Canopy photosynthesis, rather than leaf photosynthesis, is highly related to plant biomass and yield formation. Studying canopy photosynthesis and identifying the parameters that control it can help optimize agricultural management and achieve crop yield potential. Compared with traditional parameters, canopy occupation volume (COV) offers an integrative parameter on canopy architecture related to canopy photosynthetic rates. In this study, we developed a high-throughput method to derive COV for different rice varieties. We first used multi-perspective two-dimensional imaging to reconstruct three-dimensional point clouds of rice plants and developed a suite of pipelines to calculate plant height, leaf number, tiller number, and biomass, with R2 values of 91.8%, 95.9%, 82.3%, and 94.3%, respectively. We further employed point cloud data to reconstruct the surfaces of rice plants and construct a virtual canopy model of the rice population. Light distribution was simulated using a ray-tracing algorithm and canopy photosynthetic rates were simulated via photosynthetic rate-incident light intensity curve fitting. Furthermore, we systematically explored the relationships between canopy phenotypes and photosynthetic rates, and found that COV was the most effective predictor of canopy photosynthesis, achieving an R2 value of 92.1%. Adjustment in atmospheric transmittance showed that COV strongly correlated with canopy photosynthesis under different light conditions, with higher accuracy observed under diffuse light. Variations in planting density confirmed that this correlation remained strong at the community level. In summary, this study demonstrates that COV is closely linked to simulated canopy photosynthesis and the developed pipeline can support future agronomic and breeding research.

Photosynthesis fuels crop growth and yield, yet the regulatory networks coordinating photosynthetic gene expression with carbon allocation remain incompletely understood. Here, we construct a gene regulatory network (GRN) for rice photosynthesis by integrating time-resolved RNA-seq, ATAC-seq, and promoter cis-element analyses. We identify nine hub transcription factors (TFs), four of which (OsPIL13, OsbZIP72, OsCGA1, and OsGLK1) exhibit strong leaf-specific, light-inducible expression patterns. Overexpression of OsPIL13, OsbZIP72, or OsGLK1 using photosynthetic tissue-specific promoters significantly enhanced the light-saturated photosynthetic rate (Asat) across developmental stages, with OsPIL13 overexpression increasing Asat by up to 57% during grain filling. While several hub TFs boosted photosynthetic capacity, consistent improvements in biomass and grain yield under field conditions were rare. Notably, OsGLK1 overexpression confers stable yield gains across multiple growing seasons. Comparative transcriptomic analysis indicates that OsGLK1 also upregulates genes involved in brassinosteroid biosynthesis and sugar and lipid transporter genes, potentially linking photosynthetic output to growth and resource allocation. Collectively, our findings indicate that enhancing photosynthesis alone is insufficient to guarantee yield improvement; rather, the coordinated regulation of photosynthetic capacity and downstream carbon utilization is essential for sustainable productivity gains in rice.

The evolutionary convergence of complex biological features offers valuable insights into the interplay among environmental factors, organismal traits, and evolutionary outcomes. C4 photosynthesis exemplifies an adaptive syndrome derived from ancestral C3 photosynthesis in response to environmental stresses. In this study, we investigated the inducibility of C4 photosynthetic characteristics in various Flaveria species, including one of the youngest C4 species on Earth, under low CO2 conditions (100 ppm). Species used include F. robusta (proto-Kranz type), F. sonorensis (type I C3-C4), F. linearis (clade B C3-C4), F. ramosissima (type II C3-C4), and F. trinervia (C4). After 4 weeks of low CO2 treatment, F. sonorensis exhibited the highest inducibility of C4-related traits, as evidenced by enhanced chloroplast content in bundle sheath cells, reduced CO2 compensation point of photosynthesis (Г), increased apparent maximum carboxylation rate of Rubisco, and elevated cyclic electron transport. Conversely, F. linearis and F. ramosissima, despite possessing more preexisting C4-related traits, demonstrated less induction of C4-related features, with no significant enhancement of cyclic electron transport observed. These results indicate that environmental stresses can induce C4-related characteristics in C3-C4 intermediate species. Furthermore, an inducible cyclic electron transport may represent a critical precondition for the evolutionary transition from C3-C4 photosynthetic metabolism to a C4 type.

