Chinese jujube (Ziziphus jujuba) is an important fruit tree in China, and soil salinity is the main constraint affecting jujube production. It is unclear how arbuscular mycorrhizal (AM) symbiosis supports jujube adaptation to salt stress. Herein, we performed comparative physiological, ion flux, fatty acid (FA) metabolomic, and transcriptomic analyses to examine the mechanism of AM jujube responding to salt stress. AM seedlings showed better performance during salt stress. AM symbiosis altered phytohormonal levels: indole-3-acetic acid and abscisic acid contents were significantly increased in AM roots and reduced by salt stress. Mycorrhizal colonization enhanced root H⁺ efflux and K⁺ influx, while inducing expression of plasma membrane-type ATPase 7 (ZjAHA7) and high-affinity K⁺ transporter 2 (ZjHAK2) in roots. High K⁺/Na⁺ homeostasis was maintained throughout salt exposure. FA content was elevated in AM leaves as well as roots, especially for palmitic acid, oleic acid, trans oleic acid, and linoleic acid, and similar effects were also observed in AM poplar (Populus. alba × Populus. glandulosa cv. 84K) and Medicago truncatula, indicating AM symbiosis elevating FA levels could be a conserved physiological effect. Gene co-expression network analyses uncovered a core gene set including 267 genes in roots associated with AM symbiosis and conserved transcriptional responses, for example, FA metabolism, phytohormone signal transduction, SNARE interaction in vesicular transport, and biotin metabolism. In contrast to widely up-regulated genes related to FA metabolism in AM roots, limited genes were affected in leaves. We propose a model of AM symbiosis-linked reprogramming of FA metabolism and provide a comprehensive insight into AM symbiosis with a woody species adaptation to salt stress.

Leaf area index(LAI) is an important indicator of crop growth and water status. With the continuous development of precision agriculture, estimating LAI using an unmanned aerial vehicle (UAV) remote sensing has received extensive attention due to its low cost, high throughput and accuracy. In this study, multispectral and light detection and ranging (LiDAR) sensors carried by a UAV were used to obtain multisource data of a cotton field. The method to accurately relate ground measured data with UAV data was built using empirical statistical regression models and machine learning algorithm models (RFR, SVR and ANN). In addition to the traditional spectral parameters, it is also feasible to estimate LAI using UAVs with LiDAR to obtain structural parameters. Machine learning models, especially the RFR model (R² = 0.950, RMSE = 0.332), can estimate cotton LAI more accurately than empirical statistical regression models. Different plots and years of cotton datasets were used to test the model robustness and generality; although the accuracy of the machine learning model decreased overall, the estimation accuracy based on structural and multisources was still acceptable. However, selecting appropriate input parameters for different canopy opening and closing statuses can alleviate the degradation of accuracy, where input parameters select multisource parameters before canopy closure while structural parameters are selected after canopy closure. Finally, we propose a gap fraction model based on a LAI_max threshold at various periods of cotton growth that can estimate cotton LAI with high accuracy, particularly when the calculation grid is 20 cm (R² = 0.952, NRMSE = 12.6%). This method does not require much data modeling and has strong universality. It can be widely used in cotton LAI prediction in a variety of environments.

The yield, quality, and water–fertilizer use efficiency of crops are important parameters for assessing rational water and fertilizer management. For an optimal water and fertilizer system with respect to the nutrient solution irrigation of greenhouse tomatoes using cultivation substrates, a two-year greenhouse cultivation experiment was conducted from 2022 to 2023. Three drip fertigation treatments (T1, T2, and T3) were implemented in the experiment, where nutrient solutions were supplied when the substrate’s water content reached 60%, 70%, and 80%. The frequency of nutrient solution applications is based on weighing coconut coir strips in the morning and evening at 7:00 to determine the daily water consumption of plants. Nutrient solutions were supplied when the substrate’s water content reached the lower limit, and the upper limit for nutrient supply was set at 100% of the substrate water content. The nutrient solution application was carried out multiple times throughout the day, avoiding the midday heat. The nutrient solution formula used was the soilless tomato cultivation formula from South China Agricultural University. The results show that plant height and the leaf area index rapidly increased in the early and middle stages, and later growth tended to stabilize; the daily transpiration of tomatoes increased with an increase in nutrient solution supply, and it was the greatest in the T3 treatment. Between the amount of nutrient solution application and the number of years, the yield increased with the increase of the amount of nutrient solution, showing T3 > T2 > T1. Although the average yield of the T2 treatment was slightly lower than that of the T3 treatment by 3.65%, the average irrigation water use efficiency, water use efficiency, and partial fertilizer productivity of the T2 treatment were significantly higher than those of the T3 treatment by 29.10%, 19.99%, and 28.89%, respectively (p < 0.05). Additionally, soluble solid, vitamin C, and soluble sugar contents and the sugar–acid ratio of tomatoes in the T2 treatment were greater than those in the other two treatments (p < 0.05). Using the TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method, it was concluded that the nutrient solution application rate of 70% can significantly increase water and fertilizer use efficiency and markedly improve the nutritional and flavor quality of the fruit without a significant reduction in yield. This finding provides significant guidance for the high-yield, high-quality, and efficient production of coconut coir-based cultivated tomatoes in greenhouses.

Farmland application of soil amendments is a usual method to mitigate soil salinization. Most studies have concluded that soil properties can be improved by soil amendment, which indirectly affects the soil microbial community structures.