[Therapeutic effect and mechanism of Jingfang Granules on acute lung injury based on intestinal flora and fecal metabolomics].

This study aims to elucidate the therapeutic effect and mechanism of Jingfang Granules on acute lung injury, and to investigate the regulatory effect of Jingfang Granules on the metabolic disorders of endogenous metabolites in feces and the homeostasis of intestinal microbiota in acute lung injury, mice were randomly divided into a sham group, a model group, and a Jingfang Granules group. After modeling, the mice were continuously administered for 6 days. Using ultra-high performance liquid chromatography quadrupole/electrostatic field orbital trap high-resolution mass spectrometry(UHPLC-HESI-QE-Orbitrap-MS/MS) metabolomics technology and 16S rRNA high-throughput sequencing technology, changes in endogenous small molecule substances and gut microbiota in mouse intestines were determined, and potential biomarkers were identified. The results showed that Jingfang Granules can regulate 11 biomarkers, including L-glutamic acid, succinic acid, arachidonic acid, linoleic acid, linolenic acid, phenylalanine, sphingosine, 2-hydroxy-2-methyl butyric acid, pyruvate, tryptophan, and palmitic acid. Metabolic pathway analysis was conducted on these 11 biomarkers using the online software MetaboAnalyst, identifying potential major metabolic pathways. Among them, a total of 10 metabolic pathways are closely related to the treatment of acute lung injury with Jingfang Granules, including alanine, aspartate and glutamate metabolism, aminoacyl-tRNA biosynthesis, citrate cycle(TCA cycle), alyoxylate and dicarboxylate metabolism, arginine and proline metabolism, linoleic acid metabolism and linolenic acid metabolism, nitrogen metabolism, D-glutamine and D-gluta-matemetabolism, phenylalanine, tyrosine and tryptophan biosynthesis, phenylalanine metabolism. The results of gut microbiota showed significant differences in bacteria, mainly including Bacteroides, Akkermansia, Lachnospiraceae＿NK4A136＿group, Lachnochlostridium, and Klebsiella. Spearman analysis confirms that Akkermansia and Lachnospiraceae＿NK4A136＿group is a significant positive correlation between the abundance of succinic acid, arachidonic acid, linolenic acid, linoleic acid, butyric acid, and pyruvate in the group; Bacteroides, Klebsiella, Lachnochlostrium are significantly positively correlated with the abundance of L-glutamic acid, phenylalanine, and sphingosine. The above results indicate that the therapeutic effect of Jingfang Granules on acute lung injury is achieved by improving the imbalance of gut microbiota in mice with acute lung injury, balancing the metabolism of alanine, biosynthesis of aminoacyl tRNA, aspartic acid, glutamate, tricarboxylic acid cycle, biosynthesis of phenylalanine, tyrosine, tryptophan, and metabolism of linoleic acid.

Small cell lung cancer transformation: From pathogenesis to treatment.

Small cell lung cancer (SCLC) is a type of neuroendocrine tumor with high malignancy and poor prognosis. Besides the de novo SCLC, there is transformed SCLC, which has similar characteristics of pathological morphology, molecular characteristics, clinical manifestations and drug sensitivity. However, de novo SCLC and transformed SCLC have different pathogenesis and tumor microenvironment. SCLC transformation is one of the mechanisms of resistance to chemotherapy, immunotherapy, and targeted therapy in NSCLC. Two hypotheses have been used to explain the pathogenesis of SCLC transformation. Although SCLC transformation is not common in clinical practice, it has been repeatedly identified in many small patient series and case reports. It usually occurs in epidermal growth factor receptor (EGFR) mutant lung adenocarcinoma after treatment with tyrosine kinase inhibitors (TKIs). SCLC transformation can also occur in anaplastic lymphoma kinase (ALK)-positive lung cancer after treatment with ALK inhibitors and in wild-type EGFR or ALK NSCLC treated with immunotherapy. Chemotherapy was previously used to treat transformed SCLC, yet it is associated with an unsatisfactory prognosis. We comprehensively review the advancements in transformed SCLC, including clinical and pathological characteristics, and the potential effective treatment after SCLC transformation, aiming to give a better understanding of transformed SCLC and provide support for clinical uses.

Simulation of winter wheat response to variable sowing dates and densities in a high-yielding environment.

