Improve Rice Yield Research Articles

Prohexadione Calcium Improves Rice Yield Under Salt Stress by Regulating Source–Sink Relationships During the Filling Period

Salt stress is an important factor affecting the growth and development of rice, and prohexadione calcium (Pro-Ca) plays an important role in alleviating rice salt stress and improving rice yield. However, there are few studies on how Pro-Ca improves rice yield under salt stress by regulating the source–sink metabolism. In this study, we used Guanghong 3 (salt-tolerant variety) and Huanghuazhan (salt-sensitive variety) as experimental materials to investigate the dynamic changes in the synthesis and partitioning of nonstructural carbohydrates among source–sink, the dynamic changes in related enzyme activities, the effects of the source–sink metabolism on yield in rice under salt stress and the effect of Pro-Ca during the filling period. The results of this study showed that Pro-Ca improved photosynthetic efficiency by increasing leaf photosynthetic gas exchange parameters and other stomatal factors on the one hand and, on the other hand, promoted sugar catabolism and reduced sugar synthesis by increasing leaf sucrose synthase activity and decreasing sucrose phosphate synthase activity, alleviating the inhibitory effect of high concentrations of sugars in the leaves on photosynthesis. Meanwhile, Pro-Ca promotes the transport of sugars from source (leaves) to sink (seeds), increases the sugar content in the seeds, and promotes starch synthesis in the seeds by increasing starch phosphorylase, which promotes seed filling, thus increasing the number of solid grains on the primary and secondary branches of the panicle in rice, increasing the 1000-grain weight, and ultimately increasing the seed setting rate and yield. These results indicated that Pro-Ca alleviated the inhibitory effect of salt stress on rice leaf photosynthesis through stomatal and non-stomatal factors. Meanwhile, Pro-Ca promotes the transport of rice sugars from source to sink under salt stress, regulates the source–sink relationship during the filling period of rice, promotes starch synthesis, and ultimately improves rice yield.

Genome-wide association study of rice (Oryza sativa L.) inflorescence architecture.

Rice yield strongly depends on panicle size and architecture but the genetics underlying these traits and their coordination with environmental cues through various signaling pathways have remained elusive. A genome-wide association study (GWAS) was performed to pinpoint the underlying genetic determinants for rice panicle architecture by analyzing 20 panicle-related traits using a data set consisting of 44,100 SNPs. We defined QTL windows around significant SNPs by the rate of LD decay for each chromosome and used these windows to identify putative candidate genes associated with the trait. Using a publicly available RNA-seq data set we performed analyses to identify the differentially expressed genes between stem and panicle with putative functions in panicle architecture. In total, 52 significant SNPs were identified, corresponding to 41 unique QTLs across the 12 rice chromosomes, with the most signals appearing on chromosome 1 (nine associated SNPs), and seven significant SNPs for each of chromosomes 8 and 12. Some novel genes such as Ankyrin, Duf, Kinesin and Brassinosteroid insensitive were found to be associated with panicle size. A haplotype analysis showed that genetic variation in haplotypes qMIL2 and qNSBBH21 were related to two traits, MIL, the greatest distance between two nodes on the rachis, and NSBBH, the number of primary branches in the bottom half of a panicle, respectively. Analysis of epistatic interactions revealed a marker affecting clustered traits. Several QTLs were identified on different chromosomes for the first time which may explain the phenotypic diversity of rice panicle architecture we observe in our collection of accessions. The identified candidate genes and haplotypes could be used in marker-assisted selection to improve rice yield through gene pyramiding.

Trait Association and Genetic Variability Study in Rice MAGIC Lines for Yield Related Traits

The study was aimed to assess genetic variability and associations between grain yield and morphological traits in 50 multi-parent advanced generation intercross (MAGIC) lines generated by single seed descent utilizing eight diverse parents (to impart tolerance to biotic and abiotic stresses) were utilized in the study along with eight checks at Zonal agricultural and horticultural research station, Navile, Shivamogga during kharif 2022. Improvement in grain yield, a quantitative trait is one of the key objective of plant breeders which depends upon the magnitude of genetic variability existing in the genetic material utilized in the breeding programme. The analysis of variance revealed the presence of significant differences were found among lines for all traits. Moderate to high genotypic and phenotypic variation, heritability and genetic advance were recorded for plant height, tillers per plant, filled grains per panicle, test weight and grain yield. Correlation analysis revealed positive associations between grain yield and tillers per plant, panicle length, filled grains per panicle, panicle fertility and test weight. These findings emphasize the importance of indirect selection for these traits to improve grain yield in rice. The results suggest that breeders can utilize these traits as selection criteria to enhance grain yield, ultimately contributing to rice yield improvement programs. Our study provides valuable insights for rice breeders to develop high-yielding rice varieties with improved tolerance to biotic and abiotic stresses.

