Search Results (966)

Sustainable nitrogen management is crucial for long-term food security and environmental protection in rice farming systems. However, the spatial patterns of nitrogen availability at the community level remain poorly understood, hindering the development of effective sustainable management strategies. This study introduces a novel application of spatial autoregressive analysis to investigate available nitrogen distribution in paddy soils across a rice farming community in Kyoto, Japan. Soil samples from 61 plots, including organically farmed ones, were analyzed for available nitrogen and various physicochemical properties. Contrary to the hypothesis of high variability between adjacent plots, significant positive spatial autocorrelation in available nitrogen was observed, revealing previously unrecognized community-level patterns. The spatial Durbin model outperformed traditional regression approaches and revealed complex spatial interactions in soil properties. Water-soluble organic carbon and humus content showed strong but opposing effects, with a positive direct impact but negative spatial interaction, suggesting topography-driven accumulation processes. Water-soluble nitrogen exhibited reverse patterns with negative direct effects but positive spatial interaction, indicating potential nutrient transport through water movement. These findings highlight the importance of considering both direct and indirect spatial effects in understanding soil fertility patterns, challenging the conventional plot-by-plot management approach. This methodological advancement provides new perspectives for more effective, community-scale soil management strategies in rice farming systems. Moreover, it demonstrates an innovative approach to maximizing the value of outsourced soil analysis data, providing a model for more comprehensive utilization of such data in agricultural research. By enabling more targeted and efficient nitrogen management practices that consider both plot-level processes and landscape-scale interactions, this study potentially contributes to the development of more sustainable and resilient rice production systems. Full article

Pest infestations significantly impact rice production and threaten food security. Remote sensing offers a vital tool for the non-destructive, rapid detection of rice pests. Existing studies often focus on laboratory conditions at the leaf level, limiting their applicability for precise pesticide application. Therefore, this study aimed to develop a method for detecting rice pests (rice leaf folders) in paddy fields based on unmanned aerial vehicle (UAV) hyperspectral data. Firstly, a UAV imaging system collected hyperspectral images of rice plants in both the jointing and heading stages. A total of 222 field plots for investigating rice leaf folders was established during these two periods. Secondly, 23 vegetation indices were calculated as candidates for identifying rice pests. Then, hyperspectral data and field investigation data from the jointing stage were used to construct a machine learning (extreme gradient boosting, XGBoost) algorithm for detecting rice pests. The results showed that the XGBoost model exhibited the best performance when eight vegetation indices were considered as the selected input features for model construction: the Red-edge Normalized Difference Vegetation Index (red-edge NDVI), Structure Insensitive Pigment Index (SIPI), Enhanced Vegetation Index (EVI), Atmospherically Resistant Vegetation Index (ARVI), Soil-Adjusted Vegetation Index (SAVI), Red-edge Chlorophyll Index (CIred-edge), Pigment-Specific Simple Ratio₆₈₀ (PSSR₆₈₀), and Carotenoid Reflectance Index₇₀₀ (CPI₇₀₀). The training and testing accuracies reached 87.46% and 86%, respectively. Furthermore, the heading stage application confirmed the model’s feasibility. Thus, the XGBoost model with input features of eight vegetation indices provides an effective and reliable method for detecting rice leaf folders, supporting real-time, precise pesticide use in rice cultivation. Full article

The winter planting of green manure (GM) is widely used in South China to reduce chemical nitrogen (N) fertilizer use, improve soil fertility, and maintain rice yields, but its effect on N runoff loss in paddy fields remains unclear. This study combines multi-site field experiments with a process model (WHCNS-Rice) to assess how GM with reduced N fertilizer impacts N runoff loss and its forms in the Yangtze River’s middle and lower reaches, considering different rainfall years. The network field experiments included four treatments: conventional fertilization (FR), conventional fertilization plus straw return (FRS), GM with a 40% N reduction (MR), and GM-straw combined return with a 40% N reduction (MRS). Monitoring the results showed that compared to the winter fallow treatment, the GM treatments reduced the peak and average total N (TN) concentrations by 11.1–57.9% (average 26.9%) and 17.1–27.3% (average 22.3%), respectively. The TN runoff loss under the GM treatment decreased by 3.50–10.61 kg N ha⁻¹ (22.5–42.1%). GM primarily reduced the runoff loss of dissolved inorganic N (DIN), with reductions at different sites ranging from 0.22 to 9.66 kg N ha⁻¹ (8.4–43.4%), indicating GM effectively decreases N runoff by reducing DIN. Model simulations of ponding water depth, runoff, TN concentration in surface water, and TN loss in paddy fields produced the consistency indices and simulation efficiencies of 0.738–0.985, 0.737–0.986, 0.912–0.986, and 0.674–0.972, respectively, indicating that the model can be used to evaluate water consumption and N runoff loss in the GM-paddy system. The simulations showed that GM with a 40% N fertilizer significantly reduced N runoff loss under all rainfall conditions, with the greatest reductions in wet years. Under wet, normal, and dry conditions, the GM treatments significantly reduced average TN loss by 0.37–5.53 kg N ha⁻¹ (12.77–29.17%), 0.21–5.32 kg N ha⁻¹ (9.95–24.51%), and 0.02–3.2 kg N ha⁻¹ (1.78–23.19%), respectively, compared to the winter fallow treatment. These results indicate that the combination of GM and a 40% reduction in N fertilizer can significantly reduce N runoff loss from paddy fields, demonstrating good effectiveness under various rainfall conditions, making it a green production model worth promoting. Full article