Crop multi-model ensembles (MME) have proven to be effective in increasing the accuracy of simulations in modelling experiments. However, the ability of MME to capture crop responses to changes in sowing dates and densities has not yet been investigated. These management interventions are some of the main levers for adapting cropping systems to climate change. Here, we explore the performance of a MME of 29 wheat crop models to predict the effect of changing sowing dates and rates on yield and yield components, on two sites located in a high-yielding environment in New Zealand. The experiment was conducted for 6 years and provided 50 combinations of sowing date, sowing density and growing season. We show that the MME simulates seasonal growth of wheat well under standard sowing conditions, but fails under early sowing and high sowing rates. The comparison between observed and simulated in-season fraction of intercepted photosynthetically active radiation (FIPAR) for early sown wheat shows that the MME does not capture the decrease of crop above ground biomass during winter months due to senescence. Models need to better account for tiller competition for light, nutrients, and water during vegetative growth, and early tiller senescence and tiller mortality, which are exacerbated by early sowing, high sowing densities, and warmer winter temperatures.

[Therapeutic effect of Jingfang Granules on CCl_4-induced liver fibrosis in mice and its mechanism].

To investigate the therapeutic effect of Jingfang Granules on carbon tetrachloride(CCl_4)-induced liver fibrosis in mice and its mechanism. Forty-nine 8-week-old male C57 BL/6 J mice were randomly divided into a blank group, a CCl_4 group, a silybin group(positive control, 100 mg·kg~(-1))+CCl_4, a Jingfang high-dose(16 g·kg~(-1)) group, a Jingfang high-dose(16 g·kg~(-1))+CCl_4 group, a Jingfang medium-dose(8 g·kg~(-1))+CCl_4 group, and a Jingfang low-dose(4 g·kg~(-1))+CCl_4 group, with 7 mice in each group. The mice in the blank group and Jingfang high-dose group were intraperitoneally injected olive oil solution, and mice in other groups were intraperitoneally injected with 10% CCl_4 olive oil solution(5 mL·kg~(-1)) to induce liver fibrosis, twice a week with an interval of 3 d, for 8 weeks. At the same time, except for the blank group and CCl_4 group, which were given deionized water, the mice in other groups were given the corresponding dose of drugs by gavage once daily for 8 weeks with the gavage volume of 10 mL·kg~(-1). All mice were fasted and freely drank for 12 h after the last administration, and then the eyeballs were removed for blood collection. The liver and spleen were collected, and the organ index was calculated. The levels of alanine aminotransferase(ALT), aspartate aminotransferase(AST), total bile acid(TBA), and triglyceride(TG) in the serum of mice were detected by an automated analyzer. Tumor necrosis factor-α(TNF-α), interleukin-6(IL-6) and interleukin-1ß(IL-1ß) levels were detected by enzyme-linked immunosorbent assay(ELISA). Kits were used to detect the contents of superoxide dismutase(SOD), malondialdehyde(MDA), and glutathione(GSH) in the liver tissue. Pathological changes in the liver tissue were observed by hematoxylin-eosin(HE), Masson, and Sirius red staining. Western blot was used to detect protein expressions of transforming growth factor-ß(TGF-ß), α-smooth muscle actin(α-SMA) and Smad4 in the liver tissue. The results indicated that Jingfang Granules significantly reduced the organ index, levels of ALT, AST, TBA,TG, TNF-α, IL-6, and IL-1ß in the serum, and the content of MDA in the liver tissue of mice with CCl_4-induced liver fibrosis. Jingfang Granules also significantly increased the content of SOD and GSH in the liver tissue. Meanwhile, Jingfang Granules down-regulated the protein levels of TGF-ß, α-SMA, and Smad4. Furthermore, Jingfang Granules had no significant effect on the liver tissue morphology and the above indexes in the normal mice. In conclusion, Jingfang Granules has obvious therapeutic effect on CCl_4-induced liver fibrosis, and its mechanism may be related to reducing the expression of pro-inflammatory factors, anti-oxidation, and regulating TGF-ß/Smad4 signaling pathway.

Parameter estimation for functional-structural plant models when data are scarce: using multiple patterns for rejecting unsuitable parameter sets.

