Wheat, rice, maize, and soybean provide two-thirds of human caloric intake. Assessing the impact of global temperature increase on production of these crops is therefore critical to maintaining global food supply, but different studies have yielded different results. Here, we investigated the impacts of temperature on yields of the four crops by compiling extensive published results from four analytical methods: global grid-based and local point-based models, statistical regressions, and field-warming experiments. Results from the different methods consistently showed negative temperature impacts on crop yield at the global scale, generally underpinned by similar impacts at country and site scales. Without CO 2 fertilization, effective adaptation, and genetic improvement, each degree-Celsius increase in global mean temperature would, on average, reduce global yields of wheat by 6.0%, rice by 3.2%, maize by 7.4%, and soybean by 3.1%. Results are highly heterogeneous across crops and geographical areas, with some positive impact estimates. Multimethod analyses improved the confidence in assessments of future climate impacts on global major crops and suggest crop-and regionspecific adaptation strategies to ensure food security for an increasing world population.climate change impact | global food security | major food crops | temperature increase | yield C rops are sensitive to climate change, including changes in temperature and precipitation, and to rising atmospheric CO 2 concentration (1, 2). Among the changes, temperature increase has the most likely negative impact on crop yields (3, 4), and regional temperature changes can be projected from climate models with more certainty than precipitation. Meteorological records show that mean annual temperatures over areas where wheat, rice, maize, and soybean are grown have increased by ∼1°C during the last century (Fig. 1A) and are expected to continue to increase over the next century (Fig. 1B) -more so if greenhouse gas emissions continue to increase. It is thus necessary to quantify the impact of temperature increase on global crop yields, including any spatial variations, to first assess the risk to world food security, and then to develop targeted adaptive strategies to feed a burgeoning world population (5).Several methods have been developed to assess the impact of temperature increase on crop yields (6). Process-based crop models characterize crop growth and development in daily time steps and can be used to simulate the temperature response of yield either in areas around the globe defined by grids or at selected field sites or points (1, 7). A third method, statistical modeling, uses observed regional yields and historical weather records to fit regression functions to predict crop responses (8,9). A fourth method is to artificially warm crops under nearnatural field conditions to directly measure the impact of increased Significance Agricultural production is vulnerable to climate change. Understanding climate change, especially the temperature impacts, is...

Global terrestrial ecosystems absorbed carbon at a rate of 1-4 Pg yr(-1) during the 1980s and 1990s, offsetting 10-60 per cent of the fossil-fuel emissions. The regional patterns and causes of terrestrial carbon sources and sinks, however, remain uncertain. With increasing scientific and political interest in regional aspects of the global carbon cycle, there is a strong impetus to better understand the carbon balance of China. This is not only because China is the world's most populous country and the largest emitter of fossil-fuel CO(2) into the atmosphere, but also because it has experienced regionally distinct land-use histories and climate trends, which together control the carbon budget of its ecosystems. Here we analyse the current terrestrial carbon balance of China and its driving mechanisms during the 1980s and 1990s using three different methods: biomass and soil carbon inventories extrapolated by satellite greenness measurements, ecosystem models and atmospheric inversions. The three methods produce similar estimates of a net carbon sink in the range of 0.19-0.26 Pg carbon (PgC) per year, which is smaller than that in the conterminous United States but comparable to that in geographic Europe. We find that northeast China is a net source of CO(2) to the atmosphere owing to overharvesting and degradation of forests. By contrast, southern China accounts for more than 65 per cent of the carbon sink, which can be attributed to regional climate change, large-scale plantation programmes active since the 1980s and shrub recovery. Shrub recovery is identified as the most uncertain factor contributing to the carbon sink. Our data and model results together indicate that China's terrestrial ecosystems absorbed 28-37 per cent of its cumulated fossil carbon emissions during the 1980s and 1990s.

