Rice Season Research Articles

Effect of Seedling Rates on Crop Yield and Methane Emissions from Rice Paddies

Agricultural strategies are urgently needed to mitigate greenhouse gas emissions without reducing crop yield. Seedling rate per hill will affect the quantity and quality of tillers, which may affect rice yield and CH4 emissions. Therefore, it is hypothesized that high yields with low yield-scaled CH4 emissions could be achieved with optimal seedling rate per hill. A field experiment was conducted with three densities (low seedling rate, LSR; moderate seedling rate, MSR; and high seedling rate, HSR) for two consecutive rice seasons. The CH4 fluxes were determined by the static chamber–GC method. The results showed no significant differences in rice yields, seasonal CH4 emissions, or yield-scaled CH4 emissions between the three treatments. For early rice, the HSR tended to achieve high yield without increasing yield-scaled CH4 emissions. As for late rice, the MSR showed similar rice yield, and tended to have lower yield-scaled CH4 emissions in comparison with the HSR. The results suggest that choosing an appropriate seedling rate per hill to increase grain yield while maintaining lower or comparable yield-scaled CH4 emissions can be a promising option to reduce CH4 emissions from rice paddies.

Upscaling and downscaling approaches for early season rice yield prediction using Sentinel-2 and machine learning for precision nitrogen fertilisation

Early season yield prediction could support rice farmers in adopting precision agriculture for nitrogen fertilisation management. Remote sensing and machine learning (ML) can be used to predict and map crop yield during phenological stages relevant to nitrogen application, like tillering in rice, at both within-field and field scales. This study evaluated the transferability of ML models in early season yield prediction through upscaling and downscaling approaches. The effects of two prediction times (tillering and ripening stages) and training/testing set sizes on ML models performance were evaluated over five rice growing seasons (from 2018 to 2022) in northern Italy, using whole-field-average yields and yield maps. Vegetation indices from Sentinel-2 imagery using the Google Earth Engine platform fed five ML algorithms (Cubist-CUB, Gaussian Process Regression-GPR, Neural Network-NNET, Random Forest-RF, and Support Vector Machines-SVM). ML algorithms were trained with yield maps and tested with whole-field-average yields to obtain a downscaling approach, while the opposite was done to obtain an upscaling approach. The downscaling approach showed higher accuracy than upscaling approach. Ripening stage predictions were more accurate than tillering stages, although the downscaling approach showed small differences between tillering and ripening stages. The highest tillering stage accuracy was achieved by SVM for both downscaling and upscaling approaches with 20 % and 27.8 % of Normalized Root Mean Square Error (NRMSE), and 0.99 and 0.99 of Simple Additive Weighting (SAW) score, respectively. Set size and data distribution effected ML models accuracy, with the highest performance achieved by RF and GPR with 0.80 and 1.00 of SAW score for the downscaling and upscaling approaches, respectively. This study demonstrated how ML models and downscaling approach could support rice farmers to calculate the nitrogen dose using the predicted yield at the tillering stage, enabling them to apply a site-specific nitrogen fertilisation based on the within-field yield prediction variability.

The Comprehensive Effects of Biochar Amendments on Soil Organic Carbon Accumulation, Soil Acidification Amelioration and Heavy Metal Availability in the Soil–Rice System

In recent years, biochar (BC) and biochar-based soil amendments (CSAs) have been widely used in agriculture and the environment. In the present study, a two-rice-season field study was conducted to explore the comprehensive effects of applying BC (1%) and CSA (0.5% and 1%) on soil organic carbon accumulation, soil acidification amelioration and heavy metal availability in a soil–rice system. The results show that soil pH was increased by 0.5–1.7 units and 0.3–1.0 units, respectively, in the early rice season and late rice season treated by the amendments compared with CK. Soil organic contents were increased by 18–30% in the early rice season and by 15–25% in the late rice season in the amended treatments. In addition, soil available phosphorus contents were largely increased as a result of BC and CSA addition. Soil CaCl2 extractable heavy metals (Cd, Ni, Cu and Zn) were simultaneously decreased by BC or CSA amendments. In addition, Cd contents in early rice grain and late rice grain were significantly reduced by 25–48% and 52–83% in amended treatments, while Zn contents were generally not affected. The uptake of Cu and Ni was also decreased by BC and CSA. This study demonstrates that biochar application alone or combinates with inorganic amendments (limestone, sepiolite and potassium dihydrogen phosphate) can significantly improve soil properties and nutrient content and decrease the heavy metal (especially for Cd and Ni) uptake and accumulation from soil to rice grain, where the combination application is more effective.

