High-yielding Rice Research Articles

Positive and Negative Effects of Inter‐Annual Climate Variability on Rice (Oryza sativa L.) Crop in Agro‐Climatic Zones of Punjab

ABSTRACTGlobally, climate changes have significantly shifted the phenological phases and stages of rice, altered the duration of the growing season and negatively affected rice productivity due to flooding and drought. However, in the present study, the positive and negative impacts of inter‐annual climate variability on rice crops during phenological stages in agro‐climatic zones of Punjab for the period from 1989 to 2018. Initially, first difference approach was applied to minimise the impact of technological factors. Then, skewness and kurtosis tests were used to check the normalisation of the data. The standardisation method was used to normalise the data. Pearson correlation was used to determine the significant effects of climate variables on rice yield. The residuals were formed to confirm the effects of inter‐annual climate variability on rice yield in the phenological phases. The analysis revealed that a high variability of rice yields was investigated in the western region compared to the southern and western regions. The results showed a negative impact of heavy rainfall (flooding) on the years with low yields (2010, 2013 and 2016) in the Central region. Similarly, the years with low rice yields (1996, 2010, 2013 and 2014) in the Southern region were negatively affected by flooding at the time of sowing. A positive effect of rainfall was observed in the years with high rice yields (1995, 2002, 2009 and 2018) in the Western region. In contrast, the low‐yielding years 1994, 2003 and 2010 were negatively affected by flooding in the same years during the tillering stage. A high interannual maximum temperature variability was analysed in the Southern > Western > Central regions, leading to yield losses due to biotic stress during tillering and stem elongation stages. This is due to the immense reason of drought stress. The minimum temperature negatively affects the low‐yield years (2001, 2008, 2013 and 2016) in the central zone and the low‐yield years (2014, 2015 and 2016) in the southern zone during the reproductive stage. This research will help to develop new rice varieties that are more productive at high temperatures and require less water, leading to sustainable development in arid and semi‐arid regions.

Two splicing variants of amino acid transporter-like 4 (OsATL4) negatively regulate rice tillering and yield by mediating the transport of amino acids

Amino acids are the primary form of nitrogen utilization in higher plants, mainly transported by amino acid transporters. In this study, we analyzed the natural variation of amino acid transporter-like 4 (OsATL4) in rice germplasm resources, identified its spatiotemporal expression characteristics, determined its substrate transport, and validated its function using transgenic plants. We found that the promoter sequence of OsATL4 varied across 498 rice varieties. The expression level of OsATL4 was higher in japonica rice, which was negatively correlated with tiller number and grain yield. OsATL4 was highly expressed in the basal part, leaf sheath, stem, and young panicle, with its two splicing variants localized to the cell membrane. OsATL4a (the long splicing variant) had a high affinity for transporting Ser, Leu, Phe, and Thr, while OsATL4b (the short splicing variant) had a high affinity for transporting Ser, Leu, and Phe. Blocking OsATL4 promoted axillary bud outgrowth, rice tillering, and grain yield, whereas overexpression lines exhibited the opposite phenotype. Exogenous application of low concentrations of Ser promoted axillary bud outgrowth in overexpression lines, while high concentrations of Ser inhibited it. Conversely, the mutant lines showed the opposite response. Altered expression of OsATL4 might affect the expression of genes in nitrogen, auxin, and cytokinin pathways. We propose that two splicing variants of OsATL4 negatively regulate rice tillering and yield by mediating the transport of amino acids, making it a significant target for high-yield rice breeding.

