The wheat canopy genome harbors abundant yet untapped genetic variation that could be harnessed to enhance yield potential. The green area index (GAI) is a structural metric that reflects the photosynthetically active canopy surface and is closely linked to final grain yield. Current image-based GAI retrieval methods often suffer from signal saturation and coarse structural depiction, constraining downstream genetic analyses. To address this limitation, we constructed a comprehensive image dataset spanning eight field experiments across China and France, encompassing approximately 600 genotypes under six distinct management regimes. Leveraging this diverse data, we developed a multimodal deep-learning framework augmented by simulated-to-realistic (sim2-real) synthetic data transfer. This framework fuses nadir and oblique RGB images with accumulated thermal time to produce high-precision, time-series GAI estimates. Validated on independent testing datasets from both China and France, the multimodal approach demonstrated robust performance with an accuracy of R 2 = 0.88 and an RMSE of 0.49 m 2 m-2 , representing an improvement of about 22% over the traditional gap fraction method. In three site-year field experiments involving 565 genotypes, the GAI dynamics derived from the multimodal approach showed higher broad-sense heritability (0.20-0.48) than those from the gap fraction approach (0.02-0.13) and stronger genotypic correlations with yield (0.19-0.40 versus 0.09-0.31). Furthermore, genetic analysis confirmed the biological fidelity of the estimated traits, identifying loci that co-localize with known architectural regulators such as Rht-D1, TaTB1-4D, and TaBGC1-4D. Consistently, the multimodal-derived phenotypes were specifically enriched in cell-wall remodeling and hormonal signaling pathways (e.g., brassinosteroid) that directly regulate canopy expansion. Overall, the proposed method offers a powerful tool for unlocking genetic gain in canopy architecture and accelerating canopy-targeted wheat improvement.

Producing more food with reduced environmental impact remains a critical challenge. Previous agricultural management strategies have predominantly emphasized crop varieties, fertilization and irrigation, often requiring substantial resource inputs and technical expertise. However, the role of crop canopy architecture, which remarkably influences plant growth and ecosystem processes, has been largely overlooked. Here we integrate satellite-based and field observations to assess the global impacts of canopy architecture on crop yield and nitrous oxide (N2O) emissions for rice, wheat, maize and soybean during the past two decades. Our findings reveal that crops with clumped canopy architectures achieve higher yields and lower N2O emissions, a pattern consistently observed across all four major crops, even though soil properties also critically regulate N2O emissions. This effect is possibly driven by enhanced light interception and gross primary production, along with increased canopy nitrogen demand. Aligning crop canopy architecture with the global average can potentially increase crop production by 336 million tons annually, generating economic benefits of US$108 billion per year while simultaneously reducing N2O emissions by 41.6% globally. These results highlight the critical role of canopy architecture in global food security and present a novel strategy for enhancing agricultural productivity and sustainability on a global scale.

Rice paddies are one of the main anthropogenic sources of methane (CH4), and their emissions are strongly influenced by nitrogen (N) fertilization. However, the temporal variation in the effects of N fertilization on CH4 emissions remains poorly understood. Leveraging a unique 43-year field experiment, this study provides the first direct investigation of the different effects of short- versus long-term N fertilization on CH4 emissions. We found that N fertilization stimulated CH4 emissions more strongly in long-term than in short-term plots. Short-term fertilization increased CH4 emissions by 49-60 %, whereas long-term N fertilization increased emissions by 324-339 %. Compared with short-term N fertilization, long-term N fertilization resulted in higher soil dissolved organic carbon (DOC) and NH4+-N concentrations and increased methanogenic abundance by 56 %. Moreover, it shifted methanogenic and methanotrophic communities toward more efficient CH4 production (an increase in the relative abundance of Methanoregula) and less efficient CH4 oxidation (a decrease in the relative abundance of Methylocystis). These findings indicated that long-term N fertilization exacerbated CH4 emissions compared to short-term primarily due to legacy effects on substrate availability and microbial community structure. Our findings suggest that current estimates of the impact of N fertilization on CH4 emissions may be underestimated, underscoring the urgent need for targeted mitigation strategies in intensively managed rice systems to effectively reduce CH4 emissions.

