Search Results (874)

Allelopathic interactions between crops in an intercropping system can directly affect crop yields. Faba beans may release allelochemicals to the cropping system. However, the allelopathic effects in the faba bean–maize relay intercropping system are still unclear. Maize seeds and seedlings were treated with a 50 mL of 100 g L⁻¹ faba bean leaf extract (L1), 150 g L⁻¹ faba bean leaf extract (L2), 100 g L⁻¹ faba bean stem extract (S1), or 150 g L⁻¹ faba bean stem extract (S2) and sterile water (CK) to study the allelopathic effects of faba bean extracts on maize seed germination and seedling growth. The α-amylase activities, antioxidant enzyme activities, phytohormones and allelochemical content in maize seeds were determined to evaluate the allelopathic effects of faba bean extracts on maize seed germination. The agronomic traits, photosynthetic parameters and nutrient absorption characteristics of maize seedlings were determined to explore the allelopathic effects of faba bean extracts on maize seedling growth. High-concentration (150 g L⁻¹) faba bean stem extracts released allelochemicals, such as 4-hydroxybenzoic acid, hydrocinnamic acid, trans-cinnamic acid, and benzoic acid. These allelochemicals entered the interior of maize seeds and increased the abscisic acid, salicylic acid and indole-3-acetic acid content in maize seeds but decreased the aminocyclopropane carboxylic acid in maize seeds. High-concentration (150 g L⁻¹) faba bean stem extracts increased the superoxide dismutase and peroxidase activity and decreased the α-amylase activity in maize seeds at germination (36 h). Faba bean extracts released nitrogen, potassium and phosphorus and increased nitrogen content, phosphorus content, potassium content and photosynthesis of maize seedling. In summary, faba bean extracts released allelochemicals that inhibited the germination of maize seeds but released nutrients and promoted the growth and development of maize seedlings. The research results provide a basis for improving the Faba bean–maize relay strip intercropping. Full article

Nitrogen is the main nutrient element in the growth process of white radish, and accurate monitoring of radish leaf nitrogen content (LNC) is an important guide for precise fertilization decisions for radish in the field. Using white radish LNC monitoring as an object, research on radish nitrogen hyperspectral estimation methods was carried out based on leaf hyperspectral and field sample nitrogen data at multiple growth stages using feature selection and integrated learning algorithm models. First, the Vegetation Index (VI) was constructed from hyperspectral data. We extracted sensitive features of hyperspectral data and VI response to radish LNC based on Pearson’s feature-selection approach. Second, a stacking-integrated learning approach is proposed using machine learning algorithms such as Support Vector Machine (SVM), Random Forest (RF), and Ridge and K-Nearest Neighbor (KNN) as the base model in the first layer of the architecture, and the Lasso algorithm as the meta-model in the second layer of the architecture, to realize the hyperspectral estimation of radish LNC. The analysis results show the following: (1) The sensitive bands of the radish LNC are mainly centered around 600–700 nm and 1950 nm, and the constructed sensitive VIs are also concentrated in this band range. (2) The Stacking model with spectral features as inputs achieved good prediction accuracy at the radish spectral leaf, with $R^2$ = 0.7, MAE = 0.16, MSE = 0.05 estimated over the whole growth stage of radish. (3) The Lasso algorithm with variable filtering function was chosen as the meta-model, which has a redundant model-selection effect on the base model and helps to improve the quality of the integrated learning framework. This study demonstrates the potential of the stacking-integrated learning method based on hyperspectral data for spectral estimation of nitrogen content in radish at multiple growth stages. Full article

Leaf nitrogen content (LNC) is a vital agronomic parameter in rice, commonly used to evaluate photosynthetic capacity and diagnose crop nutrient levels. Nitrogen deficiency can significantly reduce yield, underscoring the importance of accurate LNC estimation for practical applications. This study utilizes hyperspectral UAV imagery to acquire rice canopy data, applying various machine learning regression algorithms (MLR) to develop an LNC estimation model and create a nitrogen concentration distribution map, offering valuable guidance for subsequent field nitrogen management. The analysis incorporates four types of spectral data extracted throughout the rice growth cycle: original reflectance bands (OR bands), vegetation indices (VIs), first-derivative spectral bands (FD bands), and hyperspectral variable parameters (HSPs) as model inputs, while measured nitrogen concentration serves as the output. Results demonstrate that the random forest regression (RFR) and gradient boosting decision tree (GBDT) algorithms performed effectively, with the GBDT achieving the highest average R² of 0.76 across different nitrogen treatments. Among the nitrogen estimation models for various rice varieties, RFR exhibited superior accuracy, achieving an R² of 0.95 for the SuXiangJing100 variety, while the GBDT reached 0.93. Meanwhile, the support vector machine regression (SVMR) showed slightly lower accuracy, and partial least-squares regression (PLSR) was the least effective. This study developed an LNC estimation method applicable to the whole growth stage of common rice varieties. The method is suitable for estimating rice LNC across different growth stages, varieties, and nitrogen treatments, and it also provides a reference for nitrogen estimation and fertilization planning at flight altitudes other than the 120 m used in this study. Full article