Seasonal methane emissions and agronomic performance of Indonesia’s high-yielding rice cultivars on the north coastal rice fields of Central Java, Indonesia

Seasonal methane emissions and agronomic performance of Indonesia’s high-yielding rice cultivars on the north coastal rice fields of Central Java, Indonesia

<b>Performance of Candidate Varieties of Basmati Rice in Uniform Yield Trials</b>

The primary focus of rice breeding research is now on developing high-yielding rice varieties with early maturing and physio-chemical features, such as improved cooking quality, aroma, and extra-long grain. This shift in focus is driven by the growing global demand for high-quality rice. Using traditional breeding methods to trait pyramid these extremely important traits while maintaining yield and quality is a challenging task. Good grain quality(aromatic), fertilizer responsiveness, and high-yielding varieties can help the farmers in Pakistan to increase their revenue despite global population growth and changing international market patterns. At the Rice Research Institute, Kala Shah Kaku (RRI KSK), new, improved, high-yielding, extra-long grain lines with the qualities of early maturity, small stature, and good cooking with aroma are being developed through traditional breeding methods. The present study aimed to assess the genetic variability of these lines in terms of paddy yield and to discover superior high-yielding cultivars suitable for widespread cultivation. There were notable variations in paddy yield among the different entries in the pooled analysis. The highest average paddy yield (5010 Kg/ha) was seen in entry 10640-10-1-1-1, followed by entry 10684-3-1-2 (4990 Kg/ha) and PK 10434-6-2-1 (4550 Kg/ha) and suggested for general cultivation based on their mean paddy yield. A strong positive correlation was seen between the grain thickness grading of over 2.2 mm and the number of panicles per plant. The overall quality of the Uniform Yield Trials of Fine rice samples can be reported as excellent for PK 10306-15-5, PK PB-8 and PK 10678-2-1-1 over check Basmati-515. There will be a simultaneous increase in grain yield with improvements in these qualities.

GW3, encoding a member of the P450 subfamily, controls grain width by regulating the GA4 content in spikelets of rice (Oryza sativa L.).

A stable QTL, GW3, controlling grain width was identified in two populations. Its causal gene LOC_Os03g04680 was verified by gene-based haplotype analysis, expression analysis, gene knockout and complementation transgenic tests. Grain width (GW) is one of the key traits affecting grain size and determines grain yield and appearance quality in rice. Mining gene loci and elite alleles controlling GW is necessary. The GW phenotypes of the two populations were investigated in three environments, which showed abundant phenotypic variation. GW3, encoding a P450 subfamily protein, was identified and validated as a causal gene by gene-based haplotype analysis, expression analysis, gene knockout and complementation transgenic tests. The accessions with large GW values had high gene expression levels. In addition, the GW of the accessions with the GG allele was significantly greater than that of the accessions with the AA allele. The Hap 1 and Hap 3 were identified as elite haplotypes, which can increase GW. The expression levels of OsKO1, OsGA3ox1, OsGA20ox1 and OsGA20ox2 in the young panicle of A7444 were significantly greater than those in the young panicle of the mutants, indicating that GW3 may be involved in the gibberellins (GA) biosynthesis pathway to regulate GW. GA4 content detection and electron scanning analysis revealed that GA4 regulates GW by affecting glume cell size. These results provide new insights for studying the genetic mechanism of rice GW and provide a material basis for breeding high-yield rice varieties.

Estimating Total Rice Biomass and Crop Yield at Field Scale Using PlanetScope Imagery Through Hybrid Machine Learning Models

Near real-time crop monitoring has been a challenging due to the lack of high-resolution remote sensing images suitable for agricultural applications. The PlanetScope constellation, comprising approximately 130 Dove satellites, collects images of the entire Earth daily, with a resolution of 3.7 m. The high-resolution images from the PlanetScope satellite, along with vegetation indices, geo-environmental data, and soil and crop parameters, were utilized and analysed using machine learning models to enhance the accuracy of predicting total biomass and rice crop yield at the field scale. The study area, covering nearly 214 sample rice plots, was located in the Tarekswar block of Hooghly, West Bengal, India. Alongside ten vegetation indices and three Principal Component Analysis (PCA) soil nutrient levels, approximately thirty-six factors were analyzed to predict rice total biomass and crop yield using ten machine learning (ML) models, namely Random forest (RF), Extreme Gradient Boosting (XGB), Support Vector Machine (SVM), Bagging Tree (Treebag), Generalized Additive Models (gamSpline), Elastic Net (enet), Ordinary regression with LASSO penalty (rqlasso), Tree Models from Genetic Algorithm (evtree), Bayesian Regularized Neutral Networks (brnn), cubist models, and there hybrid of ensembles. Boruta and multi-collinearity analysis were also conducted for the selected factors to explore their influence levels. The study area exhibited robust rice yields ranging from 5 to 10 t/ha, accompanied by healthy biomass growth. Four ML models ─cubist, random forest, enet, and the hybrid model—showed promising predictions with R2 > 0.88. Most models classified less than 20 ha of the study area as falling into the “very-low suitable class”, showing the region’s suitability for rice cultivation due to its highly fertile alluvial soil. Boruta sensitive analysis revealed that nearly 24 individual factors significantly influenced the final crop yield including, organic carbon (OC), phosphorus (P), electrical conductivity (EC), mechanization level, and the majority of the vegetation indices. A critical analysis carried out through the Map query tool showed that five vegetation indices estimated via PlanetScope displayed strong correlations (exceeding 89%) in identifying areas with high to very high rice yields. The study can serve as a guideline for near-real-time crop monitoring in the near future, using high-resolution PlanetScope images.