Context: Global warming threatens rice production and increases methane (CH4) emissions from paddy fields. Although nitrogen (N) fertilizer alleviates yield losses caused by warming, its interaction with carbon allocation and the subsequent impact on CH4 emissions remains unclear. Objectives: This study aimed to elucidate the impact of carbon allocation on CH4 emissions under warming and the regulatory mechanism of N fertilizer. Methods: A two-year field experiment was conducted employing a free-air temperature enhancement system. Four treatments were applied: normal temperature (CK), warming (ET), normal temperature with extra N fertilizer (CKN), and warming with extra N fertilizer (ETN). Yield, biomass, soluble sugar content, gene expression, root exudates, CH4 emissions, and rhizosphere microbial communities were determined. Results: Warming significantly increased CH4 emissions by increasing root exudation and enriching methanogenic substrates in the soil. It also increased soluble sugar content in grains and leaves but downregulated the gene expression of starch synthesis-related enzymes, decreasing carbon utilization efficiency. Conversely, warming with extra N fertilizer promoted the accumulation of photosynthetic products in grains and reduced carbon output from roots by upregulating starch synthesis-related genes and reducing root exudate-related genes expressions. Consequently, N fertilizer significantly mitigated CH4 emissions from rice paddies under warming by 29.5 %-31.6 %, while simultaneously increasing rice yield by 15.7 %-19.1 % and biomass by 12.5 %-19.8 %, compared to the warming-only treatment. Conclusion & implications: This study demonstrates that appropriate N fertilizer under warming conditions can effectively redirect carbon allocation from roots to grains, enhancing rice yield and reducing CH4 emissions from paddy fields. It provides both a practical strategy and a theoretical foundation for sustainable rice cultivation under global warming.

Climate warming poses a significant threat to global rice security. While research heavily emphasizes aboveground physiological acclimation to heat stress, belowground dynamics—particularly how temperature-induced shifts in rhizosphere ecology affect sustained nutrient availability—remain largely unresolved. Using a two-year field-based free-air temperature enhancement system, we investigated the temporal dynamics of rhizosphere microbial functioning, nitrogen (N) cycling, and rice performance from jointing to maturity. Warming initially stimulated plant N accumulation before heading but induced a severe N deficit during critical reproductive phases, ultimately reducing grain yield by 22.80%-23.44%. This yield penalty was driven by substantial depletions in soil organic carbon and available inorganic N pools during heading and grain filling. Furthermore, warming reshaped the rhizosphere microbiome, reducing α-diversity while increasing network complexity, indicating intensified biotic interactions under resource limitation. Taxonomically, warming drove a successional shift toward oligotrophic and stress-tolerant assemblages (e.g. Chloroflexi and Acidobacteriota). Functionally, it intensified organic matter decomposition and denitrification while suppressing nitrification. These findings demonstrate a critical “temporal decoupling” between rhizosphere N supply and crop demand: although warming transiently increases early-season N availability via stimulated rhizosphere priming, it fails to sustain N supply due to accelerated carbon depletion and gaseous N loss. This study elucidates the microbial feedbacks governing rice resilience to climate stress and highlights the necessity of stage-specific N regulation to maintain yield stability in warming agroecosystems.

Context and objective: Chemical nitrogen fertilization (CNF) is essential for sustaining global cereal production. However, how crop yield responses to CNF shift with increasing application duration, and whether these shifts differ among the three major staple cereals, remains poorly understood. Methods Here, we synthesize 1638 observations from field experiments worldwide, complemented by a 13-year rice field experiment, and show that temporal dynamics of yield response differ markedly among maize, wheat, and rice. Results and conclusions CNF significantly increased yields across all three crops, with the largest response in wheat (68.5%), followed by maize (57.0%) and rice (45.9%). In maize, the relative yield gain from CNF (lnRR) increased with application duration (+0.035 per year), and a within-study analysis confirmed that this trend reflected temporal change within experiments rather than differences among sites. In wheat, the between-study duration gradient was steeper (+0.073 per year) but was not confirmed by within-study analysis, leaving the temporal trajectory unresolved. Rice showed a weak aggregate trend (−0.008 per year); however, a significant positive trend emerged at moderate nitrogen rates (150–200 kg N ha−1), and positive trends were also observed when all higher nitrogen rates were pooled. This pattern was consistent with our 13-year field experiment, in which mineral-associated and microbial soil nitrogen pools were larger under moderate than under the highest nitrogen input. Significance These results indicate that the temporal dynamics of yield response to CNF are crop-specific, and that long-term nitrogen management should be considered separately for upland and paddy cereals.

