Accurate soil salinity estimation under small-sample agricultural conditions continues to pose a formidable challenge, attributed to the scarcity of labeled data, inherent representational limitations of single-backbone neural networks, and the heightened complexity of subsurface salinity inversion. To mitigate these intertwined challenges, this study developed a UAV-enabled soil salinity estimation framework that integrated lightweight convolutional neural networks and staged feature optimization, leveraging both RGB and multispectral imagery. A feature selection framework integrating random forest recursive feature elimination (RF-RFE), the one-standard-error (One-SE) criterion, and variance inflation factor (VIF) analysis was employed to reduce 129 candidate variables to a unified 16-channel feature set, which served as the common input for estimating both surface and subsurface soil salinity. Three lightweight single-backbone (VGGNet, ResNet, and DenseNet) and dual-backbone feature-level fusion networks (DenseResNet, DenseVGGNet, and ResVGGNet) were constructed and systematically evaluated for their performance in estimating both surface and subsurface soil salinity. Among the single-backbone networks, ResNet yielded the highest overall statistical accuracy, while DenseNet exhibited superior performance in preserving estimation trends. For surface soil salinity estimation, ResVGGNet achieved the best performance among all evaluated models, with an R2 of 0.820, RMSE of 0.626 g/kg, MAE of 0.409 g/kg, and RPD of 2.31 on the test dataset. SHAP analysis further highlighted the dominant role of vegetation and salinity-sensitive indices, together with selected spectral mean features, and revealed spatially complementary response patterns among major input channels. Collectively, the integration of lightweight multi-backbone feature-level fusion with streamlined feature optimization strategies effectively enhances the accuracy, robustness, and interpretability of UAV-enabled soil salinity estimation, particularly under the constraint of small agricultural sample sizes.

Understanding the root-soil water interactions is essential for improving the sustainability of agroforestry systems in semiarid regions. This study investigated how different agroforestry systems regulate fine root spatial patterns and soil water utilization in long-term rainfed jujube (Ziziphus jujuba Mill.) orchards. Soil samples were collected to 500 cm depth in jujube orchards intercropped with canola (Brassica napus L.) or daylily (Hemerocallis fulva L.), and compared with monoculture. Fine root length density and soil water content were measured to evaluate root distribution and hydrological status. Jujube intercropped with canola facilitated a cooperative root distribution, by increasing the abundance of jujube fine roots in shallow soil layers (0–60 cm). This system improved hydrological conditions, with a significantly thicker surface wetting soil layer (mean 93.3 cm) and a reduced dried soil layer thickness (83.3 cm) compared to the jujube monoculture (73.3 cm and 141.6 cm, respectively). Conversely, jujube intercropped with daylily triggered competitive displacement, forcing jujube fine roots into deeper soil layers (120–300 cm). This downward migration resulted in a thinner surface wetting layer (65.0 cm) and extended dried soil layer thickness (95.0 cm), particularly in inter-row zones. Intercropping species influence root plasticity and soil water redistribution. Intercropping with canola improved hydrological sustainability through shallow root niche overlap and enhanced soil water conservation, whereas intercropping with daylily intensified deep soil water consumption by promoting downward root proliferation. Compatible intercrop selection is thus critical for sustainable water management in water-limited orchards.

As a core parameter characterizing vegetation growth status, the accurate estimation of the leaf area index (LAI) is crucial for the efficient management and optimized cultivation of cotton crops. While most existing studies rely on single data sources and individual algorithms to develop LAI estimation models, limitations persist in the selection of feature variables from multi-source data and the implementation of cross-algorithm ensemble learning strategies. This study aimed to develop a multi-source data-driven ensemble learning model for estimating cotton LAI. Three types of cotton canopy image data, including visible light (RGB), multispectral (MS), and thermal infrared (TIR), along with corresponding in-situ LAI measurements, were collected throughout the entire cotton growth period under a waterlogging stress experimental framework. A comprehensive set of feature variables was extracted from each of these three data modalities. Correlation analysis, decision tree-based feature importance ranking and recursive feature elimination (RFE) were sequentially employed to screen and optimize remote sensing feature variables, after which seven distinct data fusion schemes were designed. Integrating 10-fold cross-validation and paired t-tests, the robustness and performance disparities of LAI estimation models were systematically evaluated across varying conditions: distinct single machine learning algorithms, diverse ensemble learning approaches, and varied data fusion schemes.The results revealed that color and vegetation indices exhibited the strongest correlation with LAI (r = 0.841), whereas texture features generally showed weak correlations. The integration of tree model-based feature importance ranking and RFE effectively filtered feature variables, reducing the initial set of 106 variables to a refined subset of 29 variables with relatively high predictive importance. Among single machine learning algorithms, the artificial neural network (ANN), decision trees (DT), and gaussian process (GP) models demonstrated comparable performance, whereas the support vector machine (SVM) model exhibited inferior performance and high sensitivity to data schemes. Among ensemble learning approaches, the partial least squares (PLS) meta-model achieved the best performance, with the TIR univariate, MS + TIR bivariate, and RGB + MS + TIR trivariate schemes emerging as the optimal data configurations. Furthermore, ensemble learning models, particularly the PLS meta-model, significantly outperformed single machine learning models (e.g., SVM) in terms of accuracy, with improvements reaching up to 277% for the trivariate scheme. Independent validation conducted using the optimal ensemble scheme showed results consistent with those obtained during training, where the MS + TIR bivariate data fusion yielded the highest performance (R2 = 0.90, MSE = 0.21, MAE = 0.34). Overall, this study underscores the significant potential of ensemble learning methods for cotton LAI retrieval, which can facilitate the advancement of cotton production management and agricultural decision-making.