High-yield rice with rich nutrition and low toxicity can be obtained under potato-rice cropping system.

Rice is often rotated with dryland crops to produce sufficient foodstuff, as rice is the main food crop of humans. In order to verify whether under the intensive rice-based cropping system, high yield and good quality of rice can be achieved simultaneously to ensure food security. Five long-term paddy-upland rotations - wheat-rice (WR), rapeseed-rice (RR), garlic-rice (GR), broad beans-rice (BR) and potato-rice (PR) - were conducted from 2014 to investigate rice yield, along with the profiling of 24 elements in rice grain. Mg, Zn, Cu, As, Mo and Sb concentrations were highest in the aleurone layer, and Ag and Cd concentrations showed little variation among different parts of the rice grain. Al, Ti, V, Si, Fe and Tl concentrations in the endosperm under GR were higher, while the Se concentration under PR was the highest. Furthermore, the yield of GR and PR were higher than the other three rotations with N supplementation, and the sustainable yield index of PR and WR were larger than 0.8. When we consider the concentration of toxic (As, Cd and Pb) and nutrient elements (Ca, Fe, Zn, Se, Cu and Mg) in the endosperm and grain yields, PR can simultaneously achieve high yield, high nutrition and low toxicity with different nitrogen treatments. Here we provide novel insights regarding the selection of rice-based cropping systems, focused on producing nutritious and safe rice with high grain yield. © 2024 Society of Chemical Industry.

Phenotyping of Panicle Number and Shape in Rice Breeding Materials Based on Unmanned Aerial Vehicle Imagery.

The number of panicles per unit area (PNpA) is one of the key factors contributing to the grain yield of rice crops. Accurate PNpA quantification is vital for breeding high-yield rice cultivars. Previous studies were based on proximal sensing with fixed observation platforms or unmanned aerial vehicles (UAVs). The near-canopy images produced in these studies suffer from inefficiency and complex image processing pipelines that require manual image cropping and annotation. This study aims to develop an automated, high-throughput UAV imagery-based approach for field plot segmentation and panicle number quantification, along with a novel classification method for different panicle types, enhancing PNpA quantification at the plot level. RGB images of the rice canopy were efficiently captured at an altitude of 15 m, followed by image stitching and plot boundary recognition via a mask region-based convolutional neural network (Mask R-CNN). The images were then segmented into plot-scale subgraphs, which were categorized into 3 growth stages. The panicle vision transformer (Panicle-ViT), which integrates a multipath vision transformer and replaces the Mask R-CNN backbone, accurately detects panicles. Additionally, the Res2Net50 architecture classified panicle types with 4 angles of 0°, 15°, 45°, and 90°. The results confirm that the performance of Plot-Seg is comparable to that of manual segmentation. Panicle-ViT outperforms the traditional Mask R-CNN across all the datasets, with the average precision at 50% intersection over union (AP50) improved by 3.5% to 20.5%. The PNpA quantification for the full dataset achieved superior performance, with a coefficient of determination (R 2) of 0.73 and a root mean square error (RMSE) of 28.3, and the overall panicle classification accuracy reached 94.8%. The proposed approach enhances operational efficiency and automates the process from plot cropping to PNpA prediction, which is promising for accelerating the selection of desired traits in rice breeding.

