Search Results (76)

Remote sensing tools have been proposed to assist with rice crop monitoring but have been developed and validated on ponded rice. This two-year study was conducted on a commercial rice farm with irrigation automation technology aimed to (i) understand how canopy reflectance differs between high-yielding ponded and aerobic rice, (ii) validate the feasibility of using the squared simplified canopy chlorophyll content index (SCCCI²) for N uptake estimates, and (iii) explore the SCCCI² and similar chlorophyll-sensitive indices for grain quality monitoring. Multispectral images were collected from an unmanned aerial vehicle during both rice-growing seasons. Above-ground biomass and nitrogen (N) uptake were measured at panicle initiation (PI). The performance of single-vegetation-index models in estimating rice N uptake, as previously published, was assessed. Yield and grain quality were determined at harvest. Results showed that canopy reflectance in the visible and near-infrared regions differed between aerobic and ponded rice early in the growing season. Chlorophyll-sensitive indices showed lower values in aerobic rice than in the ponded rice at PI, despite having similar yields at harvest. The SCCCI² model (RMSE = 20.52, Bias = −6.21 Kg N ha⁻¹, and MAPE = 11.95%) outperformed other models assessed. The SCCCI², squared normalized difference red edge index, and chlorophyll green index correlated at PI with the percentage of cracked grain, immature grain, and quality score, suggesting that grain milling quality parameters could be associated with N uptake at PI. This study highlights canopy reflectance differences between high-yielding aerobic (averaging 15 Mg ha⁻¹) and ponded rice at key phenological stages and confirms the validity of a single-vegetation-index model based on the SCCCI² for N uptake estimates in ponded and non-ponded rice crops. Full article

Flood risk to rice production has previously been examined in terms of river basins or administrative units, incorporating data about the flood year, inundated area, precipitation, elevation, and impacts. However, there is limited knowledge about this topic, as most flood impact studies have focused on loss and damage to people and the economy. It remains important to identify how flood risk to rice production can be better identified within a long-term, community-based, analytical framework. In addition, flood risk studies in Sri Lanka tend to focus on single-year flood events within an administrative boundary, making it difficult to fully comprehend risks to rice production. This paper aims to fill these gaps by investigating long-term flood risk levels on rice production. With this aim, we collected and analyzed information about rice production, geospatial data, and 15-year precipitation records. Temporal-spatial maps were generated using Google Earth Engine JavaScript coding, Google Earth Pro, and OpenStreetMap. In addition, focus group discussions with farmers and key informant interviews were conducted to verify the accuracy of online information. The collected data were analyzed using descriptive statistics, GIS, and linear regression analysis methods. Regarding rice production impacts, we found that floods in the years 2006–2007, 2010–2011, and 2014–2015 had significant impacts on rice production with 20.5%, 75.8%, and 16.6% reductions, respectively. Flood risk maps identified low-, medium-, and high-risk areas based on 15-year flood events, elevation, proximity to water bodies, and 15-year flood-induced damage to rice fields. High risk areas were further studied through field discussions and interviews, showing the connection between past floods and poor water governance practices in terms of dam management. Our linear regression analysis found a marginal negative correlation between total seasonal rainfall and rice production. Full article

Straw incorporation effectively improves soil fertility and crop yield, and its adaptation to single-season rice production in cold temperate regions is a current research focus. This study conducted a two-year continuous in situ field experiment with four treatments: no straw incorporation (CK), straw incorporation with autumn rotary tillage (SC), straw incorporation with autumn plowing (SH), and straw incorporation with spring rotary tillage (ST). This study investigated the effects of straw incorporation on rice growth and the soil environment to understand the soil-crop interactions and their impact on rice yield. The results indicate that in the single-season rice production system of Northeast China, straw incorporation reduces the number of tillers, dry matter accumulation, and leaf area index in the early rice growth stage but promotes dry matter accumulation in the later stages. Straw incorporation over two consecutive years increased the rice yield by 2.07%, with the SC treatments showing optimal performance. This increased yield could lead to higher economic returns for the farmers. Additionally, straw incorporation potentially increases the total nitrogen and soil organic matter (SOM) content in the topsoil, thus providing environmental benefits by reducing the need for synthetic fertilizers. Factor analysis reveals that the SC treatments enhances dry matter accumulation by influencing soil nutrient levels in the later rice growth stages, thereby improving rice yield and nitrogen recovery efficiency. By altering soil nutrient availability at different growth stages, different straw incorporation regimes regulate the material production strategy of rice and the ‘source-sink’ relationship. This research provides a theoretical basis for enhancing soil fertility and rice yield in cold temperate regions through improved straw management strategies. These findings support policy initiatives that promote large-scale straw incorporation in commercial rice production for its potential economic and environmental benefits. Full article

