Suchergebnisse

Part I. Background -- Chapter 1. Coupled Earth System and Human Processes - An Introduction to the Book and SPACES and the Book -- Chapter 2. Unique Southern African Terrestrial and Oceanic Biomes and Their Relation to Steep Environmental Gradients -- Chapter 3. Environmental Challenges to Meeting Sustainable Development Goals in Southern Africa -- Chapter 4. Overview of the Macro-Economic Drivers of the Region -- Part II. Drivers of Climatic Variability and Change in Southern Africa -- Chapter 5. Past Climate Variability in the Past Millennium -- Chapter 6. Southern Africa Climate over the Recent Decades: Description, Variability, and Trends -- Chapter 7. Projections of Future Climate Change in Southern Africa and the Potential for Regional Tipping Points -- Chapter 8. The Agulhas Current System as an Important Driver for Oceanic and Terrestrial Climate -- Chapter 9. Physical Drivers of Southwest African Coastal Upwelling and Its Response to Climate Variability and Change -- Chapter 10. Regional Land-Atmosphere Interactions in Southern Africa: Potential Impact and Sensitivity of Forest and Plantation Change -- Part III. Science in Support of Ecosystem Management -- Chapter 11. Studies of the Ecology of the Benguela Current Upwelling System – the TRAFFIC Approach -- Chapter 12. The Application of Palaeoenvironmental Research in Supporting Land Management Approaches and Conservation in South Africa -- Chapter 13. Soil Erosion Research and Soil Conservation Policy in South Africa -- Chapter 14. Biome Change in Southern Africa -- Chapter 15. Biodiversity and Ecosystem Functions in Southern African Savanna Rangelands: Threats, Impacts and Solutions -- Chapter 16. Managing Southern African Rangeland Systems in the Face of Drought – A Synthesis of Observation, Experimentation, and Modeling for Policy and Decision Support -- Chapter 17. A Fine Line Between Carbon Source and Sink – Potential CO2 Sequestration Through Sustainable Grazing Management in the Nama-Karoo -- Chapter 18. Trends and Barriers to Wildlife-Based Options for Sustainable Management of Savanna Resources – The Namibian Case -- Chapter 19. Feed Gaps among Cattle Keepers in Semiarid and Arid Southern African Regions: A Case Study in the Limpopo Province, South Africa -- Chapter 20. Agricultural Land-Use Systems and Management Challenges -- Chapter 21. The Need for Sustainable Agricultural Land-Use Systems: Benefits from Integrated Agroforestry Systems -- Chapter 22. Management Options for Macadamia Orchards with Special Focus on Water Management and Ecosystem Services -- Chapter 23. Potential of Improved Technologies to Enhance Land Management Practices of Small-Scale Farmers in Limpopo Province, South Africa -- Part IV. Monitoring and Modelling Tools -- Chapter 24. A New Era of Earth Observation for the Environment – Spatio-Temporal Monitoring Capabilities for Land Degradation -- Chapter 25. The Marine Carbon Footprint: Challenges in the Quantification of CO2 Uptake by the Biological Carbon Pump in the Benguela Upwelling System -- Chapter 26. Dynamics and Drivers of Net Primary Production (NPP) in Southern Africa Based on Estimates from Earth Observation and Process-Based Dynamic Vegetation Modelling -- Chapter 27. Comparison of Different Normalisers for Identifying Metal Enrichment of Sediment – A Case Study from Richards Bay Harbour, South Africa -- Chapter 28. Catchment and Depositional Studies for the Reconstruction of Past Environmental Change in Southern Africa -- Chapter 29. Observational Support for Regional Policy Implementation – Land Surface Change under Anthropogenic and Climate Pressure in Saldi Study Sites -- Part V. Synthesis and Outlook -- Chapter 30. Research Infrastructures as Anchor Points for Long-Term Environmental Observation -- Chapter 31. Lessons Learned from a North-South Science Partnership for Sustainable Development -- Chapter 32. Synthesis and Outlook on Future Research and Scientific Education in Southern Africa.

Abstract
Lack of nitrogen limits food production in poor countries while excessive nitrogen use in industrial countries has led to transgression of the planetary boundary. However, the potential of spatial redistribution of nitrogen input for food security when returning to the safe boundary has not been quantified in a robust manner. Using an emulator of a global gridded crop model ensemble, we found that redistribution of current nitrogen input to major cereals among countries can double production in the most food-insecure countries, while increasing global production of these crops by 12% with no notable regional loss or reducing the nitrogen input to the current production by one-third. Redistribution of the input within the boundary increased production by 6–8% compared to the current relative distribution, increasing production in the food-insecure countries by two-thirds. Our findings provide georeferenced guidelines for redistributing nitrogen use to enhance food security while safeguarding the planet.

