Search Results (79)

Rice yield is a complex quantitative trait. Although a lot of genes for yield have been cloned, their genetic basis remains unknown. In the present study, a set of chromosome segment substitution line population (CSSL) was developed, derived from the indica variety Huanghuazhan as the recipient parent and the Aus variety N22 as the donor parent, and a high-density bin map containing 609 bins was constructed by resequencing. The CSSL population comprised 155 families with an average background recovery rate of 93.02%. Nine yield-related traits, including plant height, panicle number, panicle length, primary branch number, spikelet number per panicle, grain number per panicle, seed setting rate, 1000-grain weight, and grain yield per plant, were evaluated across four environments. The results showed significant differences in yield-related traits between the two parents across four environments. All nine traits showed continuous distribution with transgressive segregation. Spikelet number per panicle, grain number per panicle and 1000-grain weight showed strong correlations with each other, whereas panicle number had weak correlations with them. A total of 80 main-effect quantitative trait loci (QTLs) affecting yield-related traits were identified, among which 13 QTLs were repeatedly detected in multiple environments, 45 QTLs were located in 8 pleiotropic QTL regions, and 47 QTLs showed significant interactions with environments. In addition, 260 pairs of epistatic QTLs underlying yield-related traits were identified, of which 2 pairs stably expressed across different environments, and 11 pairs controlled more than two traits. These findings provide a theoretical basis for clarifying the genetic differentiation between indica and Aus and cloning yield-related genes, and offer valuable gene resources for molecular breeding of high-yield rice varieties. Full article

Speed breeding represents a pivotal technology for enhancing crop breeding efficiency. This study systematically examined the regulation of LED light environments, planting density, and gibberellic acid (GA₃) on rice growth cycle progression in plant factories, establishing an integrated speed breeding protocol. The experimental design comprised three components: (1) coupling seedling age (9–25 days, variety-dependent) with LED environments and planting densities (25–100 plants/tray); (2) combining light intensity gradients (450 and 900 μmol·m⁻²·s⁻¹) with photoperiod control; (3) applying GA₃ gradients (0–120 ppm) to enhance immature seed germination. Results indicated that high planting densities (>50 plants/tray) prolonged the growth cycle and decreased yield, whereas 25 plants/tray optimally balanced growth cycle shortening and yield maximization. Under short-day induction, Nipponbare (Nip) and Wufeng B (WFB) reached heading at 39 and 58 days after sowing (DAS), respectively. Stage-specific light responses were observed: 450 μmol·m⁻²·s⁻¹ during the basic vegetative phase (BVP) promoted morphological development, whereas 900 μmol·m⁻²·s⁻¹ during the photoperiod-sensitive phase (PSP) accelerated tillering and panicle differentiation. GA₃ treatment (60 ppm) enhanced the germination rate of immature seeds by 31%. The optimized lightregimes comprised natural light + 900 μmol·m⁻²·s⁻¹ (NL–900) and 450 μmol·m⁻²·s⁻¹ + 900 μmol·m⁻²·s⁻¹ (450–900), combined with density control (25 plants/tray) and GA₃-mediated immature seed utilization, shortened the generation time to 54 days and 70 days for Nip and WFB, respectively. This integrated protocol establishes an efficient strategy for rice speed breeding in plant factories. Full article