Kale (Brassica napus) and collard (Brassica oleracea) are two leafy greens in the family Brassicaceae. The leaves are rich sources of numerous health-beneficial compounds and are commonly used either fresh or cooked. This study aimed to optimize the nutrient management of kale and collard in hydroponic production for greater yield and crop quality. ‘Red Russian’ kale and ‘Flash F1’ collard were grown for 4 weeks after transplanting in a double polyethylene-plastic-covered greenhouse using a nutrient film technique (NFT) system with 18 channels. Kale and collard were alternately grown in each channel at four different electrical conductivity (EC) levels (1.2, 1.5, 1.8, and 2.1 mS·cm⁻¹). Fresh and dry yields of kale increased linearly with increasing EC levels, while those of collard did not increase when EC was higher than 1.8 mS·cm⁻¹. Kale leaves had significantly higher P, K, Mn, Zn, Cu, and B than the collard at all EC levels. Additionally, mineral nutrients (except N and Zn) in leaf tissue were highest at EC 1.5 and EC 1.8 in both the kale and collard. However, the changing trend of the total N and NO3- of the leaves showed a linear trend; these levels were highest under EC 2.1, followed by EC 1.8 and EC 1.5. EC levels also affected phytochemical accumulation in leaf tissue. In general, the kale leaves had significantly higher total anthocyanin, vitamin C, phenolic compounds, and glucosinolates but lower total chlorophylls and carotenoids than the collard. In addition, although EC levels affected neither the total chlorophyll or carotenoid content in kale nor glucosinolate content in either kale or collard, other important health-beneficial compounds (especially vitamin C, anthocyanin, and phenolic compounds) in kale and collard leaves reduced with the increasing EC levels. In conclusion, the kale leaf had more nutritional and phytochemical compounds than the collard. An EC level of 1.8 mS·cm⁻¹ was the optimum EC level for the collard, while the kale yielded more at 2.1 mS·cm⁻¹. Further investigations are needed to optimize nitrogen nutrition for hydroponically grown kale. Full article

Accurate estimation of chlorophyll content is essential for understanding the growth status and optimizing the cultivation practices of Ginkgo, a dominant multi-functional tree species in China. Traditional methods based on chemical analysis for determining chlorophyll content are labor-intensive and time-consuming, making them unsuitable for large-scale dynamic monitoring and high-throughput phenotyping. To accurately quantify chlorophyll content in Ginkgo seedlings under different nitrogen levels, this study employed a hyperspectral imaging camera to capture canopy hyperspectral images of seedlings throughout their annual growth periods. Reflectance derived from pure leaf pixels of Ginkgo seedlings was extracted to construct a set of spectral parameters, including original reflectance, logarithmic reflectance, and first derivative reflectance, along with spectral index combinations. A one-dimensional convolutional neural network (1D-CNN) model was then developed to estimate chlorophyll content, and its performance was compared with four common machine learning methods, including Gaussian Process Regression (GPR), Partial Least Squares Regression (PLSR), Support Vector Regression (SVR), and Random Forest (RF). The results demonstrated that the 1D-CNN model outperformed others with the first derivative spectra, achieving higher CV-R² and lower RMSE values (CV-R² = 0.80, RMSE = 3.4). Furthermore, incorporating spectral index combinations enhanced the model’s performance, with the 1D-CNN model achieving the best performance (CV-R² = 0.82, RMSE = 3.3). These findings highlight the potential of the 1D-CNN model in strengthening the chlorophyll estimations, providing strong technical support for the precise cultivation and the fertilization management of Ginkgo seedlings. Full article

