The underlying mechanisms of warming effects on rice protein content have not been thoroughly investigated in the double rice cropping system. Here, a 2-year field experiment was conducted to clarify the physiological mechanisms related to nitrogen (N) uptake and assimilation under warming. The results showed that warming significantly increased albumin (16.7%), globulin (2.9%), and glutelin (26.1%) contents in early rice, while it increased prolamin (6.3%) and glutelin (13.4%) contents in late rice. The increased protein content under warming was associated with the elevated N concentration in the panicle, which was partly caused by the enhanced N uptake in early rice but not in late rice. A ¹⁵N pot experiment demonstrated that warming improved total N uptake from soil in both early and late rice; however, N uptake from fertilizer was increased and decreased by warming in early and late rice, respectively, resulting in improved total N uptake in early rice but not in late rice. Additionally, our results confirmed that increased soil net N mineralization rate and root activity contributed to the increased N uptake from soil under warming for both early and late rice. Furthermore, the activities of key enzymes, including glutamine synthetase, glutamate synthase, glutamic-oxaloacetic transaminase, and glutamate-pyruvate transferase, were increased, while protein hydrolysis was suppressed by warming in both early and late rice. Our findings indicate that the increase in protein components under warming conditions is due to improved N uptake in early rice and increased protein synthesis in both early and late rice.

Elevated atmospheric carbon dioxide (eCO₂) concentration generally boosts the photosynthetic rate of rice and tends to reduce the concentration of foliar nitrogen (N). However, there is limited evidence concerning how this shift in N allocation affects the plant's overall response to eCO₂. Therefore, this study integrated data from free-air CO₂ enhancement (FACE) experiments, open-top chamber (OTC) experiments, meta-analysis, and pot experiments with N fertilizer gradients to comprehensively investigate the physiological mechanisms of the rice CO₂ fertilization effect (CFE) and its intrinsic relationship with leaf N allocation strategy. Results showed that eCO₂ significantly enhanced rice carbon sequestration but led to reduced N allocation in the carboxylation system (PN_cb) and electron transport components (PN_et). The established least-squares regression model indicated that PN_cb and PN_et jointly control CFE (R² ​= ​0.73). Additionally, a global meta-analysis further confirmed the global applicability of the model (R² ​= ​0.75). The N addition gradient experiment revealed that higher N levels significantly alleviated the negative impacts of PN_cb and PN_et constraints on the CFE. Structural equation modeling (SEM) analysis showed that N fertilizer application indirectly influenced CFE by regulating PN_cb (path coefficient of 0.74) and PN_et (path coefficient of 0.80), with the role of N allocation strategies being significantly stronger than the direct effect of N fertilizer (path coefficient of 0.49). These findings highlight the critical role of the foliar N allocation strategy in CFE. This study broadens our understanding of the synergistic regulation mechanisms between carbon and N in crops.

Soybean is the most important oilseed and forage crop globally. Advancements in high-throughput phenotyping technologies are critical for accelerating genetic improvement in modern breeding research. However, conventional methods for assessing soybean maturity remain labor intensive. This study developed high-throughput phenotyping algorithms based on unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning to monitor the maturity process of 30 soybean cultivars in large-scale breeding trials. UAV images and plant water content (PWC) data were collected to classify soybean maturity into four distinct phases: immaturity (i.e. the period before R5 stage), late pod filling (i.e. R5 to R6), physiological maturity (i.e. R7), and harvesting maturity (i.e. R8). We evaluated the performance of three classification approaches: (1) a computer vision model utilizing UAV-derived color features, (2) a PWC-based model retrieving PWC dynamics using UAV-derived feature, and (3) a multimodal fusion model integrating computer vision and PWC dynamics. Computer vision model effectively distinguished immature and mature plants but showed limitations in resolving specific maturity phases due to genetic variation in canopy color among cultivars (training set accuracy: 0.71; validation set accuracy: 0.69). The sensitive UAV-derived features were applied to establish the prediction model of PWC using convolutional neural network, which achieved the highest R² (training set: R² ​= ​0.95; validation set: R² ​= ​0.86) between the predicted and measured PWC. The PWC-based algorithm outperformed the computer vision approach, achieving higher classification accuracy (training set: 0.78; validation set: 0.79). Strong correlations between PWC and pod water content, stem water content, and leaf water content underscored the physiological relevance of PWC in tracking maturation dynamics. Further improvement in classification accuracy was achieved with the multimodal fusion model (training set: 0.84; validation set: 0.83), which combined the information of computer vision and PWC dynamics. It was also confirmed that the multimodal fusion model achieved the lowest misclassification rate in the validation analysis across diverse soybean cultivars. These findings emphasize the potential of integrating UAV-based computer vision and PWC features to improve the accuracy and efficiency of soybean maturity classification. The proposed multimodal approach offers a robust framework for phenotypic selection and trait evaluation, providing valuable insights for soybean breeding programs.