A novel transcription factor OsMYB73 affects grain size and chalkiness by regulating endosperm storage substances' accumulation-mediated auxin biosynthesis signalling pathway in rice.

Enhanced grain yield and quality traits are everlasting breeding goals. It is therefore of great significance to uncover more genetic resources associated with these two important agronomic traits. Plant MYB family transcription factors play important regulatory roles in diverse biological processes. However, studies on genetic functions of MYB in rice yield and quality are rarely to be reported. Here, we investigated a nucleus-localized transcription factor OsMYB73 which is preferentially expressed in the early developing pericarp and endosperm. We generated targeted mutagenesis of OsMYB73 in rice, and the mutants had longer grains with obvious white-belly chalky endosperm appearance phenotype. The mutants displayed various changes in starch physicochemical characteristics and lipid components. Transcriptome sequencing analysis showed that OsMYB73 was chiefly involved in cell wall development and starch metabolism. OsMYB73 mutation affects the expression of genes related to grain size, starch and lipid biosynthesis and auxin biosynthesis. Moreover, inactivation of OsMYB73 triggers broad changes in secondary metabolites. We speculate that rice OsMYB73 and OsNF-YB1 play synergistic pivotal role in simultaneously as transcription activators to regulate grain filling and storage compounds accumulation to affect endosperm development and grain chalkiness through binding OsISA2, OsLTPL36 and OsYUC11. The study provides important germplasm resources and theoretical basis for genetic improvement of rice yield and quality. In addition, we enriches the potential biological functions of rice MYB family transcription factors.

Leaf senescence characteristics and economic benefits of rice under alternate wetting and drying irrigation and blended use of polymer-coated and common urea.

Water-saving irrigation and the mixed application of controlled-release nitrogen fertilizer (CRNF) and common urea (CU; with a higher nitrogen release rate) have shown promise in improving rice yield with high resource use efficiency. However, the physiological mechanism underlying this effect remains largely unknown. This study involved a field experiment on rice in Jingzhou City, Central China, in 2020 and 2021. Two irrigation regimes were employed [alternate wetting and drying irrigation (AWD) and conventional flood irrigation (CI)], with three nitrogen (N) compounding modes [00% CU (N1), 60% CRNF + 40% CU (N2), and 100% CRNF (N3)] with an equal N rate of 240kg ha-1. The results indicated a significant interactive effect of watering regimes and N compounding modes on net photosynthetic rate (P n), leaf area index (LAI), and SPAD values; activities of superoxide dismutase (SOD), peroxidases (POD), catalase (CAT), glutamine synthetase (GS), glutamine 2-oxoglutarate amidotransferase (GOGAT), and nitrate reductase (NR); and the contents of malondialdehyde (MDA) and soluble protein in rice leaves. Compared with N1, N2 and N3 increased the P n, LAI, and SPAD values; activities of SOD, POD, CAT, NR, GS, and GOGAT; and soluble protein content but decreased MDA content in the post-growth (heading and maturity) stages by 8.7%-31.2% under the two irrigation regimes. Compared to CI (traditional irrigation), AWD had higher P n, LAI, and SPAD values; activities of SOD, POD, CAT, NR, GS, and GOGAT; and soluble protein content (increased by 12.1%-38.0%, and lower MDA content (reduced by 13.1%-27.6%) irrespective of N compounding modes. This suggested that AWD combined with N2 and N3 could delay the leaf senescence of rice, thus achieving a larger grain yield. Moreover, AWD significantly decreased water costs (irrigation amount) and labor costs (irrigation frequency), thus increasing total income. N2 decreased fertilizer costs with a higher or comparable total income compared with N3. Therefore, the AWDN2 treatment achieved the highest net income (13,907.1 CNY ha-1 in 2020 and 14,085.7 CNY ha-1 in 2021). AWD interacted with 60% CRNF + 40% (N2) to delay leaf senescence by improving photosynthesis, antioxidant defense system, osmoregulation, and N assimilation, contributing to high grain yield and net income in rice.