BACKGROUND AND AIMS: Functional-structural plant (FSP) models provide insights into the complex interactions between plant architecture and underlying developmental mechanisms. However, parameter estimation of FSP models remains challenging. We therefore used pattern-oriented modelling (POM) to test whether parameterization of FSP models can be made more efficient, systematic and powerful. With POM, a set of weak patterns is used to determine uncertain parameter values, instead of measuring them in experiments or observations, which often is infeasible. METHODS: We used an existing FSP model of avocado (Persea americana 'Hass') and tested whether POM parameterization would converge to an existing manual parameterization. The model was run for 10 000 parameter sets and model outputs were compared with verification patterns. Each verification pattern served as a filter for rejecting unrealistic parameter sets. The model was then validated by running it with the surviving parameter sets that passed all filters and then comparing their pooled model outputs with additional validation patterns that were not used for parameterization. KEY RESULTS: POM calibration led to 22 surviving parameter sets. Within these sets, most individual parameters varied over a large range. One of the resulting sets was similar to the manually parameterized set. Using the entire suite of surviving parameter sets, the model successfully predicted all validation patterns. However, two of the surviving parameter sets could not make the model predict all validation patterns. CONCLUSIONS: Our findings suggest strong interactions among model parameters and their corresponding processes, respectively. Using all surviving parameter sets takes these interactions into account fully, thereby improving model performance regarding validation and model output uncertainty. We conclude that POM calibration allows FSP models to be developed in a timely manner without having to rely on field or laboratory experiments, or on cumbersome manual parameterization. POM also increases the predictive power of FSP models.

Modelling impact of early vigour on wheat yield in dryland regions.

Early vigour, or faster early leaf area development, has been considered an important trait for rainfed wheat in dryland regions such as Australia. However, early vigour is a genetically complex trait, and results from field experiments have been highly variable. Whether early vigour can lead to improved water use efficiency and crop yields is strongly dependent on climate and management conditions across the entire growing season. Here, we present a modelling framework for simulating the impact of early vigour on wheat growth and yield at eight sites representing the major climate types in Australia. On a typical soil with plant available water capacity (PAWC) of 147 mm, simulated yield increase with early vigour associated with larger seed size was on average 4% higher compared with normal vigour wheat. Early vigour through selection of doubled early leaf sizes could increase yield by 16%. Increase in yield was mainly from increase in biomass and grain number, and was reduced at sites with seasonal rainfall plus initial soil water <300 mm. Opportunities exists for development of early vigour wheat varieties for wetter sites. Soil PAWC could play a significant role in delivering the benefit of early vigour and would require particular attention.

Improving process-based crop models to better capture genotype×environment×management interactions.

In spite of the increasing expectation for process-based crop modelling to capture genotype (G) by environment (E) by management (M) interactions to support breeding selections, it remains a challenge to use current crop models to accurately predict phenotypes from genotypes or from candidate genes. We use wheat as a target crop and the APSIM farming systems model (Holzworth et al., 2014) as an example to analyse the current status of process-based crop models with a major focus on need to improve simulation of specific eco-physiological processes and their linkage to underlying genetic controls. For challenging production environments in Australia, we examine the potential opportunities to capture physiological traits, and to integrate genetic and molecular approaches for future model development and applications. Model improvement will require both reducing the uncertainty in simulating key physiological processes and enhancing the capture of key observable traits and underlying genetic control of key physiological responses to environment. An approach consisting of three interactive stages is outlined to (i) improve modelling of crop physiology, (ii) develop linkage from model parameter to genotypes and further to loci or alleles, and (iii) further link to gene expression pathways. This helps to facilitate the integration of modelling, phenotyping, and functional gene detection and to effectively advance modelling of G×E×M interactions. While gene-based modelling is not always needed to simulate G×E×M, including well-understood gene effects can improve the estimation of genotype effects and prediction of phenotypes. Specific examples are given for enhanced modelling of wheat in the APSIM framework.

Global wheat production with 1.5 and 2.0°C above pre-industrial warming.

Efforts to limit global warming to below 2°C in relation to the pre-industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre-industrial period) on global wheat production and local yield variability. A multi-crop and multi-climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by -2.3% to 7.0% under the 1.5°C scenario and -2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980-2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter-annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer-India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade.

Modelling the dynamic physical protection of soil organic carbon: Insights into carbon predictions and explanation of the priming effect.