Critical yield components for achieving high annual grain yield in ratoon rice.

Ratoon rice is considered an eco-friendly and resource-efficient method for rice cultivation, providing innovative strategies to mitigate the global food crisis. To clarify the critical yield components for achieving high annual grain yield in ratoon rice, data from 136 widely cultivated rice cultivars were collected through a six-year field experiment. The study analyzed the correlations between yield components and yields for both the main season and the ratoon season, indicating that main yields vary between 5.9 and 10.9 Mg ha-1, exhibiting a highly significant positive correlation with spikelets per panicle; ratoon yields range from 1.8 to 7.1 Mg ha-1, showing a highly significant positive correlation with panicles per m2, grain filling rate, and 1000-grain weight. Path analysis reveals that, in terms of contributing to ratoon yield, the grain filling rate is the most influential component, followed by panicles per m2, and 1000-grain weight. Therefore, by selecting large-panicle cultivars in the main season and enhancing panicles per m2, grain filling rate, and grain weight in the ratoon season, high annual yield in ratoon rice can be realized.

Pemanfaatan Analisis Komponen Utama dalam Mendeskripsikan Ciri Morfoagronomi Padi yang Berkorelasi dengan Yield

Rice is an important food crop with a crucial role in global food security. Study on correlation between morphoagronomic traits and yield is an important step in the development of superior rice varieties. Research data were collected from 20 rice varieties grown in the experimental field. A total of 10 morphoagronomic trait variables were measured, including plant height, number of productive tillers, flowering age, dry weight of biomass, hollow grain, 1,000-grain weight, grain weight, panicle number, panicle length, and harvest index. Data were analyzed using variance, correlation, and principal component analyses. The results of PCA showed that 3 principal components (PC) explained 80.88% of the data variation. PC1 was associated with plant height, flowering age, biomass dry weight, empty grain, panicle length, grain weight per clump. PC2 was associated with 1,000-grain weight and harvest index, while PC3 was associated with number of productive tillers and number of panicles. Correlation analysis showed that 1,000-grain weight was positively correlated with harvest index, while biomass dry weight was negatively correlated. These results suggest that all observed morphoagronomic traits except biomass dry weight have an important role in increasing rice yield. Furthermore, the results of this study provide basic informations for the development of high-yielding rice varieties.

Advanced prediction of rice yield gaps under climate uncertainty using machine learning techniques in Eastern India

The current study focuses on applying machine learning approaches to forecast future Kharif rice yield gaps in eastern India while accounting for climate change implications. To achieve the United Nations Sustainable Development Goals (SDGs), food security must be prioritized. Rice yield has been predicted using Cubist, GBM, MARS, RF, SVM, and XGB machine learning methods, considering six factors: elevation, soil moisture, precipitation, temperature, soil temperature, and actual evapotranspiration. Climatic change scenarios were generated using the latest climatic Coupled Model Intercomparison Project Phase 6 (CMIP6 MIROC6) Shared Socioeconomic Pathways (SSP) 2–4.5 and SSP5-8.5 datasets between 1990 and 2030. Finally, machine learning algorithms were used to identify rice yield gaps to achieve sustainable agricultural intensification. The rice yield validation was completed with 1889 field-based farmer observation records. The results suggest that Murshidabad and Purba Bardhaman districts had very high rice yields (5.60–3.45 t/ha) when using the Cubist model compared to another model. The findings also reveal a poor rice-yielding zone (1.44–0.39 t/ha) in the western region (Purulia) and a northwestern region (half of the west of Birbhum). The Cubist and RF models' maximum and minimum R2 values were 0.73 and 0.72, respectively. The R2 values were also negligible for the XGB, GBM, SVM, and MARS, machine learning models. Projections for rice production in 2030 indicate that the northern and north-eastern regions (Murshidabad and Purba Bardhaman) as well as the southeastern areas (Jhargram and Paschim Medinipur) have the highest yields, categorized as extremely very high (5.56–3.49 t/ha) and high (3.49–2.49 t/ha). A significant rice yield gap (50-40 %) was found in the center and south-east areas (Bankura, Jhargram, and Paschim Medinipur), the northern region (Murshidabad and Birbhum), and the western region (Purulia). In 2030, the north-western region (Birbhum), as well as the middle and south-eastern regions (Bankura, Jhargram, and Paschim Medinipur districts), had the highest proportion of high (50%–40 %) and very high (60%–50 %) rice yield gap. Our findings can contribute to a new viewpoint on agricultural planning and management for sustainable growth in the face of changing climate circumstances.