Context and research question Elevated CO2 (ECO2) generally promotes rice growth and yield, but prolonged exposure can induce photosynthetic acclimation, limiting potential benefits. Selenium (Se) is known to enhance chlorophyll content, photosynthesis, and crop productivity, yet its interaction with ECO2 remains largely unexplored. Methods In this study, a two-year field-based OTC experiment was conducted using two CO2 levels (ambient and elevated) combined with two anthesis stage treatments (spray water or foliar Se application). Results Under elevated CO2, Se application significantly increased rice yield by 18.39% and further enhanced the positive effect of ECO2 by 9.48%. Selenium treatment also promoted dry matter accumulation during grain filling, enhanced net photosynthetic rate and antioxidant enzyme activities, alleviated declines in chlorophyll and leaf nitrogen content, and maintained higher nitrogen metabolism and Rubisco activity. Additionally, Se stimulated sucrose synthesis in leaves and starch accumulation in grains. Conclusions The findings of this study indicate that foliar selenium at the grain-filling stage effectively enhances rice yield and amplifies the CO2 fertilization effect, offering a practical strategy for sustaining rice productivity under future elevated CO2 environments.

Root-zone nitrogen (N) banding is widely used to improve N use efficiency in crops. However, the effect of lateral fertilizer placement on rice performance remains unclear. We conducted two-year field experiments (2023–2024) in China, with a multi-variety validation trial at a second site, comparing single-side banded fertilization (BF-S, urea placed 5 cm deep and 5 cm lateral to one side of the plant row) against symmetrical double-side banding (BF-D) and conventional surface broadcasting. BF-S achieved 13.0% higher grain yield (9.2 vs 8.1 t ha−1) and 57.1% higher N recovery efficiency (NRE, 56.1% vs 35.7%) than BF-D, with consistent advantages across four rice varieties. The higher performance of BF-S was associated with a pronounced lateral heterogeneity of NH4+ that persisted for approximately 30 d after transplanting, accompanied by preferential root growth toward the fertilized zone. Multivariate analyses showed that grain yield and NRE were more closely related to subsurface NH4+ availability and its spatial asymmetry than to root architectural traits. Consistent patterns of NH4+ heterogeneity and root lateral asymmetry were also observed across sites and varieties in the validation experiment. These results highlight the importance of spatial nutrient heterogeneity in paddy N management and suggest that single-side banding is a promising fertilization strategy for improving rice productivity and N use efficiency.

Brassinosteroids (BRs), a class of essential plant steroid hormones, have emerged as central regulators in optimizing crop architecture, yield potential, and nutrient use efficiency (NUE). Through crosstalk with gibberellin (GA), auxin (IAA), strigolactone (SL), and nitrogen (N) signaling pathways, BRs coordinate cell elongation, tillering, and nutrient assimilation to optimize growth-resource balance. Allelic variations affecting BR biosynthesis or perception often generate compact, erect-leaf plant types suited for dense planting and enhanced lodging resistance-key traits for high-yield ideotypes. This review outlines BR signaling networks and crosstalk with GA, IAA, SL, and N pathways in cereals. Two principal regulatory hubs are emphasized: the Zinc Finger protein (ZnF)-BRASSINOSTEROID INSENSITIVE1 KINASE INHIBITOR1 (BKI1)-BRASSINOSTEROID INSENSITIVE1 (BRI1) receptor module, which fine-tunes BR perception and determines architectural traits, and the GLYCOGEN SYNTHASE KINASE 2 (GSK2)-BRASSINAZOLE-RESISTANT1 (BZR1)-DWARF AND LOW-TILLERING (DLT)-SMALL ORGAN SIZE1 (SMOS1)-GROWTH-REGULATING FACTOR4 (GRF4)-DELLA regulatory module, which integrates BR signaling with GA responsiveness and nitrogen metabolism. Moreover, deletion of the "r-e-z" haploblock, encompassing Rht-B1b, EamA-B, and ZnF-B, elicits a semi-dwarf phenotype with 6.48%-15.25% yield increases. These interconnected networks establish a molecular framework for engineering BR-driven cereal ideotypes. Future breeding could improve resource efficiency by fine-tuning BR activity in shoots for compact growth and promoting it in roots for enhanced nutrient uptake. Integrating genomics and precision gene editing will enable fine-tuning of BR signaling intensity and its crosstalk with other hormonal and nutrient pathways. By prioritizing growth optimization over mere growth maximization, BR-based strategies offer a sustainable path toward high-yield, nitrogen-efficient cereal production.