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. Full article

Weeds significantly impact paddy yields, and herbicides offer a cost-effective, rapid, and efficient solution compared to manual weeding, ensuring agricultural productivity. Tripyrasulfone, a novel 4-hydroxyphenylpyruvate dioxygenase (HPPD) inhibitor developed by Qingdao Kingagroot Chemicals Co., Ltd., has demonstrated high efficacy when applied post-emergence, causing characteristic foliar bleaching in susceptible weed species, distinct from conventional acetolactate synthase, acetyl-CoA carboxylase, and synthetic auxin herbicides. This study investigates the impact of tripyrasulfone on the activity of key soil enzymes (urease (UE), acid phosphatase (ACP), sucrase (SC), catalase (CAT), and dehydrogenase (DHA)) in paddy soils from Jilin Province and Shandong Province. Different doses of tripyrasulfone (0.1, 1.0, and 2.5 mg kg⁻¹) were applied, and the enzymatic activities were measured. Results indicated that tripyrasulfone initially inhibited UE and ACP activities before activating them. On the 20th day after treatment, UE activity had returned to control levels, whereas ACP activity remained significantly higher, showing long-lasting activation. SC and CAT activities were inhibited but gradually recovered to control levels. Furthermore, DHA activity was activated with a sustained effect, remaining significantly higher than the control group even 20 days after treatment. Overall, the impact of tripyrasulfone on soil enzyme activities diminished over time, suggesting that tripyrasulfone posed minimal long-term ecological risk to soil health. 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

In South Korea, paddy fields are increasingly being planted with soybeans to address rice supply and demand issues and increase soybean self-sufficiency. The field crops cannot grow healthily without adequate drainage due to the paddy fields storing water easily. In this study, we identified that Rice Husk Filling Drainage Method (RHDM) technology improved soil permeability and soil aeration. We also found that the soil moisture content was reduced and the water table remained at a lower level in the RHDM plot as compared to the control plot. The soybean moisture stress index showed that in the RHDM plot, the safety standard for stress due to excessive moisture at the 2 m interval was met. The soybean yield was increased by up to 35% in the 2 m RHDM construction interval compared to the control plot. In addition, the high hydraulic conductivity of the rice husk used as the hydrophobic material confirmed sufficient drainage performance and was considered economically advantageous. Therefore, our results show that RHDM is a highly efficient and economical drainage method in poorly drained paddy soils. Drainage management is essential for stable crop production in poorly drained paddy fields. Our research findings suggest that an efficient open field water management method is viable, which we believe will lead to future advances in open field smart agriculture. Full article

The rice variety Bomba is grown in Calasparra—a rice of origin in southeast Spain—resulting in a product with excellent cooking quality, although its profitability has declined in recent years due to low grain yields and susceptibility to rice blast disease (Pyricularia oryzae Cavara). An innovation project to test the efficacy of mechanized transplanting against traditional direct seed sowing was conducted in 2022 and 2023 at four locations for the first time. A lower plant density (67–82 plants m⁻²) and shorter plants with higher leaf nitrogen content were observed in transplanted plots compared with seed sowing (130–137 plants m⁻²) in the first year. The optimal climatic conditions for P. oryzae symptom appearance were determined as temperatures of 25–29 °C and a 50–77% relative humidity. The most-affected sowing plots presented 3–20% leaf area damage and a reduction in yield to values of 1.5 t ha⁻¹ in the first year and 2.12 t ha⁻¹ in the second year. In transplanted plots, there was generally less humidity at the plant level and therefore, disease incidence was low in both seasons. Grain yields did not significantly differ among the treatments studied; however, there were differences in the yield components of panicle density and the number of grains for panicles. Principal component analysis revealed two principal components that explained 81% of the variability. Variables related to yield contributed positively to the first component, while plant biomass variables contributed to the second component. Plant density, tiller density, and panicle density were found to be positively correlated (r > 0.81 ***). Overall, transplanting (frame of 30 × 15–18 cm²) resulted in uniform crop growth with less rice blast disease, as well as higher grain yields (2.92–3.89 t ha⁻¹), in comparison with the average for the whole D.O. Calasparra (2.3–2.5 t ha⁻¹) in both seasons and a good percentage of whole grains at milling. This is novel knowledge which can be considered useful for farmers operating in the region. Full article

