Search Results (33)

Maize is one of the top three crops globally, ranking only behind rice and wheat, making it an important crop of interest. Aboveground biomass is a key indicator for assessing maize growth and its yield potential. This study developed an efficient and stable biomass prediction model to estimate the aboveground biomass (AGB) of spring maize (Zea mays L.) under subsurface drip irrigation in arid regions, based on UAV multispectral remote sensing and machine learning techniques. Focusing on typical subsurface drip-irrigated spring maize in arid Xinjiang, multispectral images and field-measured AGB data were collected from 96 sample points (selected via stratified random sampling across 24 plots) over four key phenological stages in 2024 and 2025. Sixteen vegetation indices were calculated and 40 texture features were extracted using the gray-level co-occurrence matrix method, while an integrated feature-selection strategy combining Elastic Net and Random Forest was employed to effectively screen key predictor variables. Based on the selected features, six machine learning models were constructed, including Elastic Net Regression (ENR), Gradient Boosting Decision Trees (GBDT), Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Extreme Gradient Boosting (XGB). Results showed that the fused feature set comprised four vegetation indices (GRDVI, RERVI, GRVI, NDVI) and five texture features (R_Corr, NIR_Mean, NIR_Vari, B_Mean, B_Corr), thereby retaining red-edge and visible-light texture information highly sensitive to AGB. The GPR model based on the fused features exhibited the best performance (test set R² = 0.852, RMSE = 2890.74 kg ha⁻¹, MAE = 1676.70 kg ha⁻¹), demonstrating high fitting accuracy and stable predictive ability across both the training and test sets. Spatial inversions over the two growing seasons of 2024 and 2025, derived from the fused-feature GPR optimal model at four key phenological stages, revealed pronounced spatiotemporal heterogeneity and stage-dependent dynamics of spring maize AGB: the biomass accumulates rapidly from jointing to grain filling, slows thereafter, and peaks at maturity. At a constant planting density, AGB increased markedly with nitrogen inputs from N0 to N3 (420 kg N ha⁻¹), with the high-nitrogen N3 treatment producing the greatest biomass; this successfully captured the regulatory effect of the nitrogen gradient on maize growth, provided reliable data for variable-rate fertilization, and is highly relevant for optimizing water–fertilizer coordination in subsurface drip irrigation systems. Future research may extend this integrated feature selection and modeling framework to monitor the growth and estimate the yield of other crops, such as rice and cotton, thereby validating its generalizability and robustness in diverse agricultural scenarios. Full article

Against the backdrop of global warming and a reshaped hydrothermal regime, the albic soil belt of the Sanjiang Plain, a major grain base, requires farm-scale evidence of how meteorological variability couples with staple-crop yields. Using meteorological and yield records from 2000 to 2023 at three large farms (859, 850, and 852), this study applied the Mann–Kendall test, wavelet and cross-wavelet coherence, Pearson correlation, gray relational analysis, and principal component analysis to track the evolution of air temperature, precipitation, evaporation, sunshine duration, relative humidity, and surface temperature, and to assess their multi-scale impacts on rice, corn, and soybean yields. The region warmed and became wetter overall, with dominant periodicities near 21a and 8a. Across the three farms, yields were significantly and positively associated with precipitation and air temperature (R > 0.60). Rice yield correlated strongly and negatively with evaporation at Farm 850 (R = −0.61) and at Farm 852 (R = −0.503). At Farm 859, gray relational analysis ranked precipitation highest for rice, corn, and soybean (γ = 0.853, 0.844, and 0.826), followed by air temperature. The first two principal components explained 67.66% of the variance; PC1 (41.80%) loaded positively for air temperature, and PC2 (25.86%) for precipitation and relative humidity. Cross-wavelet coherence indicated stable coupling between yields and hydrothermal variables, with the strongest coupling for rice with precipitation and air temperature, prominent coupling for corn with air temperature and sunshine duration, and stage-dependent responses of soybean to precipitation and evaporation. These results show that long-term trends together with phase-specific oscillations jointly shape yield variability. The findings support translating phase identification and sensitive windows into crop-specific rules for sowing or transplanting arrangements, irrigation timing, and early warning, providing a quantitative basis for climate-adaptive management on the study farms and, where soils, management, and microclimate are comparable, for the wider Sanjiang Plain. Full article