In response to increasing global food demand and the significant water requirements of rice cultivation, this study aims to enhance water use efficiency in rice farming. Focusing on Jiayu County, a subtropical humid region in China, where rice is grown as a single crop every year, we investigated temporal variations in rice water requirements and the influence of effective rainfall on irrigation strategies. Data were collected from an experimental station within the Sanhulianjiang Reservoir in Jiayu County. Utilizing the Mann–Kendall trend test and the Seasonal–Trend Decomposition using the LOESS (STL) method, we analyzed historical data on rice water requirement (ET_c) and effective rainfall ($R_e$ ). Our findings reveal that annual water requirements for rice range between 432 mm and 746 mm, with peaks corresponding to critical growth stages such as tillering and jointing–booting. Effective rainfall contributes significantly to meeting these needs, providing 27–35% of the total water requirement during specific periods. Developed water-saving irrigation strategies, including optimized irrigation scheduling and the introduction of drought-resistant rice varieties, demonstrate a potential reduction in irrigation demands by approximately 33.84%. This study underscores the importance of integrating effective rainfall data into irrigation practices to enhance water use efficiency and promote sustainable rice production amidst climate variability challenges. Full article

In recent years, surface ozone (O₃) pollution has emerged as a significant barrier to the continued improvement of air quality in China, making O₃ risk assessment a critical research priority. Using nationwide O₃ monitoring data, this research investigated the spatial characteristics of O₃ pollution and assessed its potential impacts on human health and crop yields. The results showed that the maximum daily 8 h average O₃ (MDA8 O₃) exhibited higher concentrations in eastern and northern regions, and lower concentrations in the western and southern regions of China. Long-term O₃ exposure was associated with an estimated 175,154 all-cause deaths nationwide, with the highest health risks observed in Shandong, Henan, and Jiangsu provinces. The AOT40 values for the winter wheat and single-rice growing seasons in China were 9.30 × 10³ ppb·h and 1.29 × 10⁴ ppb·h, respectively. Moreover, O₃ exposure led to relative yield losses of 22.1% for winter wheat and 9.3% for single rice, corresponding to crop yield losses (CPLs) of 63 million metric tons and 14 million metric tons, respectively. Higher winter wheat CPL values were primarily concentrated in Henan, Shandong, and Hebei, while higher single rice CPL values were observed in Jiangsu, Hubei, and Anhui. This study presents a novel coupling of O₃ pollution exposure with human health and agricultural risk assessments across China, emphasizing the need for region-specific O₃ management strategies to protect public health and ensure agricultural sustainability. In conclusion, this study highlights the importance of targeted O₃ control in densely populated and major crop-producing areas to mitigate health risks and yield losses, thus safeguarding ecosystem health and food security. Full article

Rice is one of the most extensively cultivated food crops in Northeast China. Estimating pre-harvest rice yield is important for accurately formulating field management strategies and swiftly assessing overall rice production. This can be achieved using a pixel-scale model, which estimates crop yield based on information from each pixel. Previous studies predominantly employed remote sensing indices, climatic data, and yield statistics of rice across either single or all growth periods for yield estimation. These approaches are limited by a delay in yield estimation and are insufficient in the exploration of time-series feature variables at the pixel scale. This study presents the development of a novel deep-learning framework designed for the early estimation of rice yield in Qian Gorlos County, Northeast China. The framework utilizes a long short-term memory neural network integrated with an attention mechanism (ALSTM). In this framework, the heading stage–milk ripening stage is the time window for early yield estimation, and the vegetation indices Normalized Difference Red Edge (NDRE), Green Chlorophyll Vegetation Index (GCVI), and Normalized Difference Water Index (NDWI) from the rice transplanting to the milk ripening stage are time-series feature variables. The yield estimation precision is R² = 0.88, RMSE = 341.82 kg/ha, MAE = 280.29 kg/ha, outperforming LASSO (R² = 0.71, RMSE = 567.10 kg/ha, MAE = 487.38 kg/ha), RF (R² = 0.79, RMSE = 506.70 kg/ha, MAE = 418.90 kg/ha), and LSTM (R² = 0.83, RMSE = 451.11 kg/ha, MAE = 326.31 kg/ha). The ALSTM introduced in this paper demonstrates its robustness after being generalized to the 2022 growing season. It can forecast the current-year rice yield two months prior to harvest, providing a valuable reference for developing field management strategies to enhance rice productivity. Full article