The role and significance of physically protected soil organic carbon (SOC) in regulating SOC dynamics remains unclear. Here, we developed a simple theoretical model (DP model) considering dynamic physical protection to simulate the dynamics of protected (Cp ) and unprotected SOC (Cu ), and compared the modelling results with a conventional two-pool (fast vs. slow) model considering chemical recalcitrance. The two models were first constrained using extensive SOC data collected from soils with and without fresh carbon (C) inputs under incubation conditions, and then applied to project SOC dynamics and explore mechanisms underpinning the priming effect (PE). Overall, both models explained more than 99% of the variances in observed SOC dynamics. The DP model predicted that Cp accounted for the majority of total SOC. As decomposition proceeds, the proportion of Cp reached >90% and kept relatively constant. Although the similar performance of the two models in simulating observed total SOC dynamics, their predictions of future SOC dynamics were divergent, challenging the predictions of widely used pool-based models. The DP model also suggested alternative mechanisms underpinning the priming of SOC decomposition by fresh C inputs. The two-pool model suggested that the PE was caused by the stimulated decomposition rates, especially for the slow recalcitrant pool, while the DP model suggested that the PE might be the combined consequence of stimulated Cu decomposition, the liberation of Cp to decomposition and the inhibition of the protection of unprotected SOC. The model-data integration provided a new explanation for the PE, highlighting the importance of liberation of initially physically protected SOC to decomposition by new C inputs. Our model-data integration demonstrated the importance of simulating physical protection processes for reliable SOC predictions, and provided new insights into mechanistic understanding of the priming effect.

Soil organic carbon dynamics jointly controlled by climate, carbon inputs, soil properties and soil carbon fractions.

Soil organic carbon (SOC) dynamics are regulated by the complex interplay of climatic, edaphic and biotic conditions. However, the interrelation of SOC and these drivers and their potential connection networks are rarely assessed quantitatively. Using observations of SOC dynamics with detailed soil properties from 90 field trials at 28 sites under different agroecosystems across the Australian cropping regions, we investigated the direct and indirect effects of climate, soil properties, carbon (C) inputs and soil C pools (a total of 17 variables) on SOC change rate (rC , Mg C ha-1  yr-1 ). Among these variables, we found that the most influential variables on rC were the average C input amount and annual precipitation, and the total SOC stock at the beginning of the trials. Overall, C inputs (including C input amount and pasture frequency in the crop rotation system) accounted for 27% of the relative influence on rC , followed by climate 25% (including precipitation and temperature), soil C pools 24% (including pool size and composition) and soil properties (such as cation exchange capacity, clay content, bulk density) 24%. Path analysis identified a network of intercorrelations of climate, soil properties, C inputs and soil C pools in determining rC . The direct correlation of rC with climate was significantly weakened if removing the effects of soil properties and C pools, and vice versa. These results reveal the relative importance of climate, soil properties, C inputs and C pools and their complex interconnections in regulating SOC dynamics. Ignorance of the impact of changes in soil properties, C pool composition and C input (quantity and quality) on SOC dynamics is likely one of the main sources of uncertainty in SOC predictions from the process-based SOC models.

Uncertain future soil carbon dynamics under global change predicted by models constrained by total carbon measurements.

Pool-based carbon (C) models are widely applied to predict soil C dynamics under global change and infer underlying mechanisms. However, it is unclear about the credibility of model-predicted C pool size, decay rate (k), and/or microbial C use efficiency (e) as only data on bulked total C is usually available for model constraining. Using observing system simulation experiments (OSSE), we constrained a two-pool model using simulated data sets of total soil C dynamics under topical hypotheses on responses of soil C dynamics to warming and elevated CO2 (i.e., global change scenarios). The results indicated that the model predicted great uncertainties in C pool size, k, and e under all global change scenarios, resulting in the difficulty to correctly infer the presupposed "real" values of those parameters that are used to generate the simulated total soil C for constraining the model. Furthermore, the model using the constrained parameters generated divergent future soil C dynamics. Compared with the predictions using the presupposed real parameters (i.e., the real future C dynamics), the percentage uncertainty in 100-yr predictions using the constrained parameters was up to 45% depending on global change scenarios and data availability for model-constraining. Such great uncertainty was mainly due to the high collinearity among the model parameters. Using pool-based models, we argue that soil C pool size, k, and/or e and their responses to global change have to be estimated explicitly and empirically, rather than through model-fitting, in order to accurately predict C dynamics and infer underlying mechanisms. The OSSE approach provides a powerful way to identify data requirement for the new generation of model development and test model performance.

Fresh carbon input differentially impacts soil carbon decomposition across natural and managed systems.