Soil microbial biodiversity supports the delivery of multiple ecosystem functions under elevated CO2 and warming

The contribution of the soil microbes to agroecosystem multifunctionality under global change remains poorly understood. Here, based on data from a field experiment involving elevated carbon dioxide (CO2) and warming in a rice-wheat agroecosystem, we found that soil microbes influence the impact of climate change on agroecosystem functions. The stability of food production during the rice season increased under elevated CO2 but decreased under warming, with no significant changes in the wheat season. The interactive influences of elevated CO2 and warming on agroecosystem multifunctionality were found to be minimal. The abundance of soil fungi and nematode was associated with agroecosystem stability during the rice and wheat seasons, respectively. Soil archaeal diversity and bacterial abundance were linked to agroecosystem multifunctionality in the rice and wheat seasons, respectively. Our work proves the positive effects of soil microbes on agroecosystem functions and highlights the implications of maintaining microbial diversity for agroecosystem health under climate change.

Exploration of three native strains of entomopathogenic fungi against rice yellow hairy caterpillar, <i>Psalis pennatula</i> (Lepidoptera: Lymantriidae): An emerging pest of rainfed rice in Assam

Rice yellow hairy caterpillar Psalis pennatula Fabricius (Lepidoptera: Lymantriidae) is an emerging pest of rice and an increased incidence is expected in different rice-growing areas of Assam in the coming years. The caterpillar is a voracious leaf feeder and can consume the whole leaves within a day. Keeping this in view, an attempt was made to evaluate the bio-efficacy of three important native isolates of entomopathogenic fungal strains viz. Beauveria bassiana (KR855715), Cordyceps javanica (OM321438) and Isaria fumosorosea (MH414514) against P. pennatula larvae under laboratory conditions during the Sali season of rice. All three EPF strains at different conidial dilutions (1x103, 1x105,1x107, 1x108 and1x109 conidia/ml) were found to be pathogenic to the 3rd instar larvae of P. pennatula. Among the tested fungal strains, the strain Isaria fumosorosea (MH414514) showed the highest mortality (100%) at 1x107 conidia/ml and Cordyceps javanica (OM321438) showed the lowest mortality (51%) at 1x107conidia/ml at 10th day of post-treatment. The tested fungal isolates could play a vital role as microbial biopesticides in suppressing the P. pennatula population in Assam and could be incorporated into IPM strategies.

Effects of Fallow Season Water and Straw Management on Methane Emissions and Associated Microorganisms

The effects of fallow season water and straw management on methane (CH4) emissions during the fallow season and the subsequent rice-growing season are rarely reported, and the underlying microbial mechanisms remain unclear. A field experiment was conducted with four treatments: (1) fields flooded in both the fallow and rice seasons (FF), (2) fields drained in the fallow season and flooded in the rice season (DF), (3) FF with straw retention (FFS), and (4) DF with straw retention (DFS). The CH4 emissions in fields under different water and straw treatments were monitored using the static closed chamber method. Methanogenic and methanotrophic communities in these fields were examined using terminal restriction fragment length polymorphism (T-RFLP) analysis based on the mcrA gene and pmoA gene encoding methyl coenzyme M reductase and particulate methane monooxygenase, respectively. The results showed that CH4 emissions were significantly affected by water management, straw retention, season, and their interactions. Over 80% of CH4 emissions occurred during the rice season. Field drainage during the fallow season reduced CH4 emissions by 47.0% and 53.8% with and without straw during the rice season, respectively. Water management altered the abundance and composition of methanogens and methanotrophs, whereas the effects of straw retention were less pronounced. The quantitative polymerase chain reaction (qPCR) assay revealed that field drainage in the fallow season decreased the mcrA gene abundance by 30.0% and 23.2% with and without straw in rice season, respectively, and increased the pmoA gene abundance by 108.9% and 213.7% with and without straw in rice season, respectively. CH4 flux was significantly positively associated with mcrA gene copy number and the ratio of mcrA to pmoA gene copy number, whereas it was significantly negatively correlated with the pmoA gene copy number. Results indicated that fallow drainage greatly decreased CH4 emission not only during the fallow season but also during the subsequent rice season by altering the community composition of methanogens and methanotrophs. These findings provide scientific insight into the role of water and straw management in controlling CH4 emissions through microbial community dynamics.