The purpose of this study was to evaluate 10 process-based terrestrial biosphere models that were used for the IPCC fifth Assessment Report. The simulated gross primary productivity (GPP) is compared with flux-tower-based estimates by Jung et al. [Journal of Geophysical Research 116 (2011) G00J07] (JU11). The net primary productivity (NPP) apparent sensitivity to climate variability and atmospheric CO2 trends is diagnosed from each model output, using statistical functions. The temperature sensitivity is compared against ecosystem field warming experiments results. The CO2 sensitivity of NPP is compared to the results from four Free-Air CO2 Enrichment (FACE) experiments. The simulated global net biome productivity (NBP) is compared with the residual land sink (RLS) of the global carbon budget from Friedlingstein et al. [Nature Geoscience 3 (2010) 811] (FR10). We found that models produce a higher GPP (133 ± 15 Pg C yr(-1) ) than JU11 (118 ± 6 Pg C yr(-1) ). In response to rising atmospheric CO2 concentration, modeled NPP increases on average by 16% (5-20%) per 100 ppm, a slightly larger apparent sensitivity of NPP to CO2 than that measured at the FACE experiment locations (13% per 100 ppm). Global NBP differs markedly among individual models, although the mean value of 2.0 ± 0.8 Pg C yr(-1) is remarkably close to the mean value of RLS (2.1 ± 1.2 Pg C yr(-1) ). The interannual variability in modeled NBP is significantly correlated with that of RLS for the period 1980-2009. Both model-to-model and interannual variation in model GPP is larger than that in model NBP due to the strong coupling causing a positive correlation between ecosystem respiration and GPP in the model. The average linear regression slope of global NBP vs. temperature across the 10 models is -3.0 ± 1.5 Pg C yr(-1) °C(-1) , within the uncertainty of what derived from RLS (-3.9 ± 1.1 Pg C yr(-1) °C(-1) ). However, 9 of 10 models overestimate the regression slope of NBP vs. precipitation, compared with the slope of the observed RLS vs. precipitation. With most models lacking processes that control GPP and NBP in addition to CO2 and climate, the agreement between modeled and observation-based GPP and NBP can be fortuitous. Carbon-nitrogen interactions (only separable in one model) significantly influence the simulated response of carbon cycle to temperature and atmospheric CO2 concentration, suggesting that nutrients limitations should be included in the next generation of terrestrial biosphere models.

Highlights d scRNA-seq analyses highlight conserved myeloid subsets in human and murine CRC d Two distinct TAM subsets show inflammatory and angiogenic signatures, respectively d Two distinct TAM subsets show differential sensitivity to CSF1R blockade d Anti-CD40 activates specific cDC1s and expands Th1-like and CD8 + memory T cells

l e t t e r sBamboo represents the only major lineage of grasses that is native to forests and is one of the most important nontimber forest products in the world. However, no species in the Bambusoideae subfamily has been sequenced. Here, we report a high-quality draft genome sequence of moso bamboo (P. heterocycla var. pubescens). The 2.05-Gb assembly covers 95% of the genomic region. Gene prediction modeling identified 31,987 genes, most of which are supported by cDNA and deep RNA sequencing data. Analyses of clustered gene families and gene collinearity show that bamboo underwent whole-genome duplication 7-12 million years ago. Identification of gene families that are key in cell wall biosynthesis suggests that the whole-genome duplication event generated more gene duplicates involved in bamboo shoot development. RNA sequencing analysis of bamboo flowering tissues suggests a potential connection between droughtresponsive and flowering genes.Bamboo is one of the most important non-timber forest products in the world. About 2.5 billion people depend economically on bamboo, and international trade in bamboo amounts to over 2.5 billion US dollars per year 1 . Bamboo has a rather striking life history, characterized by a prolonged vegetative phase lasting decades before flowering, thereby inhibiting genetic improvement. Recent genomic studies in bamboo have included genome-wide full-length cDNA sequencing 2 , chloroplast genome sequencing 3 , identification of syntenic genes between bamboo and other grasses 4 and phylogenetic analysis of Bambusoideae subspecies 5 . Fifty-nine simple sequence repeat markers from rice and sugarcane were used in the genetic diversity analyses of 23 bamboo species 6 , and 2 species-specific sequence-characterized amplified region markers were developed in the identification of different bamboo species 7 .Here, we report the draft genome of moso bamboo, a large woody bamboo that has ecological, economic and cultural value in Asia and accounts for ~70% of the total bamboo growth area. Comparative genome-wide analyses of bamboo to other grass species, including rice, maize and sorghum, yielded new genetic insights into the rapid and marked phenotypic and ecological divergence of bamboo and closely related grasses.The moso bamboo genome contains 24 pairs of chromosomes 8 (2n = 48) and is characteristic of a diploid (Supplementary Fig. 1a). We conducted a flow cytometry analysis and estimated that it had a genome size of 2.075 Gb (2C = 4.24 pg; Supplementary Fig. 1b), which was very close to that estimated in a previous report 9 .Because it is difficult to generate an inbred line of moso bamboo, owing to its infrequent sexual reproduction and the long periods of time between flowering intervals, we selected five plants from a single individual rhizome of the moso bamboo ecotype (P. heterocycla var. pubescens) and performed whole-genome shotgun sequencing. We generated 295 Gb of raw sequence data (approximately 147-fold coverage), including Illumina short reads and 10,327 pairs of BAC end ...