The amount of fresh carbon input into soil is experiencing substantial changes under global change. It is unclear what will be the consequences of such input changes on native soil carbon decomposition across ecosystems. By synthesizing data from 143 experimental comparisons, we show that, on average, fresh carbon input stimulates soil carbon decomposition by 14%. The response was lower in forest soils (1%) compared with soils from other ecosystems (> 24%), and higher following inputs of plant residue-like substrates (31%) compared to root exudate-like substrates (9%). The responses decrease with the baseline soil carbon decomposition rate under no additional carbon input, but increase with the fresh carbon input rate. The rates of these changes vary significantly across ecosystems and with the carbon substrates being added. These findings can be applied to provide robust estimates of soil carbon balance across ecosystems under changing aboveground and belowground inputs as consequence of climate and land management changes.

Can citrate efflux from roots improve phosphorus uptake by plants? Testing the hypothesis with near-isogenic lines of wheat.

Phosphorus (P) deficiency in some plant species triggers the release of organic anions such as citrate and malate from roots. These anions are widely suggested to enhance the availability of phosphate for plant uptake by mobilizing sparingly-soluble forms in the soil. Carazinho is an old wheat (Triticum aestivum) cultivar from Brazil, which secretes citrate constitutively from its root apices, and here we show that it also produces relatively more biomass on soils with low P availability than two recent Australian cultivars that lack citrate efflux. To test whether citrate efflux explains this phenotype, we generated two sets of near-isogenic lines that differ in citrate efflux and compared their biomass production in different soil types and with different P treatments in glasshouse experiments and field trials. Citrate efflux improved relative biomass production in two of six glasshouse trials but only at the lowest P treatments where growth was most severely limited by P availability. Furthermore, citrate efflux provided no consistent advantage for biomass production or yield in multiple field trials. Theoretical modeling indicates that the effectiveness of citrate efflux in mobilizing soil P is greater as the volume of soil into which it diffuses increases. As efflux from these wheat plants is restricted to the root apices, the potential for citrate to mobilize sufficient P to increase shoot biomass may be limited. We conclude that Carazinho has other attributes that contribute to its comparatively good performance in low-P soils.

Complete sequencing and phylogenetic analysis of porcine kobuvirus in domestic pigs in Northwest China.

Porcine kobuvirus, a member of the genus Kobuvirus that is associated with diarrhea, has been reported in many countries. We determined the complete genome sequence and investigated the genetic evolution of the kobuvirus strain swKoV CH441, which was detected in the highland of Gansu province in Northwest China. The viral genome is 8149 nucleotides (nt) long, including a 29-nt poly(A) tail of the 3' end, and is 90 nt shorter in the 2B coding region than those of other kobuvirus strains whose sequences are available in the GenBank database. Phylogenetic analysis showed that swKoV CH441 was most closely related to porcine kobuvirus CH/HNXX-4 but more distantly related to other strains, including the strains GS-1/2012/CH and GS-2/2012/CH, which were detected in Gansu province, indicating that porcine kobuvirus may have geographic and host differences in evolution.

Complete sequence and phylogenetic analysis of a porcine bocavirus strain swBoV CH437.

Porcine bocavirus (PBoV), a member of genus Bocavirus, family Parvoviridae, was first identified in 2009 in Swedish swine herds suffering from postweaning multisystemic wasting syndrome. Up to date, the different species of PBoVs have been reported in different countries. Especially, the virus isolated in China was complicated. In this study, we detected a novel PBoV strain swBoV CH437 from clinical samples collected in Gansu Province, Northwest China. The complete genome of swBoV CH437 was 5,275 nucleotides (nt) in length and contains three ORFs: ORF1 encodes NS1 (2,004 nt, 667 aa), ORF3 encodes NP1 (681 nt, 226 aa), and ORF2 encodes VP1 (2,049 nt, 682 aa) and VP2 (1,641 nt, 546 aa). Sequence analysis demonstrated that the NS1 gene shared 24.2-88.6 % nucleotide sequence identity, the NP1 shared 21.3-89.9 %, less than 95 % nucleotide sequence identity with other PBoV strains. Therefore, we propose that swBoV CH437 should be classified as a novel PBoV species.

Complete sequence and phylogenetic analysis of a porcine sapovirus strain isolated from western China.