Optimized Tillage Method Increased Rice Yield in Rice Ratooning System

Ratoon rice occupies an important position in rice production owing to its time-saving, labor-saving and low-pollution planting, and increased benefits. However, the impact of tillage management on the yield in rice ratooning system has not yet been reported. Thus, field experiments were carried out to investigate the impact of seven tillage methods on the yield of ratoon rice crop in Jingzhou City in 2021–2022. The managements included winter plowing + rotary 2 times (PTw + RT2) or 3 times (PTw + RT3), spring plowing + rotary 2 times (PTs + RT2) or 3 times (PTs + RT3), no plowing + rotary 2 times (P0 + RT2) or 3 times (P0 + RT3) and no tillage (NT). PTw + RT3 had the highest total rice yield. The experimental data were collected in 2021 and 2022. In terms of main season rice yield, the order of ranking was PTw > PTs ≈ NT ≈ P0, while for ratoon rice yield, the ranking was NT > PTw ≈ PTs > P0. Generally, the root function ranked as PTw > PTs > P0 > NT. The photosynthetic capacity of the main season rice always maximized in PTw, those of the ratoon rice all maximized in NT, and those of both the main season rice and ratoon rice always minimized in P0. In the three tillage modes (PTw, PTs, P0), an additional rotary tillage did not affect the growth or yield of rice. PTw + 3RT was the highest yielding tillage management, but it is still necessary to explore other PTw + 3RT methods and more economical tillage management to increase the yield of ratoon rice.

Evaluating river basin plans’ effects on water stress in Thailand’s economic development area

ABSTRACT Surging water demand necessitates strategic water management to maintain efficiency and accessibility. In Thailand’s Eastern Economic Corridor Project, water demand escalated from 2005 to 2018 by 54%, prompting water management plans using pipe diversion. However, this plan merely shifts scarcity issues due to limitations and climate uncertainties. The Water Stress Index assessed the impact using distributed hydrological modelling, revealing the 3Rs scenario, and a dry season rice cultivation lowered by 26%. Specific basins still face extreme stress. Integrating scenarios can refine and alleviate stress, suggesting a combined scenario approach, improving Water Stress Index and efficiently identifying vital water reduction strategies.

Seasonal alternation of soil phosphorus saturation in rice-wheat rotation systems under multiple fertilization strategies

The rice-wheat rotation system has been shown to improve agricultural productivity with associated economic benefits. The degree of phosphorus saturation (DPS) refers to saturation of soil sorption sites with phosphorus (P), and reflects the tendency of soil P fixation or release. A current knowledge gap is to investigate how a rice-wheat rotation system affects soil DPS, which is closely related to P loss to the environment. In the present field study, a rice-wheat rotation was established and different fertilization treatments were implemented, and the Anthrosol soils collected from the rice season and the wheat season, were evaluated. Sequential extraction, Langmuir model, and structural equation modeling were used to reveal the proportion of P fractions, P sorption capacity, and the relationships between the degree of P saturation and soil properties, in order to reveal the geochemical mechanisms of P transformation. A higher degree of P saturation is always found in the rice season soils rather than in the wheat season soils, which should be attributed to the lower P retention capacity and the higher presence of P in forms with greater mobility potential in the rice season soils. The reduction of the proportion of residual P fraction coupled with the enhancement of NaHCO3-P/NaOH-P/HCl-P fractions indicated a prevalence of P mobilization due to flooding during the rice planting period. Simultaneously, lower P sorption capacity was also found in the rice season soils. Structural equation modelling was able to confirm that the change of P sorption capacity together with the enhancement proportion labile P in total P, and mobilization of soil residual P combined to affect the degree of P saturation. Management interventions, such as flooding, induced changes from crystalline iron oxides to amorphous iron oxides and this may have enhanced the release of P retained by crystalline iron oxides, which contributed to the mobilization of P in the soils after the rice season. This knowledge can be utilized to avoid P losses to the environment, while still achieving efficient P utilization.