Greenhouse gas (GHG) emissions from rice paddies, a major global source, are primarily driven by water, organic matter, and nitrogen management. While optimizing these practices can reduce emissions, previous assessments have predominantly focused on their theoretical mitigation potential, often overlooking current adoption rates, real-world implementation constraints, and consequences for food security. Here, we use data synthesis and a decision tree model to demonstrate that integrated management practices could boost global rice yields by 11.4% and deliver $62 billion in societal benefits. However, these practices increase area-scaled and yield-scaled GHG emissions by 17.1% and 5.1%, respectively. The raised emissions are largely attributed to developing countries such as India, Myanmar, and Bangladesh, where widespread straw incorporation is the most feasible method for boosting yields. Our findings highlight the importance of improved irrigation, advanced agricultural machinery, and prudent straw management to limit GHG emissions, suggesting that without substantial investments, the potential for global GHG mitigation from rice agriculture is smaller than previously thought.

Paddy fields are a major agricultural hotspot for nitrous oxide (N2O), contributing approximately 11% of global agricultural emissions. While elevated CO2 and warming individually regulate N2O emissions by modulating soil carbon, nitrogen (N) availability and microbial activity, the effects of concurrent elevated CO2 and temperature (ECT) and the underlying microbial mechanisms under field conditions remain poorly understood. Here, we used a free-air CO2 enrichment and temperature increase (T-FACE) system in a rice–wheat cropping system to investigate the impacts of ECT on N2O emissions from rice paddies and identify the underlying biogeochemical and microbial mechanisms. Results showed that ECT increased area-scaled and yield-scaled N2O emissions by 15.3% and 17.6%, respectively. Mechanistically, during the peak emission period, ECT significantly increased soil NH4+–N content by 40.2% and the denitrification gene ratio [(nirK + nirS)/nosZ] by 27.5%. Furthermore, ECT increased the diversity of nitrifying communities but decreased that of denitrifying communities, while reshaping the composition of ammonia-oxidizing archaea and denitrifiers, thereby altering nitrification and denitrification. Overall, our field-based evidence suggests that ECT can stimulate N2O emissions primarily by increasing soil N substrate availability and shifting denitrifier communities in ways that may favor N2O accumulation. These findings offer mechanistic insights into climate-driven N2O emissions.

Paddy fields with excessive fertilizer application are potential N2O emission hotspots, which profoundly affect the greenhouse effect. Unlike prior regional models assuming uniform fertilization and static atmospheric N2O concentration, this study focuses on simulating the effects of fertilization heterogeneity and dynamic atmospheric N2O concentration on daily field-scale N2O emissions. Accordingly, this study presents a hybrid model (NAU-RSP-N2O) that combines multi-source remotely-sensed data, ML algorithms, and the water-air gas exchange model to predict daily field-scale N2O emissions in paddies. Firstly, we built Bayesian-optimized ML models using critical water quality parameters to predict dissolved N2O and incorporated SHAP analysis for interpretability. Secondly, the remotely-sensed data (fertilization information, LST) were used to drive models of water quality parameters, capturing their complex spatiotemporal changes. Finally, the water-air interface gas model considering the dynamic atmospheric N2O concentration simulated daily field-scale N2O emissions in paddy fields. The model demonstrated an average R2 of 0.72, with MAE of 1.56 mg center dot m-2 center dot d-1 and RMSE of 1.65 mg center dot m-2 center dot d-1. The NAU-RSP-N2O model effectively simulated daily field-scale N2O emissions and spatiotemporal patterns, highlighting the critical role of nitrogen management and atmospheric N2O levels in controlling emissions. Our findings present a novel approach for the large-scale prediction of N2O emissions from paddy fields, applicable across diverse rice-growing regions in China.

