Search Results (4,107)

Elephant grass (Pennisetum purpureum Schumach, EG) is a promising biomass energy crop due to its high productivity and adaptability to harsh environments. In the transition to renewable energy, varietal evaluation is essential to identify cultivars that maximize biomass and energy yield. This study assessed three varieties (VS-19, VA-06, and VDP as control) across three harvest cycles (new planting, first regrowth, and second regrowth) between 2022 and 2024 at the Cotton and Agricultural Development Research Institute, Ninh Thuan Province, Vietnam. The site was characterized by mean temperatures of 25–36 °C, relative humidity of 65–82%, and average precipitation of 75.7 mm per month. Agronomic traits, energy potential (heating oil equivalent per hectare, HOE/ha), forage quality, and soil amendment value of the EG were examined to address the research question whether EG can be integrated into a three-cycle utilization model (energy, forage, soil amendment) to support a circular bioeconomy in Vietnam. All cultivars showed good growth, strong drought tolerance, and resistance to pests and diseases. VS-19 showed superior tillering, strong lodging resistance, and the highest biomass yield (63.8 t/ha) with an energy output of 32,636 HOE/ha, while VA-06 (56.1 t/ha; 28,699 HOE/ha) and VDP (54.7 t/ha; 27,952 HOE/ha) produced slightly lower but comparable outputs. Forage evaluation indicated moderate nutritional quality, while residues from the third cycle showed favorable carbon and nutrients content, making EG suitable as a soil amendment. EG thus demonstrates high biomass and energy yields, forage potential, and soil improvement capacity, reinforcing its role in integrated bioenergy and agricultural systems. Full article

Management of invasive species is especially difficult when the organisms involved are endophagous and their interactions complex. Such is the case with laurel wilt disease (LWD), a lethal vascular condition caused by Harringtonia lauricola, the fungal symbiont of the non-native redbay ambrosia beetle (RAB), Xyleborus glabratus Eichoff (Coleoptera: Curculionidae). LWD has caused extensive mortality of coastal redbay, Persea borbonia, and is expanding to utilize additional lauraceous hosts, including sassafras, Sassafras albidum. Current management has not been successful in preventing its spread, warranting investigation into additional techniques. RNA interference (RNAi) is a highly specific gene-silencing mechanism used for integrated pest management of crop pests and currently being investigated for use in forests. When targeting essential genes, RNAi can cause rapid insect mortality. Here we focus on RAB, identifying for the first time species-specific reference genes for quantitative real-time PCR (qPCR) and assessing mortality and gene expression after oral ingestion of double-stranded RNAs (dsRNAs) targeting essential genes (hsp, shi, and iap). Our study validates reference genes for expression analyses and shows significant mortality and changes in gene expression for all three target genes. Our research aims to contribute to the development of innovative management strategies for this invasive pest complex. Full article

The escalating pressure on water resources in agricultural regions has become a catalyst for water conflicts. The adoption of innovative approaches to estimate actual evapotranspiration (ETa) offers potential solutions to mitigate conflicts related to water usage. This research presents the application of a remote sensing-based methodology for estimating actual evapotranspiration (ETa) based on a two-source energy balance model (TSEB) for riparian vegetation in Nebraska, US using the Spatial EvapoTranspiration Modeling Interface (SETMI). Estimated results through SETMI and field data using the eddy covariance system (EC) considering the period 2008–2013 were used to validate the energy balance components and ETa. Modeled energy balance components showed a strong correlation to the ground data from EC, with ET presenting R2 equal to 0.96 and RMSE of 0.73 mm.d⁻¹. In 2012, the lowest adjusted crop coefficient (Kcadj) values were observed across all land covers, with a mean value of 0.49. The years 2013 and 2012, due to the dry conditions, recorded the highest accumulated ETa values (706 mm and 664 mm, respectively). Soybeans and corn exhibited the highest ETa values, recording 699 mm and 773 mm, respectively. Corn and soybeans, together accounting for a substantial portion of the land cover at 15% and 3%, respectively, play a significant role. Given that most fields cultivating these crops are irrigated, both pumped groundwater and surface water directly impact the water source of the Republican River. The SETMI model has generated appropriate estimated daily ETa values, thereby affirming the model’s utility as a tool for assisting water management and decision-makers in riparian zones. Full article

