Timing of forest fine root production advances with reduced snow cover in northern Japan : implications for climate-induced change in understory and overstory competition

To investigate the effect of reduced snow cover on fine root dynamics in a cool-temperate forest in northern Japan because of decreases in snowfall at high latitudes due to global warming, we monitored root length, production, and mortality before and after snow removal with an in-ground root scanner. We measured root dynamics of both overstory deciduous oak (Quercus crispula) and understory evergreen dwarf bamboo (Sasa nipponica), the two major species in the forest. Snow removal advanced the timing of peak root production by a month both in total and in Sasa, but not in oak. There was a significant interaction between snow removal and plant form on root production; this indicates that enhanced Sasa root production following snow removal might increase its ability to compete with oak. In contrast, snow removal did not enhance root mor-tality, suggesting that the roots of these species tolerate soil freezing. The earlier snow disappearance in the snow removal plot expanded the growing season in Sasa. We speculate that this change in the understory environment would advance the timing of root production by Sasa by extending the photosynthetic period in spring. We propose that different responses of root production to reduced snow cover between the two species would change the competitive interactions of overstory and understory vegetation, influencing net primary production and biogeochemistry (e.g., carbon and nitrogen cycles) in the forest ecosystem

Global modeling of SDG indicators related to small-scale farmers: testing in a changing climate

Some indicators used to track the progress of the Sustainable Development Goals (SDGs) suffer from a lack of reported data, and therefore need estimates to fill the data gaps. Using crop model outputs and global cropping system datasets, we present a modeling of small-scale farmer productivity and agricultural output (conceptually similar to the formal SDG 2.3.1 and 2.3.2 indicator, respectively). We analyze the responses of the indicators for 106 low- and middle-income countries for the periods 2051–2060 and 2091–2100, relative to 2001–2010, to various scenarios of climate, socioeconomic development, cost-free adaptation, and irrigation expansion. The results show the potentials of modeling in gap-filling of reported national data, and that the agricultural output indicator indicates the positive effect of climate mitigation to small-scale farmers. The contributions of adaptation are evident when agricultural output indicator is used but are no longer visible, or even wrongly interpreted, when productivity indicator is used, underling the importance of selecting robust indicators to track SDG goals in a changing climate. Also discussed are the caveats identified in the SDG 2.3 indicators that enable the design of indicators more aligned with the other development goals, such as poverty eradication

Flood impacts on global crop production: advances and limitations

Considering the anticipated rise in wet extremes due to climate change, effective management of flood risks in global agriculture necessitates an initial assessment of the impact of floods on crop production. Such estimation can inform the development of strategies to enhance the resilience of the global agricultural system against floods, particularly in the face of growing demand for food. To this end, a worldwide calculation of inundation areas’ return periods was conducted using a global river and inundation model output. This information was then linked to a global historical yield map, allowing for the identification of flood-induced crop yield changes. The findings revealed that for return periods over ten years, global average yield losses were estimated to be 4% for soy, 3% for rice, 2% for wheat, and 1% for maize. These losses amounted to a total production loss of 5.5 billion United States dollars during the 1982–2016 period. This first global estimation of flood impacts on crop production contributes to the advancement of flood risk management in agriculture, although the limitations identified in this study need to be addressed in future research