Effects of Harvest Time on Grinding Quality, Appearance Quality and Physical and Chemical Quality of Japonica Rice.

Harvest time is very important to rice due to its high correlation to rice yield, eating quality, etc.; however, the impact of harvest time on quality is still unclear. In this study, Nangeng 5718, a japonica rice planted in three regions in Jiangsu Province of China, was used to analyze and compare the milling quality, appearance quality, and physicochemical quality of japonica rice at different harvest times. The results showed that the 1000-grain weight of Nangeng 5718 exhibited no significant change at different harvest times (p > 0.05). The brown rice rate and rice yield at different harvest times were 82.3-85.4% and 66.3-76.1%, respectively. Harvest time had no significant effect on the brown of rice (p > 0.05). However, Nangeng 5718 planted in Nanjing had the highest rice yield at 50 days after heading, which was significantly different from that of rice harvested 65 days after heading (p < 0.05). Nangeng 5718 planted in Huai'an had the highest rice yield at 55 days after heading, which was significantly different from that of rice harvested 60 days after heading (p < 0.05). Harvest time had little effect on the length, width, and thickness of rice. The immature grain rate showed a decreasing trend with the increase in maturity. There were little differences in the protein content of Nangeng 5718 at different harvest times. Nangeng 5718 planted in Nanjing had the highest protein content at 50 days after heading. There was a significant difference between the rice harvested and the rice harvested 60 days after heading (p < 0.05). There were no significant differences between the other two regions (p > 0.05). The accumulated temperature in Nanjing was relatively high, and the RVA curve and RVA eigenvalues of rice varied greatly. The setback value of rice harvested at 50 days was significantly lower than that at 55 days and 60 days (p < 0.05). Rice has good gelatinizing properties. Therefore, timely harvesting and appropriate accumulated temperature could increase 1000-grain weight and rice yield, reduce the immature grain rate, and improve the gelatinization characteristics. Overall, the quality of Nangeng 5718 reached a good level when it was harvested 50 days after heading, with the accumulated temperature reaching 1051 °C. In fact, the harvest time should be chosen flexibly according to the weather conditions. Nangeng 5718 planted in Nanjing should be harvested earlier than 50 days, and rice from Huai'an and Lianyungang was of better quality when the harvest time was 50 days.

Yield Difference between Different Cultivation Techniques under Ultrasonic Treatment Driven by Radiation Use Efficiency.

Ultrasonic treatment and optimal cultivation techniques are both conducive to the high yield of super rice in South China. Many previous studies have shown that the increase in intercepted photosynthetically active radiation (IPAR) and radiation use efficiency (RUE) is an important reason for high rice yield. Field experiments were conducted over two years to evaluate the effects of IPAR and RUE on the yield under different treatments (CK: conventional cultivation technique without ultrasonic treatment; T1: conventional cultivation technique with ultrasonic treatment; T2: super rice-specific cultivation technique without ultrasonic treatment and T3: super rice-specific cultivation technique with ultrasonic treatment), with two representative rice varieties, Wufengyou-615 (WFY) and Jingnongsimiao (JNSM) during the late seasons of rice cultivation in South China. The super rice-specific cultivation technique and the ultrasonic treatment could significantly increase the yield, which was significantly (p < 0.01) and positively correlated with panicle number, grain-filling rate, and aboveground total dry weight. The higher grain yield depended more highly on higher RUE in the mid-tillering stage and maturity stage. The results of multiple-regression models also showed that the contributions of IPAR and RUE to yield were significant (p < 0.01). Conclusively, IPAR and RUE contributed a lot to yield progress of super rice in both super rice-specific cultivation techniques with fewer times of topdressing and ultrasonic treatment in South China. It is worth further studying how to reasonably improve the RUE of high-RUE varieties through other means.