Influence of various organic nutrient management technologies on microbial abundance, growth, and yield of rice (Oryza sativa L.) under field conditions

A field study (three years) was carried out to evaluate the outcome of various organic nutrient management technologies including farmyard manure (FYM) and poultry manure (PM) integrated with chemical fertilizers for microbial dynamics and growth and yield of rice at Experimental Farm of Nuclear Institute of Agriculture (NIA), Tandojam, Sindh, Pakistan. The most broadly grown rice variety, NIA-Shandar was selected for this study. The experiment involved integrating organic technologies, specifically Farm Yard Manure (FYM) and Poultry Manure (PM), each applied at 10 tons per hectare. These organic technologies were combined with varying levels of inorganic fertilizers: half (60 kg N ha-1 and 45 kg P2O5 ha-1) and full (120 kg N ha-1 and 90 kg P2O5 ha-1) recommended doses. Additionally, zinc was applied at 10 kg ha-1. The study employed a randomized complete block design (RCBD) with three replications to assess treatment effects. Both organic technologies perform better for microbial dynamics and rice growth and yield enhancement either alone technology or combined with chemical fertilizers during three rice seasons. The maximum general microbial abundance (9.70 log10 CFU g soil-1), N2 fixing (7.37 log10 CFU g soil-1), P solubilizing (7.97 log10 CFU g soil-1) and Zn solubilizing(6.75 log10 CFU g soil-1) population was recorded in FYM alone or with half N and P2O5 fertilizer application. The highest plant height (106 cm), tiller numbers(17.10), panicle numbers (26.53), panicle length (26.32 cm), one thousand grainweight (26.53 g) and rice grain yield (7.92 t ha-1) were perceived in FYM with full rate N and P2O5 fertilizer application. Both technologies along with chemical fertilizer induced soil properties and soil nutrients which reflected to increase soil microbial abundance and growth and yield of rice however, FYM showed higher potential than poultry manure during the three years rice cultivation seasons. Hence, it was indicated that a significant contribution to the development of sustainable agricultural technologies that prioritize soil health,environmental stewardship, and food security.

Investigation of the effects of polyethylene microplastics at environmentally relevant concentrations on the plant-soil-microbiota system: A two-year field trial

Microplastics are a potential threat to agricultural sustainability. However, the effects of microplastics at environmentally relevant concentrations on the plant-soil-microbiota system in realistic field conditions are largely unknown. Herein, we conducted a two-year field trial to study the effects of polyethylene (PE) microplastics at 0, 100, and 600 mg/kg on crop growth, soil properties, and the composition and function of microbial communities in a farmland with rice-wheat rotation. PE did not affect wheat growth but it increased the rice grain weight by 42.5 % at 600 mg/kg, and enhanced rice height by 35.4 % and 30.2 % at 100 and 600 mg/kg, respectively. The presence of PE significantly decreased soil available phosphorus during the wheat season, while it reduced soil total nitrogen, NH4+-N and available phosphorus during the rice season. There were five and sixteen bacterial orders identified changed by PE in wheat and rice soils, respectively. Specifically, PE at different concentrations differentially altered the abundances of sulfate-reducing bacteria Thermodesulfovibrionia, Thermoactinomycetales and Syntrophobacterales, and further modified soil sulfate respiration in wheat soils. During the rice season, PE (100 mg/kg) increased the abundance of Xanthomonadales by 98.0 % and enriched the functional groups of intracellular parasites, while PE (600 mg/kg) inhibited twelve cluster of orthologous group function classes and disturbed bacterial metabolism. This study suggests that PE exhibits a greater impact on the plant-soil-microbiota system during the rice season compared to the previous year's wheat season, highlighting the importance of crop type and cultivation practices in determining the environmental risks of microplastics in agroecosystems.

