Extreme temperatures are predicted to become increasingly common due to climate change, threatening the sustainability and profitability of global rice production. Manure amendment is a common agricultural practice to improve soil fertility and increase crop yields, but whether this practice modulates the effect of extreme temperatures on crop yield is unclear. Here we show through a series of experiments and meta-analysis that long-term manure amendment reduces losses of rice yield due to extreme temperatures. We propose that by increasing soil fertility, manure amendment increased net photosynthetic rate and plant physiological resistance to extreme temperatures. Without considering the impact of other global change factors, we estimate that manure amendment could potentially reduce global losses of rice yield due to extreme temperatures from 33.6 to 25.1%. Thus, our findings indicate that manure amendment may play a key role in improving food security in a changing climate.

Enhancing the photosynthetic rate is one of the effective ways to increase rice yield, given that photosynthesis is the basis of crop productivity. At the leaf level, crops’ photosynthetic rate is mainly determined by photosynthetic functional traits including the maximum carboxylation rate (V_cmax) and stomatal conductance (gs). Accurate quantification of these functional traits is important to simulate and predict the growth status of rice. In recent studies, the emerging sun-induced chlorophyll fluorescence (SIF) provides us an unprecedented opportunity to estimate crops’ photosynthetic traits, owing to its direct and mechanistic links to photosynthesis. Therefore, in this study, we proposed a practical semimechanistic model to estimate the seasonal V_cmax and gs time-series based on SIF. We firstly generated the coupling relationship between the open ratio of photosystem II (qL) and photosynthetically active radiation (PAR), then estimate the electron transport rate (ETR) based on the proposed mechanistic relationship between SIF and ETR. Finally, V_cmax and gs were estimated by linking to ETR based on the principle of evolutionary optimality and the photosynthetic pathway. Validation with field observations showed that our proposed model can estimate V_cmax and gs with high accuracy (R² > 0.8). Compared to simple linear regression model, the proposed model could increase the accuracy of V_cmax estimates by >40%. Therefore, the proposed method effectively enhanced the estimation accuracy of crops’ functional traits, which sheds new light on developing high-throughput monitoring techniques to estimate plant functional traits, and also can improve our understating of crops’ physiological response to climate change.

Due to the substantial gross exchange fluxes with the atmosphere, the terrestrial carbon cycle plays a significant role in the global carbon budget. Drought commonly affects terrestrial carbon absorption negatively. Terrestrial biosphere models exhibit significant uncertainties in capturing the carbon flux response to drought, which have an impact on estimates of the global carbon budget. Through plant physiological processes, soil moisture tightly regulates the carbon cycle in the environment. Therefore, accurate observations of soil moisture may enhance the modeling of carbon fluxes in a model–data fusion framework. We employ the Carbon Cycle Data Assimilation System (CCDAS) to assimilate 36-year satellite-derived surface soil moisture observations in combination with flask samples of atmospheric CO₂ concentrations. We find that, compared to the default model, the performance of optimized net ecosystem productivity (NEP) and gross primary productivity (GPP) has increased with the RMSEs reduced by 1.62 gC/m²/month and 10.84 gC/m²/month, which indicates the added value of the ESA-CCI soil moisture observations as a constraint on the terrestrial carbon cycle. Additionally, the combination of soil moisture and CO₂ concentration in this study improves the representation of inter-annual variability of terrestrial carbon fluxes as well as the atmospheric CO₂ growth rate. We thereby investigate the ability of the optimized GPP in responding to drought by comparing continentally aggregated GPP with the drought index. The assimilation of surface soil moisture has been shown to efficiently capture the influences of the sub-annual (≤9 months drought durations) and large-scale (e.g., regional to continental scales) droughts on GPP. This study highlights the significant potential of satellite soil moisture for constraining inter-annual models of the terrestrial biosphere’s carbon cycle and for illustrating how GPP responds to drought at a continental scale.

Rice is the staple food for more than half of the population in China. Therefore, enhancing the grain yield of rice is the core of ensuring food security. In recent decades, the increase of atmospheric carbon dioxide (CO₂) concentration generally increases the photosynthetic rate of rice and then the rice yield. This phenomenon is generally termed the CO₂ fertilization effect (CFE), which plays a pivotal role in sustaining global rice yield in the context of climate change. In order to accurately understand the impact of elevated CO₂ on rice productivity and yield, this study first presents the mechanisms of CFE impacts on rice yield at the foliar and canopy levels. Elevated CO₂ will promote the net photosynthetic rate, reduce the stomatal conductance, and thus increase the water use efficiency. Currently, at foliar, canopy, regional and global scales, controlled experiments, process-based models and statistical attribution models are the main approaches to estimate CFE. Based on these methods, at the leaf and canopy scales, elevated CO₂ will lead to an increase in the number of panicles per unit area and the spikelet number per panicle of rice, finally resulting in the enhancement of grain yield. State-of-the-art crop models suggested that the global averaged CFE on rice yield is about 24% per 100 ppm increase in CO₂, albeit with large differences between different models. Rice varieties, nutrient management, water regimes, planting density and temperature are the main factors that affect the CFE. Finally, based on the existing research basis and existing problems, we proposed several future research directions including the understanding of the mechanism of CFE from the perspective of rice roots, soil microbes, key genes and rice breeding, and also the upscaling of the results from site-level to regional- and global-level through the integration between experiments and models. By systematically reviewing the response of rice physiology and yield to elevated CO₂ using multiple methods at various scales, this study will be beneficial for the formulation of adaptive cultivation strategies to enhance the rice yield and thus to sustain global food security in the context of climate change.

The accurate estimation of gross primary productivity (GPP) plays an important role in accurately projecting the terrestrial carbon cycle and climate change. Satellite-driven near-infrared reflectance (NIRv) can be used to estimate GPP based on their nearly linear relationship. Notably, previous studies have reported that the relationship between NIRv and GPP seems to be biome-specific (or land cover) at the ecosystem scale due to both biotic and abiotic effects. Hence, the NIRv-based estimation of GPP may be influenced by land cover changes (LCC) and the discrepancies in multisource products (DMP). However, these issues have not been well understood until now. Therefore, this study took the Yellow River basin (YRB) as the study area. This area has experienced remarkable land cover changes in recent decades. We used Moderate-Resolution Imaging Spectroradiometer (MODIS) and European Space Agency (ESA) Climate Change Initiative (CCI) land cover products (termed MCD12C1 and ESACCI, respectively) during 2001–2018 to explore the impact of land cover on NIRv-estimated GPP. Paired comparisons between the static and dynamic schemes of land cover using the two products were carried out to investigate the influences of LCC and DMP on GPP estimation by NIRv. Our results showed that the dominant land cover types in the YRB were grassland, followed by cropland and forest. Meanwhile, the main transfer was characterized by the conversion from other land cover types (e.g., barren) to grassland in the northwest of the YRB and from grassland and shrubland to cropland in the southeast of the YRB during the study period. Moreover, the temporal and spatial pattern of GPP was highly consistent with that of NIRv, and the average increase in GPP was 2.14 gCm⁻²yr⁻¹ across the YRB. Nevertheless, it is shown that both LCC and DMP had significant influences on the estimation of GPP by NIRv. That is, the areas with obvious differences in NIRv-based GPP closely correspond to the areas where land cover types dramatically changed. The achievements of this study indicate that considering the land cover change and discrepancies in multisource products would help to improve the accuracy of NIRv-based estimated GPP.

1. The terrestrial ecosystem removes over 30% of anthropogenic carbon dioxide (CO₂) emissions and plays a critical role in modulating the growth rate of atmospheric CO₂ concentrations, making it a ...