Greenhouse gas emissions during the rice season are reduced by a low soil C:N ratio using different upland-paddy rotation systems

ContextUpland-paddy rotation can improve multiple-cropping index and crop yields; however, the mechanisms underlying the effects of dry-season crop diversification on rice yields and greenhouse gas (GHG) emissions under multiple rotation systems remain unclear. ObjectiveHere, we aimed to clarify the intrinsic mechanisms whereby rice yields and GHG emissions respond to the diversification of dry-season crops and lay a theoretical foundation for developing agronomic measures that can stabilize yields and reduce GHG emissions. MethodsUsing a positioning experimental site for upland-paddy rotation, we measured rice-season CH4 and N2O emissions, crop yields, GHG-emission intensity (GHGI) levels, soil physical and chemical properties in garlic–rice (GR), wheat–rice (WR) systems for 3 years (2019–2020, and 2022), and in a rapeseed–rice (RR) system for 1 year (2022). The soil microbial dynamics of the three systems were only tested in 2022. ResultsThe WR system had the highest CO2 emission equivalent (CO2-eq), with a 3-year interval value of 1898.24–16794.30 kg·ha−1, the lowest yield (8490.10–9773.46 kg·ha−1), and the highest GHGI (0.22–1.83). The GR system had the highest rice yield (9718.91–10769.75 kg ha−1), a lower CO2-eq (1588.55–12567.51 kg·ha−1), and therefore a lower GHGI (0.16–1.24). The RR system had the lowest GHGI in 2022 (benefiting from the lowest CO2-eq) and a slightly higher yield than that of the WR system. CH4 contributed to >88 % of the CO2-eq under the three systems in 2020 and 2022. The higher soil C:N ratio of the WR system stimulated methanogenic microorganisms, coupled with higher microbial biomass C levels, and ultimately increased CH4 emissions substantially. The soil C:N ratios of the GR and RR systems were significantly lower than that of the WR system because the soil total nitrogen (TN) of both systems was higher and increased CH4 emissions were avoided. The higher levels of N nutrients (TN, NO3--N, and NH4+-N) in the GR and RR systems also enhanced rice yields, with respective increases of 10.37 % and 1.22 %, compared with that of the WR system. ConclusionsThe diversified cultivation of dry-season crops in upland-paddy rotation systems affected rice yields and GHG emissions by changing the ratios of C and N. ImplicationsOur findings highlight the importance of future research involving comprehensive agronomic measures to help reduce emissions, including fertilizer management, straw management, and tillage methods.

Impact of Hybrid Varieties on the Productivity of Rice Cultivation: The Case of Sindh, Pakistan

Agriculture is a very important sector and crucial for poverty reduction and employment generation. The use of hybrid seeds leading to increased yield of crops and farmers’ income has been widely discussed in the literature. Through this study, an effort has been made to identify factors that impact obtaining high crop yield of rice by using the hybrid varieties. This study focuses on the use of hybrid varieties in the rice crop and presents a comparison in yield with the traditional open-pollinated varieties. The plot level information collected from eight major rice-producing districts of Sindh province of Pakistan was used to conduct this study and a propensity score matching technique was used to estimate the results of the study by using plot level variation between hybrid varieties and open-pollinated varieties. The results of the study showed significant improvement in rice yield with the adoption of hybrid rice varieties in the study area. This study highlights that the farmers have achieved a higher average yield of rice by using hybrid seeds as compared to open-pollinated varieties. Besides the crop yield, the overall income and profit of farmers have also increased by using hybrid seeds in the Sindh province. The study illustrates different interesting patterns relating to different characteristics of the household cultivating rice crops, different plot-level characteristics, and their comparison with hybrid and traditional varieties.