Nitrogen fertilization is a challenging task that usually requires intensive use of resources, such as fertilizers, management and water. This study explored the potential of VIS-NIR-SWIR remote sensing for quantifying leaf nitrogen content (LNC) in sugarcane from different regions and vegetative stages. Conducted in three regions of São Paulo, Brazil (Jaú, Piracicaba and Santa Maria), the research involved three experiments, one per location. The spectral data were obtained at 140, 170, 200, 230 and 260 days after cutting (DAC). From the hyperspectral data, clustering analysis was performed to identify the patterns between the spectral bands for each region where the spectral readings were made, using the Partitioning Around Medoids (PAM) algorithm. Then, the LNC values were used to generate spectral models using Partial Least Squares Regression (PLSR). Subsequently, the generalization of the models was tested with the leave-one-date-out cross-validation (LOOCV) technique. The results showed that although the variation in leaf N was small, the sensor demonstrated the ability to detect these variations. Furthermore, it was possible to determine the influence of N concentrations on the leaf spectra and how this impacted cluster formation. It was observed that the greater the average variation in N content in each cluster, the better defined and denser the groups formed were. The best time to quantify N concentrations was at 140 DAC (R² = 0.90 and RMSE = 0.74 g kg⁻¹). From LOOCV, the areas with sandier soil texture presented a lower model performance compared to areas with clayey soil, with R² < 0.54. The spatial generalization of the models recorded the best performance at 140 DAC (R² = 0.69, RMSE = 1.18 g kg⁻¹ and dr = 0.61), decreasing in accuracy at the crop-maturation stage (260 DAC), R² of 0.05, RMSE of 1.73 g kg⁻¹ and dr of 0.38. Although the technique needs further studies to be improved, our results demonstrated potential, which tends to provide support and benefits for the quantification of nutrients in sugarcane in the long term. Full article

The replacement of urea with polymer-coated urea (PCU) fertilizer and the application of organic fertilizers (OFs) are effective strategies for reducing N loss in farmland and preventing soil degradation. However, limited research has been conducted on the synergistic effects of OF combined with inorganic N fertilizer, particularly PCU, on rice yield, quality, and profit. To address this issue, a two-year field experiment was conducted involving five fertilization treatments: no nitrogen fertilizer (0N), urea applied at the full local rate of 270 kg N ha⁻¹ (CK), PCU at a reduced rate of 240 kg N ha⁻¹ (T1), a combination of 70% PCU and 30% urea at 240 kg N ha⁻¹ (T2), and T2 supplemented with 4500 kg ha⁻¹ of OF (T3). The results showed that, compared with CK, the T1 treatment improved the appearance quality and taste value but slightly reduced the other quality indices. In contrast, the T2 and T3 treatments enhanced the grain yield, especially for T3, with an advantage in the tiller number, shoot dry weight, and leaf area index, which promoted the panicle number, filled grain, and grain weight, thereby significantly increasing the yield. The T2 improved the processing, appearance, and taste qualities by reducing the protein content, increasing the amylose content and gel consistency, and optimizing the starch viscosity characteristics (increasing the peak viscosity and breakdown while reducing the setback and consistency), with the addition of OF (T3) further expanding the benefits. Furthermore, the nutritional quality was also enhanced by optimizing the protein components and increasing the protein yield. Although the agricultural inputs in the T2 and T3 treatments were higher, the profit from the increased grain yield could cover these inputs, thereby maintaining profit with T3 or increasing profit with T2. In summary, the combined application of PCU with urea and OF can synergistically improve the rice yield, quality, and profit. Full article

Climate change alters vegetation patterns, pushing subtropical forests further north. These forests play a crucial role for carbon neutrality efforts due to their significant CO₂ assimilation potential. This study investigated CO₂ assimilation rate along with growth, morphological, and physiological traits in 23 half-sib families of Quercus acuta and 26 half-sib families of Q. glauca, two prominent East Asian evergreen broadleaf species. Q. acuta exhibited significantly higher growth rates, with diameter at breast height (DBH) and aboveground biomass exceeding those for Q. glauca by 12.1% and 69.9%, respectively (p < 0.001). Leaf traits, including leaf mass pear area (LMA), leaf nitrogen, and chlorophyll content, were also greater in Q. acuta, showing 24.5%, 45.8%, and 15.6% higher values (p < 0.001). While photosynthetic traits were similar, Q. acuta exhibited a 12.7% higher intrinsic water-use efficiency (_iWUE) (p < 0.01). Among half-sib families, marginal differences were observed in growth traits (p < 0.1), and significant differences in leaf morphology and physiological traits (p < 0.05) were observed. A positive correlation was found between growth and physiological traits associated with the CO₂ assimilation rate in Q. acuta, but not in Q. glauca. These findings highlight the potential of Q. acuta and Q. glauca for supporting future carbon neutrality efforts and provide traits supporting carbon uptake, valuable for selecting tree species with enhanced carbon sequestration potential. Full article