Impact of foliar application of phyllosphere yeast strains combined with soil fertilizer application on rice growth and yield

BackgroundThe application of beneficial microbes in agriculture is gaining increasing attention as a means to reduce reliance on chemical fertilizers. This approach can potentially mitigate negative impacts on soil, animal, and human health, as well as decrease climate-changing factors. Among these microbes, yeast has been the least explored, particularly within the phyllosphere compartment. This study addresses this knowledge gap by investigating the potential of phyllosphere yeast to improve rice yield while reducing fertilizer dosage.ResultsFrom fifty-two rice yeast phyllosphere isolates, we identified three yeast strains—Rhodotorula paludigena Y1, Pseudozyma sp. Y71, and Cryptococcus sp. Y72—that could thrive at 36 °C and possessed significant multifarious plant growth-promoting traits, enhancing rice root and shoot length upon seed inoculation. These three strains demonstrated favorable compatibility, leading to the creation of a yeast consortium. We assessed the combined effect of foliar application of this yeast consortium and individual strains with two distinct recommended doses of chemical fertilizers (RDCFs) (75 and 100%), as well as RDCFs alone (75 and 100%), in rice maintained in pot-culture and field experiments. The pot-culture experiment investigated the leaf microbial community, plant biochemicals, root and shoot length during the stem elongation, flowering, and dough phases, and yield-related parameters at harvest. The field experiment determined the actual yield. Integrated results from both experiments revealed that the yeast consortium with 75% RDCFs was more effective than the yeast consortium with 100% RDCFs, single strain applications with RDCFs (75 and 100%), and RDCFs alone (75 and 100%). Additionally, this treatment improved leaf metabolite levels compared to control rice plants.ConclusionsOverall, a 25% reduction in soil chemical fertilizers combined with yeast consortium foliar application improved rice growth, biochemicals, and yield. This study also advances the field of phyllosphere yeast research in agriculture.

Comparative study of rice morphological and physiological characteristics of rice grown under organic and inorganic farming

Organic farming practices have shown a potential to improve rice yields, aside from the popular conventional ones. This study aimed to comparatively analyze the morpho-physiological characteristics of rice plants grown under organic and inorganic farming. This research was conducted at the Polinela Organic Farm experimental station (5°21'10"S 105°13'43"E, 114 m sea above level), from February to April 2023 using a completely randomized design. The results showed that rice grown organically exhibited a notably higher chlorophyll index and actual water use efficiency compared to those grown inorganically. Conversely, inorganic farming resulted in a greater number of tillers than organic farming. However, there were no significant differences between the two systems regarding CO2 efficiency and plant height. An interesting fact is the improved water use efficiency by organic farming helps rice plants to achieve similar growth performance while requiring less water.

The DENSE AND ERECT PANICLE1-GRAIN NUMBER ASSOCIATED module enhances rice yield by repressing CYTOKININ OXIDASE 2 expression.

The phytohormone cytokinin (CK) positively regulates the activity of the inflorescence meristem (IM). Cytokinin oxidase 2/Grain number 1a (OsCKX2/Gn1a)-mediated degradation of CK in rice (Oryza sativa L.) negatively regulates panicle grain number, whereas DENSE AND ERECT PANICLE 1 (DEP1) positively regulates grain number per panicle (GNP). However, the detailed regulatory mechanism between DEP1 and OsCKX2 remains elusive. Here, we report the GRAS (GIBBERELLIN ACID INSENSITIVE, REPRESSOR OF GA1, and SCARECROW) transcription factor GRAIN NUMBER ASSOCIATED (GNA), previously thought to be involved in the Brassinosteroids (BRs) signaling pathway, directly inhibits OsCKX2 expression in the IM through a DEP1-GNA regulatory module. Overexpressing GNA leads to increased CK levels and consequently higher branch number, GNP, and yield. Both DEP1 and dep1 enhance the inhibitory effect of GNA on OsCKX2 expression through interacting with GNA. GNA promotes the translocation of DEP1 to the nucleus, while the gain-of-function mutant dep1 translocates into the nucleus in the absence of GNA. Our findings provide insight into the regulatory mechanism underlying OsCKX2 and a strategy to improve rice yield.

