territory is currently suitable for barley cultivation without additional irrigation, with these regions concentrated primarily in temperate southern areas. In contrast, 18.62% of the region is already unsuitable due to excessive heat. Projections under future climate scenarios indicate a shrinking of suitable areas, alongside an expansion of unsuitable regions. In the worst-case scenario, only 1.48% of the territory would remain viable for barley farming. These findings emphasize the crop's vulnerability to climate change, underscoring the urgency of developing agricultural adaptation strategies. The predicted contraction in suitable barley cultivation areas demonstrates the profound impact of climate change on agriculture and highlights the need for proactive measures to ensure sustainable barley production in South America.

‘IPCC-envy’: Why do other science-policy issues want an IPCC, and should they?

The UN's Intergovernmental Panel on Climate Change (IPCC) has been at the forefront of climate science and policy for more than 35 years. Many commentators hold up the IPCC as a model for how global knowledge should be assessed, communicated and employed in complex, transnational, public policy challenges. In this essay, I lay out some reasons why other science-policy issues might indeed be envious of the IPCC, but also suggest a number of reasons why replication of the IPCC in other topic areas might be difficult and why it might not necessarily be desirable.

An evaluation of soil carbon models and their role on finding ways to net-zero carbon in agricultural systems

The estimation of changes in soil organic carbon (SOC) is a key issue for national green-house gasses (GHG) inventories, climate change mitigation programs and the estimation of carbon footprint of farm products in life cycle assessments. Any strategy related to net-zero carbon in agricultural systems needs to quantify the SOC balance. In this way, SOC models help decision makers involved in agriculture to understand the dynamics of the SOC and the interaction between all variables related to soil, climate, land use, and management, to design the best solution to reduce emissions or enable carbon sequestration. Likewise, it is important to identify suitable models for the region. This study aims to address three main subjects: a) a discussion on the importance of SOC estimation for GHG inventories and the carbon footprint of crops, using the Intergovernmental Panel on Climate Change (IPCC) Tier 1 method and AMG model; b) an evaluation and brief description of the IPCC “Steady State Method” (SSM), using experimental data from two sites in Argentina, comparing these results to AMG and RothC models (both previously validated at those sites); and c) a brief discussion about the potential use of SOC models for what-if management scenarios, their real limitations and future research needs. The three models were consistent in predicting the impact of tillage and the long-term trends in changes in SOC stocks under different management practices. The SSM model was evaluated for the first time in Argentina and performed even better than the other two models. It was consistent with the observed values, when predicting the effect of tillage system under different crop rotations, including pasture systems. Regarding efficiencies of the models, they showed acceptable Nash-Sutcliffe Efficiency (NSE) values, and the root mean square error (RMSE) was also acceptable between 3 % and 7 %, within a range of 4–5 Mg C.ha−1. Therefore, the SSM model proved to be a valuable tool to estimate SOC trends for crop and pasture rotations under different management scenarios (i.e., tillage systems and fertilization), to identify best practices that allow for a zero or positive SOC balance, in two different soil and climate conditions of the Pampean Region of Argentina. In our study, the SSM did have a better fit to the data and, furthermore, this Tier 2 method is simpler than the Tier 3 models, and, therefore, is advantageous for conducting regional assessments and GHG inventories.

Future directions for deep ocean climate science and evidence-based decision making