The sustainability of rice-cropping systems hinges on balancing resources, output, and environmental impacts. China is revitalizing the ancient ratoon rice (RR) system for input savings and environmental benefits. Prior research has explored the RR system’s performance using various individual indicators, but few studies have focused on its overall balance of these factors. Environmental efficiency (EE) analysis addresses this gap. Using field survey data from Hunan Province in China and the slacks-based data envelopment analysis method, we quantified the EE of the RR, double-season rice (DR), and single-season rice (SR) systems. Key findings include: (1) the RR system outperforms in carbon emissions and non-point source pollution; (2) the RR system’s EE is 0.67, significantly higher than the DR (0.58) and SR (0.57) systems, indicating superior performance; and (3) despite its relatively high EE, the RR system can still improve, mainly due to input redundancy and production value shortfall. These findings provide strategies for optimizing RR systems to enhance agricultural sustainability. Full article

Based on a soil analysis of individual crops, lower nitrogen (N) inputs may affect soil fertility and the soil’s capacity for carbon sequestration. This study investigates the changes in soil nitrogen levels, the amounts of labile and recalcitrant carbon fractions, and their relationship to soil organic carbon (SOC) over the course of a single crop season. We conducted this study on seven crops in the provinces of Chiang Mai, Lamphun, and Lampang in northern Thailand, from February 2022 to December 2023. The farmer plots, which included litchi, mango, banana, maize, cabbage, garlic, and paddy rice, underwent three nitrogen addition treatments: high-nitrogen fertilizer (FP), reduced-nitrogen fertilizer informed via soil analysis (FS), and fertilizer absence (FZ). Soil samples were collected from a depth of 0 to 30 cm following the harvest of each crop. Subsequently, we utilized these samples to distinguish between labile and recalcitrant carbon fractions and assessed the impact of reduction through a one-way ANOVA. This study indicated a reduced availability of nitrogen, with the recalcitrant carbon fractions being the fine fraction (FF) and less labile carbon (LLB_C). The labile organic carbon fraction, referred to as LB_C, exhibited no change in FP treatment, in contrast to the non-fine fraction (NFF) and permanganate-oxidizable carbon (POXC). Our concern was to reduce the quantity of synthetic nitrogen fertilizer to achieve a lower level of soil organic carbon (SOC) and decreased nitrogen availability. These findings underscore the importance of considering N management when assessing soil carbon dynamics in agricultural soils, and, in future work, we should therefore model the optimal N input for crop yield, soil fertility, and soil carbon storage. Full article

Single-season rice describes the area under rice cultivation from May–October of the year. Many scholars have used lower-resolution data to study single-season rice in different regions, but using high-precision and high-resolution single-season rice data can reveal new phenomena. This paper uses a long-time-series, high-precision, and high-resolution single-season rice cultivation dataset to conduct an in-depth analysis of the spatial–temporal variability characteristics of single-season rice in Jiangsu Province, China, from 2017 to 2021. It explores the correlation between meteorological factors and greenhouse gasses for single-season rice. It analyzes the driving role of social factors on single-season rice. The results showed that single-season rice was mainly grown in the central and northeastern regions of the study area. The single-season rice cultivation was significantly reduced in 2020 due to the impact of COVID-19. Single-season rice strongly correlates with meteorological factors in time but shows a weak spatial correlation. This is because human factors largely dominate the area under single-season rice cultivation. Methane emissions in the study area are mainly influenced by anthropogenic activities rather than single-season rice. Social factors are essential in controlling single-season rice cultivation in the study area. This study was conducted in Jiangsu Province, China. Still, the methodology and results have important implications for agricultural production and environmental management studies in other regions, and some findings have general applicability. Full article

