Distribution regularity of downwash airflow under rotors of agricultural UAV for plant protection

This study developed an innovative model integrating straw subsoil deep burial (SD) and mixing plow to mitigate albic soil’s physical and chemical constraints and enhance crop yield. A field experiment with four treatments, including conventional tillage (CT), straw mulching (SM), straw subsoil deep burial (SD), and straw burning (SR), was conducted to assess impacts on soil enzyme activity, nutrient dynamics, crop yield, and soil physical properties. Results showed that SD treatment significantly improved albic soil properties compared to conventional tillage: catalase activity in the albic horizon decreased by 13.51%, reducing peroxide toxicity. In the albic horizon, alkaline hydrolysis nitrogen, total nitrogen, available phosphorus, total phosphorus, available potassium, total potassium, and organic matter increased by 29.98%, 58.70%, 36.86%, 20.46%, 5.00%, 21.70%, and 40.46%, respectively. Correspondingly, maize and soybean yield under SD reached 8686.6 kg/ha and 2245.3 kg/ha, increasing by 15.39% and 19.94% compared to CT, respectively. Additionally, SD treatment improved physical properties of the albic horizon: soil hardness reduced by 43.56%, with enhanced water-holding capacity, permeability coefficient, porosity, and hydraulic conductivity. Its findings not only boost agronomic productivity by improving crop yields but also support environmental sustainability by enhancing soil fertility, which is of great significance for ensuring food security.

Background: As a major food crop, maize is highly susceptible to pathogenic bacteria, which greatly reduces its yield and quality. Metabolomics reveals physiological and biochemical changes in organisms and aids in analyzing metabolic changes caused by various factors. Methods: This study utilized metabolomics to examine maize’s metabolic changes after NCLB infestation, aiming to uncover related pathways and potential biomarkers. The metabolite measurements were performed during the maize silking stage. Results: PCA showed an obvious dispersion between the treated and untreated groups. OPLS-DA identified 1274 differential metabolites, with 242 being downregulated (mainly phenolics and esters) and 1032 upregulated (primarily organic acids, amino acids, sugars, and derivatives). KEGG annotation revealed 50 affected metabolic pathways, and the biosynthesis of secondary metab-olites and amino acids was significantly enriched. Conclusions: We hypothesized that metabolic pathways related to sugar metabolism, proline metabolism, and jasmonic acid synthesis are associated with NCLB susceptibility. These findings provide critical insights into the metabolic responses of maize to biotic stress, offering a theoretical basis for future research on plant resistance mechanisms.

Nutrient fertilizer application to agricultural land has led to greenhouse gas emissions and has altered soil nitrogen (N) deposition. In soil, N can be degraded in four ways: entering surface water through water flow, absorption by plants and microorganisms, decomposition into gas, and deposition as minerals. This study proposes the concept of N degradability and aims to clarify how farming activities affect N degradability in soil. Over 260 soil profiles were excavated, and the effective soil depth, coordinates, soil types, and vegetation were recorded at each measurement point. The following characteristics were determined in the soil samples: pH, organic matter, total N, total phosphorus, total potassium, total soluble N, available phosphorus, and available potassium. The sample characteristics were subjected to Pearson correlation analysis, principal component analysis, and one-way analysis of variance. The 260 samples included four soil types: dark brown soil, black soil, albic black soil, and meadow soil. Black soil exhibited more stable N levels compared with the other three soil types, showing a tendency towards N accumulation. Ground vegetation was categorized into seven types: forest, rice, maize, red adzuki bean, grassland, soybean, and others. Forests contributed the most to N deposition. Conversely, planting maize led to a tendency for N loss compared with forests. This study can provide a reference for the sustainable development of agriculture and the balance of ecological protection.

Low productivity in albic soils often results in excessive nitrogen input, while straw return further increases nitrogen accumulation through decomposition. To address this issue, a three-year field experiment was conducted in albic soils of high, medium, and low fertility. Two nitrogen management strategies were assessed: nitrogen addition and reduction. Addition treatments included conventional nitrogen application rate alone (N), straw return (8250 kg ha⁻¹) with conventional nitrogen application rate (SN), and straw return with increased nitrogen (SN₊). Reduction treatments comprised SN and straw return with 10%, 20%, and 30% reduced nitrogen (SN_0.9, SN_0.8, and SN_0.7). Soil physical properties, nutrient content, and rice yield were evaluated. Results showed that SN_0.9 exhibited advantages in high-fertility albic soils, as it increased rice yield and improved some soil quality while reducing the nitrogen input by 10%. However, yield under SN_0.9 declined progressively over the three years, indicating limitations of long-term application. SN performed better than both N and SN₊ in medium- and low-fertility albic soils, offering better yield and soil quality improvements. However, nitrogen overaccumulation risk under continuous application should not be overlooked. These findings highlight that fertility-based nitrogen adjustment combined with straw return can simultaneously improve rice productivity and soil quality while reducing nitrogen input in albic soils.

As an important factor affecting the yield and quality of grain crops and threatening grain security, traditional pest and disease monitoring can no longer meet the needs of accurate and efficient agricultural production. The development of remote sensing technology provides a new monitoring method, which is specific, accurate and efficient, and provides real-time, rapid and non-destructive spectral data information for the identification of the occurrence and severity of pests and diseases and can realize large-scale monitoring of grain crop pests and diseases. In this paper, through the statistics and analysis of the published literature on remote sensing monitoring of grain crop diseases and pests, the research hotspots and directions of remote sensing monitoring of grain crop diseases and pests are clarified. Based on this foundation, this paper systematically elaborates the mechanism underlying remote sensing-based monitoring and prediction of diseases and insect pests in grain crops. It reviews various remote sensing monitoring approaches for such diseases and pests by leveraging multi-source remote sensing data. Furthermore, it summarizes methodologies for constructing monitoring and prediction models for grain crop diseases and insect pests. Finally, the paper discusses current challenges and future development trends in this field.