Deficit irrigation of reclaimed water relieves oat drought stress while controlling the risk of PAEs pollution in microplastics-polluted soil.

Reclaimed water irrigation has emerged as a critical alternative in agricultural regions facing water scarcity. However, soil pollution with microplastics (MPs) greatly increases the exposure risk and toxic effects of reclaimed water contaminations, such as phthalate esters (PAEs). A field experiment consisting of soil column pots evaluated the feasibility of using PAEs-contaminated water to irrigate oats (Avena sativa L.) in drought seasons. Three irrigation regimens based on soil matric potential thresholds (-10 kPa, -30 kPa, -50 kPa) explored the impact of PAE-contaminated water on oat physiology and environmental pollution in soil with and without MPs contamination. The results showed that treating oats at the SMP of -30 kPa boosted shoot biomass by 3.1%-14.0% compared to the drought condition at -50 kPa, and the root biomass of oats was significantly increased. The physiological metrics of oats indicated that irrigation at -50 kPa induced drought stress and oxidative damage in oats, particularly during the milk stage. Different irrigation treatments influenced the accumulation of PAEs in plants, soil, and leachate. The ratios of leachate to irrigation water in -10 kPa treatment with and without MPs addition were 1.18% and 4.48%, respectively, which aggravated the accumulation of pollutants in deep soil layers and may cause groundwater pollution. MPs pollution in soil increased the content of PAEs in the harvested oats and reduced the transport and accumulation of PAEs in deep soil layers (20-50 cm) and leachate. The coupling of PAEs in irrigation water with soil MPs pollution may exacerbate plant damage. However, the damage can be minimized under the scheduled irrigation at -30 kPa which could balance crop yield and potential risks.

Simulating soil salinity dynamics, cotton yield and evapotranspiration under drip irrigation by ensemble machine learning.

Cotton is widely used in textile, decoration, and industry, but it is also threatened by soil salinization. Drip irrigation plays an important role in improving water and fertilization utilization efficiency and ensuring crop production in arid areas. Accurate prediction of soil salinity and crop evapotranspiration under drip irrigation is essential to guide water management practices in arid and saline areas. However, traditional hydrological models such as Hydrus require more variety of input parameters and user expertise, which limits its application in practice, and machine learning (ML) provides a potential alternative. Based on a global dataset collected from 134 pieces of literature, we proposed a method to comprehensively simulate soil salinity, evapotranspiration (ET) and cotton yield. Results showed that it was recommended to predict soil salinity, crop evapotranspiration and cotton yield based on soil data (bulk density), meteorological factors, irrigation data and other data. Among them, meteorological factors include annual average temperature, total precipitation, year. Irrigation data include salinity in irrigation water, soil matric potential and irrigation water volume, while other data include soil depth, distance from dripper, days after sowing (for EC and soil salinity), fertilization rate (for yield and ET). The accuracy of the model has reached a satisfactory level, R2 in 0.78-0.99. The performance of stacking ensemble ML was better than that of a single model, i.e., gradient boosting decision tree (GBDT); random forest (RF); extreme gradient boosting regression (XGBR), with R2 increased by 0.02%-19.31%. In all input combinations, other data have a greater impact on the model accuracy, while the RMSE of the S1 scenario (input without meteorological factors) without meteorological data has little difference, which is -34.22%~19.20% higher than that of full input. Given the wide application of drip irrigation in cotton, we recommend the application of ensemble ML to predict soil salinity and crop evapotranspiration, thus serving as the basis for adjusting the irrigation schedule.

Distinguishing Planting Structures of Different Complexity from UAV Multispectral Images.

This study explores the classification potential of a multispectral classification model for farmland with planting structures of different complexity. Unmanned aerial vehicle (UAV) remote sensing technology is used to obtain multispectral images of three study areas with low-, medium-, and high-complexity planting structures, containing three, five, and eight types of crops, respectively. The feature subsets of three study areas are selected by recursive feature elimination (RFE). Object-oriented random forest (OB-RF) and object-oriented support vector machine (OB-SVM) classification models are established for the three study areas. After training the models with the feature subsets, the classification results are evaluated using a confusion matrix. The OB-RF and OB-SVM models' classification accuracies are 97.09% and 99.13%, respectively, for the low-complexity planting structure. The equivalent values are 92.61% and 99.08% for the medium-complexity planting structure and 88.99% and 97.21% for the high-complexity planting structure. For farmland with fragmentary plots and a high-complexity planting structure, as the planting structure complexity changed from low to high, both models' overall accuracy levels decreased. The overall accuracy of the OB-RF model decreased by 8.1%, and that of the OB-SVM model only decreased by 1.92%. OB-SVM achieves an overall classification accuracy of 97.21%, and a single-crop extraction accuracy of at least 85.65%. Therefore, UAV multispectral remote sensing can be used for classification applications in highly complex planting structures.