Assessing Leaf Nitrogen Content (LNC) is critical for evaluating crop nutritional status and monitoring growth. While Unmanned Aerial Vehicle (UAV) remote sensing has become a pivotal tool for nitrogen monitoring at the field scale, current research predominantly relies on uni-modal feature variables. Consequently, the integration of multidimensional feature information for nitrogen assessment remains largely underutilized in existing literature. In this study, the four types of feature variables (two kinds of spectral indices, color space parameters and texture features from UAV images of RGB and multispectral sensors) were extracted from three dimensions, and crop nitrogen-sensitive feature variables were selected by GCA (Gray Correlation Analysis), followed by one fused deep neural network (DNN-F2) for remote sensing monitoring of rice nitrogen and a comparative analysis with five common machine learning algorithms (RF, GPR, PLSR, SVM and ANN). Experimental results indicate that the DNN-F2 model consistently outperformed conventional machine learning algorithms across all three growth stages. Notably, the model achieved an average R² improvement of 40%, peaking at the rice jointing stage with R² of 0.72, RMSE of 0.08, and NRMSE of 0.019. The study shows that the fusion of multidimensional feature information from UAVs combined with deep learning algorithms has great potential for nitrogen nutrient monitoring in rice crops, and can also provide technical support to guide decisions on fertilizer application in rice fields. Full article

In the present study, a new Ganoderma sp. (ZHM1939) was collected from Lincang, Yunnan, China, and described on the basis of morphological characters and multigene phylogenetic analysis of rDNA-ITS, TEF1α and RPB2 sequences. This fungus is characterized by the exceptionally large basidiomata, oval shape, a pileus measuring 63.86 cm long, 52.35 cm wide, and 21.63 cm thick, and a fresh weight of 80.51 kg. The skeleton hyphae from the basidiocarp are grayish to grayish-red in color, septate, and 1.41–2.75 μm in diameter, with frequently dichotomous branched and broadly ellipsoid basidiospores. The basidiospores are monocellular, ellipsoid, with round ends or one slightly pointed end, brown–gray in color, and measured 6.52–10.26 μm × 4.68–7.17 μm (n = 30). When cultured for 9 days at 25 ± 2 °C on PDA, the colony was white, ellipsoid or oval, with slightly ragged edges, measured Φ58.26 ± 3.05 mm (n = 5), and the growth rate = 6.47 mm/day; prosperous blast-spores formed after culturing for 21 days, making the colony surface powdery-white. The mycelia were septate, hyaline, branching at near-right angles, measured Φ1.28–3.32 μm (n = 30), and had some connections. The blast-spores were one-celled, elliptic or barley-seed shaped, and measured 6.52–10.26 μm × 4.68–7.17 μm (n = 30). Its rDNA-ITS, TEF1α and RPB2 sequences amplified through PCR were 602 bp, 550 bp and 729 bp, respectively. Blast-n comparison with these sequences showed that ZHM1939 was 99.67–100% identical to related strains of Ganoderma mutabile. A maximum likelihood phylogenic tree using the concatenated sequence of rDNA-ITS, TEF1α and RPB2 was constructed and it showed that ZHM1939 clustered on the same terminal branch of the phylogenic tree with the strains Cui1718 and YUAN 2289 of G. mutabile (Bootstrap support = 100%). ZHM1939 could grow on all the 15 original inoculum substrates tested, among which the best growth was shown on substrate 2 (cornmeal 40 g, sucrose 10 g, agar 20 g), with the fastest colony growth rate (6.79 mm/day). Of the five propagation substrates tested, substrate 1 (wheat grains 500 g, gypsum powder 6.5 g and calcium carbonate 2 g) resulted in the highest mycelium growth rate (7.78 mm/day). Among the six cultivation substrates tested, ZHM1939 grew best in substrate 2 (cottonseed hulls 75 g, rice bran 12 g, tree leaves 5 g, cornmeal 5 g, lime powder 1 g, sucrose 1 g and red soil 1 g) with a mycelium growth rate of 7.64 mm/day. In conclusion, ZHM1939 was identified as Ganoderma mutabile, which is a huge mushroom and rare medicinal macrofungus resource. The original inoculum substrate 9, propagation substrate 1 and cultivation substrate 2 were the most optimal substrates for producing the original propagation and cultivation inocula of this macrofungus. This is the first report on successful growing conditions for mycelial production, but basidiocarp production could not be achieved. The results of the present work establish a scientific foundation for further studies, resource protection and application development of G. mutabile. Full article

