Towards Sustainable Livestock Production Systems: Analyzing Ecological Constraints to Grazing Intensity

Increasing food production from cropland and grassland is essential to meet the future food demand of a growing world population without further land-use expansion. It is estimated that until 2050, food production has to increase strongly to meet future food demands. Increasing food production from grasslands in a sustainable way (e.g., by not degrading essential ecosystem services) is important, yet requires a good understanding of the major determinants and constraints of the global livestock production systems and the associated socio-economic and ecological patterns. The spatially explicit analysis of grazing intensity (GI; e.g., the fraction of available Net Primary Production (NPP) that is consumed by grazing animals in a year) using monthly data allow us to analyse the role of seasonality for limits to grazing intensity. Seasonality creates in many regions of the world shortage and surplus periods of NPP, which can (partly) be overcome by social organization, such as the employment of storage technologies or by imports. By comparing the current livestock density to the ecologically maximum density (EMD) determined by biomass availability during shortage periods we show that management has contributed to substantial higher livestock density in many world-regions whereas in others it is still close to the EMD. Our analysis shows to which expense (e.g., length of shortage period to overcome) the increase in livestock-density comes in different world regions and where potential for further biomass extraction exists. This study contributes to an improved understanding of the systemic inter-linkages between GI, seasonal biomass supply, and socioeconomic and ecological trade-offs, and provides essential information for analyzing intensification potentials of grasslands

Towards sustainable livestock production systems: Analyzing ecological constraints to grazing intensity

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MemeGraphs: Linking Memes to Knowledge Graphs

Memes are a popular form of communicating trends and ideas in social media and on the internet in general, combining the modalities of images and text. They can express humor and sarcasm but can also have offensive content. Analyzing and classifying memes automatically is challenging since their interpretation relies on the understanding of visual elements, language, and background knowledge. Thus, it is important to meaningfully represent these sources and the interaction between them in order to classify a meme as a whole. In this work, we propose to use scene graphs, that express images in terms of objects and their visual relations, and knowledge graphs as structured representations for meme classification with a Transformer-based architecture. We compare our approach with ImgBERT, a multimodal model that uses only learned (instead of structured) representations of the meme, and observe consistent improvements. We further provide a dataset with human graph annotations that we compare to automatically generated graphs and entity linking. Analysis shows that automatic methods link more entities than human annotators and that automatically generated graphs are better suited for hatefulness classification in memes

Biomass turnover time in terrestrial ecosystems halved by land use

The terrestrial carbon cycle is not well quantified1. Biomass turnover time is a crucial parameter in the global carbon cycle2–4, and contributes to the feedback between the terrestrial carbon cycle and climate2–7. Biomass turnover time varies substantially in time and space, but its determinants are not well known8,9, making predictions of future global carbon cycle dynamics uncertain5,10–13. Land use—the sum of activities that aim at enhancing terrestrial ecosystem services14—alters plant growth15 and reduces biomass stocks16, and is hence expected to aect biomass turnover. Here we explore land-use-induced alterations of biomass turnover at the global scale by comparing the biomass turnover of the actual vegetation with that of a hypothetical vegetation state with no land use under current climate conditions. We find that, in the global average, biomass turnover is 1.9 times faster with land use. This acceleration aects all biomes roughly equally, but with large dierences between land-use types. Land conversion, for example fromforests to agricultural fields, is responsible for59%of the acceleration; the use of forestsand natural grazing land accounts for 26% and 15% respectively. Reductions in biomass stocks are partly compensated by reductions in net primary productivity. We conclude that land use significantly and systematically aects the fundamental trade-off between carbon turnover and carbon stocks