The leaf area index (LAI) and leaf chlorophyll content (LCC) are key indicators of crop photosynthetic efficiency and nitrogen status. This study explores the integration of UAV-based multispectral (MS) and thermal infrared (TIR) data to improve the estimation of maize LAI and LCC across different growth stages, aiming to enhance nitrogen (N) management. In field trials from 2022 to 2023, UAVs captured canopy images of maize under varied water and nitrogen treatments, while the LAI and LCC were measured. Estimation models, including partial least squares regression (PLS), convolutional neural networks (CNNs), and random forest (RF), were developed using spectral, thermal, and textural data. The results showed that MS data (spectral and textural features) had strong correlations with the LAI and LCC, and CNN models yielded accurate estimates (LAI: R² = 0.61–0.79, RMSE = 0.02–0.38; LCC: R² = 0.63–0.78, RMSE = 2.24–0.39 μg/cm²). Thermal data reflected maize growth but had limitations in estimating the LAI and LCC. Combining MS and TIR data significantly improved the estimation accuracy, increasing R² values for the LAI and LCC by up to 23.06% and 19.01%, respectively. Nitrogen dilution curves using estimated LAIs effectively diagnosed crop N status. Deficit irrigation reduced the N uptake, intensifying the N deficiency, while proper water and N management enhanced the LAI and LCC. Full article

The increasing frequency and severity of drought, driven by global climate change, has emerged as a critical factor constraining the growth of landscaping trees in urban ecosystems. The local or non-local status of tree species is an important driver of plant function traits, which regulate plant performance. However, the differential impact of varying drought intensities on the functional traits of both non-local and local trees remains poorly understood. This study investigated the responses of leaf and root traits of seven typical tropical landscaping tree seedlings (three local species and four non-local species) to simulated drought conditions in a year-long greenhouse experiment. The results showed that drought significantly increased the specific leaf area, leaf thickness, and root exudate rate, while reducing root nitrogen content and leaf dry matter content, with differences observed between local and non-local species. The non-local species exhibited pronounced fluctuations in leaf and root traits between control and drought conditions. Local species tended to enhance the relationship between leaves and roots under drought, while non-local species showed a weakening of this relationship. Principal component analysis revealed that local species adopted a more conservative strategy under control conditions and a more acquisitive strategy under drought, while root strategies remained stable across conditions. The subordination function method in fuzzy mathematics identified Terminalia neotaliala (non-local) as the most drought-resistant species and Artocarpus heterophyllus (non-local) as the least drought-resistant species. Non-local species demonstrated greater drought resistance in leaf traits compared to local species, but the opposite was observed for root traits. These results underscore the importance of understanding the species-specific responses of local and non-local trees to drought stress. These findings provide a scientific basis for developing effective screening and management protocols for drought-resistant landscaping tree species. Full article

Traditional techniques for measuring leaf nitrogen content (LNC) involve slow and laborious processes, and radiometric data have been used to assist in the nutritional analysis of plants. Therefore, this study aimed to evaluate the performance of LNC predictions in corn plants based on laboratory hyperspectral Vis-NIR-SWIR data. The treatments corresponded to 60, 120, 180, and 240 kg ha⁻¹ of nitrogen, in addition to the control (0 kg ha⁻¹), and they were distributed using a randomized complete block design. At the laboratory, hyperspectral data of the leaves and LNC were obtained. The hyperspectral data were used in the calculation of different vegetation indices (VIs), which were applied in a predictive model—partial least squares regression (PLSR)—and the capacity of the prediction was assessed. The combination of bands and VIs generated a better prediction (0.74 < R² < 0.87; 1.00 < RMSE < 1.50 kg ha⁻¹) in comparison with the individual prediction by band (0.69 < R² < 0.85; 1.00 < RMSE < 1.77 kg ha⁻¹) and by VI (0.55 < R² < 0.68; 1.00 < RMSE < 1.78 kg ha⁻¹). Hyperspectral data offer a new opportunity to monitor the LNC in corn plants, especially in the region comprising the bands from 450 to 750 nm, since these were the bands that were most sensitive to the LNC. Full article

