Accurate prediction of food crop yield is of great significance for global food security and regional trade stability. Since remote sensing data collected from unmanned aerial vehicle (UAV) platforms have the features of flexibility and high resolution, these data can be used as samples to develop regional regression models for accurate prediction of crop yield at a field scale. The primary objective of this study was to construct regional prediction models for winter wheat yield based on multi-spectral UAV data and machine learning methods. Six machine learning methods including Gaussian process regression (GPR), support vector machine regression (SVR) and random forest regression (RFR) were used for the construction of the yield prediction models. Ten vegetation indices (VIs) extracted from canopy spectral images of winter wheat acquired from a multi-spectral UAV at five key growth stages in Xuzhou City, Jiangsu Province, China in 2021 were selected as the variables of the models. In addition, in situ measurements of wheat yield were obtained in a destructive sampling manner for prediction algorithm modeling and validation. Prediction results of single growth stages showed that the optimal model was GPR constructed from extremely strong correlated VIs (ESCVIs) at the filling stage (R² = 0.87, RMSE = 49.22 g/m², MAE = 42.74 g/m²). The results of multiple stages showed GPR achieved the highest accuracy (R² = 0.88, RMSE = 49.18 g/m², MAE = 42.57 g/m²) when the ESCVIs of the flowering and filling stages were used. Larger sampling plots were adopted to verify the accuracy of yield prediction; the results indicated that the GPR model has strong adaptability at different scales. These findings suggest that using machine learning methods and multi-spectral UAV data can accurately predict crop yield at the field scale and deliver a valuable application reference for farm-scale field crop management.

Nutrient-depleted soil is a major constraint for crop production, particularly for fruits. Here, we investigated the different response of nitrogen (N), phosphorus (P), and potassium (K) deficiency on the growth and development of strawberry ( Fragaria ×ananassa Duch.) in sand culture under greenhouse conditions. Compared with K, the lack of N or P is more unfavorable to strawberry growth and development. N deficiency affected shoot-root (S/R) ratio at different growth stages, and decreased the shoot biomass. P deficiency greatly increased the N content but decreased K content of the plants, which means P is of advantage to regulate the absorption and utilization of N and K nutrients in plants. Meanwhile, P has a profound influence on fruit quality, such as total soluble (TSS) sugar content. K deficiency is not conducive to fruit coloring and the formation of high-quality commercial fruits. The results contribute to a better understanding of the difference of N, P, or K deficiency on strawberry growth, nutrient absorption, and fruit quality during the whole growth period.

Low nitrogen stress severely impedes crop growth and productivity. There has been substantial research on root adaptation to low nitrogen conditions in many plant species. However, the mechanism underlying the morphological response of the strawberry (Fragaria × ananassa Duch.) root to low-NO₃⁻ or low-NH₄⁺ stress remains poorly understood. Strawberry plants were hydroponically cultivated under 1 mM NO₃⁻, 1 mM NH₄⁺, and control (15 mM NO₃⁻) conditions to assess the physiological responses of their roots to low nitrogen stress. As a result, low nitrogen stresses increased the fresh weight of root, lateral root density, and root surface area, as well as enhanced the accumulation of indole-3-acetic acid and jasmonic acid while significantly reducing salicylic acid in the roots. Correspondingly, low nitrogen stresses increased PM H⁺-ATPase activity. Low-NO₃⁻ stress enhanced the activities of nitrate reductase and glutamine synthetase, whereas low-NH₄⁺ treatment led to higher glutamine synthetase and glutamate synthase activities. Collectively, the present results demonstrate that low nitrogen stresses enhance nitrogen uptake of strawberry roots by regulating hormones (indole-3-acetic acid, jasmonic acid, and salicylic acid) and thereby mediating PM H⁺-ATPase activity, while promoting nitrogen metabolism by upregulating the activities of nitrate reductase, glutamine synthetase, and glutamate synthase. In conclusion, low nitrogen conditions may facilitate more efficient acquisition of available N from the soil by strawberry root system.

In the old course of the Yellow River area, most orchards are over-applied with nitrogen (N) fertilizers. To improve N management in this area, a ¹⁵N tracing experiment was conducted to investigate the absorption, distribution and loss of spring-topdressing urea in pear orchards from March to August 2019. The 7-year-old Sucui 1 pear was used as the test material, and 277.5 g of ordinary urea and 15 g of ¹⁵N-urea N were evenly applied to each plant. The N absorption, distribution and utilization efficiency of different organs from the flowering stage to the post-harvest stage were analyzed, and the residual and loss of N in the soil were also discussed. The N fertilizer utilization rate increased with the advancement of the phenological period. The N fertilizer utilization rate in the full bloom period is 10.39%, which is the fastest growing period, and reached a maximum of 23.62% in the post-harvest stage. In the young fruit stage, the amount of N derived from labeled fertilizer (%Ndff) of the fruit was only 1.02%, and most of the new vegetative organs were above 1%. Residual amount in the 20–40 cm soil layer was significantly higher than that in other soil layers. Direction of N fertilizer is N fertilizer loss>soil residue>tree absorption. N loss in the fruit expansion stage and the harvest stage is higher, which are 3.76 g and 3.74 g, respectively. N utilization rate in this area is low throughout the year. There is nutrient competition between reproductive growth and vegetative growth, which can be effectively alleviated by spring top-dressing. The N loss during fruit expansion and harvesting is serious. Attention should be paid to split fertilizer application and the timely supplementation of an appropriate amount of N fertilizer to improve N use efficiency.

Phosphorus-solubilizing microorganisms play an important role in soil nutrient phosphorus cycling. In order to clarify the effect of glucose (C₆H₁₂O₆) on soil phosphorus transformation, the effects of glucose additions on the bacterial community, soil phosphorus status, and plant phosphorus uptake in apple rhizosphere soil were investigated. A 90-day pot experiment was carried out, and the experiment was repeated three times. Glucose additions were 0, 2.5, 5, 7.5, 10, and 12.5 g glucose per kg of soil. We measured soil bacteria and phosphorus related indexes using Illumina MiSeq sequencing technology and chemical methods. The results showed that when the glucose application rate was 2.5–7.5 g·kg⁻¹, the soil total phosphorus content decreased by 4.4–7.3%; however, the soil acid phosphatase activity increased by 0.5–1.3 times, and the microbial biomass phosphorus increased by 29.1% and 37.0%. The content of Al-P and Fe-P in the rhizosphere soil decreased by 14.4 to 32.7 mg·kg⁻¹ and 16.04 to 28.7 mg·kg⁻¹, respectively. The compositional difference of the bacterial community became larger, and the relative abundance of 11 bacterial phyla changed significantly, among which the most significant change was found in Proteobacteria. This study also found that the relative abundances of Bacillus, Pseudomonas, Arthrobacter, and Cuprococcus increased by 0.9%, 2.2%, 2.4%, and 0.8%, respectively. Applying 7.5 g glucose per kg of soil can significantly increase the relative abundance of phosphorus solubilizing bacteria (Bacillus, Pseudomonas, Arthrobacter et al.) in rhizosphere soil, activate Al-P and Fe-P, and improve the availability of soil phosphorus.