In order to effectively decrease cadmium (Cd) in rice grains in contaminated paddy soil and maintain the safe production of rice, identifying excessive Cd in rice caused by rice varieties or soil Cd is critical, but it is currently lacking. In the present study, the soil ethylenediaminetetraacetic acid (EDTA)-extractable Cd (EDTA-Cd) and the bioaccumulation factors of rice based on EDTA-Cd (BCF_EDTA-Cd) were used to develop an approach to identify excessive Cd in rice caused by rice varieties or soil Cd. Based on an empirical soil–plant transfer model and species sensitivity distribution (SSD), BCF_EDTA-Cd and EDTA-Cd were divided into five grades. The results showed that the five grades of the EDTA-Cd (minimum value less than 0.11 mg/kg and maximum value greater than 2.93 mg/kg) and BCF_EDTA-Cd (minimum value less than 0.09 and maximum value greater than 1.40) were classified in the normal soil pH range. Further, the conversion equation between EDTA-Cd and diethylene triamine pentaacetic acid (DTPA)-Cd was obtained through linear regression analysis using 67 sets of soil data from the literature. In addition, the four selected rounding thresholds for the percentage of EDTA-Cd to total soil Cd (EDTA-Cd) (%) were 52.5, 67.5, 82.5, and 97.5%. A selected soil EDTA-Cd (%) (about 75%) can be used to identify the status of soil bioavailability, especially in soil with high background Cd. Finally, a set of 1084 pairs of rice and soil data for Cd-contaminated soils was used to investigate the respective contributions of rice varieties and soil Cd when Cd in rice exceeds the limit (0.2 mg/kg). Based on field experiment data, a systematic identification approach for the causes of rice Cd exceeding the limit, soil Cd or rice variety, was established and applied. In conclusion, under Cd exposure conditions, the importance of the causes of Cd in soil and rice varieties can be identified, and their contributions can be distinguished, thus helping to identify the causes of Cd contamination in rice. Full article

We examined the postharvest rice value chain among farmers in the Arequipa region of Peru, focusing on the stages of value creation after harvest. Our study is complemented by an economic analysis that provides insights into farmers’ decisions on whether or not to store rice after harvest. We found that farmers produced, on average, 65 tons of paddy rice on a 5 ha farm. Most farmers (over 85%) milled their paddy rice immediately after harvest, usually by paying a fee to a private mill. Milled rice was then sold to intermediaries (wholesalers and retailers). Approximately 13% and less than 1% of farmers stored their paddy rice before and after milling, respectively. Storage provided minimal financial benefits once grain preservation costs and price arbitrage were considered. Our findings offer guidance for policymakers and investment partners to enhance the efficiency of the postharvest rice value chain and to improve outcomes for farmers in Peru and other developing countries. Full article

The impact of climate change on methane (CH₄) emissions from rice production systems in the Coimbatore region (Tamil Nadu, India) was studied by leveraging field experiments across two main treatments and four sub-treatments in a split-plot design. Utilizing the closed-chamber method for gas collection and gas chromatography analysis, this study identified significant differences in CH₄ emissions between conventional cultivation methods and the system of rice intensification (henceforth SRI). Over two growing seasons, conventional cultivation methods reported higher CH₄ emissions (range: from 36.9 to 59.3 kg CH₄ ha⁻¹ season⁻¹) compared with SRI (range: from 2.2 to 12.8 kg CH₄ ha⁻¹ season⁻¹). Experimental data were subsequently used to guide parametrization and validation of the DeNitrification–DeComposition (DNDC) model. The validation of the model showed good agreement between the measured and modeled data, as denoted by the statistical tests performed, which included CRM (0.09), D-index (0.99), RMSE (7.16), EF (0.96), and R² (0.92). The validated model was then used to develop future CH₄ emissions projections under various shared socio-economic pathways (henceforth SSPs) for the mid- (2021–2050) and late (2051–2080) century. The analysis revealed a potential increase in CH₄ emissions for the simulated scenarios, which was dependent on specific soil and irrigation management practices. Conventional cultivation produced the highest CH₄ emissions, but it was shown that they could be reduced if the current practice was replaced by minimal flooding or through irrigation with alternating wetting and drying cycles. Emissions were predicted to rise until SSP 370, with a marginal increase in SSP 585 thereafter. The findings of this work underscored an urgency to develop climate-smart location-specific mitigation strategies focused on simultaneously improving current water and nutrient management practices. The use of methanotrophs to reduce CH₄ production from rice systems should be considered in future work. This research also highlighted the critical interaction that exists between agricultural practices and climate change, and emphasized the need to implement adaptive crop management strategies that can sustain productivity and mitigate the environmental impacts of rice-based systems in southern India. Full article