Cover crops are promoted for sustainable nitrogen (N) management, yet quantitative evidence for their N benefits across crop types and environments remains limited. We synthesized 1433 paired field observations worldwide to quantify effects on cash crop yield, aboveground N accumulation (ANA), and soil N, with an additional 44 observations for soil organic carbon (SOC). On average, cover crops increased yield by 7.7% and ANA by 9.0%. Soil N dynamics revealed contrasting temporal patterns: soil mineral N declined by 21.1% at cover crop termination, whereas soil total N increased by 10.0% at cash crop harvest. Distinguishing by cover crop type revealed strongly context-dependent responses. Legume cover crops showed consistent N-supplying benefits, enhancing yield, ANA, and soil N at harvest, driven primarily by soil N status and cash crop type. Non-legume cover crops did not improve yield or N uptake, but reduced soil mineral N at termination, increased SOC at both time points, and were most sensitive to temperature and precipitation. The effects of cover crop mixtures were largely neutral on average and were mainly governed by fertilization and residue management. By linking these outcomes to processes of N fixation, decomposition, retention, and synchrony between N release and crop demand, our synthesis provides a comprehensive understanding of how different cover crop types regulate N dynamics across diverse environments.

Satellite solar-induced chlorophyll fluorescence (SIF) provides a direct proxy for vegetation photosynthesis, yet the short lifespan and degradation of satellite sensors limit long-term global SIF records. Here, we developed a hybrid modeling framework integrating light use efficiency (LUE) and machine learning to reconstruct a spatiotemporally continuous global long-term daily and monthly SIF dataset (LTSIF) at 0.05° resolution from 1981 to 2023. Key drivers were identified using a far-red SIF decomposition framework, LUE theory, and random forest (RF) modeling. Hybrid models combining LUE with RF, extreme gradient boosting, and neural networks were developed, among which the LUE-RF model achieved the best performance (R2 = 0.97, RMSE = 0.08 mW m−2 sr−1 nm−1, KGE = 0.97) and showed robust performance across vegetation types and climate zones. Independent validation against tower-based SIF, OCO-2 SIF, and widely reconstructed SIF datasets demonstrated that LTSIF reliably captured global photosynthetic dynamics. From 1981 to 2023, LTSIF revealed increasing photosynthetic activity over 89.01% of global vegetated areas, with a global mean trend of 0.016 mW m−2 sr−1 nm−1 per decade (p < 0.01). LTSIF provides a valuable long-term dataset for assessing terrestrial photosynthesis responses to climate change.

Human diets are commonly deficient in iron. Hence, increasing the iron content in rice, a major staple crop globally, will help overcome this iron deficiency. The phloem is an important route for iron distribution. However, little is known about the effects of manipulating iron transport in the phloem on iron content. The phloem-specific OsSUT1 promoter is used to drive the expression of iron transport genes OsYSL9 and OsIRT1. The phloem exudate iron content, iron harvest index and remobilization contribution rate are enhanced in pOsSUT1::OsYSL9 (SY) and pOsSUT1::OsIRT1 (SI) transgenic lines. The iron content of brown rice in SY and SI lines is increased by 1.27- and 1.36-fold, respectively. The iron content of milled rice is also increased by 1.44- and 1.60-fold, respectively. The grain weight of SY and SI lines is higher than that of wild type. Furthermore, the chalky rice rate and chalkiness degree are significantly decreased in the SY and SI lines due to a longer active grain-filling period. Overexpression of iron transport genes under the control of the phloem-specific promoter can increase the iron content in grains and also improve the milling and appearance quality of rice.

Green Area Index (GAI) is a key crop trait obtained through remote sensing with wide applications in agriculture. Although 3D model-driven approaches to retrieve GAI from multispectral reflectance observations are appealing, they are constrained by limitations in the realism of simulated datasets used for training. This study comprehensively explored how to integrate prior information-such as soil background, leaf optical properties, and canopy structure-into radiative transfer models to improve GAI retrieval. A suite of models (MARMIT-2 for soil reflectance, PROSPECT for leaf optical properties, ADEL-Wheat for canopy structure, and LESS for radiative transfer) was employed to generate five simulation datasets incorporating different combinations of prior information. Support Vector Regression (SVR) models were independently trained on these simulated datasets and validated against an extensive data set made of 310 samples of GAI ground measurements and the corresponding SuperDove satellite data. Our results show that stage-specific GAI retrieval integrating detailed prior information on soil and leaf properties (R2 = 0.93, RMSE = 0.47) notably outperforms standard model inversion approaches (R2 = 0.82, RMSE = 0.73). The improved realism of the training dataset stems from three key strategies was discussed in detail including: (1) employing models that integrates physical and biological knowledge; (2) narrowing the training space; and (3) minimizing distribution shifts. While this study focused on GAI estimation for wheat crops using SuperDove observations, the findings can be extended to other crops, vegetation variables, and satellite systems.