Monitoring changes in vegetation growth has been the subject of considerable research during the past several decades, because of the important role of vegetation in regulating the terrestrial carbon cycle and the climate system. In this study, we combined datasets of satellite-derived Normalized Difference Vegetation Index (NDVI) and climatic factors to analyze spatio-temporal patterns of changes in vegetation growth and their linkage with changes in temperature and precipitation in temperate and boreal regions of Eurasia (423.51N) from 1982 to 2006. At the continental scale, although a statistically significant positive trend of average growing season NDVI is observed (0.5 Â 10 À3 year À1 , P 5 0.03) during the entire study period, there are two distinct periods with opposite trends in growing season NDVI. Growing season NDVI has first significantly increased from 1982 to 1997 (1.8 Â 10 À3 year À1 , Po0.001), and then decreased from 1997 to 2006 (À1.3 Â 10 À3 year À1 , P 5 0.055). This reversal in the growing season NDVI trends over Eurasia are largely contributed by spring and summer NDVI changes. Both spring and summer NDVI significantly increased from 1982 to 1997 (2.1 Â 10 À3 year À1 , P 5 0.01; 1.6 Â 10 À3 year À1 Po0.001, respectively), but then decreased from 1997 to 2006, particularly summer NDVI which may be related to the remarkable decrease in summer precipitation (À2.7 mm yr À1 , P 5 0.009). Further spatial analyses supports the idea that the vegetation greening trend in spring and summer that occurred during the earlier study period 1982-1997 was either stalled or reversed during the following study period 1997-2006. But the turning point of vegetation NDVI is found to vary across different regions.

Satellite-derived Normalized Difference Vegetation Index (NDVI), a proxy of vegetation productivity, is known to be correlated with temperature in northern ecosystems. This relationship, however, may change over time following alternations in other environmental factors. Here we show that above 30°N, the strength of the relationship between the interannual variability of growing season NDVI and temperature (partial correlation coefficient R NDVI-GT ) declined substantially between 1982 and 2011. This decrease in R NDVI-GT is mainly observed in temperate and arctic ecosystems, and is also partly reproduced by process-based ecosystem model results. In the temperate ecosystem, the decrease in R NDVI-GT coincides with an increase in drought. In the arctic ecosystem, it may be related to a nonlinear response of photosynthesis to temperature, increase of hot extreme days and shrub expansion over grass-dominated tundra. Our results caution the use of results from interannual time scales to constrain the decadal response of plants to ongoing warming.

Earth system models project that the tropical land carbon sink will decrease in size in response to an increase in warming and drought during this century, probably causing a positive climate feedback. But available data are too limited at present to test the predicted changes in the tropical carbon balance in response to climate change. Long-term atmospheric carbon dioxide data provide a global record that integrates the interannual variability of the global carbon balance. Multiple lines of evidence demonstrate that most of this variability originates in the terrestrial biosphere. In particular, the year-to-year variations in the atmospheric carbon dioxide growth rate (CGR) are thought to be the result of fluctuations in the carbon fluxes of tropical land areas. Recently, the response of CGR to tropical climate interannual variability was used to put a constraint on the sensitivity of tropical land carbon to climate change. Here we use the long-term CGR record from Mauna Loa and the South Pole to show that the sensitivity of CGR to tropical temperature interannual variability has increased by a factor of 1.9 ± 0.3 in the past five decades. We find that this sensitivity was greater when tropical land regions experienced drier conditions. This suggests that the sensitivity of CGR to interannual temperature variations is regulated by moisture conditions, even though the direct correlation between CGR and tropical precipitation is weak. We also find that present terrestrial carbon cycle models do not capture the observed enhancement in CGR sensitivity in the past five decades. More realistic model predictions of future carbon cycle and climate feedbacks require a better understanding of the processes driving the response of tropical ecosystems to drought and warming.