Under the global climate change, variations in climatic elements such as temperature, precipitation, and sunshine duration significantly impact the growth, development, and yield formation of winter wheat. A precise understanding of the impact of climate change on winter wheat growth and the scientific use of meteorological resources are crucial for ensuring food security, optimizing agricultural planting structures and agricultural sustainability. This study uses statistical methods and focuses on the Beijing–Tianjin–Hebei region, utilizing data from 25 meteorological stations from 1961 to 2010 and winter wheat yield data from 1978 to 2010. Twelve refined indicators encompassing temperature, precipitation, and sunshine duration were constructed. Path analysis was employed to determine their weights, establishing a comprehensive climate indicator model. Results indicate: Temperature indicators in the region show an upward trend, with accumulated temperature of the whole growth period increasing at a rate of 61.1 °C·d/10a. Precipitation indicators reveal precipitation of the whole growth period rising at 3.9 mm/10a and pre-winter precipitation increasing at 4.2 mm/10a. Sunshine duration exhibits a declining trend, decreasing at 72.7 h/10a during the whole growth period. Comprehensive climate indicators decrease from south to north, with the southwest region exhibiting the highest tendency rate (18.41), while the central and southern regions show greater variability. This study provides scientific basis for optimizing winter wheat planting patterns and rational utilization of climate resources in the Beijing–Tianjin–Hebei region. It recommends prioritizing cultivation in western southern Hebei and improving water conditions in the central and northern areas through irrigation technology to support sustainable crop production. Full article

In recent years, the cultivated area of hazelnuts in Chile has increased significantly. Along with this rapid expansion, biotic constraints that affect the optimal development of the crop have been identified. Among these, bacterial blight disease caused by Xanthomonas arboricola pv. corylina has been particularly relevant. This pathogen has a global distribution and is present in all hazelnut-producing countries. In the spring of 2023, hazelnut orchards were sampled from the Maule to Biobío Regions of Chile. The Chilean isolates recovered from hazelnut tissues showing symptoms of bacterial blight were characterized by their ability to grow on different semi-selective media, their carbohydrate utilization profiles, hypersensitivity response in tobacco plants, and biochemical tests. Additionally, the isolates were identified based on the 16S rRNA gene and multilocus sequence analysis (MLSA) on the rpoD, gyrB, and atpD genes. The results showed that the X. arboricola pv. corylina Chilean isolates differed from previously reported isolates in other geographic areas as they are capable of metabolizing sorbitol and mannitol. Using MLSA and average nucleotide identity (ANI) comparison, these isolates were grouped into four and five phylogenetic clades, respectively, representing a significant difference from what has been reported in similar international studies. Full article

Agriculture is a major economic industry that sustains life. Moreover, plant health is a crucial aspect of a highly functional agricultural system. Because stress agents can damage crops and plants, it is important to understand what effect these agents can have and be able to detect this negative impact early in the process. Machine learning technology can help to prevent these undesirable consequences. This research investigates machine learning applications for plant health analysis and classification. Specifically, Residual Networks (ResNet) and Long Short-Term Memory (LSTM) models are utilized to detect and classify plants response to abiotic external stressors. Two types of plants, azalea (shrub) and Chinese tallow (tree), were used in this research study and different concentrations of sodium chloride (NaCL) and acetic acid were used to treat the plants. Data from cameras and soil sensors were analyzed by the machine learning algorithms. The ResNet34 and LSTM models achieved accuracies of 96% and 97.8%, respectively, in classifying plants with good, medium, or bad health status on test data sets. These results demonstrate that machine learning algorithms can be used to accurately detect plant health status as well as healthy and unhealthy plant conditions and thus potentially prevent negative long-term effects in agriculture. Full article