IntroductionA defining aspect of the Intergovernmental Panel on Climate Change (IPCC) assessment reports (AR) is a formal uncertainty language framework that emphasizes higher certainty issues across the reports, especially in the executive summaries and short summaries for policymakers. As a result, potentially significant risks involving understudied components of the climate system are shielded from view.MethodsHere we seek to address this in the latest, sixth assessment report (AR6) for one such component—the deep ocean—by summarizing major uncertainties (based on discussions of low confidence issues or gaps) regarding its role in our changing climate system. The goal is to identify key research priorities to improve IPCC confidence levels in deep ocean systems and facilitate the dissemination of IPCC results regarding potentially high impact deep ocean processes to decision-makers. This will accelerate improvement of global climate projections and aid in informing efforts to mitigate climate change impacts. An analysis of 3,000 pages across the six selected AR6 reports revealed 219 major science gaps related to the deep ocean. These were categorized by climate stressor and nature of impacts.ResultsHalf of these are biological science gaps, primarily surrounding our understanding of changes in ocean ecosystems, fisheries, and primary productivity. The remaining science gaps are related to uncertainties in the physical (32%) and biogeochemical (15%) ocean states and processes. Model deficiencies are the leading cited cause of low certainty in the physical ocean and ice states, whereas causes of biological uncertainties are most often attributed to limited studies and observations or conflicting results.DiscussionKey areas for coordinated effort within the deep ocean observing and modeling community have emerged, which will improve confidence in the deep ocean state and its ongoing changes for the next assessment report. This list of key “known unknowns” includes meridional overturning circulation, ocean deoxygenation and acidification, primary production, food supply and the ocean carbon cycle, climate change impacts on ocean ecosystems and fisheries, and ocean-based climate interventions. From these findings, we offer recommendations for AR7 to avoid omitting low confidence-high risk changes in the climate system.

Fish distribution shifts due to climate change in the Northeast Atlantic: Using a hierarchical filtering approach on marine-estuarine opportunist species

Marine-estuarine opportunist (MEO) species are fish that occur in the continental shelf and use estuaries and/or shallow coastal areas as nurseries. These commercially important resources are facing significant environmental modifications caused by direct and/or indirect anthropogenic climate change effects. In this study, we investigated the directionality and the magnitude of the distribution shifts (i.e., range size, gravity centroids, and margins) in marine environment suitability for six main MEO fish species within the Northeast Atlantic expected for the end of the 21st century. In the framework of this study, we have distinguished ‘sub-boreal’ from ‘sub-tropical’ species. The ‘hierarchical filters’ concept was adopted for modelling the potential species distributions and combined the predictions of i) a bioclimatic model with ii) a habitat model. The bioclimatic model is based on large-scale and time-variant variables while variables of the habitat model are fine-grained and time-invariant. Two Intergovernmental Panel on Climate Change (IPCC) scenarios are tested: an intermediate (SSP2-4.5) and a pessimistic one (SSP5-8.5). We applied this framework using international databases of biodiversity occurrences, ensemble forecasting producing consensual predictions, and innovative indices of distribution shifts. A visible north-westward shift was predicted for all six species in our study area. However, the northward expansion was greater for ‘sub-tropical’ than for ‘sub-boreal’ species due to faster gravity centroid displacement shifts and faster margins shifts. These range shifts may lead to major ecological impacts (e.g., changes in recruitment to estuarine and coastal nurseries, as well as changes in spawning grounds) that may alter populations' connectivity.

THE ROLE OF CARBON DIOXIDE IN “GLOBAL WARMING” AND ITS APPROACH IN CHEMISTRY TEXTBOOKS

This study aimed to investigate the approaches taken to hypotheses describing climate drivers, especially the warming theory , in Brazilian high school chemistry textbooks from the early 21st century. We assume that the concept of the leading role of CO2 in global climate change is based mainly on theoretical data and fragments of information derived from computational climate models. In parallel, several empirical studies have pointed to the natural and cyclical nature of climate phenomena that have occurred throughout history. Therefore, we proposed to compare these studies with the content available in some of the main high school textbooks used in Brazil in the early 2000s, using Discursive Textual Analysis . According to the results, all the collections analyzed opted for unilateral warming approaches, derived from the IPCC ( Intergovernmental Panel on Climate Change ) reports, that is, without a comprehensive description of antagonistic and natural hypotheses about current climate change.

