Soil texture affects rice nutrient uptake and yield formation by influencing soil structure, microbial activity, and soil nutrient supply capacity. Analyzing the relationship between soil texture, nutrient content, and rice agronomic traits is of great significance for precise and efficient fertilizer application. The tillage layer (0–20 cm) of 31 paddy fields in China’s main rice-producing areas was collected to perform rice pot experiments, and soil texture characteristics, physicochemical properties, microbial-related indicators, and rice agronomic traits were measured and analyzed. The results showed that these soils could be classified into four types of soil texture: loamy sandy soil, sandy loam soil, silty loam soil, and silty soil. Analysis of variance showed that the available nitrogen (AN), available potassium (AK), and available phosphorus (AP) contents were the highest in silty loam, silty, and sandy loam soils, respectively, and silt loamy soil had the highest CEC. Principal component analysis (PCA) also showed that soil physicochemical properties can be distinguished to a certain extent according to soil texture types. For the relationship of soil texture parameters and soil physicochemical properties, soil organic matter (OM), total nitrogen (TN), AN, ammonium nitrogen (NH₄⁺-N), and microbial carbon (MBC) contents were positively correlated with soil clay content, AK was positively correlated with silt content, and soil phosphorus status was significantly related to pH. Mantel’s test revealed significant correlations between rice N, P, and K nutrient status, dry matter accumulation, and yield, and soil available nutrient content, MBC, pH, and soil texture parameters. Structural equation modeling (SEM) indicated that sand affected soil available nutrients by regulating pH, while clay can positively influence soil available nutrients by affecting soil organic matter mineralization and microbial activity, thus influencing nutrient absorption and yield formation in rice. Overall, in rice production, the silty and silty loam paddy soil with fine texture and higher clay content facilitates the mineralization of soil organic matter and the activity of soil microbes, resulting in more available soil nutrients, which benefits the rice absorption and accumulation of nutrients. Furthermore, a higher content of clay also promotes the distribution of dry matter to the panicle, thereby promoting rice yield formation.

N fertilizer is usually supplied via multiple applications in rice production in China. Due to the high N-recovery efficiency (NRE) of panicle fertilizer, applying large amounts of fertilizer at the booting stage is considered to be an effective measure of increasing yields, although it has adverse effects on eating quality. In this study, using six inbred and four hybrid japonica varieties, we postponed topdressing-N to increase the ratio of panicle N from 20% to 40% by correspondingly reducing the N amount applied only at the tillering stage. We also analyzed the effects of postponing the topdressing-N on grain yield and dry matter accumulation in both high- and low-fertility blue clayey paddy fields in 2016 and 2017. The effects of postponing topdressing-N applications on japonica rice were related to variety, meteorological conditions, and soil fertility. With respect to the inbred varieties, regardless of whether panicle N was applied as a single or split application, increasing the ratio of panicle N had no effect on the yield components or dry matter accumulation of plants grown in either high- and low-fertility soils. Regarding the hybrid varieties grown in the high-fertility soil, although postponing the topdressing-N application had no effect on yield under good weather conditions (no low-temperature stress during grain-filling), a single application of 40% of the total N at the panicle initiation stage significantly decreased both the dry matter accumulation after heading and the seed-setting rate of varieties that presented long growth periods under low-temperature conditions. With respect to hybrid varieties grown in low-fertility soil, postponing the application of topdressing-N had an adverse effect on the number of effective panicles. Our results suggested that the proportion of panicle N applied to japonica rice should not exceed 30% in clayey paddy fields and that fertilizer management with respect to rice production should be adjusted according to soil type, soil fertility, and variety.

Crop plant height is a critical parameter for assessing crop physiological properties, such as above-ground biomass and grain yield and crop health. Current dominant plant height estimation methods are based on digital surface model (DSM) and vegetation indexes (VIs). However, DSM-based methods usually estimate plant height by growth stages, which would result in some discontinuity between growth stages due to different fitting curves. Additionally, there has been limited research on the application of VI-based plant height estimation for multiple crop species. Thus, this study investigated the validity and challenges associated with these methods for estimating canopy heights of multi-variety rice throughout the entire growing season. A total of 474 rice varieties were cultivated in a single season, and RGB images including red, green, and blue bands, DSMs, multispectral images including near infrared and red edge bands, and manually measured plant heights were collected in 2022. DSMs and 26 commonly used VIs were employed to estimate rice canopy heights during the growing season. The plant height estimation using DSMs was performed using different quantiles (50th, 75th, and 95th), while two-stage linear regression (TLR) models based on each VI were developed. The DSM-based method at the 95th quantile showed high accuracy, with an R² value of 0.94 and an RMSE value of 0.06 m. However, the plant height estimation at the early growth stage showed lower effectiveness, with an R² < 0. For the VIs, height estimation with MTCI yielded the best results, with an R² of 0.704. The first stage of the TLR model (maximum R² = 0.664) was significantly better than the second stage (maximum R² = 0.133), which indicated that the VIs were more suitable for estimating canopy height at the early growth stage. By grouping the 474 varieties into 15 clusters, the R² values of the VI-based TLR models exhibited inconsistencies across clusters (maximum R² = 0.984; minimum R² = 0.042), which meant that the VIs were suitable for estimating canopy height in the cultivation of similar or specific rice varieties. However, the DSM-based method showed little difference in performance among the varieties, which meant that the DSM-based method was suitable for multi-variety rice breeding. But for specific clusters, the VI-based methods were better than the DSM-based methods for plant height estimation. In conclusion, the DSM-based method at the 95th quantile was suitable for plant height estimation in the multi-variety rice breeding process, and we recommend using DSMs for plant height estimation after 26 DAT. Furthermore, the MTCI-based TLR model was suitable for plant height estimation in monoculture planting or as a correction for DSM-based plant height estimation in the pre-growth period of rice.