To evaluate the impact of climate change on maize production,accurately measuring the radiation use efficiency(RUE)of maize is critical.This study focused on three maize cultivars in Jilin Province,China:Zhengdan 958(ZD958),Xianyu 335(XY335),and Liangyu 99(LY99).Under the optimal growing conditions for high density planting(9 plants m-2),the maize RUE was determined during the vegetative and reproductive phases,and the entire growth period.The results showed that the canopy light interception for maize peaked during anthesis.After anthesis,maize plant biomass continued to accumulate.The maize RUE was calculated based on the absorbed photosynthetically active radiation(APAR).During the entire growth period,maize RUE averaged 5.71 g MJ-1 APAR among the three cultivars,with a high-to-low order of ZD958(5.85 g MJ-1 APAR)>XY335(5.64 g MJ-1 APAR)>LY99(5.07 g MJ-1 APAR).Within the vegetative and reproductive growth periods,maize RUE averaged 6.85 and 5.64 g MJ-1 APAR,respectively.When utilizing maize models that depend on RUE to predict aboveground biomass accumulation,such as APSIM,the current RUE value of 3.6 g MJ-1 APAR is considerably lower than the measured value obtained under high-density optimal growing conditions.Consequently,to derive the optimal potential yield for maize in such planting conditions,we recommend adjusting the RUE to a range of 5.07-5.85 g MJ-1 APAR.

C4 photosynthesis possess complex traits associated with high efficiencies in light, nitrogen and water utilization. To investigate the coupling of carbon and nitrogen stoichiometry within the systematic metabolism along the evolutionary path from C3 to C4 photosynthesis, we applied the nuclear magnetic resonance (NMR)-based metabolomics and advanced lipidomic approaches to profile 38 metabolites and 246 lipids in leaves of representative C3 ancestral, C3–C4 intermediate, and C4 species in the genus Flaveria. Our results revealed that progressive changes in metabolic profiles associated with amino acids/amines, organic acids, choline/its derivatives, and phospholipids along the C3 to C4 transition, implicating these pathways in cellular responses functionally oriented toward protein synthesis and degradation, tricarboxylic acid (TCA) cycle activity, and oxidative stress. In contrast, the metabolism related to carbohydrate and lipid biosynthesis exhibited a declining trend from C3 to C4 species. Among C3–C4 intermediates, the type II F. ramosissima displayed a unique metabolic profile, combining characteristics of both C3 species (elevated carbohydrate levels) and C4 species (elevated amino acid and organic acid levels). Taking together, these comprehensive metabolic phenotypes delineate distinct biochemical signatures associated with photosynthetic types in Flaveria, providing novel insights into the metabolic reprogramming underpinning C4 photosynthetic evolution. C4 photosynthetic evolution in Flaveriais characterized by a progressive metabolic shift from carbohydrate and lipid biosynthesis towards enhanced nitrogen-rich compound accumulation, delineating a biochemical phenotype across C3, intermediate, and C4 species.

Compared to the C3 plant rice (Oryza sativa), veins in the leaves of the C4 plant maize (Zea mays) are more densely arranged, but the mechanisms controlling vein initiation and differentiation remain unclear. This study systematically investigated the developmental patterning of vein formation in leaf primordia of maize and rice. Single-nucleus transcriptomic atlas of maize primordia revealed distinct middle ground tissue and procambium cell populations and a role for auxin in vein initiation inferred. Pseudo-time trajectory analysis further facilitated the identification of marker genes in ground tissue and procambial cell types including ETHYLENE-RESPONSIVE ELEMENT BINDING 114 (ZmEREB114), AUXIN IMPORT CARRIER 3 (ZmAIC3), ZmEREB161, and AUXIN AMIDO SYNTHETASE 2 (ZmAAS2). An experimental system was established in which the interplay between SHORT-ROOT1 (ZmSHR1) and auxin was used to suppress or restore vein formation in rice leaf primordia. Notably, expression of the rice genes GRETCHEN HAGEN 3.8 (OsGH3.8, homologous to ZmAAS2) and AUXIN RESISTANT 1 (OsAUX1, homologous to ZmAIC3) was suppressed by ZmSHR1 but promoted by auxin. Collectively this study provides single-cell resolved resources for early stages of grass leaf development, an experimental system for manipulating vein initiation in rice, and a model about how the interplay between SHR function and auxin response regulates vein patterning in maize and rice leaves.