Sea Level Rise in Europe: Summary for Policymakers

Abstract. Sea level rise (SLR) is a global concern for low-lying coastal areas, including many European coasts. The European Knowledge Hub on Sea Level Rise (KH-SLR), a collaborative effort by the Joint Programming Initiatives for “Connecting Climate Knowledge for Europe” (JPI Climate) and for “Healthy and Productive Seas and Oceans” (JPI Oceans), has developed the 1st Assessment Report (SLRE1) to address the challenges posed by SLR in Europe. The report's target audience includes national and subnational bodies focused on research and policy advice for coastal management and climate adaptation, as well as European experts who contribute to shaping policy frameworks and collecting information at a pan-European scale. This report, preceded by a series of targeted surveys and workshops with researchers and stakeholders (e.g. coastal decision-makers), has synthesized the current scientific knowledge on SLR drivers, impacts, and policies at local, national, and European basin scales. It provides in-depth and basin-specific analyses on local sea level changes, compared with relevant global assessments of the Intergovernmental Panel on Climate Change (IPCC). In addition, it identified critical knowledge gaps needed to support the development of actionable information. The Summary for Policymakers (SPM) distils the key findings of the SLRE1, presenting information specific to the six European basins: Mediterranean Sea, Black Sea, North Sea, Baltic Sea, Atlantic, and Arctic. The SPM highlights basin-specific trends, vulnerabilities, and potential impacts, while also orienting future requirements.​​​​​​​

Assessing climate strategies of major energy corporations and examining projections in relation to Paris Agreement objectives within the framework of sustainable energy

The study presents a comparative analysis of emission scenarios proposed by key institutions, including Shell, British Petroleum (BP), the International Energy Agency (IEA), and the Intergovernmental Panel on Climate Change (IPCC), within the framework of the Paris Agreement's ambitious goals. The Agreement seeks to limit global temperature rise to below 2 °C, ideally to 1.5 °C. Using a comprehensive analytical framework, the study evaluates each institution's projected carbon pathways, energy compositions, and policy recommendations. The findings reveal that IPCC scenarios demonstrate the strongest alignment with the Paris Agreement's targets, emphasizing a rapid transition to renewable energy and stringent mitigation measures. In contrast, the scenarios put forward by Shell and BP, although showing significant carbon reductions, remain heavily dependent on fossil fuels, raising concerns about the ability to meet the 1.5 °C and 2 °C targets. The IEA scenarios provide a middle ground, promoting decarbonization while still supporting natural gas as a transitional energy source. Disparities in transparency and methodological consistency are also identified across the scenarios, with the IPCC leading in clarity and scientific rigor. Ultimately, the research underscores the importance of harmonizing the strengths of different institutional approaches, while addressing the respective limitations, to ensure that the global community can stay on track to meet or exceed the climate objectives outlined in the Paris Agreement's. The study concludes that collective action, accelerated technological advancement, and policy shifts are crucial to achieving a sustainable, Net Zero future.

Influence of climate change on water quality in Terra Nova River basin, Pernambuco, Brazil

Climate changes tend to intensify water scarcity in arid and semi-arid regions, making water management in these areas more challenging, directly influencing hydrological dynamics, causing impacts on ecosystems and society. The Terra Nova River basin is crucial for the water security of the semi-arid region of Pernambuco, especially after the construction of the São Francisco River Integration Project (PISF), which saw the construction of six reservoirs to supply water for human consumption, agricultural, and industrial activities. Therefore, this study aims to analyzes the impacts of climate changes on water quality in the Terra Nova River basin in Pernambuco, using long-term data from the Hydrological Response Unit System for Pernambuco (SUPer). The ARIMA (Autoregressive Integrated Moving Averages) model is employed to predict water quality parameters in climate change scenarios, using data from the Intergovernmental Panel on Climate Change (IPCC) and the Oswaldo Cruz Foundation (Fiocruz). The variables include air temperature, precipitation, dissolved oxygen, nitrogen, and phosphorus. Simulated scenarios were compared with CONAMA No. 357/2005 limits, revealing potential decreases in dissolved oxygen and phosphorus concentrations, alongside an increase in nitrogen concentrations. Irregular rainfall rates, high air temperatures, and evapotranspiration, combined with conflicts by water resources, may exacerbate water access issues in the semi-arid region, worsening the water crisis and threatening water security.