Continued population growth demands a significant increase in agricultural production to ensure food security. However, agricultural output is limited by environmental crises and the negative impacts of open-field farm practices. As an alternative, vertical farming techniques, such as aeroponics, can be utilized to optimize the use of resources. However, the uneven size and distribution of spray droplets in aeroponics, issues that affect root development and nutrient delivery, continue to be problematic in spray performance analysis. In aeroponics, nutrient solutions are delivered to plant roots through pressurized nozzles, and the effectiveness of this delivery depends on the spray characteristics. Variations in flow rates directly affect droplet size, density, and coverage, which in turn influence nutrient uptake and crop growth. In this study, the flow rate was adjusted (3, 4.5, and 6 L/min) to quantitatively analyze spray performance using water-sensitive paper (WSP) as a deposit collector via a quick assessment method. Subsequently, image-processing techniques such as threshold segmentation and morphological operations were applied to isolate individual spray droplets on the WSP images. This technique enabled the quantification of the droplet’s coverage area, size, density, and uniformity to effectively evaluate spray performance. One-way ANOVA indicated that all the spray parameters varied significantly with respect to the flow rate (p < 0.05): For example, the average diameters of the droplets increased from 0.73 mm at 3 L/min to 1.29 mm at 6 L/min. The droplets’ densities decreased from 85.53 drops/cm² to 30.00 drops/cm² across the same flow range. The average uniformity index improved from 30.53 to 15.95 as the flow rate increased. These results indicate that the application of WSP is an effective and scalable approach for analyzing spray performance in aeroponics, as WSP can be rapidly digitized with simple tools, such as a cell phone camera, avoiding the limitations of flatbed scanners or specialized imaging systems. Full article

The win–win situation of dye degradation and nitrogen fixation in wastewater using non-thermal plasma (NTP) were investigated in this study. Specifically, the feasibility of utilizing plasma-treated dye-contaminated wastewater for seed germination and plant growth was explored. Crystal Violet (CV) and Rhodamine B (RhB) dyes were used as model pollutants, while Sorghum bicolor (great millet) seeds were used to assess germination rates and plant growth responses. In untreated wastewater containing CV and RhB, approximately 45% of seeds germinated after three days, but no significant stem or root growth was observed after 11 days. Plasma treatment significantly enhanced dye degradation, with efficiency improving as treatment time and input power increased. After 16 min of plasma treatment at 1.3 ± 0.2 W input power, about 99% degradation efficiency was achieved for both CV (0.0122 mM) and RhB (0.0104 mM). This degradation was primarily driven by reactive oxygen and nitrogen species (RONS) generated by plasma discharge. When sorghum seeds were germinated using plasma-treated wastewater, the germination rate increased to 65% after three days—20% higher than with untreated wastewater. Furthermore, after 11 days, the average stem length reached 9 cm, while the average root length extended to 7 cm. These findings highlight NTP as a promising and sustainable method for degrading textile industry pollutants while simultaneously enhancing crop productivity through the reuse of treated wastewater. Full article

The traditional continuous, quantitative spraying technology ignores the severity of pests, diseases and grasses, spatial distribution and other differences, resulting in low effective utilization of pesticides, environmental pollution and other problems. Variable-rate spray technology has become an important development direction in the field of precision agriculture by dynamically sensing crop canopy morphology, pest and disease distribution, and environmental parameters, adjusting the application amount in real time, and significantly improving pesticide utilization. In this study, we systematically review the core progress of variable-rate spray technology; focus on the technical system of information detection, spray volume model, and control system; analyze the current bottlenecks; and propose an optimization path to adapt to the complex agricultural conditions. At the level of information perception, LiDAR, machine vision, and multi-source sensor fusion technology constitute the main perception architecture, and infrared and ultrasonic sensors assist target recognition in complex scenes. In the construction of the spray volume model, models based on canopy volume, leaf area density, etc., are used to realize dynamic application decision by fusing equipment operating parameters, pest and disease levels, meteorological conditions, and so on. The control system takes the solenoid valve + PID control as the core program, and improves the response speed through PWM regulation and closed-loop feedback. The current technical bottlenecks are mainly concentrated in the sensor dynamic detection accuracy, model environmental adaptability, and the reliability of the execution parts. In the future, it is necessary to further promote anti-jamming multi-source heterogeneous sensor data fusion, multi-factor adaptive spray model development, lightweight edge computing deployment, and solenoid valve structural parameter optimization and other technical research, with a view to promoting the application of variable-rate spray technology to the field on a large scale and providing a theoretical reference and technological support for the green transformation of agriculture. Full article