The objective of this study was to identify an application strategy for a blend of two controlled-release nitrogen fertilizers (CRNFs) that optimized yield and N-use efficiency of late japonica rice in the Yangtze River Delta. In a two-year field experiment using high-yield split-applied urea (CK) and no-N fertilization as control (N0), nine CRNF treatments were evaluated for their effects on grain yield, N uptake (NUP), soil ammonium nitrogen (NH4+-N) dynamics, and ammonia volatilization (AV). The treatments included sulfur-coated urea (SCU), urease inhibitor urea (AHA), 90-d polymer-coated urea (P90), 120-d polymer-coated urea (P120), and five BBFs prepared by mixing CRNFs at a 3:7 ratio (AHAP90, SP90, AHAP120, SP120, and P90P120). Based on N release characteristics, CRNFs were categorized into four release modes: pre-positioned single-peak (PrSRM), post-positioned single-peak (PoSRM), decreasing double-peak (DDRM), and increasing double-peak (IDRM). Synchronization between soil NH4+-N dynamics under CRNFs and plant N uptake rate (NUPR) under CK was quantified using dynamic time warping (DTW), with smaller values indicating higher synchrony. Results showed that single-peak release modes significantly reduced grain yield and NUP by 12.6% and 10.5%, respectively. The IDRM treatment, a blend of 90-d and 120-d polymer-coated urea, showed NH4+-N supply dynamics most closely matching the N demand of high-yielding rice, with lower two-year average DTW values (SSRDTW 1.01, NUPSDTW 1.72) than DDRM (1.03 and 2.00), which translated into increases in spikelet number, grain yield, and NUP by 4.49%, 6.03%, and 4.85%, respectively, while decreasing AV by 86.7% compared with CK. One-time application of IDRM fertilizer can align soil NH4+-N supply with rice N demand, ensure high yield, and reduce N losses, providing an optimized fertilization strategy for sustainable rice production in the Yangtze River Delta.

Breeding rice varieties that are both salt-tolerant and high-yielding is essential for utilizing saline-alkaline lands and ensuring food security. However, However, high-throughput and accurate phenotyping at early growth stages remains a major bottleneck in breeding programs. In this study, unmanned aerial vehicle (UAV) imaging was employed to screen salt-tolerant and high-yielding varieties among 60 rice varieties under saline-alkaline field conditions. Red-green-blue (RGB), multispectral, and thermal canopy images were acquired throughout the growing season by UAV, from which 41 phenotypic traits were extracted at each growth stage. These traits were categorized into early-stage (tillering and jointing), late-stage (booting, flowering, and maturity), and whole-growth-stage (from tillering to maturity) and subsequently used to screen salt-tolerant and high-yielding rice varieties. Results showed that: (1) An early high-throughput screening method for salt-tolerant rice varieties was developed based on the membership function and UAV phenotypes (MFuav), achieving high performance (Precision >0.8, OA > 0.7). MFuav demonstrated the highest accuracy at the early-stage, with Precision increasing by 0.29 and 0.43 compared to the late- and whole-stage models, respectively. (2) A machine learning based UAV phenotypes framework (MLuav) was developed to further improve salt-tolerance screening performance. Within this framework, the partial least squares regression (PLSR) was employed for early-stage salt-tolerance screening, which achieved a Precision of 0.97 and an OA of 0.78, outperforming the MFuav by 0.11 and 0.08, respectively. In addition, within the same MLuav framework, early-stage UAV phenotypes were further used for actual yield prediction using a Random Forest (RF) model. The model achieved a high Recall for high-yielding varieties (Recall = 1.00), ensuring that no potentially high-yielding germplasm was missed, although this was accompanied by a moderate Precision (0.51) and an overall accuracy of 0.70. (3) The MLuav consistently outperformed the MFuav in screening salt-tolerant and high-yielding varieties across all 60 rice varieties. Among the five referenced salt-tolerant and high-yielding rice varieties, the MLuav correctly screened four using early-stage phenotypes, whereas the MFuav only screened three. Overall, the proposed method enables early screening of salt-tolerant and high-yielding rice varieties, offering an efficient tool for the screening and utilization of elite stress-resilient germplasm.