Study on the Destruction and Removal Efficiency (DRE) of Perfluorinated Compound Gases Used in Semiconductor and Display Processes in Korea: DRE and Uncertainty by Scrubber Type

In Republic of Korea, plasma scrubbers are a common technology employed in the semiconductor and display industries. However, there are numerous other types of scrubbers in use. The Intergovernmental Panel on Climate Change (IPCC) does not provide destruction and removal efficiencies (DREs) specific to individual scrubbers, only DREs specific to greenhouse gases, which makes it challenging to consider the specific effects of different types of scrubbers. The semiconductor and display industries in Korea represent a significant market share and are experiencing a steady increase in emissions, underscoring the need for research on GHG-reducing DREs to effectively manage these emissions. In this study, data on Tier 3 DREs developed based on measurements from companies subject to GHG management were collected. The findings indicated that C3F8 gas exhibited a comparable performance to the 2019 IPCC G/L (guideline) for plasma and combustion wet scrubbers, although it surpassed the 2006 IPCC G/L value. The catalytic scrubbers exhibited values that were comparable to the 2006 IPCC G/L, but lower than the 2019 IPCC G/L. The uncertainties were minimal for c-C4F8 gases, both in the absence of consideration of the scrubber type and when each scrubber type was taken into account. In contrast, the uncertainties associated with CF4 gases were relatively high, depending on the scrubber type. Although there are differences between the greenhouse gases in question, failing to take the type of scrubber into account could result in under- or overestimates of certain emissions. Therefore, it would be beneficial to develop coefficients that take this into account, particularly where information on scrubbers by gas type is available.

Geography and climate vulnerabilities

AbstractGeography and geographers have a long and influential history of research on climate change, with a significant impact on scholarship, international assessments and public policy. In this commentary, I focus on geographic work on climate vulnerability, arguing that geographers have been fundamental to our understanding of the impacts of climate variability and climate hazards and to the assessments of Working Group 2 of the Intergovernmental Panel on Climate Change (IPCC). Geographic work on climate vulnerability is traced to pioneering work in hazards and cultural ecology and to the rise of critical perspectives on the political economy and political ecology of hazards and then climate change. I discuss the role of geographers in the IPCC from 1990 to present and the importance of concepts originating in geography such as vulnerability mapping, double exposure, borderless climate risks, discourses, intersectionality, climate‐resilient development, labour vulnerabilities and maladaptation.

Uncertainty-informed selection of CMIP6 Earth system model subsets for use in multisectoral and impact models

Abstract. Earth system models (ESMs) and general circulation models (GCMs) are heavily used to provide inputs to sectoral impact and multisector dynamic models, which include representations of energy, water, land, economics, and their interactions. Therefore, representing the full range of model uncertainty, scenario uncertainty, and interannual variability that ensembles of these models capture is critical to the exploration of the future co-evolution of the integrated human–Earth system. The pre-eminent source of these ensembles has been the Coupled Model Intercomparison Project (CMIP). With more modeling centers participating in each new CMIP phase, the size of the model archive is rapidly increasing, which can be intractable for impact modelers to effectively utilize due to computational constraints and the challenges of analyzing large datasets. In this work, we present a method to select a subset of the latest phase, CMIP6, featuring models for use as inputs to a sectoral impact or multisector dynamics models, while prioritizing preservation of the range of model uncertainty, scenario uncertainty, and interannual variability in the full CMIP6 ensemble results. This method is intended to help impact modelers select climate information from the CMIP archive efficiently for use in downstream models that require global coverage of climate information. This is particularly critical for large-ensemble experiments of multisector dynamic models that may be varying additional features beyond climate inputs in a factorial design, thus putting constraints on the number of climate simulations that can be used. We focus on temperature and precipitation outputs of CMIP6 models, as these are two of the most used variables among impact models, and many other key input variables for impacts are at least correlated with one or both of temperature and precipitation (e.g., relative humidity). Besides preserving the multi-model ensemble variance characteristics, we prioritize selecting CMIP6 models in the subset that preserve the very likely distribution of equilibrium climate sensitivity values as assessed by the latest Intergovernmental Panel on Climate Change (IPCC) report. This approach could be applied to other output variables of climate models and, possibly when combined with emulators, offers a flexible framework for designing more efficient experiments on human-relevant climate impacts. It can also provide greater insight into the properties of existing CMIP6 models.