Advances in plant nutrition have driven substantial progress in modern fertilization technologies. Nevertheless, excessive chemical fertilizer application, low nutrient-use efficiency, and the resulting environmental pollution remain widespread. We have reviewed the research progress and existing limitations in the field of plant nutrition and fertilization technology. Based on the traditional plant nutrition diagnosis and integrating visual diagnosis methods, this study explores the intrinsic relationship between plant growth status, nutrient supply conditions, and crop yield and proposed the concept of “status nutrition”. Variations in environmental nutrient conditions lead plants to exhibit distinct growth status in terms of vigor and phenotype. We define the plant nutritional status reflected by this growth status as “status nutrition”. Based on growth characteristics, plant growth status can be classified as weak, normal, or vigorous, corresponding to deficient, appropriate, and excessive environmental nutrient supply, respectively. Guided by this concept, an innovative rice “extremely dense planting” technology is integrated by increasing planting density, eliminating tiller-stage fertilization, and optimizing nitrogen management. The technology adapts to growth status with low nutrient demand, coordinates population growth and main-stem panicle formation, and achieves high yield with reduced fertilizer inputs. Further research is needed on the nutrient metabolism mechanisms of plants under different growth statuses and the growth status grading system. The promotion of “extremely dense planting” is constrained by crop variety traits and soil fertility, and its parameters urgently need to be optimized. Overall, the framework of “status nutrition” provides important theoretical support for the development and application of crop high-yield cultivation technologies. Full article

Shendao3^# (SD3^#) exhibits perennial characteristics. Identifying the QTLs underlying the yield-related traits in SD3^# provides a theoretical basis for future perennial rice breeding. In this study, SD3^# and an F₂ population derived from a cross between SD3^# and XieqingzaoB (XQZB) and its bi-parents were selected as experimental materials. A total of fifteen yield-related traits including plant height, effective panicle per plant and thousand-grain weight in the SD3^#-population and its bi-parents were investigated for both phenotypic analysis and QTL mapping. Results indicated that the fifteen yield-related traits in the SD3^#-population exhibited quantitative genetic characteristics suitable for QTL analysis. Altogether, 25 QTLs underlying the yield-related traits and 26 pairs of epistatic QTLs were identified; these explained phenotypic variances ranging from 4.21% to 27.30% and 1.24% to 19.30%. Of these, nine novel QTLs underlying unfilled grain per panicle (UGP), spikelet per panicle (SP), seed setting density (SSD), grain yield per plant (GYP) and thousand-grain weight (TGW) with additive effects derived from SD3^# were detected on the first, second, fourth, eighth, ninth, and tenth chromosomes. Six pleiotropic QTLs underlying two or more traits were detected on the first, fourth, seventh, eighth, and eleventh chromosomes. This work lays a good foundation for both the yield-related gene mined from SD3^# and future perennial Chinese rice breeding. Full article

Chinese milk vetch (CMV; Astragalus sinicus L.), serving as winter green manure in rice cropping systems, is widely adopted in the southern China. Field experiments including different incorporation regimes (CMV incorporation, urea substitution incorporation and fertilizer-free incorporation), times (45 days, 30 days and 15 days before rice transplanting) and methods (no flooding, intermittent flooding and continuous flooding) were conducted from 2022 to 2024 to determine the optimal time and method for CMV incorporation that could improve soil nutrients, reduce rice-associated weed infestation, and increase rice yield. Delaying CMV incorporation was beneficial to the accumulation of dry matter and organic matter content in CMV shoots and the increase in the total nitrogen content of the soil before rice transplanting. Broadleaf weed infestation was significantly influenced by flooding method, CMV incorporation and incorporation time. Delaying CMV incorporation combined with flooding significantly reduced the density of broadleaf weeds. Grassy weed infestation was only significantly affected by the flooding method, with significantly lower density under flooding conditions compared to non-flooding conditions when other treatments were consistent. Sedge weed infestation was not affected by any of the experimental treatments. Compared with conventional CMV incorporation (incorporated 30 days before rice transplanting without flooding), incorporating CMV 15 days before rice transplanting with flooding (continuous or intermittent flooding) resulted in a 59.20–66.86% reduction in rice-associated weed infestation. Rice yield was also increased with a delay in CMV incorporation, which mainly manifested in increases in panicle number and seed setting rate. Incorporating CMV 15 days before rice transplanting increased rice yield by 5.34–13.24% compared to conventional CMV incorporation. Therefore, considering the comprehensive effects on soil nutrients, weed infestation and rice yield, incorporating CMV 15 days before rice transplanting combined with intermittent flooding is a recommended green manure management practice in green manure–rice rotation systems. Full article