Rice fields are one of the most important and extensive agro-ecosystems in the world. Italy is a major non-Asian rice producer, with a significant proportion of its yield originating from a vast area within the Po Valley, a region nourished by the waters of the Alps. While the biodiversity of these rice fields has been extensively documented for certain faunal groups, such as birds, there remains a paucity of research on the biodiversity of aquatic insects. A further challenge is the limited dissemination of findings, which have been primarily published in “gray” literature (local journals, newsletters and similar). Moreover, rice fields are of particular significance in the field of invasion biology, given their role in the arrival and spread of alien species. While the efficacy of rice fields as a substitute for the now-disappeared lowland natural environments is well documented, it is equally evident that traditional rice-growing techniques can require an unsustainable use of water resources, which threatens the biodiversity of the surrounding lotic systems. Here, we summarize and review multiple sources of entomological information from Italian rice fields, analyzing both publications in ISI journals and papers published in local journals (gray literature). In the near future, strategies that reduce the demand for irrigation, promote the cultivation of drought-tolerant crops, and utilize precision farming techniques will be implemented. The challenge will be balancing the need to reduce water withdrawal from rivers with the maintenance of wetlands where possible to support this pivotal component of regional biodiversity. Full article

Under the influence of water resource conservation policies, the annual water diversion volumes in irrigation areas have been steadily decreasing, leading to substantial changes in regional cropping systems. These shifts have profoundly impacted agricultural reactive nitrogen (Nr) emissions and surface water quality. This study focuses on the Yellow River Irrigation area of Ningxia, China, and employs a life cycle assessment method to quantitatively analyze fluctuations in the nitrogen footprint (NF) and gray water footprint (GWF) across three cropping systems—rice-maize intercropping, rice monoculture, and maize monoculture—during 2021–2023. The results indicate that rice monoculture exhibited significant variability in NF values (197.89–497.57 kg Neq·ha⁻¹), with NO₃⁻ leaching identified as the primary loss pathway (102.33–269.48 kg Neq·ha⁻¹). The GWF analysis revealed that in 2021, the region’s GWF peaked at 23.18 × 10⁴ m³·ha⁻¹, with water pollution predominantly concentrated in Pingluo County (8 × 10⁴ m³·ha⁻¹). LMDI analysis identified nitrogen fertilizer application as the main contributor to variations in NF, while surface water pollution was indirectly influenced by crop yield. Furthermore, gray correlation analysis highlighted a significant coupling relationship between NF and GWF, with nitrogen fertilizer application having the most pronounced impact on GWF. Therefore, in the face of the gradual tightening of water resources in the irrigation areas, the current situation of reduced water diversion should be adopted as early as possible, and initiatives such as the reduction of nitrogen fertilizer application and the adjustment of the planting area of dryland crops should be accelerated to cope with the problem of nitrogen pollution brought about by changes in the cropping system. Full article

Predicting rice yield accurately is crucial for enhancing farming practices and securing food supplies. This research aims to estimate rice yield in Peru’s Lambayeque region by utilizing spectral and textural indices derived from unmanned aerial vehicle (UAV) imagery, which offers a cost-effective alternative to traditional approaches. UAV data collection in commercial areas involved seven flights in 2022 and ten in 2023, focusing on key growth stages such as flowering, milk, and dough, each showing significant predictive capability. Vegetation indices like NDVI, SP, DVI, NDRE, GNDVI, and EVI2, along with textural features from the gray-level co-occurrence matrix (GLCM) such as ENE, ENT, COR, IDM, CON, SA, and VAR, were combined to form a comprehensive dataset for model training. Among the machine learning models tested, including Multiple Linear Regression (MLR), Support Vector Machines (SVR), and Random Forest (RF), MLR demonstrated high reliability for annual data with an R² of 0.69 during the flowering and milk stages, and an R² of 0.78 for the dough stage in 2022. The RF model excelled in the combined analysis of 2022–2023 data, achieving an R² of 0.58 for the dough stage, all confirmed through cross-validation. Integrating spectral and textural data from UAV imagery enhances early yield prediction, aiding precision agriculture and informed decision-making in rice management. These results emphasize the need to incorporate climate variables to refine predictions under diverse environmental conditions, offering a scalable solution to improve agricultural management and market planning. Full article

