Coupled effects of soil drying and salinity on soil-plant hydraulics

Nature-based remediation of mine tailings: Synergistic effects of narrow-leafed lupine and organo-mineral amendments on soil nutrient-acquiring enzymes and microbial activity

Rising global metal demand has led to extensive mining, leaving post-mining landscapes with degraded soil and metal contamination. The exacerbated heavy metals concentrations deteriorate soil microbial activity and consequent microbial biomass, enzymatic activities, and organic matter are impaired. This study explores nature-based solutions, focusing on assisted natural remediation and organo-mineral amendments: marble waste (Mw), clay (Cy), and compost (Cp). Lupinus angustifolius L., a key bioremediator, is highlighted for its role in mine rehabilitation, adaptation to extreme edaphic conditions, and contribution to enhanced nutritional status. The specific aim of this study is to evaluate the synergetic impact of the use of L. angustifolius with four soil combined treatments (Com): Com1: Cy2.5-Cp2.5-Mw10; Com2: Cy2.5-Cp5-Mw5; Com3: Cy7.5-Cp2.5-Mw7.5; and Com4: Cy10-Cp10-Mw10. As a practical approach to sustainable mining soil rehabilitation, it emphasizes soil microbial biomass and activity, soil fertility, plant growth, and heavy metal immobilization in a concise and impactful manner. These combinations were used as the soil substrate material for a four-month greenhouse experiment where plant growth parameters, heavy metal accumulation, soil properties, microbial activity, and bioavailable metal content were determined. The study underscored the positive effects of the treatments Com1, Com3, and Com4 on heavy metal mobility, microbial biomass, and carbon, nitrogen, and phosphorus-acquiring enzymes. Notably, bioavailable heavy metals were effectively reduced, with copper, zinc, and lead decreasing up to 2-fold, 2-fold, and 1.8-fold, respectively. Microbial biomass and soil enzyme activities responded positively to our amendments, indicating improved nutrient cycling. Microbial biomass carbon increased up to 4-fold, and similarly, β-glucosidase, N-acetyl-ß-glucosaminidases, L-Arginase, and acid phosphatase (Pho) increased up to 1.9-fold, 47-fold, 12.85-fold, and 2-fold, respectively. Furthermore, soil carbon and nitrogen contents increased up to 11.15-fold and 9.41-fold, respectively. This study suggested a positive and impactful influence on the intricate processes of soil carbon and nitrogen cycling, indicative of increased microbial activity, and offered a nature-based solution to mitigate the environmental impact of extensive mining.

Inversion model of soil salinity in alfalfa covered farmland based on sensitive variable selection and machine learning algorithms

Purpose

Timely and accurate monitoring of soil salinity content (SSC) is essential for precise irrigation management of large-scale farmland. Uncrewed aerial vehicle (UAV) low-altitude remote sensing with high spatial and temporal resolution provides a scientific and effective technical means for SSC monitoring. Many existing soil salinity inversion models have only been tested by a single variable selection method or machine learning algorithm, and the influence of variable selection method combined with machine learning algorithm on the accuracy of soil salinity inversion remain further studied.

Methods

Firstly, based on UAV multispectral remote sensing data, by extracting the spectral reflectance of each sampling point to construct 30 spectral indexes, and using the pearson correlation coefficient (PCC), gray relational analysis (GRA), variable projection importance (VIP), and support vector machine-recursive feature elimination (SVM-RFE) to screen spectral index and realize the selection of sensitive variables. Subsequently, screened and unscreened variables as model input independent variables, constructed 20 soil salinity inversion models based on the support vector machine regression (SVM), back propagation neural network (BPNN), extreme learning machine (ELM), and random forest (RF) machine learning algorithms, the aim is to explore the feasibility of different variable selection methods combined with machine learning algorithms in SSC inversion of crop-covered farmland. To evaluate the performance of the soil salinity inversion model, the determination coefficient (R2), root mean square error (RMSE) and performance deviation ratio (RPD) were used to evaluate the model performance, and determined the best variable selection method and soil salinity inversion model by taking alfalfa covered farmland in arid oasis irrigation areas of China as the research object.

Results

The variable selection combined with machine learning algorithm can significantly improve the accuracy of remote sensing inversion of soil salinity. The performance of the models has been improved markedly using the four variable selection methods, and the applicability varied among the four methods, the GRA variable selection method is suitable for SVM, BPNN, and ELM modeling, while the PCC method is suitable for RF modeling. The GRA-SVM is the best soil salinity inversion model in alfalfa cover farmland, with Rv 2 of 0.8888, RMSEv of 0.1780, and RPD of 1.8115 based on the model verification dataset, and the spatial distribution map of soil salinity can truly reflect the degree of soil salinization in the study area.

Conclusion

Based on our findings, the variable selection combined with machine learning algorithm is an effective method to improve the accuracy of soil salinity remote sensing inversion, which provides a new approach for timely and accurate acquisition of crops covered farmland soil salinity information.