The incidence of duckweed (Lemna minor L.) outbreaks in paddy fields has increased in recent years, but how it impacts rice production is still under debate. This study assessed duckweed’s effects on rice yield and quality under different fertilizer regimes: organic fertilizer (OF), chemical fertilizer (CF), a mix (one-third OF and two-thirds CF based on nitrogen content, COF), and no fertilizer (NF) as a control. For each fertilizer regime, two duckweed treatments were applied: duckweed coverage (Duckweed) and no duckweed coverage (Control). A light wet–dry alternate irrigation method was used in the experimental field. Averaged across all fertilizer treatments, duckweed coverage in paddy fields increased grain yield by 8.3%, mainly due to increased panicle density. Duckweed coverage increased chalky grain percentage by 17.0% under NF, but decreased it by 33.7% under CF, with nonsignificant changes under COF and OF conditions. Similar fertilizer-by-duckweed interactions were also found for chalkiness degree, white degree, breakdown and setback values of the starch rapid visco analyzer (RVA) profile, palatability index, and protein and amino acid concentrations. Duckweed coverage decreased protein and amino acid concentrations but improved the taste of cooked rice under NF, while the opposite trend was observed under CF. Duckweed coverage significantly decreased copper and zinc concentrations in milled rice, which may aggravate the “hidden hunger” risk for rice consumers. Full article

Low-temperature (LT) stress remains a challenge in rice cultivation and breeding. Despite global warming, cold waves cause damage to rice plants, particularly during pollen development. LTs during early panicle formation worsen pollen formation defects, but the underlying mechanisms remain unclear. We investigated the effects of low temperatures (19.0 °C and 18.5 °C) throughout reproductive growth on the panicle architecture and fertility of 28 japonica rice varieties with different LT tolerances. LT-sensitive varieties like Sasanishiki and conventional LT-tolerant varieties like Hitomebore showed increased spikelet densities on basal branches, whereas extremely LT-tolerant varieties like Tohoku 234 maintained a stable panicle architecture. RNA sequencing of the early panicles revealed LT-induced expression of stress response genes in all varieties. Compared with Hitomebore and Sasanishiki, in Tohoku 234, the expression of genes involved in flowering and sugar metabolism—such as OsGI and OsTOC1—showed stepwise induction with decreasing temperatures, while the expression of genes related to the cell cycle exhibited stepwise suppression. In addition, 24 genes with variety-specific expression patterns were identified. These findings suggested that LTs during the early reproductive stage increased spikelet numbers, along with total anther numbers, which may reduce the pollen formation capacity within each anther in LT-susceptible varieties. This study offers insights into rice’s LT tolerance mechanisms. Full article