Sweet corn (Zea mays var. rugosa Bonaf.) is a crop with a high economic benefit in tropical and subtropical regions. Heat tolerance analysis and heat-tolerant gene mining are of great significance for breeding heat-resistant varieties. By combining improved genotyping using targeted sequencing (GBTS) with liquid chip (LC) technology, a high-density marker array containing 40 K multiple single polynucleotide polymorphisms (mSNPs) was used to genotype 376 sweet corn inbred lines and their heat-stress tolerance was evaluated in the spring and summer of 2019. In general, plant height, ear height and the number of lateral branches at the first level of the male flowers were reduced by 24.0%, 36.3%, and 19.8%, respectively. High temperatures in the summer accelerated the growth process of the sweet corn, shortening the days to shedding pollen by an average of 21.6% compared to the spring. A genome-wide association study (GWAS) identified 85 significant SNPs distributed on 10 chromosomes. Phenotypes in the spring and summer were associated with the 21 and 15 loci, respectively, and significant phenotypic differences between the two seasons caused by the temperature change were associated with the 49 SNP loci. The seed setting rate (SSR) was more susceptible to heat stress. An annotation analysis identified six candidate genes, which are either heat shock transcription factors (Hsfs) or heat shock proteins (Hsps) in Arabidopsis and rice (Oryza sativa), and these candidate genes were directly and indirectly involved in the heat-resistant response in the sweet corn. The current findings provide genetic resources for improving the heat-stress tolerance of sweet corn by molecular breeding. Full article

Along with climate warming, extreme heat events have become more frequent, severe, and seriously threaten rice production. Precisely evaluating rice heat levels based on heat duration and a cumulative intensity index dominated by temperature and humidity is of great merit to effectively assess regional heat risk and minimize the deleterious impact of rice heat along the middle and lower reaches of the Yangtze River (MLRYR). This study quantified the response mechanism of daytime heat accumulation, night-time temperature, and relative humidity to disaster-causing intensity in three categories of single-season rice heat (dry, medium, and wet conditions) using Fisher discriminant analysis to obtain the Heat Comprehensive Intensity Index daily (HCIId). It is indicated that relative humidity exhibited a negative contribution under dry heat, i.e., heat disaster-causing intensity increased with decreasing relative humidity, with the opposite being true for medium and wet heat. The Kappa coefficient, combined with heat duration and cumulative HCIId, was implemented to determine classification thresholds for different disaster levels (mild, moderate, and severe) to construct heat evaluation levels. Afterwards, spatiotemporal changes in heat risk for single-season rice through the periods of 1986–2005, 2046–2065 and 2080–2099 under SSP2-4.5 and SSP5-8.5 were evaluated using climate scenario datasets and heat evaluation levels carefully constructed. Regional risk projection explicitly revealed that future risk would reach its maximum at booting and flowering, followed by the tillering stage, and its minimum at filling. The future heat risk for single-season rice significantly increased under SSP5-8.5 than SSP2-4.5 in MLRYR. The higher risk would be highlighted in eastern Hubei, eastern Hunan, most of Jiangxi, and northern Anhui. As time goes on, the heat risk for single-season rice in eastern Jiangsu and southern Zhejiang will progressively shift from low to mid-high by the end of the twenty-first century. Understanding the potential risk of heat exposure at different growth stages can help decision-makers guide the implementation of targeted measures to address climate change. The proposed methodology also provides the possibility of assessing other crops exposure to heat stress or other extreme events. Full article

Satellite-derived evapotranspiration (ETa) products serve global applications, including drought monitoring and food security assessment. This study examines the applicability of ETa data from two distinct sources, aiming to analyze its correlation with crop yield (rice, maize, barley, soybean). Given the critical role of crop yield in economic and food security contexts, monthly and yearly satellite-derived ETa data were assessed for decision-makers, particularly in drought-prone and food-insecure regions. Utilizing QGIS, zonal statistics operations and time series graphs were employed to compare ETa with crop yield and ET anomaly. Data processing involved converting NRSC daily data to monthly and extracting single-pixel ET data using R Studio. Results reveal USGSFEWS as a more reliable ETa source, offering better accuracy and data continuity, especially during monsoon seasons. However, the correlation between crop yield and ETa ranged from 12% to 35%, while with ET anomaly, it ranged from 35% to 55%. Enhanced collection of satellite-based ETa and crop-yield data is imperative for informed decision-making in these regions. Despite limitations, ETa can moderately guide decisions regarding crop-yield management. Full article