Sapovirus (SaV) is a type of calicivirus that can cause acute viral gastroenteritis in humans and animals. SaVs have been found in several mammalian species, including humans, pigs, minks, dogs, and bats. Porcine sapovirus (PoSaV) was first identified in 1980 in the United States and has been found to be circulating throughout China in recent years. In this study, the complete genomic characterization of PoSaV CH430, first found in west China, was reported and analyzed. The genome was 7,342 bp excluding the 30 nt poly(A) tail at the 3' terminus and comprised two major open reading frames. Comprehensive evolutionary and phylogenetic analyses indicated that the CH430 strain belongs to genotype III SaVs. However, this particular isolate and DG24 strain occupied an independent branch of the phylogenetic tree we generated, indicating that they could form a separate subgenotype in the near future. We predicted the cleavage sites for the ORF1 polyprotein located at Q56/G57, Q310/A311, E649/A650, E934/A935, E1047/G1048, and E1712/A1713, separately. This is the first PoSaV strain isolated from western China to be fully sequenced and characterized. It provided a reliable experimental basis for studying the genetic nature of emerging PoSaVs.

Spatiotemporal co-optimization of agricultural management practices towards climate-smart crop production.

Co-optimization of multiple management practices may facilitate climate-smart agriculture, but is challenged by complex climate-crop-soil management interconnections across space and over time. Here we develop a hybrid approach combining agricultural system modelling, machine learning and life cycle assessment to spatiotemporally co-optimize fertilizer application, irrigation and residue management to achieve yield potential of wheat and maize and minimize greenhouse gas emissions in the North China Plain. We found that the optimal fertilizer application rate and irrigation for the historical period (1995-2014) are lower than local farmers' practices as well as trial-derived recommendations. With the optimized practices, the projected annual requirement of fertilizer, irrigation water and residue inputs across the North China Plain in the period 2051-2070 is reduced by 16% (14-21%) (mean with 95% confidence interval), 19% (7-32%) and 20% (16-26%), respectively, compared with the current supposed optimal management in the historical reference period, with substantial greenhouse gas emission reductions. We demonstrate the potential of spatiotemporal co-optimization of multiple management practices and present digital mapping of management practices as a benchmark for site-specific management across the region.

Global needs for nitrogen fertilizer to improve wheat yield under climate change.

Increasing global food demand will require more food production1 without further exceeding the planetary boundaries2 while simultaneously adapting to climate change3. We used an ensemble of wheat simulation models with improved sink and source traits from the highest-yielding wheat genotypes4 to quantify potential yield gains and associated nitrogen requirements. This was explored for current and climate change scenarios across representative sites of major world wheat producing regions. The improved sink and source traits increased yield by 16% with current nitrogen fertilizer applications under both current climate and mid-century climate change scenarios. To achieve the full yield potential-a 52% increase in global average yield under a mid-century high warming climate scenario (RCP8.5), fertilizer use would need to increase fourfold over current use, which would unavoidably lead to higher environmental impacts from wheat production. Our results show the need to improve soil nitrogen availability and nitrogen use efficiency, along with yield potential.

Meta-modeling soil organic carbon sequestration potential and its application at regional scale.

Upscaling the results from process-based soil-plant models to assess regional soil organic carbon (SOC) change and sequestration potential is a great challenge due to the lack of detailed spatial information, particularly soil properties. Meta-modeling can be used to simplify and summarize process-based models and significantly reduce the demand for input data and thus could be easily applied on regional scales. We used the pre-validated Agricultural Production Systems sIMulator (APSIM) to simulate the impact of climate, soil, and management on SOC at 613 reference sites across Australia's cereal-growing regions under a continuous wheat system. We then developed a simple meta-model to link the APSIM-modeled SOC change to primary drivers, i.e., the amount of recalcitrant SOC, plant available water capacity of soil, soil pH, and solar radiation, temperature, and rainfall in the growing season. Based on high-resolution soil texture data and 8165 climate data points across the study area, we used the meta-model to assess SOC sequestration potential and the uncertainty associated with the variability of soil characteristics. The meta-model explained 74% of the variation of final SOC content as simulated by APSIM. Applying the meta-model to Australia's cereal-growing regions reveals regional patterns in SOC, with higher SOC stock in cool, wet regions. Overall, the potential SOC stock ranged from 21.14 to 152.71 Mg/ha with a mean of 52.18 Mg/ha. Variation of soil properties induced uncertainty ranging from 12% to 117% with higher uncertainty in warm, wet regions. In general, soils in Australia's cereal-growing regions under continuous wheat production were simulated as a sink of atmospheric carbon dioxide with a mean sequestration potential of 8.17 Mg/ha.