Bacterial wilt is a highly destructive disease affecting a wide range of crops, with no effective chemical control methods currently available. Consequently, the development of microbial strategies for disease management has become increasingly important. Among these, plant immunity-intensifying microbes have demonstrated promising efficacy in controlling bacterial wilt. However, the influence of environmental factors, particularly light intensity, on the effectiveness of these microbes remains unclear. Light intensity is a critical regulator of the photosynthetic system and plant biochemical functions, including defense responses. In this study, we specifically utilized Arabidopsis plants grown under distinct light intensities to systematically examine how light conditions affect the induction of plant immune responses and the occurrence of bacterial wilt. Our findings revealed that Arabidopsis grown under high light intensity exhibited significantly stronger immune responses and reduced disease severity, compared to plants grown under low light intensity. Further, application of Bacillus amyloliquefaciens PMB05, a plant immunity-intensifying strain, resulted in more pronounced immune signaling and disease control efficacy under high light conditions. Experiments using salicylic acid (SA)-deficient mutants demonstrated that disruption of the SA pathway abolished the enhanced suppression of bacterial wilt conferred by B. amyloliquefaciens PMB05 under high light intensity, indicating that the SA pathway is indispensable for PMB05-mediated disease resistance. Moreover, the validation experiments in tomato plants supported these results, with B. amyloliquefaciens PMB05 significantly reducing bacterial wilt development under high light intensity. Collectively, our study demonstrates that growing plants under varying light intensities provides critical insights into how environmental conditions modulate the effectiveness of plant immunity-intensifying microbes, offering a potential strategy for integrated disease management in crops. Full article

Fast and precise monitoring of soil organic matter (SOM) during maize growth periods is crucial for real-time assessment of soil quality. However, the big challenge we usually face is that many agricultural soils are covered by crops or snow, and the bare soil period is short, which makes reliable SOM prediction complex and difficult. In this study, an unmanned aerial vehicle (UAV) was utilized to acquire multi-temporal hyperspectral images of maize across the key growth stages at the field scale. The auxiliary predictors, such as spectral indices (I), field management (F), plant characteristics (V), and soil properties (S), were also introduced. We used stepwise multiple linear regression, partial least squares regression (PLSR), random forest (RF) regression, and XGBoost regression models for SOM prediction, and the results show the following: (1) Multi-temporal remote sensing information combined with multi-source predictors and their combinations can accurately estimate SOM content across the key growth periods. The best-fitting model depended on the types of models and predictors selected. With the I + F + V + S predictor combination, the best SOM prediction was achieved by using the XGBoost model (R² = 0.72, RMSE = 0.27%, nRMSE = 0.16%) in the R3 stage. (2) The relative importance of soil properties, spectral indices, plant characteristics, and field management was 55.36%, 26.09%, 9.69%, and 8.86%, respectively, for the multiple periods combination. Here, this approach can overcome the impact of the crop cover condition by using multi-temporal UAV hyperspectral images combined with valuable auxiliary variables. This study can also improve the field-scale farmland soil properties assessment and mapping accuracy, which will aid in soil carbon sequestration and soil management. Full article

The aim of our study was to make one step further to verify a method that can turn back mycotoxin-contaminated crops into the circular economy. Thus, the possibility of utilizing aflatoxin B1 (AfB1)-contaminated corn by yellow mealworms (Tenebrio molitor) was investigated to be used as fish feed components. Four different self-contaminated corn samples were used in our study, of which one was below and three were above the threshold limit (20 µg/kg) regulated by the European Union. The highest applied AfB1 concentration in our study for insect feeding was 415 µg/kg (more than twenty times higher than the threshold). After a five-week feeding period insect mortality was not increased, even in the highly contaminated group, compared to the negative control. The mycotoxin in the dried and ground insects was only detected in the case of feeding with the highest-concentration corn, however it remained as low as 2.2 µg/kg. For studying the possible physiology effects, insect grounds were used in feeding experiments of common carp (Cyprinus carpio) fries. Results showed that insect meal, even if originated from a highly mycotoxin-contaminated crop, did not have a significant effect on the examined fish fries, compared with the control groups. The AfB1 concentrations of the leftover frass after insect rearing were also measured, and in the case of the highest concentration mealworm group, it was 157.6 µg/kg (other groups were under 20 µg/kg). Toxicity of frass extracts from different contaminated groups was also studied using microinjected zebrafish (Danio rerio) embryos. Extracts of the highly contaminated frass samples caused 91.67 ± 3.33% mortality and led to numerous phenotypic changes, which highlights the need for responsible usage of the by-product. However, the effects of injected frass samples, originating from corn with lower and more environmentally relevant AfB1 concentrations, were significantly lower. Full article