Tillage practices are of critical importance in maintaining soil quality on cropland and for food production, with rice cultivation representing a significant portion of the world’s food production and greenhouse gas (GHG) emissions. While numerous studies have examined the effects of reduced and no-tillage on soil GHG emissions and rice yields, the impact of adopting a rotational approach to tillage practices on the rice cultivation cycle remains uncertain. In this study, we conducted a four-year (2017–2020) field experiment in a single rice-growing area in Northeast China with the aim of investigating the effects of different tillage practices on GHG emissions from paddy fields and rice yields under full straw return conditions. We set up three experimental treatments: rotary tillage, plowing, and rotational tillage (i.e., a combination of one year of plowing and one year of rotary tillage). The results showed that averaged across all treatments, average methane (CH₄, 302.6 ± 51.1 kg ha⁻¹) and nitrous oxide (N₂O, 0.86 ± 0.361 kg ha⁻¹) emissions and rice yield (9.0 ± 0.9 t ha⁻¹) did not exhibit significant inter-annual variability during the entire experimental period and were comparable to the average for the region. The ranking of GHG emissions during the rice-growing season was as follows: rotary tillage > plowing > rotational tillage. Across the experimental period, CH₄ and N₂O emissions were 9.1% and 8.5% lower in the plowing treatment and 21.2% and 13.1% lower in the rotational tillage treatment compared to the rotary tillage treatment. During the experimental period, there was no significant effect of tillage treatments on rice yield. This reduction in emissions may be attributed to changes in soil penetration resistance. In the rotational and plowing treatments, soil penetration resistance was in a range more adapted to rice growth and GHG emissions reduction compared to the rotary tillage treatment. The yield-scale GHG emission intensity was reduced by 12.7% and 26.1% in the plowing and rotational tillage treatments, respectively, in comparison to the rotary tillage treatment. This suggests that rotational tillage is a management practice that can achieve greenhouse gas emission reductions in paddy fields and stabilize or possibly increase rice yields. Consequently, the results demonstrated that a rotational alternation of multiple tillage practices is a synergistic strategy for achieving low carbon and high yield in rice in the cold rice-growing region of Northeast China. Full article

Regenerated rice, characterized by single planting and double harvesting, saves labor and costs, significantly contributing to global food security. Hyperspectral imaging technology, which integrates image and spectral data, provides comprehensive, non-destructive, and pollution-free vegetation canopy analysis, making it highly effective for crop nutrient diagnosis. In this study, we selected two varieties of regenerated rice for field trials. Hyperspectral images were captured during key growth stages (flush, grouting, and ripening) of both the first and regenerated seasons. Utilizing a two-dimensional convolutional neural network (2D-CNN) as a deep feature extractor and a fully connected layer for nitrogen content prediction, we developed a robust model suitable for estimating nitrogen content in regenerated rice. The experimental results demonstrate that our method achieves a mean squared error (MSE) of 0.0008, significantly outperforming the back-propagation (BP) network and multiple linear regression by reducing the MSE by 0.0151 and 0.0247, respectively. It also surpasses the one-dimensional convolutional neural network (1D-CNN) by 0.003. This approach ensures accurate nitrogen content prediction throughout the growth cycle of regenerated rice, aiding in yield and economic benefit enhancement. Full article

Methane emissions from flooded rice paddies are a major source of atmospheric methane and represent a significant greenhouse gas with high climate-forcing potential due to anthropogenic activities globally. For sustainable agriculture, it is necessary to find effective methods for mitigating greenhouse gas emissions without reducing crop productivity. We investigated mechanisms to reduce methane emissions during rice cultivation by applying rice straw, rice husk biochar, humic acid, and a humic acid–iron complex, assessing greenhouse gases and rice yield over a single season. The results demonstrated that the treatment plots with rice straw and the humic acid–iron complex significantly reduced methane emissions (563 ± 113.9 kg ha⁻¹) by 34.4% compared to plots treated with rice straw alone (859 ± 126.4 kg ha⁻¹). Rice yield was not compromised compared to the control group treated with only NPK fertilizer, and growth in terms of plant height and tiller number was enhanced in the plots treated with rice straw and the humic acid–iron complex. Conversely, the plots treated solely with rice husk biochar and humic acid did not show a methane reduction effect when compared to the NPK treatment. The humic acid–iron complex has demonstrated potential as a methane mitigation agent with practical applicability in the field, warranting further long-term studies to validate its effectiveness. Full article