Accurate prediction of the maximum carboxylation rate (Vcmax ) and maximum electron transport rate (Jmax ) from hyperspectral imagery is important for high-throughput plant phenotyping, but the appropriate modelling strategy remains unclear when labelled datasets differ in scale. We benchmarked six classical machine-learning models, three deep-learning architectures, and four transfer-learning strategies across hyperspectral datasets spanning n=48–661 plants. Models were evaluated using plant-level mean spectra and patch-level spectral inputs, with a Gaussian process (GP) post-regression applied to selected latent representations. At small sample sizes (n≈50), classical methods were most reliable: PLSR provided a stable baseline (RV2 cmax =0.33–0.40), while patch-level Lasso and SVR achieved strong performance by exploiting within-leaf spectral variation. However, patch-level sampling was not universally beneficial: on the larger datasets, it destabilised Ridge regression, where highly correlated patches shared identical plant-level labels. GP post-regression was unreliable at very small sample sizes but became beneficial at larger scales, especially when applied to PLSR latent scores. In the largest rice–wheat dataset, deep and foundation-model approaches became competitive, with single-trait LoRA combined with GP achieving the highest accuracy (RV2 cmax =0.69). Subsampling experiments indicated an empirical crossover at approximately n=200–300, beyond which deep models began to outperform the strongest classical baselines. These results show that hyperspectral trait-prediction models should be selected according to dataset scale: classical methods for small experiments, GP post-regression for intermediate to large datasets, and deep transfer learning when sufficient labelled data are available.

Coping with high light represents a major challenge for plants in nature. Under high light, 1O2 can induce MBS1 to form a low-dynamic condensate, which can effectively shade chloroplasts to avoid photodamage. This mechanism can be used to support breeding crops for both high photoprotection and high photosynthetic light use efficiency.

Dicarboxylate transporters (DiTs) mediate the exchange of dicarboxylates across the chloroplast inner membrane, playing critical roles in C/N coupling, photorespiration, chloroplast redox homeostasis, and C4 photosynthesis. DiT1 and DiT2 are Na⁺-independent exchangers of the solute carrier 13 (SLC13) family, and exhibit overlapping yet distinct substrate specificities: DiT1 transports 2-oxoglutarate, malate, and oxaloacetate, while DiT2 additionally transports glutamate and aspartate. However, the structural determinants of their substrate specificity and transport mechanism remain unclear. Here, we determined cryo-electron microscopy structures of Arabidopsis thaliana DiT1 and DiT2.1 bound to diverse substrates in dual conformational states. Structural analyses revealed that AtDiT1 possesses a singular dicarboxylate-binding site that is electrostatically incompatible with amino acid substrates, whereas AtDiT2.1 has 2 distinct sites to accommodate C4- and C5-dicarboxylates, thus allowing amino acids to bind without electrostatic repulsion. Phylogenetic analysis identified an A226S substitution in the substrate-binding site of DiT1, emerging during evolution in the charophyte ancestor of land plants. This substitution enhances oxaloacetate binding affinity in DiT1, which may have improved adaptation to terrestrial environments. Additionally, 2 conserved positively charged residues in DiTs functionally mimic Na⁺ used by SLC13 co-transporters, thereby enabling a Na⁺-independent elevator-type transport mechanism. These findings provide critical structural and mechanistic insights into the functional divergence of plant DiTs.