The multidimensionality of plant drought stress: The relative importance of edaphic and atmospheric drought

Drought threatens plant growth and related ecosystem services. The emergence of plant drought stress under edaphic drought is well studied, whilst the importance of atmospheric drought only recently gained momentum. Yet, little is known about the interaction and relative contribution of edaphic and atmospheric drought on the emergence of plant drought stress. We conducted a gradient experiment, fully crossing gravimetric water content (GWC: maximum water holding capacity-permanent wilting point) and vapour pressure deficit (VPD: 1-2.25 kPa) using five wheat varieties from three species (Triticum monococcum, T. durum & T. aestivum). We quantified the occurrence of plant drought stress on molecular (abscisic acid), cellular (stomatal conductance), organ (leaf water potential) and stand level (evapotranspiration). Plant drought stress increased with decreasing GWC across all organizational levels. This effect was magnified nonlinearly by VPD after passing a critical threshold of soil water availability. At around 20%GWC (soil matric potential 0.012 MPa), plants lost their ability to regulate leaf water potential via stomata regulation, followed by the emergence of hydraulic dysfunction. The emergence of plant drought stress is characterized by changing relative contributions of soil versus atmosphere and their non-linear interaction. This highly non-linear response is likely to abruptly alter plant-related ecosystem services in a drying world.

Seeds Priming with Melatonin Improves Root Hydraulic Conductivity of Wheat Varieties under Drought, Salinity, and Combined Stress

Drought and salinity stress reduce root hydraulic conductivity of plant seedlings, and melatonin application positively mitigates stress-induced damage. However, the underlying effect of melatonin priming on root hydraulic conductivity of seedlings under drought-salinity combined remains greatly unclear. In the current report, we investigated the influence of seeds of three wheat lines' 12 h priming with 100 μM of melatonin on root hydraulic conductivity (Lpr) and relevant physiological indicators of seedlings under PEG, NaCl, and PEG + NaCl combined stress. A previous study found that the combined PEG and NaCl stress remarkably reduced the Lpr of three wheat varieties, and its value could not be detected. Melatonin priming mitigated the adverse effects of combined PEG + NaCl stress on Lpr of H4399, Y1212, and X19 to 0.0071 mL·h-1·MPa-1, 0.2477 mL·h-1·MPa-1, and 0.4444 mL·h-1·MPa-1, respectively, by modulating translation levels of aquaporin genes and contributed root elongation and seedlings growth. The root length of H4399, Y1212, and X19 was increased by 129.07%, 141.64%, and 497.58%, respectively, after seeds pre-treatment with melatonin under PEG + NaCl combined stress. Melatonin -priming appreciably regulated antioxidant enzyme activities, reduced accumulation of osmotic regulators, decreased levels of malondialdehyde (MDA), and increased K+ content in stems and root of H4399, Y1212, and X19 under PEG + NaCl stress. The path investigation displayed that seeds primed with melatonin altered the modification of the path relationship between Lpr and leaf area under stress. The present study suggested that melatonin priming was a strategy as regards the enhancement of root hydraulic conductivity under PEG, NaCl, and PEG + NaCl stress, which efficiently enhanced wheat resistant to drought-salinity stress.

Interactive Effects of Microbial Fertilizer and Soil Salinity on the Hydraulic Properties of Salt-Affected Soil

Significant research has been conducted on the effects of fertilizers or agents on the sustainable development of agriculture in salinization areas. By contrast, limited consideration has been given to the interactive effects of microbial fertilizer (MF) and salinity on hydraulic properties in secondary salinization soil (SS) and coastal saline soil (CS). An incubation experiment was conducted to investigate the effects of saline soil types, salinity levels (non-saline, low-salinity, and high-salinity soils), and MF amounts (32.89 g kg-1 and 0 g kg-1) on soil hydraulic properties. Applied MF improved soil water holding capacity in each saline soil compared with that in CK, and SS was higher than CS. Applied MF increased saturated moisture, field capacity, capillary fracture moisture, the wilting coefficient, and the hygroscopic coefficient by 0.02-18.91% in SS, while it was increased by 11.62-181.88% in CS. It increased soil water supply capacity in SS (except for high-salinity soil) and CS by 0.02-14.53% and 0.04-2.34%, respectively, compared with that in CK. Soil available, readily available, and unavailable water were positively correlated with MF, while soil gravity and readily available and unavailable water were positively correlated with salinity in SS. Therefore, a potential fertilization program with MF should be developed to increase hydraulic properties or mitigate the adverse effects of salinity on plants in similar SS or CS areas.

Effects of Single and Combined Drought and Salinity Stress on the Root Morphological Characteristics and Root Hydraulic Conductivity of Different Winter Wheat Varieties

Water shortages and crop responses to drought and salt stress are related to the efficient use of water resources and are closely related to food security. In addition, PEG or NaCl stress alone affect the root hydraulic conductivity (Lpr). However, the effects of combined PEG and NaCl stress on Lpr and the differences among wheat varieties are unknown. We investigated the effects of combined PEG and NaCl stress on the root parameters, nitrogen (N) and carbon content, antioxidant enzymes, osmotic adjustment, changes in sodium and potassium, and root hydraulic conductivity of Yannong 1212, Heng 4399, and Xinmai 19. PEG and NaCl stress appreciably decreased the root length (RL), root surface area (RS), root volume (RV), K⁺ and N content in shoots and roots, and Lpr of the three wheat varieties, while the antioxidant enzyme activity, malondialdehyde (MDA), osmotic adjustment, nonstructural carbon and Na⁺ content in shoots and roots, etc., remarkably remained increased. Furthermore, the root hydraulic conductivity had the greatest positive association with traits such as RL, RS, and N and K⁺ content in the shoots of the three wheat varieties. Moreover, the RL/RS directly and actively determined the Lpr, and it had an extremely positive effect on the N content in the shoots of wheat seedlings. Collectively, most of the root characteristics in the wheat seedlings decreased under stress conditions, resulting in a reduction in Lpr. As a result, the ability to transport nutrients-especially N-from the roots to the shoots was affected. Therefore, our study provides a novel insight into the physiological mechanisms of Lpr.