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Fragmentation of landscapes creates a transition zone in between natural habitats or different kinds of land use. In forested and agricultural landscapes with transition zones, microclimate and matter cycling are markedly altered. This probably accelerates and is intensified by global warming. However, there is no consensus on defining transition zones and quantifying relevant variables for microclimate and matter cycling across disciplines. This article is an attempt to a) revise definitions and offer a framework for quantitative ecologists, b) review the literature on microclimate and matter cycling in transition zones and c) summarise this information using meta-analysis to better understand bio-geochemical and bio-geophysical processes and their spatial extent in transition zones. We expect altered conditions in soils of transition zones to be 10-20 m with a maximum of 50 m, and 25-50 m for above-ground space with a maximum of 125 m.
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.