The cultivated peanut (Arachis hypogaea L.) is a globally important oilseed and economic crop, but its narrow genetic base limits breeding progress. Wild Arachis species represent valuable genetic resources for enhancing the resilience of the peanut cultigen. While wild species from section Arachis are widely used in breeding programs, the detection of alien chromosomes in hybrids remains challenging due to limited molecular tools. In this study, a cost-effective and efficient system was established for generating species-specific molecular markers using low-coverage next-generation sequencing data, bypassing the need for whole-genome assembly. Utilizing the Chorus2 software, specific alien-chromosome oligo (SAO) markers were developed for four wild species, A. duranensis (accession A19), A. pusilla (A10), A. appresipilla (A33), and A. glabrata (G2 and G3). A total of 1166 primer pairs were designed, resulting in 220 SAO markers specific to A. duranensis, 77 to A. pusilla, 112 to A. appresipilla, 69 to A. glabrata G2, and 59 to A. glabrata G3, with the highest development efficiency observed in A. duranensis (55.0%). These markers span all chromosomes of the five wild accessions. Genome-wide, chromosome-specific SAO markers enable the efficient detection of introgressed alien chromosomes and provide insight into syntenic relationships among homoeologous chromosomes. These markers offer an effective tool for identifying favorable genes and facilitating targeted introgression for the genetic improvement of the cultivated peanut. Full article

This study evaluated the effects of applying a biofertilizer, alone and in combination with nanoparticles (NPs), under controlled greenhouse conditions to improve soil quality and wheat performance (soil from the region of General Cepeda, Coahuila, Mexico, was used). The integration of the biofertilizer with Fe_xO_x NPs proved particularly effective in enhancing soil physical and biological parameters as well as promoting superior crop growth compared with individual treatments. The incorporation of NPs markedly improved the biofertilizer’s biocompatibility and stability, reinforcing its potential for optimizing plant nutrition, nutrient use efficiency, and overall agricultural sustainability. In addition, the combined treatments enhanced the utilization of native microbial diversity, thereby contributing to increased soil fertility and the quality and yield of crops in the study region. The best yield obtained in previous harvests (8.3 Mg ha⁻¹) was improved to 8.48 Mg ha⁻¹ with application of the biofertilizer with Fe_xO_x NPs. Moreover, shoot length increased significantly with the combination of the biofertilizer and ZnO NPs as well as with Fe_xO_x NPs separately, whereas root length was maximized with the addition of the biofertilizer alone. These findings underscore the synergistic effects of combining biofertilizers with metal-based nanoparticles to sustainably enhance wheat growth and productivity. Full article

Rice, as one of the world’s four major staple crops, is frequently threatened by pests and diseases during its growth. With the rapid expansion of agricultural information data, the effective management and utilization of such data have become crucial for the development of agricultural informatization. Named entity recognition technology offers precise support for the early prevention and control of crop pests and diseases. However, entity recognition for rice pests and diseases faces challenges such as structural complexity and prevalent nesting issues. Inspired by biological visual mechanisms, we propose a deep learning model capable of extracting multi-granularity features. Text representations are encoded using BERT, and the model enhances its ability to capture nested boundary information through multi-granularity convolutional neural networks (CNNs). Finally, sequence modeling and labeling are performed using a bidirectional long short-term memory network (BiLSTM) combined with a conditional random field (CRF). Experimental results demonstrate that the proposed model effectively identifies entities related to rice diseases and pests, achieving an F1 score of 91.74% on a self-constructed dataset. Full article