Spatial and Temporal Variations of Climate Resources during the Growing Season of Early-Season Rice in Hunan Province

The rising temperatures and changes in precipitation due to climate change have significantly impacted agricultural production. Evaluating the effects of climate change on rice production is crucial for improving rice cultivation techniques and ensuring food security. It is essential to comprehensively examine the climatic, spatial, and temporal variations during the duration of crop production. Previous research has mainly focused on different rice planting areas, rice types, and various growth stages of rice. However, more research is needed on the climatic changes during the crop-growing season in specific regions. Therefore, this study compiled complete daily meteorological data from 37 meteorological stations in Hunan Province from 1961 to 2020. The period from 1961 to 2020 was divided into three segments: 1961–1980 (a), 1981–2000 (b), and 2001–2020 (c), to analyze the characteristics of agricultural climate resource changes during different growth stages of early-season rice in Hunan Province. Results show that the heat resources were significantly increased (accumulated temperature growth rate of 43.36 °C/10a), the sunshine resources were decreased by −14.60 h/10a, and the precipitation resources were slightly increased by 6.85 mm/10a. The increase in heat resources mainly occurs during the vegetative growth stage of early-season rice. Additionally, the high-value regions of heat resources and precipitation in period c are 97.8% and 34.2% higher than the average values of periods a and b, respectively. In contrast, the regions with high sunshine hours significantly decreased in period c compared to periods a and b. In summary, the heat, sunshine, and water resources in the central and eastern regions of Hunan Province increase simultaneously, and appropriate cultivation measures should be adopted in the future to improve the yield and resource utilization efficiency of early-season rice in a double-cropping system.

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.

Feasibility of machine learning-based rice yield prediction in India at the district level using climate reanalysis and remote sensing data

CONTEXTYield forecasting, the science of predicting agricultural productivity before the crop harvest occurs, helps a wide range of stakeholders make better decisions around agricultural planning. OBJECTIVEThis study aims to investigate whether machine learning-based yield prediction models can capably predict Kharif season rice yields at the district level in India several months before the rice harvest takes place. METHODOLOGYThe methodology involved training 19 machine learning models such as CatBoost, LightGBM, Orthogonal Matching Pursuit, and Extremely Randomized Trees on 20 years of climate, satellite, and rice yield data across 247 of India's rice-producing districts. In addition to model-building, a dynamic dashboard was built understand how the reliability of rice yield predictions varies across district. RESULTS AND CONCLUSIONSThe results of the proof-of-concept machine learning pipeline demonstrated that rice yields can be predicted with a reasonable degree of accuracy, with out-of-sample R2, MAE, and MAPE performance of up to 0.82, 0.29, and 0.16 respectively. This performance outperformed test set performance reported in related literature on rice yield modelling in other contexts and countries. In addition, SHAP value analysis was conducted to infer both the importance and directional impact of the climate and remote sensing variables included in the model. Important features driving rice yields included temperature, soil water volume, and leaf area index. In particular, higher temperatures in August correlate with increased rice yields, particularly when the leaf area index in August is also high. Building on the results, a proof-of-concept dashboard was developed to allow users to easily explore which districts may experience a rise or fall in yield relative to the previous year. The dashboard show that the model may perform better in some regions than in others. For instance, the absolute percentage error for predicted versus actual yields ranged from an average of 7.1 % in districts in Uttarakhand to an average of 14.7 % in Uttar Pradesh. SIGNIFICANCEThis study underscores the potential for policymakers to consider scaling and operationalizing machine learning approaches to rice yield prediction in the context of agricultural early warning systems to deliver timely crop yield forecasts on a rolling basis throughout the season, thereby equipping agricultural decision-makers with the ability to make informed choices on irrigation scheduling, fertilizer application, and harvest planning to optimize crop output and resource use.

Rice yield and water productivity in response to water-saving irrigation practices in China: A meta-analysis