Winter wheat yield prediction is critical for optimizing field management plans and guiding agricultural production. To address the limitations of conventional manual yield estimation methods, including low efficiency and poor interpretability, this study innovatively proposes an intelligent yield estimation method based on a convolutional neural network (CNN). A comprehensive two-factor (fertilization × irrigation) controlled field experiment was designed to thoroughly validate the applicability and effectiveness of this method. The experimental design comprised two irrigation treatments, sufficient irrigation (C) at 750 m³ ha⁻¹ and deficit irrigation (M) at 450 m³ ha⁻¹, along with five fertilization treatments (at a rate of 180 kg N ha⁻¹): (1) organic fertilizer alone, (2) organic–inorganic fertilizer blend at a 7:3 ratio, (3) organic–inorganic fertilizer blend at a 3:7 ratio, (4) inorganic fertilizer alone, and (5) no fertilizer control. The experimental protocol employed a DJI M300 RTK unmanned aerial vehicle (UAV) equipped with a multispectral sensor to systematically acquire high-resolution growth imagery of winter wheat across critical phenological stages, from heading to maturity. The acquired multispectral imagery was meticulously annotated using the Labelme professional annotation tool to construct a comprehensive experimental dataset comprising over 2000 labeled images. These annotated data were subsequently employed to train an enhanced CNN model based on ResNet50 architecture, which achieved automated generation of panicle density maps and precise panicle counting, thereby realizing yield prediction. Field experimental results demonstrated significant yield variations among fertilization treatments under sufficient irrigation, with the 3:7 organic–inorganic blend achieving the highest actual yield (9363.38 ± 468.17 kg ha⁻¹) significantly outperforming other treatments (p < 0.05), confirming the synergistic effects of optimized nitrogen and water management. The enhanced CNN model exhibited superior performance, with an average accuracy of 89.0–92.1%, representing a 3.0% improvement over YOLOv8. Notably, model accuracy showed significant correlation with yield levels (p < 0.05), suggesting more distinct panicle morphological features in high-yield plots that facilitated model identification. The CNN’s yield predictions demonstrated strong agreement with the measured values, maintaining mean relative errors below 10%. Particularly outstanding performance was observed for the organic fertilizer with full irrigation (5.5% error) and the 7:3 organic-inorganic blend with sufficient irrigation (8.0% error), indicating that the CNN network is more suitable for these management regimes. These findings provide a robust technical foundation for precision farming applications in winter wheat production. Future research will focus on integrating this technology into smart agricultural management systems to enable real-time, data-driven decision making at the farm scale. Full article

Growing cover crop mixtures is a sustainable agriculture tool that helps to reduce fertilizer use and, at the same time, ensures lower environmental pollution. The aim of this research is to assess the biomass of the aboveground part of cover crop mixtures and the nutrients accumulated in it and to determine their influence on the soil properties and productivity of spring oats (Avena sativa L.). The biomass of the aboveground part of cover crop mixtures of different botanical compositions varied from 2.33 to 2.67 Mg ha⁻¹. As the diversity of plant species in cover crop mixtures increased, the accumulation of nutrients in the aboveground part biomass increased, and the risk of nutrient leaching was reduced. The post-harvest cover crop mixture TGS GYVA 365, consisting of eight short-lived and two perennial plant species, significantly reduced the mineral nitrogen content in the soil in spring and had the strongest positive effect on organic carbon content. Post-harvest cover crop mixtures TGS GYVA 365 and TGS D STRUKT 1 did not affect the content of available potassium in the soil but significantly reduced the content of available phosphorus. All tested cover crop mixtures, including the undersown TGS BIOM 1 and the post-harvest mixtures TGS D STRUKT 1 and TGS GYVA 365, reduced soil shear strength and improved soil structure, although the reduction was not statistically significant for TGS D STRUKT 1. Cover crop mixtures left on the soil surface as mulch had a positive effect on the chlorophyll concentration in oat leaves, number of grains per panicle, and oat grain yield. A significant positive correlation was found between oat grain yield and several yield components, including crop density, plant height, number of grains per panicle, and grain mass per panicle. These findings highlight the potential of diverse cover crop mixtures to reduce fertilizer dependency and improve oat productivity under temperate climate conditions. Full article

This study aimed to investigate the regulatory effects of nitrogen (N) application rate and plant density on panicle type index (PTI), yield, grain filling characteristics, and their correlations. The low-PTI rice variety DP128 (PTI = 0.15) was cultivated under field conditions at four N supply levels (0 (N₀), 140 (N₁₄₀), 200 (N₂₀₀), and 260 (N₂₆₀) kg∙ha^–1), and two plant densities (166,755 and 333,495 plants∙ha⁻¹). Results showed that N application rate, planting density, and their interactions significantly influenced yield, PTI, grain number in middle/lower secondary branches, and total grain number in lower secondary branches of rice DP128. Parameters trends were consistent over two years. Under N₂₀₀D₁₀, the total grain number in lower secondary branches was minimized, while other indices were maximized. Further analysis indicates that under high PTI conditions, the maximum grain-filling rate (G_max), mean grain-filling rate (G_mean), sucrose content, ABA/ETH ratio, and starch content in inferior grains (IGs) were all significantly elevated. Correlation analysis indicated PTI was positively correlated with yield, grain number in middle/lower secondary branches, IGs−G_max, and IGs−G_mean and negatively correlated with the total grain number in the lower secondary branches. In summary, increasing PTI can be achieved by optimizing the distribution of secondary branch grains along the panicle axis, decreasing the number of grains on the lower secondary branches, mitigating the competition for filling materials among inferior grains, improving grain-filling capacity and, ultimately, increasing rice yield. Full article