SNP-based QTL mapping and identification of panicle structure-related genes in rice.

Rice is a staple crop providing a significant portion of the global food supply. It is then crucial to develop strategies for breeding high-yield cultivars to meet global food security challenges, including the UN's zero-hunger goal. In this study, QTL mapping was employed to pinpoint key genomic regions linked to traits influencing rice yield, with a focus on panicle structure-a critical determinant of grain number. Over two consecutive years, QTLs were identified using 88 JJ625LG/Namchan Recombinant Inbred Lines (JNRILs), revealing several candidate genes. Notably, Gn1a, a known regulator of grain number, was mapped within qNS1 and qNSSr1-1, while the sd1 gene, linked to plant height, was detected across multiple QTLs. Furthermore, a novel gene, OsNSMq3 (Os03g0843800), encoding a methyltransferase, was identified in various QTLs, with haplotype and sequence homology analysis suggesting its role in enhancing yield by influencing panicle structure development. The increase in primary and secondary branches, driven by these genes, leads to a higher number of spikelets per panicle, thereby boosting yield. These findings underscore the potential of candidate genes from stable QTLs as valuable tools in molecular breeding to develop high-yield rice cultivars, addressing global hunger and aiding food supply in refugee crises.

Development of Iron and Zinc Biofortified Rice Variety for Nutrition Improvement in Bangladesh

Depleting micronutrients such as iron and zinc poses a serious risk to a population’s health in regions where nutrient-rich meals are unavailable, such as Bangladesh. We can engross the essential minerals using biofortified rice varieties enriched with essential minerals. We experimented to develop and evaluate a new high-yielding rice variety, Advanced Chemical Industries (ACI) dhan1, specifically for the rainy season in Bangladesh. We used high-yielding rice parent lines with high iron and zinc in the hybridization process, then grew and selected the resulting breeding lines during the rainy season of Bangladesh to identify the most suitable rice lines for the rainy season. The plant height and days to maturity traits were the same as shown in both cultivars, ACI dhan1 and Bangladesh Rice Research Institute (BRRI) dhan62 (standard check), which are 104 cm and 103 days, respectively. On the other hand, ACI dhan1 produced 6.05 tons per hectare, and BRRI dhan62 yielded 4.33 tons per hectare. A quality analysis of the grain quantified that ACI dhan1 contained 8.21% protein and 25.12% apparent amylose. Similarly, ACI dhan1 had very high levels of Zn (24.8 ppm) and Fe (9 ppm). We observed a more than 70% improvement in milling capabilities and head rice recovery. In proposed variety trials, the Seed Certification Agency of Bangladesh identified an average yield advantage of more than 10% for ACI dhan1 in 10 locations. National Seed Board, Bangladesh, released it as a candidate for high-iron and zinc-enriched rice variety and approved its extensive cultivation in Bangladesh. Due to its high micronutrient content, excellent agronomic performance, and non-sticky cooking quality, ACI dhan1 presents itself as a variety of choice in Bangladesh’s pursuit of food and nutrition security. Thus, the present study demonstrates the large achievement of biofortification by ACI dhan1 and hence promises to be a strong weapon in the battle against malnutrition.

Price and Non‐Price Factors in Development of Rice Cultivation: Case Studies From Southern India and Mekong‐Delta Vietnam

ABSTRACTThe high rice prices of 2023 have highlighted the need for increasing rice productivity. But improving productivity, especially in low‐performing rice‐producing regions, should also improve incomes for the numerous smallholder cultivators. This article examines what we can learn from the experience of high‐performing rice‐growing regions, which have both high rice yields and incomes. We focus on two best‐performing villages from Kerala, southern India and Mekong Delta, Vietnam. Using a detailed case study approach combining farm budget analysis, counterfactual simulations using agricultural household models, and qualitative insights, our analysis shows the contribution of price support, irrigation, and agricultural research in these two regions to the current situation. While price support was the prominent factor in Keralaʼs village, irrigation and extending the cropping intensity was important for farm incomes in the Mekong Delta village. This comparative analysis demonstrates the need for an ongoing evaluation of price and non‐price factors and supplementing farm incomes through specific interventions for developing low‐performing rice‐producing regions.