Context: Balancing early maturity with high yield in rice has long been seen as a "zero-sum trade-off"-where early maturity typically compromises high yield. Objective: This study aimed to determine if the "high-density lodging-resistant" cultivation strategy can break this maturity-yield trade-off. Methods: A two-year field experiment was conducted during 2023-2024 in Changsha, China to compare the indica-japonica hybrid rice Yongyou 4949 (YY4949; growth period approximate to 125 d) with the late-maturing cultivar LLYHZ (LLYHZ; growth period approximate to 141 d) at two planting densities i.e., 22.5 and 37.5 & times; 10 degrees plants ha-1 denoted as D1 and D2. Results: Compared with D1, the D2 increased grain yield of YY4949 by 8.99% (12.78 t ha-1) and yield increase per day to 103.54 kg ha-1 d-1, outperforming LLYHZ despite a 16-d shorter season. Dense planting compensated for shorter duration by increasing panicles and spikelets up to 256.19 & times; 10 degrees and 6.35 & times; 108 ha-1, respectively. Under D2, the third internode of YY4949 featured thicker stems, higher dry weight per unit length, and stronger breaking strength than LLYHZ, along with optimized internodal length associated with higher lignin content and activities of antioxidant enzymes. Conclusions: The "high-density lodging-resistant" cultivation strategy effectively breaks the traditional early maturity-high yield trade-off in indica-japonica hybrid rice. This study provides a practical roadmap for breeding and cultivating early-maturing, high-yield, and stable rice varieties in short-season regions, through the coordination of population compensation, efficient dry matter allocation, and enhanced lodging resistance.

Time-series point clouds have emerged as an effective approach for precise, continuous crop monitoring and quantitative growth analysis. This study constructed a spatiotime-series point cloud dataset containing four species and eleven plant varieties, exploring crop organ instance segmentation, phenotypic parameter extraction, growth quantification, and canopy photosynthesis assessment. A skeleton-based framework for organ-level instance segmentation and time-series analysis is proposed, demonstrating robust performance across all four crops. To fully utilize the time-series data, a novel time-series leaf matching method was introduced, achieving a matching accuracy, defined as the proportion of correctly matched leaves, of over 0.823 for all species. By integrating the matching results with phenotypic parameter extraction, time-series phenotypic data were generated, and a phenotypic variation rate was defined as a suitable metric for quantifying crop growth. Furthermore, these results were integrated into a canopy photosynthesis model to derive key time-series photosynthetic metrics, including photosynthetic rate, absorbed light quantity, light energy utilization efficiency, and each crop organ's contribution to photosynthesis. These metrics provide insights into the crop’s growth patterns and photosynthetic strategy. This study offers refined quantitative analysis of crop morphology and photosynthetic parameters through time-series point cloud segmentation, contributing valuable data for advancing plant biology research and enhancing the understanding of crop growth dynamics.

Global crop production needs to increase to meet the growing demand for food. Rising atmospheric CO2 concentration offers opportunities to increase global productivity via the CO2 fertilization effect on C3 crops (including rice, Oryza sativa L.). However, whether the CO2 fertilization effect can be sustained over generations remains unclear. Here, we show that the CO2 fertilization effects on aboveground biomass, aboveground nitrogen uptake, and yield strongly depends on the number of generations in maternal elevated CO2 (eCO2) for rice cultivars Yangdao 6 (Y6; indica subspecies) and Wuyungeng 23 (W23; japonica subspecies) in free-air CO2 enrichment (FACE) experiments. DNA methylation and transcriptome changes provided epigenetic evidence on the multigenerational effects of maternal eCO2 on aboveground biomass, aboveground nitrogen uptake, and yield in both cultivars/subspecies. Our results highlight that data from multigenerational exposure to FACE experiments are needed to accurately predict crop production and food security in a future high-CO2 world.

Life related processes are characterized by high dimensionality and multi-scale properties.Understanding mechanisms underpinning life processes helps promote national healthcare,agricultural development,sustainable ecological civilization,and national security.Current life science research is confronted with an enormous challenge of dimensionality stemming from data explosion and data fragmentation,for which the recent rapid advancement of artificial intelligence(AI)provides novel solutions.AI will catalyze a paradigm shift in life science research from the current experiment based empirical induction to a new closed-loop knowledge acquisition including large scale data collection,model building,model prediction,experimental validation,and iterative of these procedures.Life science research is moving beyond fragmented descriptive exploration toward a systematic,predictable,and design-driven era.The major biological research fields that AI can provide immediate push include digital cell,health management,diagnosis and treatment,pathogen detection,crop breeding,brain-computer interfaces,ecological management,etc.To secure a competitive edge in global life science research,government now needs to develop national-level AI-friendly data warehouse and computing infrastructure,life science foundational models,as well as agent-based research platforms for both wet-lab and model-based studies.In addition,the interdisciplinary collaboration,and strong bioethics and safety governance system need to be strengthened.