Study on the structure and energy flow of the salt marsh wetland ecosystem in Liaohe Estuary based on the Ecopath model

Salt marshes are one of the three blue carbon ecosystems recognized by the Intergovernmental Panel on Climate Change (IPCC). However, coastal salt marshes in China are facing the risk of degradation. To reveal the status of the salt marsh wetland ecosystem in Liaohe Estuary, an Ecopath model composed of 14 functional groups was constructed based on the 2019 ecological survey data. A comprehensive analysis of the system’s food web structure, energy flow processes, and overall ecosystem characteristics was conducted. The results show that the energy flow in the Liaohe Estuary salt marsh wetland ecosystem is mainly distributed in three integrated trophic levels. The utilization rates of trophic levels II and III are low, easily causing blockages in the lower trophic levels of the ecosystem’s energy flow. The total system throughput of the Liaohe Estuary salt marsh wetland ecosystem is 49,099.039 t·km²·a−1;. The system connectivity index and the system omnivory index are 0.207 and 0.109, respectively. Compared with other wetland systems, the ecosystem has a larger scale, but the overall ecosystem characteristic index reveals lower stability and complexity of the Liaohe Estuary salt marsh wetland system.

Intergovernmental Panel on Climate Change (IPCC) Tier 1 forest biomass estimates from Earth Observation

Aboveground biomass density (AGBD) estimates from Earth Observation (EO) can be presented with the consistency standards mandated by United Nations Framework Convention on Climate Change (UNFCCC). This article delivers AGBD estimates, in the format of Intergovernmental Panel on Climate Change (IPCC) Tier 1 values for natural forests, sourced from National Aeronautics and Space Administration’s (NASA’s) Global Ecosystem Dynamics Investigation (GEDI) and Ice, Cloud and land Elevation Satellite (ICESat-2), and European Space Agency’s (ESA’s) Climate Change Initiative (CCI). It also provides the underlying classification used by the IPCC as geospatial layers, delineating global forests by ecozones, continents and status (primary, young (≤20 years) and old secondary (>20 years)). The approaches leverage complementary strengths of various EO-derived datasets that are compiled in an open-science framework through the Multi-mission Algorithm and Analysis Platform (MAAP). This transparency and flexibility enables the adoption of any new incoming datasets in the framework in the future. The EO-based AGBD estimates are expected to be an independent contribution to the IPCC Emission Factors Database in support of UNFCCC processes, and the forest classification expected to support the generation of other policy-relevant datasets while reflecting ongoing shifts in global forests with climate change.

Performance Analysis of Neighborhood Component Analysis on Support Vector Machine in Greenhouse Gas Emission Classification

The heatwave phenomenon has hit several countries in various parts of the world, caused by climate change. Climate change leads to greenhouse gas emissions increasing beyond the limits set by the Intergovernmental Panel on Climate Change (IPCC) Fourth Assessment Report Global Warming Potentials. This final project uses a combination of Neighborhood Component Analysis (NCA) and Support Vector Machine (SVM) methods with linear, polynomial, Radial Basis Function (RBF), and sigmoid kernel functions. The purposes of this final project are to evaluate the performance of NCA on SVM and to determine the best kernel function in this combination. Based on the analysis, it was found that classification using a combination of NCA and SVM methods can reduce variables, with the best kernel function being the Polynomial kernel function. This is because the analysis using the Polynomial kernel function achieved the highest accuracy values for training data, testing accuracy, and F1-Score, which are 98,96%, 99,15%, and 98,98% respectively. Additionally, the training analysis time and testing analysis time were the shortest at 0,15 seconds and 0,04 seconds.