In order to analyze the physiological regulation mechanisms associated with exogenous melatonin on rice blast, this study treated rice seedlings with different concentrations of melatonin (0, 20, 100, and 500 µmol/L) in order to investigate the growth characteristics, root morphology, superoxide dismutase (SOD) activity, peroxidase (POD) activity, catalase (CAT) activity, malondialdehyde (MDA) content, hydrogen peroxide (H₂O₂) content, and soluble protein content of rice seedlings. The results indicated that 100 µmol/L of melatonin exhibited a significant effect, improving the growth and antioxidant capacity of rice seedlings under rice blast fungus infection. The disease resistance level of rice seedlings against rice blast significantly decreased by 31.58% when compared to the 0 µmol/L melatonin treatment, while the plant height, stem base width, plant leaf area, total root length, aboveground dry weight, aboveground fresh weight, and underground fresh weight significantly increased by 8.72% to 91.38%. Treatment with 100 µmol/L of melatonin significantly increased catalase activities and soluble protein content, with respective increases of 94.99% and 31.14%. Simultaneously, the contents of malondialdehyde and hydrogen peroxide significantly decreased, reaching 18.65% and 38.87%, respectively. The gray relational grade analysis indicated that hydrogen peroxide content and resistance level exhibit the highest gray relational grades with melatonin concentration and, so, can be used to evaluate the effect of melatonin on the severity of rice blast fungus infection. Furthermore, the membership function analysis revealed that the 100 µmol/L melatonin treatment had the highest membership function value, indicating a significant improvement in the resistance of rice seedlings to rice blast disease. In conclusion, 100 µmol/L of melatonin enhances the resistance of rice seedlings to rice blast disease through promoting their growth and strengthening their antioxidant defenses. This study provides new insights into the tolerance mechanisms of rice seedlings against rice blast disease. Full article

Achieving high-yielding crops while also improving nitrogen use efficiency is a significant challenge for agricultural production in Bangladesh. We investigated the impacts of applying nitrogen (N) using different management options in wetland rice on a calcareous dark gray soil over three seasons. These included (1) the recommended dose of available N as prilled urea, (2) the recommended N dose plus 25% extra of available N as prilled urea, (3) 25% less than the recommended dose of available N as prilled urea, (4) the recommended dose of prilled urea in 2 t ha⁻¹ cow dung, (5) the recommended dose as urea super granules (USGs) by deep placement, (6) 4 t ha⁻¹ biochar with the recommended dose of prilled urea, and (7) Zero N. It was found that the growth, yield, and N use efficiency (NUE) were significantly different from the results obtained for prilled urea in all the alternative fertilizer options. The deep placement of USG consistently increased plant height, total number of tillers per plant, effective tillers per plant, chlorophyll content, panicle length, grains per panicle, and 1000-grain weight. The yield increases over recommended prilled urea were 5.22% for USG followed by biochar with the recommended dose. Similarly, using the deep placement of USG gave the highest yield and harvest index. In addition, compared to the recommended dose of prilled urea, the deep placement of USG increased NUE by 13%, agronomic N efficiency by 20%, and recovery N use efficiency by 19%. This suggests the rate of N application could be reduced by up to 8% without impacting yield by using deep placement of USG instead of prilled urea. The cost–benefit ratio was higher for the deep placement of USG than all other treatments. Biochar with the recommended dose of prilled urea also showed good results in terms of growth, yield, and NUE (41.8, 43.0, and 41.7, respectively, during three sequential years), but the extra cost of the biochar reduced the cost–benefit ratio. These findings suggest that the deep placement of USG is the best option for improving the yield of rice while also improving N use efficiency. 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

Neonatal hypoxia–ischemia is a major cause of infant death and disability. The only clinically accepted treatment is therapeutic hypothermia; however, cooling is less effective in the most severely encephalopathic infants. Here, we wanted to test the neuroprotective effect of the antioxidant dimethyl fumarate after severe hypoxia–ischemia in neonatal rats. We used a modified Rice–Vannucci model to generate severe hypoxic–ischemic brain damage in day 7 postnatal rats, which were randomized into four experimental groups: Sham, Sham + DMF, non-treated HI, and HI + DMF. We analyzed brain tissue loss, global and regional (cortex and hippocampus) neuropathological scores, white matter injury, and microglial and astroglial reactivity. Compared to non-treated HI animals, HI + DMF pups showed a reduced brain area loss (p = 0.0031), an improved neuropathological score (p = 0.0016), reduced white matter injuries by preserving myelin tracts (p < 0.001), and diminished astroglial (p < 0.001) and microglial (p < 0.01) activation. After severe hypoxia–ischemia in neonatal rats, DMF induced a strong neuroprotective response, reducing cerebral infarction, gray and white matter damage, and astroglial and microglial activation. Although further molecular studies are needed and its translation to human babies would need to evaluate the molecule in piglets or lambs, DMF may be a potential treatment against neonatal encephalopathy. Full article