Photosystem II Subunit S (PsbS) is a critical regulator of non-photochemical quenching (NPQ), which is a protective mechanism triggered to dissipate excess light energy as heat and prevent photodamage. However, the molecular basis of how PsbS interact with partner proteins to regulate NPQ remains unclear. In this study, we employed proximity labeling to identify PsbS interaction proteins in situ in living cells of Arabidopsis leaves via biotinylation during NPQ. Arabidopsis plants stably expressing PsbS constructs fused to proximity labeling enzyme TurboID were generated and the biotinylated proteomes were analyzed by liquid chromatography-mass spectrometry. The interactomes of PsbS under dark and under light were generated, which not only confirmed several known PsbS-interacting proteins, such as Lhcb1.3, Lhcb3, and Lhcb4.2, but also identified many novel binding proteins. Interestingly, most of these protein interactions of PsbS were unaffected by light, which suggest that PsbS might influence the NPQ through conformational changes without a large physical migration within thylakoid membrane. Analyses of the interactomes also show a few proteins enhanced (such as TLP18.3) or some proteins inhibited (such as ZEP) under the high light, suggesting that the NPQ and repair process after photoinhibition might be coordinated.

The 3D heterogeneity in nitrogen content and temperature within the canopy affects canopy photosynthesis. Currently, there are no methods for efficiently assessing the heterogeneous 3D-distribution of leaf nitrogen content and leaf temperature and integrating that information into a 3D model of canopy photosynthesis. We therefore developed a high-throughput pipeline for collecting canopy photosynthesis parameters in maize (Zea mays) by combining several innovations. First, we used readily obtained SPAD502Plus meter readings to infer local leaf nitrogen content. Second, a Bayesian inference method allowed us to parameterize a C4 leaf photosynthesis model. Third, we used neural radiance fields (NeRFs) to recreate 3D plant architecture and SPAD distribution. Finally, we developed an indoor ray tracing and energy balance model to estimate local light distribution and leaf temperature within a canopy. SPAD values showed a distinct 3D pattern, suggesting within-canopy variation in photosynthesis. Bayesian inference efficiently parameterized the C4 leaf photosynthesis model, with estimated parameter values correlating well with SPAD values. In addition, NeRF more accurately reconstructed 3D architecture and estimated 3D SPAD distribution than traditional methods. This resulted in calculated leaf temperatures being similar to measured values. Different model assumptions can cause significant differences in simulated canopy photosynthetic rate. Omitting 3D SPAD heterogeneity alone produced a 1% to 8% difference in simulated canopy photosynthetic rate. Ignoring leaf temperature heterogeneity led to a difference in the calculated canopy photosynthetic rate of only 1% to 3% near the optimal temperature, but of up to 38% at 35 °C. This pipeline can be realized by high-throughput phenotyping platforms, making it suitable for exploring genetic differences and optimizing ideotype design for improved canopy photosynthesis.

Global food security faces immense pressure from population growth and climate change, demanding sustainable agricultural intensification. While biochar offers promise for soil enhancement and carbon sequestration, its large-scale application requires significant biomass feedstock and energy-intensive production, raising economic and carbon footprint concerns. Nano-enabled foliar feeding is gaining momentum, but practical, eco-efficient field use from lab to farm remains challenging. Bridging this gap is essential for realizing nano-enabled agriculture without exacerbating environmental burdens. Here, we demonstrate on-site conversion of ecologically safe flash graphene via flash joule heating. Spraying 18 g/hectare of this graphene, produced from 75 g (<0.001%) of crop residues per hectare, on multi-crops over two seasons increased yields by 9.1%-27.3% through enhanced photosynthesis and alleviated oxidative stress. Compared to biochar, this approach reduces farmers' inputs by 86%-91% and lowers life-cycle carbon emissions by up to 10,000-fold. We offered a self-sufficient, scalable, and climate-smart circular foliar feeding pathway to advance food security sustainably.