During the flowering period of mango trees, pests often hide in the inflorescences to suck sap, affecting fruit formation. By accurately detecting the number and location of mango inflorescences in the early stages, it can help target-specific spraying equipment to perform precise pesticide application. This study focuses on mango panicles and addresses challenges such as high crop planting density, poor image quality, and complex backgrounds. A series of improvements were made to the YOLOv8 model to enhance performance for this type of detection task. Firstly, a mango panicle dataset was constructed by selecting, augmenting, and correcting samples based on actual agricultural conditions. Second, the backbone network of YOLOv8 was replaced with FasterNet. Although this led to a slight decrease in accuracy, it significantly improved inference speed and reduced model parameters, demonstrating that FasterNet effectively reduced computational complexity while optimizing accuracy. Further, the GAM (Global Attention Module) attention mechanism was introduced as an attention module in the backbone network to enhance feature extraction capabilities. Experimental results indicated that the addition of GAM improved the average precision by 2.2 percentage points, outperforming other attention mechanisms such as SE, CA, and CBAM. Finally, the model’s bounding box localization ability was enhanced by replacing the loss function with WIoU, which also accelerated model convergence and improved the mAP@.5 metric by 1.1 percentage points. Our approach demonstrates a discrepancy of less than 10% compared to manual counted results. Full article

Rice yield and quality decline due to excessive fertiliser use is problematic in China. To increase rice grain filling and improve rice yield and quality, a nitrogen reduction and density increase study in 2023 and 2024 was imposed on a long-term experimental field. The four treatments adopted for the study were normal nitrogen and normal density (CK), normal nitrogen and increased density (NN+ID), reduced nitrogen in panicle fertiliser and increased density (RPN+ID), and reduced nitrogen in basal fertiliser and increased density (RBN+ID). RPN+ID and RBN+ID, respectively, produced a 3.0% and 5.1% higher yield than CK in both years. The mean grain filling rate (Va) of superior grains in RBN+ID increased by 12.5%, while the mean grain filling rate (Va) of inferior grains in the RPN+ID treatment increased by 4.2% with respect to CK. RPN+ID caused 0.4%, 9.6%, and 13.3% decline in the brown rice rate, chalkiness degree, and chalkiness rate, respectively, while RBN+ID triggered 0.4%, 7.2%, and 11.0% decline in the brown rice rate, chalkiness degree, and chalkiness rate, respectively. RPN+ID stimulated 4.2% and 3.1% increases in flavour and straight-chain amylose values, respectively. Whereas a 20% reduction in basal nitrogen fertiliser and a 32% increase in density improved the yield and appearance quality of rice, a 20% reduction in nitrogen fertiliser at the panicle stage and a 32% increase in density promoted a higher steaming flavour quality. Therefore, an appropriate reduction in nitrogen fertiliser while simultaneously increasing rice density has a significant impact on rice quality, fertiliser pollution reduction, and is a theoretical basis for rice yield and quality improvement in Northeast China. Full article