Investigating the mechanism of auxin-mediated fulvic acid-regulated root growth in Oryza sativa through physiological and transcriptomic analyses.

As rice is one of the most crucial staple food sources worldwide, enhancing rice yield is paramount for ensuring global food security. Fulvic acid (FA), serving as a plant growth promoter and organic fertilizer, holds significant practical importance in studying its impact on rice root growth for improving rice yield and quality. This study investigated the effects of different concentrations of FA on the growth of rice seedlings. The results indicated that 0.05 g/L FA could promote the growth of rice seedlings, while 0.5 g/L FA inhibited root growth, reduced cell activity and enzyme activity in the root tips, and accumulated reactive oxygen species in root cells. To further elucidate the molecular mechanisms underlying these effects, we performed transcriptomic analysis and found that auxin (Aux) may be involved in the growth process mediated by FA. Furthermore, transcriptome heatmap analysis revealed a significant upregulation of the Aux/indoleacetic acid (Aux/IAA) gene family after FA treatment, suggesting that this gene family plays a crucial role in the impact of FA on root growth. Additionally, by detecting endogenous Aux content and adding exogenous Aux inhibitors, we confirmed the involvement of FA in rice seedling root growth as well as in the synthesis and transduction pathway of Aux.

Effects of Different Rice Varieties and Water Management Practices on Greenhouse Gas (CH4 and N2O) Emissions in the Ratoon Rice System in the Upper Yangtze River Region, China

Ratoon rice can improve rice yield by increasing the multiple cropping index in China. However, the greenhouse gas (CH4 and N2O) emission characteristics from ratoon rice fields and the cultivation methods to reduce CH4 and N2O emissions are rarely reported. This study first conducted the analysis of genotype differences in greenhouse gas emission fluxes using five strong ratoon ability rice varieties in 2020. Second, water management methods, including alternating the wet–dry irrigation (AWD) pattern and conventional flooding irrigation (CF) during the main season, were carried out in 2021. CH4 and N2O emission flux, agronomic traits, and rice yield during both main and ratoon seasons were investigated. The results showed that the CH4 emission flux during the main and ratoon seasons was 157.05–470.73 kg·ha–1 and 31.03–84.38 kg·ha–1, respectively, and the total N2O emission flux was 0.13–0.94 kg·ha–1 in the ratoon rice system over the two seasons (RRSTS). Compared with the main season, the CH4 emission flux during the ratoon season was significantly reduced, thus decreasing the greenhouse gas global warming potential (GWP) and greenhouse gas emission intensity (GHGI) in the ratoon rice system. Cliangyouhuazhan (CLYHZ) showed a high yield, and the lowest GWP and GHGI values among the five rice varieties in RRSTS. Compared with CF, the AWD pattern reduced the CH4 emission flux during the main and ratoon seasons by 67.4–95.3 kg·ha–1 and 1.7–5.1 kg·ha–1, respectively, but increased the N2O emission flux by 0.1–0.6 kg·ha–1 during the RRSTS. Further, compared with CF, the AWD pattern had a declined GWP by 14.3–19.4% and GHGI by 30.3–34.3% during the RRSTS, which was attributed to the significant reduction in GWP and GHGI during the main season. The AWD pattern significantly increased rice yield by 21.9–22.9% during the RRSTS, especially for YX203. Correlation analysis showed that CH4, GWP, and GHGI exhibited significant negative correlations with spikelet number per m2 and the harvest index during the main and ratoon seasons. Collectively, selecting the high-yield, low-emission variety CLYHZ could significantly reduce greenhouse gas emissions from ratoon rice while maintaining a high yield. The AWD pattern could reduce total CH4 emission during the main season, reducing the GWP and GHGI while increasing the ratoon rice system yield. It could be concluded that a variety of CLYHZ and AWD patterns are worthy of promotion and application to decrease greenhouse gas emissions in the ratoon rice area in the upper reaches of Yangtze River, China.