MACSUR — Modelling European Agriculture with Climate Change for Food Security — is a knowledge hub that was formally created in June 2012 as a European scientific network. The strategic aim of the knowledge hub is to create a coordinated and globally visible network of European researchers and research groups, with intra- and interdisciplinary interaction and shared expertise creating synergies for the development of scientific resources (data, models, methods) to model the impacts of climate change on agriculture and related issues. This objective encompasses a wide range of political and sociological aspects, as well as the technical development of modelling capacity through impact assessments at different scales and assessing uncertainties in model outcomes. We achieve this through model intercomparisons and model improvements, harmonization and exchange of data sets, training in the selection and use of models, assessment of benefits of ensemble modelling, and cross-disciplinary linkages of models and tools. The project engages with a diverse range of stakeholder groups and to support the development of resources for capacity building of individuals and countries. Commensurate with this broad challenge, a network of currently 300 scientists (measured by the number of individuals on the central e-mail list) from 18 countries evolved from the original set of research groups selected by FACCE. In the spirit of creating and maintaining a network for intra- and interdisciplinary knowledge exchange, network activities focused on meetings of researchers for sharing expertise and, depending on group resources (both financial and personnel), development of collaborative research activities. The outcome of these activities is the enhanced knowledge of the individual researchers within the network, contributions to conference presentations and scholarly papers, input to stakeholders and the general public, organised courses for students, junior and senior scientists. The most visible outcome are the scientific results of the network activities, represented in the contributions of MACSUR members to the impressive number of more than 200 collaborative papers in peer-reviewed publications. Here, we present a selection of overview and cross-disciplinary papers which include contributions from MACSUR members. It highlights the major scientific challenges addressed, and the methodological solutions and insights obtained. Over and above these highlights, major achievements have been reached regarding data collection, data processing, evaluation, model testing, modelling assessments of the effects of agriculture on ecosystem services, policy, and development of scenarios. Details on these achievements in the context of MACSUR can be found in our online publication FACCE MACSUR Reports at http://ojs.macsur.eu.

Increasing the accuracy of crop productivity estimates is a key element in planning adaptation strategies to ensure global food security under climate change. Process-based crop models are effective means to project climate impact on crop yield, but have large uncertainty in yield simulations. Here, we show that variations in the mathematical functions currently used to simulate temperature responses of physiological processes in 29 wheat models account for >50% of uncertainty in simulated grain yields for mean growing season temperatures from 14 °C to 33 °C. We derived a set of new temperature response functions that when substituted in four wheat models reduced the error in grain yield simulations across seven global sites with different temperature regimes by 19% to 50% (42% average). We anticipate the improved temperature responses to be a key step to improve modelling of crops under rising temperature and climate change, leading to higher skill of crop yield projections. Process-based modelling of crop growth is an effective way of representing how crop genotype, environment and management interactions affect crop production to aid tactical and strategic decision making1. Process-based crop models are increasingly used to project the impact of climate change on crop yield2. However, current models produce different results, creating large uncertainty in crop yield simulations3. A model inter-comparison study within the Agricultural Model Inter-comparison and Improvement Project (AgMIP)4 of 29 widely used wheat models against field experimental data revealed that there is more uncertainty in simulating grain yields from the different models than from 16 different climate change scenarios3. The greatest uncertainty was in modelling crop responses to temperature3,5. Similar results were found with rice and maize crops6,7. Such uncertainty should be reduced before informing decision making in agriculture and government policy. Here, we show contrasting differences in temperature response functions of key physiological processes adopted in the 29 crop models. We reveal opportunities for improving simulation of temperature response in crop models to reduce the uncertainty in yield simulations. We aim to reassess the scientific assumptions underlying model algorithms and parameterization describing temperature-sensitive physiological processes, using wheat, one of the most important staple crops globally, as an example. We hypothesized that: (1) the difference among models in assumed temperature responses is the largest source of the uncertainty in simulated yields; and (2) the uncertainty in the multi-model ensemble results can be reduced by improving the science for modelling temperature response of physiological processes. Temperature affects crop performance primarily through its impact on (1) the rate of phenological development from seed germination to crop maturity, including the fulfilment of cold requirement (vernalisation); (2) the initiation and expansion of plant organs; (3) photosynthesis and respiration, considered either separately or combined as net biomass growth simulated using radiation use efficiency (RUE)8; and (4) the senescence, sterility or abortion of plant organs. All 29 models simulate these processes, except for sterility and abortion, in response to temperature change. Here, we compare the temperature functions of these four categories of physiological processes built into the 29 wheat models and identify the representative response types. We analyse how different temperature response functions affected simulations of wheat growth compared to observations in a field experiment8,9,10, in which well-fertilized and irrigated wheat grew under contrasting sowing dates and temperature environments (Hot Serial Cereal (HSC) experiment). We further evaluate the impact of the different response types by implementing them in two models (APSIM and SiriusQuality) and analysing their results against the HSC data and an additional global dataset from the International Heat Stress Genotpye Experiment (IHSGE)8 carried out by the International Maize and Wheat Improvement Center (CIMMYT). More importantly, we derive, based on newest knowledge and data, a set of new temperature response functions for the key physiological processes of wheat and demonstrate that when substituted in four wheat models the new functions reduced the error in grain yield simulations across seven global sites with different temperature regimes covered by the IHSGE data.