Various water-saving irrigation (WSI) practices (e.g., dry cultivation, intermittent irrigation, controlled irrigation, shallow-wet irrigation, and rain-gathering irrigation) have been applied to rice cultivation mitigate water scarcity in China. However, in previous studies, these WSI practices have shown different water savings and yield increases, mainly due to different application conditions. A meta-analysis was applied to investigate the responses of the actual evapotranspiration (ETact), irrigation water (IW), rice yield (Y), and water productivity (WP) to WSI practices in different conditions, and 956 data sets were selected from 108 published papers. The results showed that, compared to traditional flood irrigation, rain-gathering irrigation decreased ETact and IW by 25.41 % and 55.7 % respectively, and increased WP greatly by 14.26 % while having a slight decrease in Y. Except for dry cultivation, all WSI practices increased WP by 4.72–14.26 % compared to traditional flood irrigation. The effects of different soil qualities on rice water consumption and production vary; medium soils with high organic content and a pH below 6.5 are better for rice growth. As for rice seasons, WSI practices had the least impact on ETact in middle rice, with an average reduction of 5.84 %, followed by early rice (–12.66 %) and late rice (–18.81 %). Higher mean annual temperature and more precipitation led to more Y under WSI practices. Differences in the effects of mean annual temperature and mean annual precipitation on WP were not significant. Our meta-analysis provides more insight into the effects of water-saving irrigation practices on rice water consumption, yield, and water productivity at various experimental sites. In general, there is considerable variation in the responses of Y and WP to different water-saving irrigation practices, and more evaluation of aspects such as rice seasons, soil properties, and meteorological conditions is needed for optimizing WSI in practice.

Comparative Proteomic Analysis Provides New Insights into Improved Grain-filling in Ratoon Season Rice

Grain-filling of rice spikelets (particularly for the later flowering inferior spikelets) is an important characteristic that affects both quality and yield. Rice ratooning technology is used to cultivate a second crop from dormant buds that sprout from stubble left after the first harvest. This study used two rice varieties, the conventional indica rice ‘Jinhui 809’ and the hybrid indica-japonica rice ‘Yongyou 1540’, to assess the impact of rice ratooning on grain-filling. The results indicated that the grain-filling process in inferior spikelets of ratoon season rice (ISR) showed significant improvement compared to inferior spikelets of main crop (late season) rice (ISL). This improvement was evident in the earlier onset of rapid grain-filling, higher seed-setting percentage, and improved grain quality. A label-free quantitative proteomic analysis using mass spectrometry identified 1724 proteins with significant abundance changes, shedding light on the molecular mechanisms behind the improved grain-filling in ISR. The functional analysis of these proteins indicated that ratooning stimulated the metabolic processes of sucrose-starch, trehalose, and hormones in rice inferior spikelets, leading to enhanced enzyme activities related to starch synthesis, elevated concentrations of trehalose-6-phosphate (T6P), indole-3-acetic acid (IAA) and zeatin riboside (ZR) during the active grain-filling phase. This research highlighted the importance of the GF14f protein as a key regulator in the grain-filling process of ISR. It revealed that GF14f transcriptional and protein levels declined more rapidly in ISR compared to ISL during grain-filling. Additionally, the GF14f-RNAi plants specific to the endosperm exhibited improved quality in inferior spikelets. These findings suggest that the enhancement of starch synthesis, increased levels of IAA, ZR, and T6P, along with the rapid decrease in GF14f protein, play a role in enhancing grain-filling in ratoon season rice.

Harmonizing soil restoration and microbial diversity: Insights from a Two-Year field experiment with Sedum-Rice rotation systems

Phytoremediation coupled with agroproduction (PCA) model contributes to sustainable agriculture and environmental management. This study investigated the impact of continuous cropping early/late season rice (RR) and Sedum alfredii-rice rotation (SR) on soil physical and chemical properties, as well as their relationships with soil microbial community. In 2022, SR treatment significantly increased pH value and organic matter content by 7 % and 17 %, respectively, compared to the levels in 2020, while RR treatment showed no change. RR treatment resulted in a significant decrease in soil concentrations of Ca, Mg, and K by 18.42 %, 29.01 %, and 7.77 %, respectively. Furthermore, SR treatment saw reductions of 29.62 % in total Cd and 38.30 % in DTPA extractable Cd in the soil. Over the two years, both treatments notably influenced the diversity, structure, and network of the rhizosphere bacterial and fungal communities, which are crucial for nutrient cycling and plant health. Notably, SR treatment exhibited a more complex network compared to RR, suggesting a greater impact on the interconnected systems. Therefore, these findings highlight the potential of Sedum rotation system to rehabilitate contaminated soils while supporting agricultural practices, which is essential for food security and environmental sustainability. This research direction holds promise for future exploration and application in the fields of phytoremediation and agroecology.