Transforming the feeding regime towards low-input increases the environmental impact of organic milk production on a case study farm in central germany

Abstract Purpose Despite the direct effect of the feeding regime on the environmental impacts of dairy farming systems, its level of intensity, particularly in organic systems, has rarely been investigated. This study compares the environmental impact of a high-input feeding regime with a grassland-based, low-input feeding regime scenario within an organic milk production system conducted on Gladbacherhof, the research farm of Justus Liebig University Giessen, in Central Germany. Methods An integrated Life Cycle Assessment (LCA) analysis was performed from a cradle-to-farm gate perspective to quantify five environmental impacts, namely Global Warming (GW), Non-Renewable Energy Use (NREU), Land Use (LU), Terrestrial Acidification (TA), and Freshwater Eutrophication (FE). All agronomic data of the Gladbacherhof research farm, averaged over the years 2010–2017, were included. When not directly measured on the farm, ecoinvent data were included. Results and discussion Contrary to our hypothesis, the results suggest that a grassland-based low-input system has a higher environmental impact as compared to a high-input system for each of the five impact categories when using fat and protein-corrected milk (FPCM) as the functional unit. A 50% reduction in concentrates and exclusion of maize silage from the feed ration in the modelled low-input production system lead to a 20% drop in milk yield. To balance the energy content in low-input feeding ration, longer grazing period and higher amount of hay, alfalfa, and grass silage are required. This in turn results in higher emissions from enteric fermentation, manure management, and feed production and hence in higher environmental impact, particularly for GW, TA, and FE. Conclusions This study is one of the few that directly explores the environmental impact of feeding intensity in an organic milk production system. Nevertheless, there is a lack of research on consolidated emission factors for several greenhouse gas (GHG) sources in organic livestock and cropping systems to perform more robust carbon footprint calculations that comply with the Intergovernmental Panel on Climate Change (IPCC) Tier 3 GHG reporting guidelines. To generalize the results at the regional or national scale, direct comparisons with a larger number of organic farms representative of high-input and low-input intensities are still essential.

Dynamics of Exposure and Sensitivity of Agricultural Sector to Climate Change in Yogyakarta Special Region as Observed by Geospatial Datasets

Abstract The impact of climate change on agricultural productivity is evident. Higher temperatures, more extreme rainfall, and shorter growing seasons are leading to decreased agricultural yields. To address this, it is crucial to map the exposure and sensitivity of the agricultural sector to climate change in the Yogyakarta Special Region. This is especially important given the region’s heavy reliance on agriculture, particularly in Sleman, Bantul, Kulon Progo, and Gunungkidul Regencies. Mapping these factors aligns with the Intergovernmental Panel on Climate Change (IPCC) assessments. Exposure and sensitivity are evaluated using biophysical indicators over three periods: 2020, 2030, and 2050, under two Representative Concentration Pathway (RCP) scenarios, 4.5 and 8.5. The findings reveal three levels of exposure and sensitivity in the region: low, moderate, and high. Exposure values are projected to rise under both RCP scenarios across the studied periods. In Gunungkidul, Saptosari, Purwosari, Tanjungsari, and Panggang consistently exhibit high exposure levels, while Pakem and Cangkringan in Sleman maintain low exposure levels. Gedangsari and Rongkop in Gunungkidul show highest sensitivity, whereas Temon in Kulon Progo and Pakem in Sleman show lowest sensitivity. Therefore, it is important to develop plans to reduce these impacts.