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Food system innovations will be instrumental to achieving multiple Sustainable Development Goals (SDGs). However, major innovation breakthroughs can trigger profound and disruptive changes, leading to simultaneous and interlinked reconfigurations of multiple parts of the global food system. The emergence of new technologies or social solutions, therefore, have very different impact profiles, with favourable consequences for some SDGs and unintended adverse side-effects for others. Stand-alone innovations seldom achieve positive outcomes over multiple sustainability dimensions. Instead, they should be embedded as part of systemic changes that facilitate the implementation of the SDGs. Emerging trade-offs need to be intentionally addressed to achieve true sustainability, particularly those involving social aspects like inequality in its many forms, social justice, and strong institutions, which remain challenging. Trade-offs with undesirable consequences are manageable through the development of well planned transition pathways, careful monitoring of key indicators, and through the implementation of transparent science targets at the local level.
We perform a statistical study calculating electromagnetic ion cyclotron (EMIC) wave amplitudes based off in situ plasma measurements taken by the Van Allen Probes’ (1.1–5.8 Re) Helium, Oxygen, Proton, Electron (HOPE) instrument. Calculated wave amplitudes are compared to EMIC waves observed by the Electric and Magnetic Field Instrument Suite and Integrated Science on board the Van Allen Probes during the same period. The survey covers a 22-month period (1 November 2012 to 31 August 2014), a full Van Allen Probe magnetic local time (MLT) precession. The linear theory proxy was used to identify EMIC wave events with plasma conditions favorable for EMIC wave excitation. Two hundred and thirty-two EMIC wave events (103 H+-band and 129 He+-band) were selected for this comparison. Nearly all events selected are observed beyond L = 4. Results show that calculated wave amplitudes exclusively using the in situ HOPE measurements produce amplitudes too low compared to the observed EMIC wave amplitudes. Hot proton anisotropy (Ahp) distributions are asymmetric in MLT within the inner (L < 7) magnetosphere with peak (minimum) Ahp, ∼0.81 to 1.00 (∼0.62), observed in the dawn (dusk), 0000 < MLT ≤ 1200 (1200 < MLT ≤ 2400), sectors. Measurements of Ahp are found to decrease in the presence of EMIC wave activity. Ahp amplification factors are determined and vary with respect to EMIC wave-band and MLT. He+-band events generally require double (quadruple) the measured Ahp for the dawn (dusk) sector to reproduce the observed EMIC wave amplitudes.
Early agriculture can be detected in palaeovegetation records, but quantification of the relative importance of climate and land use in influencing regional vegetation composition since the onset of agriculture is a topic that is rarely addressed. We present a novel approach that combines pollen-based REVEALS estimates of plant cover with climate, anthropogenic land-cover and dynamic vegetation modelling results. This is used to quantify the relative impacts of land use and climate on Holocene vegetation at a sub-continental scale, i.e. northern and western Europe north of the Alps. We use redundancy analysis and variation partitioning to quantify the percentage of variation in vegetation composition explained by the climate and land-use variables, and Monte Carlo permutation tests to assess the statistical significance of each variable. We further use a similarity index to combine pollen based REVEALS estimates with climate-driven dynamic vegetation modelling results. The overall results indicate that climate is the major driver of vegetation when the Holocene is considered as a whole and at the sub-continental scale, although land use is important regionally. Four critical phases of land-use effects on vegetation are identified. The first phase (from 7000 to 6500 BP) corresponds to the early impacts on vegetation of farming and Neolithic forest clearance and to the dominance of climate as a driver of vegetation change. During the second phase (from 4500 to 4000 BP), land use becomes a major control of vegetation. Climate is still the principal driver, although its influence decreases gradually. The third phase (from 2000 to 1500 BP) is characterised by the continued role of climate on vegetation as a consequence of late-Holocene climate shifts and specific climate events that influence vegetation as well as land use. The last phase (from 500 to 350 BP) shows an acceleration of vegetation changes, in particular during the last century, caused by new farming practices and forestry in response to population growth and industrialization. This is a unique signature of anthropogenic impact within the Holocene but European vegetation remains climatically sensitive and thus may continue to respond to ongoing climate change. (C) 2017 Elsevier Ltd. All rights reserved.
An accurate estimation of crop yield under climate change scenarios is essential to quantify our ability to feed a growing population and develop agronomic adaptations to meet future food demand. A coordinated evaluation of yield simulations from process-based eco-physiological models for climate change impact assessment is still missing for soybean, the most widely grown grain legume and the main source of protein in our food chain. In this first soybean multi-model study, we used ten prominent models capable of simulating soybean yield under varying temperature and atmospheric CO2 concentration [CO2] to quantify the uncertainty in soybean yield simulations in response to these factors. Models were first parametrized with high quality measured data from five contrasting environments. We found considerable variability among models in simulated yield responses to increasing temperature and [CO2]. For example, under a + 3 degrees C temperature rise in our coolest location in Argentina, some models simulated that yield would reduce as much as 24%, while others simulated yield increases up to 29%. In our warmest location in Brazil, the models simulated a yield reduction ranging from a 38% decrease under + 3 degrees C temperature rise to no effect on yield. Similarly, when increasing [CO2] from 360 to 540 ppm, the models simulated a yield increase that ranged from 6% to 31%. Model calibration did not reduce variability across models but had an unexpected effect on modifying yield responses to temperature for some of the models. The high uncertainty in model responses indicates the limited applicability of individual models for climate change food projections. However, the ensemble mean of simulations across models was an effective tool to reduce the high uncertainty in soybean yield simulations associated with individual models and their parametrization. Ensemble mean yield responses to temperature and [CO2] were similar to those reported from the literature. Our study is the first demonstration of the benefits achieved from using an ensemble of grain legume models for climate change food projections, and highlights that further soybean model development with experiments under elevated [CO2] and temperature is needed to reduce the uncertainty from the individual models.