With the development of sustainable agriculture, trials on the benefits of the application of organic fertilizers around the world have been conducted. Herein, we investigated the impact of the pure chemical fertilizers (CFs) combined with organic fertilizers compared with the application of CFs (100% CFs) and no fertilizers (NFs) on soil properties as well as the yield and quality of Chinese cabbage through meta-analysis. Results indicate that: (1) Compared with NFs, the application of organic fertilizers can significantly improve the yield and quality of Chinese cabbage and increase soil nutrients. (2) Compared with CFs, the application of organic fertilizers can increase the fresh weight, number of leaves, transverse diameter, leaf length, and development of Chinese cabbage per plant, with increases of 8.54%, 6.6%, 9.905%, 8.42%, and 10.03%; Meanwhile, organic fertilizers can significantly increase the yield (total amount of above-ground parts produced) and commercial yield (the portion that meets the required quality standards and is intended for sale) of Chinese cabbage to increase the yield and commercial yield by 10.08% and 35.56%, respectively. However, it has no significant impact on the income from growing Chinese cabbage. (3) Compared with CFs, the application of organic fertilizers can significantly increase the content of vitamin C (11.06%), soluble sugar (19.16%), and soluble protein (8.83%) and reduce the content of nitrate and nitrite in Chinese cabbage, with a reduction of up to 19.02% and 20.9%, respectively. The application of organic fertilizers will also have a certain impact on the absorption of heavy metals in Chinese cabbage. (4) Compared with CFs, the application of organic fertilizers can significantly improve soil organic matter, soil carbon sequestration, nitrogen absorption, and potassium absorption, showing increases of 12.73%, 13.19%, 7.91%, and 7.37%, and the application of organic fertilizers reduces soil electrical conductivity and available nitrogen, showing decreases of 36.78% and 38.75%, respectively. (5) The application of organic fertilizers significantly increased the content of soil urease and soil sucrase, increasing by 9.42% and 17.16%, respectively. This study helps inform the application of organic fertilizers in Chinese cabbage production. 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

Iron tailings from the mining process occupy vast land areas and pose a significant ecological risk. In order to reuse iron tailings resources and carry out in situ ecological restoration of a mine, in this study, a medium of mixed iron tailings and mining topsoil (m:m = 3:1) was used to plant landscape grasses, including Lolium perenne L. (L. perenne), Pennisetum alopecuroides (L.) Spreng. (P. alopecuroides), Melilotus officinalis (L.) Lam. (M. officinalis), and Medicago sativa L. (M. sativa). Biochar and chicken manure were used as biochar organic fertilizers and indigenous microorganisms were isolated from the rhizosphere soil of tested grasses. They were applied to enhance landscape grass growth by regulating rhizosphere microbial communities and nutrient conditions. The results showed that the biochar organic fertilizers significantly promoted the growth of the four landscape grasses, notably P. alopecuroides, increasing plant height, root length, root weight, and leaf fresh weight by 169%, 60%, 211%, and 388%, respectively. Additionally, L. perenne exhibited the greatest height increase (10%) following the application of bacterial solutions. Moreover, indigenous bacterial solutions enhanced chlorophyll content and phenylalanine ammonia-lyase (PAL) activity, with P. alopecuroides showing the highest chlorophyll increase of 58% and M. sativa exhibiting a 30.58% rise in PAL activity. The biochar organic fertilizer also significantly elevated soluble protein content in P. alopecuroides and M. sativa by 195% and 152%, respectively. It also effectively enhanced peroxidase (POD) activity in Poaceae grasses by 120% to 160%. After adding indigenous microorganisms, the rhizosphere soil of the landscape grass showed the highest Shannon–Wiener diversity index, reaching 3.561. The rhizosphere soil of M. officinalis had the highest microbial richness, with a value of 39. Additionally, the addition of indigenous microorganisms increased the nitrogen, phosphorus, and potassium content of the four plants by 8–19%, 6–14%, and 8–18%, respectively. This study offers a new approach for managing mining waste and ecological restoration in mining areas. Full article

As one of the main bamboo species in coastal sandy land protection forests, Bambusa tuldoides ‘Swolleninternode’ can effectively improve the structure of forest tree species, increase the diversity of tree species and enhance the protection efficiency. However, research on the cultivation and utilization of B. tuldoides is still relatively scarce. Therefore, in this study, B. tuldoides was used as the research object. By applying biochar and nitrogen fertilizer, the effects of different biochar and nitrogen fertilizer ratios on the physiology and soil characteristics of bamboo were analyzed, and the optimal ratio scheme was identified. The results showed that the ratio of the T5 (A2B2C3) treatment had the best effect on the total chlorophyll, non-structural carbohydrate and nutrient contents of B. tuldoides leaves, and the contents under treatments was generally higher than under the control, CK. The activities of soil invertase, nitrate reductase and nitrite reductase were significantly increased under biochar treatment, and the effects of the T5 treatment were the best. The results of principal component analysis showed that the absolute values of the coefficient of leaf potassium, phosphorus content and soil total nitrogen were larger and more important, and the comprehensive evaluation of the T5 treatment was the highest. Full article