Adapting Seasonal Rice Cultivation Strategies for Food Security in Response to Climate Change Impacts

An in-depth examination of the effects of climate change on rice yield in China, encompassing various rice types, is crucial for ensuring the nation’s food security. This study develops an “economy-climate” theoretical model and employs Panel Corrected Standard Error Estimation (PCSE) on panel data spanning from 1978 to 2018, sourced from China’s primary grain-producing regions. The analysis delves into the impact of climate variables, including precipitation, temperature, and sunshine duration, on overall rice production and different rice types. Overall, the findings reveal a nonlinear relationship between precipitation, temperature, sunshine duration, and rice yield, characterized by an “inverted U-shaped” pattern. However, significant variations exist in the effects on different rice types across China’s main grain-producing areas. Increasing precipitation generally enhances early rice production across provinces and also augments mid-season and one-season-late rice production in the Inner Mongolia Autonomous Region, Hebei, Jilin, Heilongjiang, and Shandong Province. Conversely, it reduces mid-season and one-season-late rice output in Liaoning, Jiangsu, Anhui, Jiangxi, Henan, Hubei, and Hunan. Sichuan Province sees a rise in temperature favoring early and double-season-late rice production, unlike other provinces. For mid-season and one-season-late rice, temperature increases benefit output in Heilongjiang Province but not in other regions. Additionally, prolonged sunshine duration boosts early and double-season-late rice production across all provinces but does not have the same effect on mid-season and one-season-late rice in China’s primary grain-producing areas.

12-year continuous biochar application: Mitigating reactive nitrogen loss in paddy fields but without rice yield enhancement

Numerous studies have documented the beneficial effects of short-term biochar application on soil carbon sequestration, greenhouse gas emissions reduction, and ecological functions enhancement. However, concerns persist regarding the impacts on soil reactive nitrogen (N) loss and crop yields under long-term biochar application (more than ten years), due to the irreversibility once biochar is incorporated into soils. This study presents the results of a 12-year field experiment in a rice-wheat rotation system with treatments including a control (CK, no corn straw or biochar), annual corn straw returning (CS, 6 Mg·ha−1·yr−1), and annual application of corn straw biochar pyrolyzed at 400°C at rates of 2.4, 6, and 12 Mg·ha−1·yr−1 (BC1, BC2, and BC3), investigating rice yields (2011–2022) and reactive N loss (2021–2022). The results showed that after 11 years of continuous biochar application until the 2021 rice season, alkaline hydrolysis N (AN, equivalent to an average of 347 kg N ha−1) within the 0–15 cm soil layer in biochar treatments was significantly higher than that of the CK (200 kg N ha−1), attributable to the biochar-induced enhancement of cation exchange capacity (CEC). The application of biochar reduced inorganic N leaching and NH3 volatilization losses by an average of 42.21 % and 30.85 %, respectively. However, N2O emission in the BC3 treatment was over 2 times as high as in the CK. Continuous biochar applications did not increase rice yield over the 12 rice seasons, as excessive soil AN level under biochar application reduced the rice harvest index. The results indicated that continuous biochar application was effective in reducing reactive N losses and increasing plant-available N supply, suggesting a potential to sustain rice production with lower fertilizer N inputs.

Evaluation of the characteristics of G1 genotypes in Tai Nguyen seasonal rice (Oryza sativa) in Tra Vinh province

Tai Nguyen variety is a local seasonal rice variety, grown in some districts in Tra Vinh province. This rice variety has delicious rice and is highly adaptable to acidic and saline soils in the Mekong delta. Phenotypic variation of agronomic characters was investigated to eliminate the genetic diversity of the Tai Nguyen populations. Forty G1 individuals with the same maturing day and uniform agro-morphological characteristics were selected for G2 generation at 3 locations as Chau Thanh, Tra Cu and Cau Ngang of Tra Vinh province. The agronomic indexes of 40 Tai Nguyen rice lines in the G1 crop reached the following average values such as culm length 128.8 cm, number of branches 40.5, leaf length 53.4 cm, leaf width 1.0 cm, number of panicles 34.8, panicle length 24.7 cm, total number of firm seeds 134.3, total number of seeds 170.7, grain length 8.4 mm, grain width 2.4 mm, dry weight 193.0 g, and weight of 1000 grain 22.9 g. The comparison results have selected the 13 best G1 lines to be used as raw materials for the G2 crop