A lightweight rice pest detection algorithm based on improved YOLOv8

Timely and accurate detection of rice pests is highly important for pest control, as well as for improving rice yield and quality. However, owing to the high interclass similarity, significant intraclass age differences, and complex backgrounds among different pests, accurately and rapidly identifying a variety of rice pests via deep neural network models poses a significant challenge. To address this issue, this paper presents a fast and accurate method for rice pest detection and identification named Rice-YOLO (You Only Look Once). This model is based on YOLOv8-N and incorporates an efficient detection head designed for the complex characteristics of pests. Additionally, deep supervision layers were introduced into the network, along with the incorporation and improvement of the dynamic upsampling module. The experimental data included the large-scale pest public dataset IP102 and the sixteen-class rice pest dataset R2000. The experimental results demonstrated that Rice-YOLO outperformed previous object detection algorithms, with 78.1% mAP@0.5, 62.9% mAP@0.5:0.95, and 74.3% F1 scores.

Combined application of green manure and straw mulch improves rice yield and mitigates methane emissions

Combined application of green manure and straw mulch improves rice yield and mitigates methane emissions

A molecular module improves rice grain quality and yield at high temperature

Abstract Excessive temperatures during grain filling can compromise endosperm starch biosynthesis and decrease grain quality and yield in rice. However, the molecular mechanisms underlying these remain unclear. Here, we show that heat shock protein OsHsp40-1 interacts with and elevates the ATPase activity of OsHsp70-2 in rice. OsHsp40-1 also interacts with the key starch biosynthetic enzymes OsGBSSI and OsPPDKB and thereby enhances their stability and activity, which is essential for maintaining rice quality and grain yield under moderate high-temperature (HT) conditions. Overexpression of OsHsp70-2 and OsHsp40-1 in rice significantly improved grain quality and yield at HT. Furthermore, a haplotype analysis identified favorable alleles of OsHsp70-2 and OsHsp40-1, which could be used for improving thermotolerance in rice. Collectively, our findings reveal a novel mechanism by which OsHsp70-2–OsHsp40-1 module ameliorates the effects of HT on starch biosynthesis, providing a new strategy for genetic improvement of rice quality and yield under HT conditions.

Genetic Analysis of Key Traits for Sheath Blight Resistance and Yield Improvement in Rice (Oryza sativa)

Rice (Oryza sativa) is a staple crop for more than half of the global population, with significant production in India. However, rice production suffers yield losses of 25-30% due to diseases, particularly sheath blight (ShB), caused by Rhizoctonia solani. This study evaluated genetic variability, heritability, and genetic advance in ShB resistance and yield-related traits in 360 recombinant inbred lines (RILs) across two seasons (dry season of 2022-23 and wet season of 2023) at ICAR-IIRR, Hyderabad. Traits such as plant height, lesion height, relative lesion height, and panicle number showed high heritability (>80%) and genetic advance (>30%), indicating their strong genetic influence and responsiveness to selection. These traits, especially relative lesion height, were identified as key indicators for breeding ShB resistance. In contrast, grain yield under inoculated conditions showed lower heritability, reflecting environmental effects and suggesting that indirect selection through stable, heritable traits may be more effective. This research offers a foundation for breeding high-yielding, disease-resistant rice cultivars, advancing sustainability in ShB-prone regions by maintaining stable yields even under pathogen pressure.

Effects of Straw and Green Manure Addition on Crop Yield, Soil Properties and CH4 Emissions: A Meta-Analysis

The incorporation of organic amendments is widely acknowledged for its capacity to enhance soil fertility and boost crop productivity. However, whether the addition of organic amendments can improve soil quality and crop production, simultaneously causing methane emissions in paddy fields, deserves further investigation. In this meta-analysis, the effects of different organic amendments on soil nutrient levels, rice yield and CH4 emissions were evaluated in paddy fields based on 328 observations from 77 field trial studies. Our results revealed that the addition of organic amendments significantly increased soil organic carbon (9.47%), microbial biomass carbon (21.13%), microbial biomass nitrogen (28.91%), urease (25.07%) and β-glucosidase (24.41%). Moreover, straw addition significantly increased the CH4 emissions by 152.68% and rice yield by 7.16%; green manure addition significantly increased CH4 emissions by 71.62% and rice yield by 10.09%, respectively. Although both increased the CH4 emissions, green manure had the ability to improve the availability of N, which could improve rice uptake. The regression results showed that the variation in crop yield, soil nutrients and CH4 emissions are influenced through the types and quality of organic amendments. Overall, this study suggests that organic amendments are beneficial in maintaining soil quality and improving rice yield, whereas it also increased the CH4 emissions. These meta-analysis results may provide some references for optimizing organic amendments incorporated into the soil to sustain soil fertility and crop production while mitigating soil constraints and methane emissions.