IntroductionOptimized photosynthesis and transport of photosynthate from the upper three leaves in a rice plant is critical for yield formation in rice.MethodsIn this study, we selected two high-yielding early-season rice cultivars, i.e. a large-panicle inbred rice Zhongzao39 (ZZ39) and a plural-panicle hybrid rice Lingliangyou268 (LLY268) with high effective panicle number, to study the translocation of photosynthate from the flag and the basipetal 2nd leaves to the other organs under different nitrogen application scenarios. 13CO2 labeling was study the proportion of newly assimilated carbon partitioned into different organs.ResultsResults demonstrate that the ratio that 13C assimilated in the flag leaves and the basipetal 2nd leaves, and the distribution ratio 13C in the organs of ZZ39 and LLY268 cultivars were not affected by nitrogen application. However, at the booting stage, the translocation rate of photosynthate was slower under N150 compared with CK in both flag and the basipetal 2nd leaves labeled with 13C. At the grain filling stage, an average of 51% of photosynthetic products labeled with 13C was translocated to the panicle in both cultivars under CK treatment; in contrast, only 43% of leaf photosynthate was translocated to panicles in the N150 treatment. At maturity, the photosynthate labeled with 13C distribution ratio in the panicle was greater in the basipetal 2nd leaves than in the flag leaves for ZZ39, whereas the opposite was observed in LLY268. These different photosynthate allocation patterns and their responses to nitrogen application were linked with their corresponding tiller number and number of grains per panicle.DiscussionThis study shows that early-season rice has the ability to flexibly adapt their carbon and nitrogen allocation patterns to gain optimized yield components for higher yield under different nitrogen status. Early season rice can be used as a model system to study the growth strategy selection of plants to changing environment conditions.

With over 60 parallel origins representing evolutionary replicates, C4 photosynthesis is well-suited for studying complex trait evolution. However, lineages with diverse C3-C4 intermediate species are scarce, leaving uncertainty in models of C4 evolution. Phenotypic characterization of 28 living species of Blepharis (Acanthaceae) is presented, including photosynthetic gas exchange, enzyme activity assays, cell ultrastructure, and δ13C assays, the latter including 92 herbarium specimens from three species with phenotypic diversity. A well-resolved transcriptome-based phylogeny provides evolutionary context. C3, proto-Kranz, C2, C4-like, and C4 phenotypes occur in Blepharis sect. Acanthodium. The phylogeny supports a stepwise progression from C3 through C2 to C4 states and up to five distinct origins of the C4 cycle. Substantial intraspecific C2-C4 variation is demonstrated in Blepharis mitrata, Blepharis furcata, and Blepharis macra. Blepharis gazensis is a monospecific C4 lineage exhibiting an NADP malic enzyme C4 pathway with features of the NAD-ME subtype, extending the ways in which the C4 cycle is known to function. Substantial photosynthetic diversity exists in Blepharis that rivals or exceeds the range of character states present in other C3 to C4 transitional lineages. This diversity in Blepharis represents a robust new model for studying convergent evolution of C4 photosynthesis and complex traits in general.

Light intensity and spectral distribution within plant canopies provides insights into the effects of optimizing canopy architecture on light use efficiency. Breeding crop varieties with a "smart" canopy, characterized by erect upper-layer leaves and flat lower-layer leaves, can be supported with a 3D canopy model which can simulate light distribution for a particular canopy architecture. Leaf optical properties are required parameters for such canopy photosynthesis model to accurately predict canopy microclimate and hence photosynthetic efficiency. In this study, we developed a strategy to estimate the leaf optical properties based on leaf anatomical features. We developed a Directional Spectrum Detection Instrument (DSDI) system and associated Bidirectional Reflectance Distribution Function (BRDF) analysis software to precisely describe leaf light distribution. BRDF parameters were quantified with high accuracy ( R 2 > 0.95 ) for adaxial and abaxial surfaces of maize, rice, cotton, and poplar leaves across canopy layers. Leaf phenotypic traits, surface roughness, pigments content, specific leaf weight and thickness were also assessed. Ensemble learning (EL) model showed excellent predictive performance for leaf optical properties based on phenotypic traits with R2 between 0.83 and 0.99. Compared to existing BRDF measurement systems, the DSDI achieves broader angular coverage (-π/36 to 35π/36) via mechanical rotation design, and the ensemble learning model establishes the first direct predictive relationship between BRDF parameters and leaf phenotypic traits. This work presents a new approach to quantify leaf optical properties and offers predictive models for leaf optical properties, which can support canopy light distribution prediction and hence support design leaf features for higher canopy photosynthesis efficiency.