Brazil, a major global rice producer outside Asia, has two key rice-growing regions: subtropical lowlands, where irrigated rice accounts for 78% of national production (1.1 ​Mha), and tropical uplands, covering 0.31 ​Mha of rainfed rice. This review provides a comprehensive analysis of rice-based cropping systems in Brazil, discussing agronomic management, sustainability challenges, and potential improvements. The integration of rice, soybean, and pasture systems has enhanced land-use efficiency, soil health, and productivity. Irrigation practices, soil fertility management, and pest control strategies play a crucial role in maximizing yield. Additionally, climate-smart agriculture and no-till adoption are emerging as key strategies for mitigating environmental impacts and improving resilience. Despite advancements, challenges such as climate variability, weed resistance, and water-use efficiency remain critical. This review highlights the importance of continued research and innovation in sustainable rice-based systems.

Rainfed lowlands account for one-third of the total rice land in tropical Asia. Rice production is often constrained by low nitrogen (N) and phosphorus (P) availability under rainfed lowland conditions. Genotypes with root phenotypic plasticity to fertilizer management may be advantageous for nutrient uptake and crop growth in rainfed lowlands. To test our hypothesis, we conducted a two-year field experiment in northeastern Thailand. Three genotypes (two advanced backcross lines and the recipient) with contrasting root morphologies were grown under sufficient and limited N and P fertilizers (four treatments). Without any drought symptoms throughout the experimental period, the average grain yield across years, treatments, and genotypes was 3.58 ​t ​ha⁻¹. Sufficient N application significantly increased grain yield, shoot biomass, and N uptake in both years; however, it reduced root length density. In contrast, the effect of sufficient P application was unclear, with a significant increase in P uptake observed only in the second year. At the panicle initiation stage, one advanced backcross line demonstrated a higher root length density in subsoil (10-30 ​cm) and higher P uptake. The positive correlation between root length density (0-30 ​cm) and P uptake suggested that greater root proliferation enhanced P acquisition in rainfed lowlands. Furthermore, genotypic differences in rhizosphere effects might have contributed to improved P solubilization in the topsoil, and N management might have altered root plasticity. These findings emphasize the importance of root traits in improving N and P use efficiencies in rainfed lowlands and offer insights for rice breeding in drought-prone environments.

Wheat genetic advances have stagnated in recent years despite genetic gains made during the Green Revolution. Thus, this research evaluated wheat varieties released during pre-Green Revolution (landraces and old varieties) and post-Green Revolution periods. The study was conducted under rainfed conditions over four seasons (2020-2021 to 2023-2024). For one season (2021-2022), we employed unmanned aerial vehicles (UAVs) and satellite-based (Skysat) high-resolution imagery. These methods were complemented by analyses of the stable carbon isotope composition (δ¹³C) of the grains over three crop seasons, the oxygen isotope composition (δ¹⁸O) of stem-base water during the 2022-2023 season, and agronomic data collected throughout all seasons. This study aimed to examine changes in phenotypic characteristics across successive breeding periods. The increase in grain yield between pre- and post-Green Revolution varieties was stronger when assessed under conditions without water stress. However, landraces showed higher yield stability than post-Green Revolution varieties. There was no significant genetic gain in yield or grain protein content across the varieties released since the Green Revolution under low rainfall conditions, whereas under wetter conditions, the genetic gain in yield was evidenced at 0.88​% yearly. In terms of phenotypic characteristics, more productive varieties were associated with improved water status (indicated by lower grain δ¹³C values), reflecting deeper water extraction (indicated by lower stem δ¹⁸O values). Vegetation indices measured at the aerial level correlated slightly better with wheat grain yield than Skysat imagery. Among the vegetation indices evaluated, the chlorophyll vegetation index (CVI) was the most effective at distinguishing breeding period groups of varieties.