Soil salinization threatens global agriculture, and seed germination is especially salt-sensitive. Although seed imbibition is a critical early phase, salt responses during this stage remain poorly understood. Reliance on final morphological traits limits nondestructive early phenotyping, thereby hindering efficient screening and mechanistic insight. To overcome this limitation, we integrated continuous physiological measurements with hyperspectral data to model dynamic responses of key indicators and further leveraged hyperspectral-derived traits for early prediction and genetic dissection of salt tolerance. The results showed that: (1) Salt stress induced a distinct temporal cascade of physiological changes during seed imbibition. Proline accumulation was the earliest response, initiating at 6 h, followed by marked increases in abscisic acid (ABA) and malondialdehyde (MDA) by 12 h, while gibberellin (GA) levels and α-amylase activity were strongly suppressed by 24 h. (2) Integrating hyperspectral reflectance with PLSR and principal component analysis (PCA) enabled high-throughput, real-time physiological monitoring and nondestructive early salt-tolerance classification. PLSR simultaneously estimated five physiological traits (R2 = 0.51-0.57), which were synthesized via PCA into a comprehensive salt-tolerance score. Across time points, this pipeline achieved 94.4% cumulative accuracy by 48 h, with 55.6% of tolerant germplasms identified within 24 h, establishing a critical early diagnostic window and offering nearly a one-week lead over traditional morphological assessments. (3) Hyperspectral-derived physiological traits exhibited heritability comparable to morphological traits, and allowed the dissection of temporally resolved genetic architectures. Using the FarmCPU model, we identified 500 significant loci associated with early salt-stress responses, characterized by a complete temporal differentiation between the 24-h and 48-h stages. Allelic variation at key loci (vg0223423016, vg0712463109, and vg0910185858) significantly shaped seedling salt tolerance. Candidate gene analysis prioritized regulators involved in osmoregulation and signaling, including two aquaporin homologs (LOC_Os02g39630 and LOC_Os07g22224) and a WAK-like kinase (LOC_Os09g16550). Together, these results establish the potential of imbibition stage physiological responses for resolving final germination salt tolerance, and demonstrate that hyperspectral analysis provides a reliable technological platform to accelerate this line of research.

Elevated CO(2)concentration (eCO(2)) significantly affects crop photosynthesis and the plant growth, yet the accurately direct monitoring of this positive effect still faces a cascade of challenges. Sun-induced chlorophyll fluorescence (SIF), as a fast and non-destructive link to plant photosynthesis, may offer us a novel approach to accurately monitor the effect of CO(2)on crop photosynthesis. However, due to the lack of direct observation-based evidence, it remains unclear whether SIF can be used to depict the changes in plant photosynthesis under eCO(2) conditions. Here, based on the free-air CO(2)enhancement experiment conducted at a rice-paddy field during 2023-2024 with changing the CO(2)concentration from 400 to 550 & micro;mol mol-1, we continuously collected canopy SIF to check its ability for capturing changes in plant photosynthesis. Results showed that canopy SIF was significantly enhanced at eCO(2) conditions compared to that for ambient CO2(33.7%, P<0.01), consistent to the enhanced leaf net photosynthetic rate (24.6%), aboveground biomass and grain yield. This phenomenon was also robust for the total SIF emissions at the leaf-and photosystem-levels. The performance of SIF to capture this positive effect was much better than that of traditional vegetation indices (VIs). The enhanced SIF at eCO(2)conditions originated from both the structural (APARchl, fesc) and physiological (Phi Fobs) components, of which the contribution from Phi Fobs was the largest, accounting for 73.3% of the SIF enhancement at eCO(2)conditions. The ability of SIF to depict the positive CO(2)impact possibly results from the changes of the energy allocated to photochemistry, fluorescence and NPQ at the leaf-level when there are no stresses. Our findings thus revealed the strength of SIF to characterize the CO(2)impacts on crop photosynthesis and indicate the possibility to monitor the response of plant photosynthesis to increasing CO(2)through satellite-based SIF at regional and global scales.