Exogenous application of wood vinegar improves rice yield and quality by elevating photosynthetic efficiency and enhancing the accumulation of total soluble sugars

Rice is an important cereal crop for over half of the world population and essential for food security, especially in developing countries where it constitutes a substantial part of daily caloric intake. Wood vinegar, obtained from biomass pyrolysis and rich in organic acids, phenols, esters, sugars, and alcohols, promotes crop growth and productivity. Its diverse composition offers an eco-friendly and cost-effective growth promoter and improves stress tolerance in different crops; however, its optimal application timings in rice cultivation remain underexplored. Our study investigated the effects of wood vinegar on rice growth, photosynthetic efficiency, chlorophyll contents, soluble sugars, phenolics, grain chalkiness, and yield at different stages and combinations such as tillering (T), jointing (J), flowering (F), grain filling (G), T + J, T + F, T + G, J + F, J + G, F + G, T + J + F, T + J + G, T + F + G, and J + F + G. Our results indicate that wood vinegar application at T + J + F stages significantly enhanced the photosynthetic rate by promoting gaseous exchange, chlorophyll contents, and SPAD index when compared with control. Additionally, total soluble sugars (9.71 mg g−1) and phenolics (2.4 mg g−1) levels were noticeably induced during wood vinegar application as compared with corresponding control. Besides, this treatment also elevated the yield by up to 32.4%, reduced grain chalkiness, and enhanced head rice percentage. Taken together, these findings suggest that wood vinegar supports sustainable agriculture by improving crop yield and quality, offering considerable benefits to farmers.

Comparative Analysis of Machine Learning Models for Predicting Rice Yield: Insights from Agricultural Inputs and Practices in Rwanda

Food security is a global challenge, especially in developing countries like Rwanda. With a growing population and limited agricultural land, Rwanda struggles to meet increasing food demands. Rice is a staple food crop in Rwanda, playing a crucial role in the country’s food security. However, factors like climate variability, soil nutrient management, and limited access to high-quality inputs hinder rice yield optimization. This paper investigates the most effective machine learning model for predicting rice crop yield in Rwanda, using agricultural inputs and practices. The study used secondary datasets from the National Institute of Statistics of Rwanda (NISR) for rice yield prediction. Eight supervised machine learning algorithms were used, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Machine, Artificial Neural Network, eXtreme Gradient Boosting Tree, and AdaBoost. The models were evaluated based on their accuracy in predicting rice yields, with RMSE, MAE, and Relative Error as primary metrics. The feature importance analysis was also conducted to identify significant factors influencing yield predictions. The study’s findings revealed that the Adaptive Boosting Tree model outperformed the other machine learning models in predicting rice yield. This model achieved RMSE, MAE and Relative Error of 0.69, 0.46 and 12.4%, respectively, indicating a high level of predictive accuracy. The feature importance analysis further highlighted the key factors that contributed to rice yield predictions, with the quantity of inorganic fertilizer, degree of erosion, season, and seed type emerging as the most influential variables. The study demonstrates the effectiveness of machine learning models, particularly the Adaptive Boosting Tree, in improving rice yield predictions and highlighting the crucial role of agricultural inputs like fertilizer and seed type, in influencing crop yields. The output from this study will help the farmers and stakeholders to make data-driven decisions about resource use and crop management.

