Impacts of future climate change on rice yield based on crop model simulation-A meta-analysis.

Rice is one of the world's major food crops. Changes in major climatic factors such as temperature, rainfall, solar radiation and carbon dioxide (CO2) concentration have an important impact on rice growth and yield. However, many of the current studies that predict the impact of future climate change on rice yield are affected by uncertainties such as climate models, climate scenarios, model parameters and structure, and showing great differences. This study was based on the assessment results of the impact of climate change on rice in the future of 111 published literature, and comprehensively analyzed the impact and uncertainty of climate change on rice yield. This study utilized local polynomial (Loess) regression analysis to investigate the impact of changes in mean temperature, minimum temperature, maximum temperature, solar radiation, and precipitation on relative rice yield variations within a complete dataset. A linear mixed-effects model was used to quantitatively analyze the relationships between the restricted datasets. The qualitative analysis based on the entire dataset revealed that rice yields decreased with increasing average temperature. The precipitation changed between 0 and 25 %, it was conducive to the stable production of rice, and when the precipitation changed >25 %, it would cause rice yield reduction. The change of solar radiation was less than -1.15 %, the rice yield increases with the increase of solar radiation, and when the change of solar radiation exceeds -1.15 %, the rice yield decreases. Elevated CO2 concentrations and management practices could mitigate the negative effects of climate change. The results of a quantitative analysis utilizing the mixed effects model revealed that average temperature, precipitation, CO2 concentration, and adaptation methods all had a substantial impact on rice production, and elevated CO2 concentrations and management practices could exert positive influences on rice production. For every 1 °C and 1 % increase in average temperature and precipitation, rice yield decreased by 3.85 % and 0.56 %, respectively. For every 100 ppm increase in CO2 concentration, rice yield increased by 7.1 %. The variation of rice yield under different climate models, study sites and climate scenarios had significant variability. Elevated CO2 concentrations and management practices could compensate for the negative effects of climate change, benefiting rice production. This study comprehensively collected and analyzed a wide range of literature and research, which provides an in-depth understanding of the impacts of climate change on rice production and informs future research and policy development.

The future distribution of the savannah biome: model-based and biogeographic contingency.

The extent of the savannah biome is expected to be profoundly altered by climatic change and increasing atmospheric CO2 concentrations. Contrasting projections are given when using different modelling approaches to estimate future distributions. Furthermore, biogeographic variation within savannahs in plant function and structure is expected to lead to divergent responses to global change. Hence the use of a single model with a single savannah tree type will likely lead to biased projections. Here we compare and contrast projections of South American, African and Australian savannah distributions from the physiologically based Thornley transport resistance statistical distribution model (TTR-SDM)-and three versions of a dynamic vegetation model (DVM) designed and parametrized separately for specific continents. We show that attempting to extrapolate any continent-specific model globally biases projections. By 2070, all DVMs generally project a decrease in the extent of savannahs at their boundary with forests, whereas the TTR-SDM projects a decrease in savannahs at their boundary with aridlands and grasslands. This difference is driven by forest and woodland expansion in response to rising atmospheric CO2 concentrations in DVMs, unaccounted for by the TTR-SDM. We suggest that the most suitable models of the savannah biome for future development are individual-based dynamic vegetation models designed for specific biogeographic regions.This article is part of the themed issue 'Tropical grassy biomes: linking ecology, human use and conservation'.