The accumulation of potential toxic elements in agricultural soil and rice is of particular concern in China. However, studies on the risk assessment of these elements in regional soil–rice systems remain limited. The aim of this study is to evaluate the pollution status and potential health risk of potential toxic elements in typical paddy soil and rice in Henan Province. A total of 80 soil samples and corresponding rice samples were collected to determine the contents of Cd, Pb, As, Cr, Cu, Zn, and Ni, and to assess their potential health risks to local consumers. Results showed that the average contents of these elements in soils were below the national risk screening values in GB15618-2018. Only the average content of Cr in rice exceeded the limit in GB 2762-2022 specified by the national food safety standard. The rates of exceeding the limits for Cd, Pb, As, and Cr in rice samples were 13.89%, 15.28%, 15.28%, and 27.78%, respectively. The health risk assessment indicated that rice intake for both adults and children caused carcinogenic and non-carcinogenic health risks to varying degrees. Local residents are advised to purchase rice from outside the study area to meet their daily needs and strictly regulate the pollution of potential toxic elements within the area. Full article

Nitrogen is a vital nutrient for rice growth; however, its inefficient use often results in nutrient loss, environmental degradation, and the emission of greenhouse gases. In this study, a rice paddy simulation was conducted under different water levels (1–4 cm), incorporating a comprehensive analysis of nitrogen dynamics, environmental factors, and microbial communities to evaluate the impact of water levels on nitrogen concentrations and microbial composition. The results indicated that the water level had a greater impact on nitrogen concentrations in surface water than in soil water. Compared to low water level conditions (1 cm), the average concentrations of ammonium nitrogen, nitrate nitrogen, and nitrite nitrogen in surface water under 2–4 cm water levels decreased by approximately 53.8%, 36.7%, and 78.9%, respectively. Water levels also influenced the microbial composition and nitrogen cycling in paddy soil, with lower water levels promoting aerobic processes such as nitrification, while higher water levels facilitated anaerobic processes such as denitrification and dissimilatory nitrate reduction to ammonium. Correspondingly, microbial composition shifted, with aerobic bacteria predominating in shallow water conditions and anaerobic bacteria flourishing under deeper water. These findings suggest that optimized water management, particularly through shallow irrigation, may mitigate nitrogen loss and improve nitrogen use efficiency. Nevertheless, additional field studies are necessary to validate these results and explore their interaction with other agricultural practices. Full article

The agronomic benefits of pesticides combined with amino acid application to increase rice production have been recognized, but they are still not well-known for greenhouse gas (GHG) emissions and mitigation in irrigated paddy fields. Thus, this study was conducted to investigate the combined effects of pesticide and amino acid application on rice yield and methane (CH₄) emissions in a Korean rice paddy. A field experiment was conducted with five levels: none (no pesticide application, T1), different conventional practices (combined application of insecticides and fungicide, T2 and T3), and GroMore^® programs (combined application of insecticides, fungicides, and amino acids, T4 and T5). Rice grain yield and yield components were obtained using agronomic measurements. To determine the greenhouse gas intensity (GHGI) of each treatment, CH₄ emissions were measured throughout the rice growing period. Results showed that the chemical applications in combination with amino acids in T4 obtained a higher grain yield and number of panicles per plant compared to T1, T2, and T3, while T4 and T5 showed no difference on filled spikelets except for T2. T3 and T5 showed lower respective cumulative CH₄ emissions by 30% and 32% during the entire rice growing season, compared to no chemical application (T1). Meanwhile, N₂O emissions were negligible in all treatments because the paddy field was flooded most of the growing season. The results of the impact of GroMore^® programs on relatively higher grain yield and lower GHG emissions are presented. In conclusion, the application of pesticides combined with amino acids obtained lower GHGI values. Full article

Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy ($V_{cmax,0}^{25}$), was quantified using various spatial information representation methods, including mean (μ_ref) and standard deviation (σ_ref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of $V_{cmax,0}^{25}$ and GPP estimation. CNN methods achieved the best $V_{cmax,0}^{25}$ estimation, with an R of 0.94, an RMSE of 19.44 μmol m⁻² s⁻¹, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 μmol m⁻² s⁻¹, and an MdAPE of 23%. The μ_ref-GLCM texture features and μ_ref-LBPH joint-driven models also gave promising results. However, σ_ref contributed less to $V_{cmax,0}^{25}$ estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The μ_ref-LBPH joint-driven model mainly prioritized reflectance information. The μ_ref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle. Full article