Flowering intensity is an important parameter to predict and control fruit yield. However, its estimation is often based on subjective evaluations of fruit growers. This study explores the application of the YOLO framework for flowering intensity estimation. YOLO is a popular computer vision solution for object-detecting tasks. It was applied to detect flowers in different studies. Still, it requires manual annotation of photographs of flowering trees, which is a complex and time-consuming process. It is hard to distinguish individual flowers in photos due to their overlapping and indistinct outlines, false positive flowers in the background, and the density of flowers in panicles. Our experiment shows that the small dataset of images (320 × 320 px) is sufficient to achieve an accuracy of 0.995 and 0.994 mAP@50 for YOLOv9m and YOLOv11m using aggregated mosaic augmentation. The AI-based method was compared with the manual method (flowering intensity estimation, 0–9 scale). The comparison was completed using data analysis and the MobileNetV2 classifier as an evaluation model. The analysis shows that the AI-based method is more effective than the manual method. Full article

Investigating the variation in and key factors influencing the yield of super hybrid rice cultivated at different altitudes but within the same latitude provides valuable insights for further improvements in super hybrid rice grain yields. Field and pot experiments were conducted using four rice varieties at the following two altitudinal locations in Sichuan Province, China: Hanyuan (high, 1000 m) and Luxian (low, 300 m). The results indicated that Hanyuan achieved an average grain yield of 13.89 t ha⁻¹ in paddy fields, with yields being from 63.6% to 94.2% higher than those at Luxian in the field experiments and from 10.8% to 68.0% higher in the pot experiments. The grain yield was consistently higher in the soil from Hanyuan compared to that from Luxian at the same sites. In the field experiments, the grain yield was influenced by location (L), plant density (P), and variety (V), but there were no significant interactions between these factors. In the pot experiments, the grain yield was significantly impacted by L, soil (S), and the interaction between L and S. Climatic factors, which varied with the altitude of the planting site, played a crucial role in achieving optimal yields of the super hybrid rice. Hanyuan exhibited more cumulative solar radiation with a longer growth duration and lower temperatures and higher soil fertility compared to Luxian. The higher grain yield observed at Hanyuan was linked to increases in panicle numbers, spikelets per panicle, grain filling, pre- and post-heading biomass production, and the harvest index. The variations in biomass production between Hanyuan and Luxian were largely due to differences in pre- and post-heading crop growth rates (CGRs) and pre-heading radiation use efficiency (RUE), which were influenced by differences in the maximum and minimum temperatures and cumulative solar radiation. This study indicated that the differences in the grain yield of super hybrid rice across various ecological sites are primarily influenced by altitude and soil fertility, and further enhancement of the grain yield can be achieved by concurrently increasing biomass production before and after heading through improvements in pre- and post-heading CGR. Full article

Rice cultivated in cadmium (Cd)-polluted acidic paddy soil poses important health risks in China. Mitigating Cd accumulation in rice is of crucial importance for food safety and human health. In this study, using Chuangliangyou 669 as the ratoon rice variety, a field experiment was conducted in paddy fields with severe Cd pollution (Cd concentration > 1.0 mg kg⁻¹). The aim was to explore the impacts of different nitrogen (N) fertilizer levels (N1-180 kg hm⁻², N2-153 kg hm⁻², N3-126 kg hm⁻²) and planting densities (D1-20 cm × 20 cm, D2-16.7 cm × 16.7 cm) in the main crop on the yield and Cd accumulation characteristics of ratoon rice. The results showed that reducing the amount of N fertilizer would lead to a decrease in the yield of ratoon rice, while increasing the planting density could increase the yield, mainly by increasing the effective panicle. Among the various combined treatments, the yields of N1M2 and N2M2 were relatively high. The planting density had no significant impact on the Cd concentration, translocation factor and bioaccumulation factor of ratoon rice. The Cd concentration in various tissues of ratoon rice decreased significantly with the reduction in N fertilizer application. Reducing N fertilizer application could increase the pH, reduce the concentration of available Cd in the soil and consequently reduce the Cd bioaccumulation factor of various tissues of ratoon rice and the Cd translocation factor from roots and stems to brown rice. Considering both the yield and the Cd concentration in brown rice, N2M2 was the optimal treatment of reducing N and increasing density, which could maintain a relatively high yield while significantly reducing the Cd concentration. Full article