Advancing food sustainability: a case study on improving rice yield prediction in Sri Lanka using weather-based, feature-engineered machine learning models

Food sustainability is crucial aspect in achieving several United Nations (UN) Sustainable Development Goals (SDGs). By integrating advanced technologies for reliable and accurate decision-making, we can advance food sustainability and, consequently, make significant advances toward achieving the UN SDGs. Rice, a staple crop in many Asian and some African nations, is crucial to Sri Lanka as well. Serving as the primary food for most Sri Lankans, it plays a vital role in sustaining the livelihoods of over 1.8 million farmers. In Sri Lanka, rice is grown during two distinct seasons of the year (Yala and Maha). This study focuses on ML with feature engineering for rice yield prediction using weather data: Rainfall, Maximum temperature, Minimum temperature, and Radiation. The data from two districts in Yala and Maha seasons collected from 1982 to 2019 were used for evaluating two sets of models respectively. Data were pre-processed to handle the outliers and missing values and scaled using normalization. The machine learning models considered are Linear Regression (LR), Support Vector Machine (SVM), k-Nearest Neighbour (KNN), and Random Forest (RF). The performance of these models was evaluated using metrics: Root Mean Squared Error (RMSE), Relative Root Mean Squared Error (RRMSE), and Mean Absolute Error (MAE). The results demonstrate that Random Forest Regression with less number of features can yield comparable results compared to the original set of features.

Improvement of Transplanting Rice Yield and Nitrogen Use Efficiency by Increasing Planting Density in Northeast China Under the Optimal Nitrogen Split-Fertilizer Applications

There are few studies on how nitrogen (N) fertilizer application rates and transplanting densities impact rice yield, root distribution, and N use efficiency in the cold regions of Northeast China. This research involved a two-year field trial utilizing Jinongda 667 as the material. In 2021, three N split-fertilizer applications—T1 (6:3:1), T2 (5:3:2), T3 (4:3:3)—and two transplanting densities—D1 (30 cm × 13.3 cm) and D2 (30 cm × 20 cm)—were compared with the conventional cultivation mode (T0: 175 kg N hm−2, 6:3:1), whereby the N application mode most suitable for increasing density was explored. In 2022, four N application levels—0 (N0), 125 (N1), 150 (N2), and 175 (N3) kg N hm−2—were assessed under the same density treatment to analyze the yield, resource utilization efficiency, and root traits of Jinongda 667. The results indicated that when the transplanting density was 30 cm × 13.3 cm, the application of 5:3:2 fertilizer was more conducive to improving rice yield. Increasing planting density under reduced N input significantly enhanced both rice yield and N use efficiency. In contrast to the conventional cultivation method (D2N3), the treatment of increased planting density (D1N2) under reduced N input led to a 21.2% rise in the number of panicles per square meter and an 8.6% boost in rice yield. Furthermore, increasing planting density under reduced N input significantly enhanced the agronomic efficiency of N fertilizer, the apparent utilization rate, and the N harvest index. It also boosted the SPAD value, photosynthetic rate, and the utilization efficiency of light and N resources in rice. However, it was noted that root enzyme activity decreased. This study demonstrated that increasing planting density, combined with the N application mode of 5:3:2 and an N application rate of 150 kg hm−2, maximized resource utilization efficiency, optimized root absorption capacity, and resulted in higher yields.

Genome-wide association study reveals novel QTLs and candidate genes for panicle number in rice.

Panicle number (PN) is one of the three key yield components in rice, maintaining stable tiller and PN is a crucial characteristic of an ideal plant architecture. Understanding the molecular mechanisms underlying PN is essential for breeders aiming to improve rice yield. To dissect the genetic architecture of panicle number, a genome-wide association study (GWAS) was conducted in 411 japonica rice varieties. GWAS analysis was carried out with the mixed linear model using 743,678 high-quality SNPs. Over two experimental years, we detected a total of seven quantitative trait loci (QTLs), located on chromosomes 1, 4, 6, 8, 11, and 12; notably, qPN1.1 and qPN8 were detected consistently in both years. By combining haplotype and expression analyses, OsCKX11 was identified as the candidate gene for qPN8, while LOC_Os01g07870 and LOC_Os01g07950 were identified as candidate genes for qPN1.1. Significant differences were observed among the haplotypes of these candidate genes. Additionally, qRT-PCR results showed that LOC_Os01g07870 expression levels were significantly lower in accessions with high panicle numbers compared to those with low panicle numbers. To understand the natural biological function of these candidate genes, further research involving overexpression or silencing in rice is needed. Despite these challenges, our results will lay the foundation for further study of panicle development and provide valuable genetic resources for developing high-panicle-number rice cultivars using genetic engineering and molecular breeding.