Mechanistic Insights and Genetic Approaches for Enhancing Submergence Tolerance in Rice

Rice, a pivotal staple crop, serves as a dietary foundation for approximately half of the global population. Its cultivation, traditionally reliant on submerged conditions, faces significant challenges due to sudden flooding events. The majority of rice varieties are highly sensitive to prolonged submersion beyond a mere seven days, resulting in substantial yield losses. As global climate patterns shift, the occurrence and intensity of flooding are predictable to increase, exacerbating this issue. Therefore, development of rice varieties having enhanced submergence tolerance is critical to sustaining productivity and ensuring food security in flood-prone regions. Understanding the physiological and biochemical mechanisms underlying submergence tolerance is crucial in this context. Key physiological mechanisms include the regulation of carbohydrate reserves for energy during low oxygen availability, ethylene-responsive factors, especially the Sub1A gene to, regulate growth and stress responses, curbing excessive elongation to conserve energy and enhance survival. Additionally, specialized tissues such as aerenchyma and adventitious roots improve oxygen transport and nutrient uptake under flooded conditions. Genetic approaches, including Marker-Assisted Selection (MAS), have been instrumental in the development of submergence-tolerant rice varieties. The integration of the Sub1 QTL, especially the Sub1A gene, into high-yielding rice varieties has led to significant advancements in breeding submergence-tolerant cultivars. These genetic improvements provide a promising solution to mitigate the detrimental effects of flooding on rice production, ensuring the stability and sustainability of this essential crop in the face of changing climate conditions. This review discusses various mechanisms adapted by rice and genetic advancements aimed at enhancing submergence tolerance in rice to secure food production for future generations.

Balancing Yield and Environmental Impact: Nitrogen Management and Planting Density for Rice in Southwest China

With growing concerns about global warming, it is crucial to adopt agronomic practices that enhance rice yields from paddy fields while reducing greenhouse gas (GHG) emissions for sustainable agriculture. An optimal nitrogen (N) fertilization rate and planting density are vital to ensure high rice yields, minimize GHG emissions, and understand emission behavior for better field management. We hypothesized that optimizing N application rates and planting density to improve nitrogen use efficiency (NUE) in rice cultivation would reduce resource losses and GHG emissions. To test this hypothesis, we implemented five treatments with a rice straw return cultural system: two planting densities (16 hills m−2 (traditional density, D1) and 20 hills m−2 (25% higher density, D2)) and three N application rates (no N fertilizer (N0), 180 kg N ha−1 (N1), and 144 kg N ha−1 (N2)). The control treatment (CK) was traditional planting density with no N fertilizer. The four new cropping modes were N1D1, N1D2, N2D1, and N2D2. We investigated the effects of N application rates and planting density on rice grain yield, NUE, and GHG emissions in multiple rice-growing seasons. The N1D2 treatment exhibited the highest grain yield over the three years, with a value of 10,452 kg ha−1, representing an increase of 12.2% compared to CK. Moreover, N uptake in N1D2 was the highest, averaging 39.2% (p &lt; 0.05) higher than CK, and 8.5%, 3.5%, and 2.8% (p &lt; 0.05) higher than N1D1, N2D1 and N2D2, respectively. N2D2 exhibited the highest NUE, with a value of 58.99 kg kg−1, surpassing all other treatments over the three years. GHG emissions, global warming potential (GWP), and greenhouse gas intensity (GHGI) in N2D2 were lower than in N1D1, N1D2, and N2D1. Additionally, reducing N application (comparing N1D1 to N2D1) and increasing plant density (comparing N1D1 to N1D2) improved N agronomic efficiency (NAE) and N partial productivity (PFPN). The negative correlation between the NAE and PFPN with GWP and GHG emissions further supports the potential for optimized N management and denser planting density to reduce environmental impact. These findings have important implications for sustainable rice cultivation practices in Southwest China and similar agroecosystems, emphasizing the need for integrated nutrient management strategies to achieve food security and climate change mitigation goals.