Location-based methods for counting rice panicles have often been underestimated, primarily due to their perceived inferior performance when compared to detection-based techniques. However, we argue that the potential of these location-based methods has not been fully realized, largely owing to the limitations of existing model architectures. In response to this challenge, we introduce LKNet, an innovative model developed on the foundation of the location-based framework P2Pnet. To enhance the performance of panicle counting across diverse types and growth stages, we implemented several key strategies. Firstly, we reconstructed the localization loss function as a predictive probability distribution to reduce the influence of manual labeling. Additionally, we dynamically adapted the receptive field to better accommodate different panicle types through the use of large kernel convolutional blocks. We evaluated LKNet on several publicly available counting task datasets and achieved state-of-the-art performance on the Diverse Rice Panicle Detection dataset. Furthermore, we employed a rice panicle dataset collected at an altitude of 7 ​m, which includes various panicle types and growth stages for model training and evaluation. The results showed that LKNet effectively accommodates variations in panicle morphology, with R² values ranging from 0.903 to 0.989. These findings highlight LKNet's potential to enhance precision in panicle counting in rice breeding programs.

Observations of the effects of long-term paddy-upland rotations on soil carbon (C) and nitrogen (N) stocks are scarce. This experiment aimed to examine soil C and N stock characteristics and their relationships with rice yield using four long-term rotation systems. The results showed that in the topsoil at 50 cm, compared to rice-fallow (RF), rice-potato with rice straw mulch and return (RP) and rice-Chinese milk vetch (RC) increased soil organic carbon (SOC) stock by 19.67% and 15.29%, total nitrogen (TN) stock by 20.40% and 18.54%, and available nitrogen (AN) density by 61.54% and 34.44%, respectively; while rice-wheat (RW) increased AN density by 30.24%. Furthermore, the increases in densities of soil C and N at 20–30 cm were likely to increase rice above biomass and yield. The highest soil C and N densities were observed in RP, which was the best choice for increasing soil nutrients and rice yield. In addition, SOC, TN, and AN had obvious stratification and surface aggregation characteristics, and more than 70% of the total stocks were concentrated at the top 30 cm. These results provide a theoretical basis for the high-yield cultivation of rice in the middle and lower reaches of the Yangtze River.

Hybrid japonica cultivars, such as the Yongyou series, have shown high yield potential in the field in both the early and late growing seasons. Moreover, understanding the responses of rice flowering dates to temperature and light is critical for improving yield performance. However, few studies have analyzed flowering genes in high-yielding japonica cultivars. Based on the five sowing date experiments from 2019 to 2020, select the sensitive cultivar Yongyou 538 and the insensitive cultivar Ninggeng 4 and take their flag leaves and panicles for transcriptome analysis. The results showed that compared with sowing date 1 (6/16), after the sowing date was postponed (sowing date 5, 7/9), 4480 and 890 differentially expressed genes (DEGs) were detected in the leaves and panicles in Ninggeng 4, 9275 and 2475 DEGs were detected in the leaves and panicles in Yongyou 538, respectively. KEGG pathway analysis showed that both Ninggeng 4 and Yongyou 538 regulated rice flowering through the plant circadian rhythm and plant hormone signal transduction pathways. Gene expression analysis showed that Os01g0566050 (OsELF3-2), Os01g0182600 (OsGI), Os11g0547000 (OsFKF1), Os06g0275000 (Hd1), and Os09g0513500 (FT-1) were expressed higher and Os02g0771100 (COP1-1) was expressed lower in Yongyou 538 compared with Ninggeng 4 as the climate conditions changed, which may be the key genes that regulate the flowering process with the change of temperature and light resources in sensitive cultivar Yongyou 538 in the late season.