Seasonality constraints to livestock grazing intensity

Increasing food production is essential to meet the future food demand of a growing world population. In the light of pressing sustainability challenges like climate change and the importance of the global livestock system for food security as well as GHG emissions, finding ways to increasing food production sustainably and without increasing competition for food crops is essential. Yet, many unknowns relate to livestock grazing, in particular grazing intensity, an essential variable to assess the sustainability of livestock systems. Here we explore ecological limits to grazing intensity (GI; i.e., the fraction of Net Primary Production consumed by grazing animals) by analysing the role of seasonality in natural grasslands. We estimate seasonal limitations to GI by combining monthly Net Primary Production data and a map of global livestock distribution with assumptions on the length of non-favourable periods that can be bridged by livestock (e.g., by browsing dead standing biomass, storage systems or biomass conservation). This allows us to derive a seasonality-limited potential GI, which we compare with the GI prevailing in 2000. We find that GI in 2000 lies below its potential on 39% of the total global natural grasslands, which has a potential for increasing biomass extraction of up to 181 MtC/yr. In contrast, on 61% of the area GI exceeds the potential, made possible by management. Mobilizing this potential could increase milk production by 5%, meat production by 4%, or contribute to free up to 2.8 Mio km² of grassland area at the global scale if the numerous socio-ecological constraints can be overcome. We discuss socio-ecological trade-offs, which may reduce the estimated potential considerably and require the establishment of sound monitoring systems and an improved understanding of livestock system’s role in the Earth system

Quantification of uncertainties in global grazing systems assessments

Livestock systems play a key role in global sustainability challenges like food security and climate change, yet, many unknowns and large uncertainties prevail. We present a systematic, spatially explicit assessment of uncertainties related to grazing intensity (GI), a key metric for assessing ecological impacts of grazing, by combining existing datasets on a) grazing feed intake, b) the spatial distribution of livestock, c) the extent of grazing land, and d) its net primary productivity (NPP). An analysis of the resulting 96 maps implies that on average 15% of the grazing land NPP is consumed by livestock. GI is low in most of worlds grazing lands but hotspots of very high GI prevail in 1% of the total grazing area. The agreement between GI maps is good on one fifth of the world's grazing area, while on the remainder it is low to very low. Largest uncertainties are found in global drylands and where grazing land bears trees (e.g., the Amazon basin or the Taiga belt). In some regions like India or Western Europe massive uncertainties even result in GI > 100% estimates. Our sensitivity analysis indicates that the input-data for NPP, animal distribution and grazing area contribute about equally to the total variability in GI maps, while grazing feed intake is a less critical variable. We argue that a general improvement in quality of the available global level datasets is a precondition for improving the understanding of the role of livestock systems in the context of global environmental change or food security

Models meet data: Challenges and opportunities inimplementing land management in Earth system models

As the applications of Earth system models (ESMs) move from general climate projections toward questions of mitigation and adaptation, the inclusion of land management practices in these models becomes crucial. We carried out a survey among modeling groups to show an evolution from models able only to deal with land‐cover change to more sophisticated approaches that allow also for the partial integration of land management changes. For the longer term a comprehensive land management representation can be anticipated for all major models. To guide the prioritization of implementation, we evaluate ten land management practices—forestry harvest, tree species selection, grazing and mowing harvest, crop harvest, crop species selection, irrigation, wetland drainage, fertilization, tillage, and fire—for (1) their importance on the Earth system, (2) the possibility of implementing them in state‐of‐the‐art ESMs, and (3) availability of required input data. Matching these criteria, we identify “low‐hanging fruits” for the inclusion in ESMs, such as basic implementations of crop and forestry harvest and fertilization. We also identify research requirements for specific communities to address the remaining land management practices. Data availability severely hampers modeling the most extensive land management practice, grazing and mowing harvest, and is a limiting factor for a comprehensive implementation of most other practices. Inadequate process understanding hampers even a basic assessment of crop species selection and tillage effects. The need for multiple advanced model structures will be the challenge for a comprehensive implementation of most practices but considerable synergy can be gained using the same structures for different practices. A continuous and closer collaboration of the modeling, Earth observation, and land system science communities is thus required to achieve the inclusion of land management in ESMs