With a shift towards plant-based diets for human and planetary health, monitoring the mineral content of staple crops is important to ensure population nutrient requirements can be met. This review aimed to explore changes in the iron content of unprocessed wheat and rice in Australia over time. A comprehensive systematic search of four electronic databases and the gray literature was conducted. A total of 25 papers published between 1930 and 2023 that measured the iron content of unprocessed wheat and rice were included. Triticum aestivum was the most common wheat type studied, including 26 cultivars; iron content ranged from 40 to 50 µg/g in the 1930s and 1970s and was more variable after this time due to the introduction of modern cultivars, with most values between 25 and 45 µg/g. The iron content of rice (Oryza sativa) was more consistent at 10–15 µg/g between the 1980s and 2020s. Variations over the years may be attributed to environmental, biological, and methodological factors but these were not well documented across all studies, limiting the interpretation of findings. As the number of individuals following plant-based diets continues to rise, the ongoing monitoring of the mineral content in commonly consumed plant-based foods is warranted. Full article

A rice classification method for the fast and non-destructive differentiation of different varieties is significant in research at present. In this study, fluorescence hyperspectral technology combined with machine learning techniques was used to distinguish five rice varieties by analyzing the fluorescence hyperspectral features of Thai jasmine rice and four rice varieties with a similar appearance to Thai jasmine rice in the wavelength range of 475–1000 nm. The fluorescence hyperspectral data were preprocessed by a first-order derivative (FD) to reduce the background and baseline drift effects of the rice samples. Then, a principal component analysis (PCA) and t-distributed stochastic neighborhood embedding (t-SNE) were used for feature reduction and 3D visualization display. A partial least squares discriminant analysis (PLS-DA), BP neural network (BP), and random forest (RF) were used to build the rice classification models. The RF classification model parameters were optimized using the gray wolf algorithm (GWO). The results show that FD-t-SNE-GWO-RF is the best model for rice classification, with accuracy values of 99.8% and 95.3% for the training and test sets, respectively. The fluorescence hyperspectral technique combined with machine learning is feasible for classifying rice varieties. Full article

Water management and soil conditions affect the bioavailability of cadmium (Cd) and inorganic arsenic (As) in the soil, and hence, their accumulation in rice grains. A field experiment was conducted to investigate the effects of two water management regimes (flooding and dry–wet alternation) on Cd and inorganic As uptake and transport in rice under different soil conditions (paddy soil developed from gray-brown alluvium, K1; paddy soil developed from weathered shale and slate, K2) in the Sichuan Basin, Western China. The results indicated that compared to the wet–dry rotation, long-term flooding led to a substantial decrease of 49.3~55.8% in soil-available Cd content (p < 0.05), accompanied by a significant increase of 16.0~74.2% in As(Ⅲ) content (p < 0.05), causing no significant difference in As(V) content at the K1 site (p > 0.05). However, differences in soil-available Cd and inorganic As content under different water management treatments were both insignificant at the K2 site (p > 0.05). Long-term flooding treatment at the K1 site resulted in a remarkable reduction of 90.2% in Cd content in rice husks and 92.2% in brown rice (p < 0.05), along with a significant increase of 263.6% and 153.3%, respectively, in As(Ⅲ) content; no significant differences in As(V) content were observed at the K2 site (p > 0.05). In conclusion, the effect of water management on rice Cd and inorganic As varied under different soil conditions, with the change in rice Cd and inorganic As in paddy soil developed from gray-brown alluvium being significantly greater than that in paddy soil developed from weathered shale and slate. Full article

The top-gray chalkiness of hybrid rice (Oryza sativa L.) seeds is a typical phenomenon in hybrid rice seeds. The chalky part of the grain is infected and is the inoculum to infect the normal seeds during storage and soaking. These seed-associated microorganisms were cultivated and sequenced using metagenomics shotgun sequencing to obtain more comprehensive information on the seed-associated microorganisms in this experiment. The results showed that fungi could grow well on the rice flour medium, similar to the ingredients of rice seed endosperms. After the assembly of metagenomic data, a gene catalog was established, comprising 250,918 genes. Function analysis showed that glycoside hydrolases were the dominant enzymes, and the genus Rhizopus accounted for the dominant microorganisms. The fungal species R. microspores, R. delemar, and R. oryzae were likely to be the candidate pathogens in the top-gray chalky grains of hybrid rice seeds. These results will provide a reference for improving hybrid rice processing after harvest. Full article