Soil salinization poses an increasing threat to arable land quality, crop growth, and regional biodiversity; therefore, accurate and efficient acquisition of salinity information is essential for remediation. Previous studies have primarily focused on single data sources or algorithms, whereas the potential of integrating multi-source remote sensing data with multiple machine learning models remains largely underexplored. In this study, quantitative experiments were conducted across gradients of soil salinity under barley cultivation to acquire visible light (RGB), multispectral (MS), and thermal infrared (TIR) imagery, together with measurements of apparent electrical conductivity of the soil profile. A total of 144 feature variables, including band brightness, spectral reflectance, and derived color indices, temperature indices, and texture features, were extracted from remote sensing image, screened and optimized using coordinated approach integrating random forest importance and recursive feature elimination (RF-RFE), and incorporated into seven data fusion schemes. Four machine learning models and three ensemble learning strategies (Average, Weighted, and stacking of two layers with ridge regression as the meta-model (St-RR)) was systematically established and assessed using cross validation with five folds and multiple evaluation metrics. Results showed that extreme learning machine achieved the highest accuracy for surface soil (R2=0.75, RMSE=0.69 %, MAE=0.45, RPIQ=0.76, RPD=1.98), whereas gaussian process regression performed best for root-zone soil (R2=0.73, RMSE=1.06 %, MAE=0.68, RPIQ=1.08, RPD=1.93). All three ensemble learning strategies improved estimation accuracy compared with single models, with St-RR achieving the most notable enhancements (surface soil: R2 increases of 6.0 %-7.6 % and RMSE reductions of 5.1 %-6.3 %; root-zone soil: R2 increases of 6.1 %-9.4 % and RMSE reductions of 8.3 %-10.6 %). Among all data fusion schemes, the St-RR based on the RGB+MS fusion achieved the highest accuracy for surface soil (R2 = 0.76, RMSE = 0.67 %, MAE = 0.43, RPIQ = 0.79, RPD = 2.04) and root-zone soils (R2 = 0.77, RMSE = 0.99 %, MAE = 0.65, RPIQ = 1.16, RPD = 2.08). Overall, these findings demonstrate the effectiveness of multisource remote sensing fusion combined with stacking ensemble learning for accurate soil salinity estimation, providing robust technical support for the management of soil salinization at a fine scale.

Soil salinization is the most prevalent form of land degradation in arid, semi-arid, and coastal regions of China, posing significant challenges to local crop yield, economic development, and environmental sustainability. However, limited research exists on estimating soil salinity at different depths under vegetation cover. This study employed field-controlled soil experiments to collect multi-source remote sensing data on soil salt content (SSC) at varying depths beneath barley growth. Three types of feature variables were derived from the images and filtered using the boosting decision tree (BDT) method. In addition, four machine learning algorithms coupled with seven variable combination groups were applied to establish comprehensively soil salinity estimation models. The performances of estimation model for different crop coverage ratios and soil depth were then evaluated. The results showed that the gaussian process regression (GPR) model, based on the whole variable group for depths of 0 ~ 10 cm and 30 ~ 40 cm, outperformed other models, achieving validation R2 values of 0.774 and 0.705, with RMSE values are 0.185% and 0.31%, respectively. For depths of 10 ~ 20 cm and 20 ~ 30 cm, the random forest (RF) models, incorporating spectral index and texture data, demonstrated superior accuracy with R2 values of 0.666 and 0.714. The study confirms that SSC can be quantitatively estimated at various depths using the machine learning model based on multi-source remote sensing, providing a valuable approach for monitoring soil salinization.

Straw mulching is an important strategy for regulating soil moisture, nutrient availability, and thermal conditions in agricultural systems. However, the mechanisms by which the mulching period, thickness, and planting density interact to influence yield formation in wheat–soybean rotation systems remain insufficiently understood. In this study, we systematically examined the combined effects of straw mulching at the seedling and jointing stages of winter wheat, as well as varying mulching thicknesses and soybean planting densities, on soil properties and crop yields through field experiments. The experimental design included straw mulching treatments during the seedling stage (T1) and the jointing stage (T2) of winter wheat, with soybean planting densities classified as low (D1, 1.8 × 105 plants·ha−1) and high (D2, 3.6 × 105 plants·ha−1). Mulching thicknesses were set at low (S1, 2830.19 kg·ha−1), medium (S2, 8490.57 kg·ha−1), and high (S3, 14,150.95 kg·ha−1), in addition to a no-mulch control (CK) for each treatment. The results demonstrated that (1) straw mulching significantly increased soil water content in the order S3 > S2 > S1 > CK and exerted a temperature-buffering effect. This resulted in increases in soil organic carbon, available phosphorus, and available potassium by 1.88−71.95%, 1.36−165.8%, and 1.92−36.34%, respectively, while decreasing available nitrogen content by 1.42−17.98%. (2) The T1 treatments increased wheat yields by 1.22% compared to the control, while the T2 treatments resulted in a 23.83% yield increase. Soybean yields increased by 23.99% under D1 and by 36.22% under D2 treatments. (3) Structural equation modeling indicated that straw mulching influenced yields by modifying interactions among soil organic carbon, available nitrogen, available phosphorus, available potassium, bulk density, soil temperature, and soil water content. Wheat yields were primarily regulated by the synergistic effects of soil temperature, water content, and available potassium, whereas soybean yields were determined by the dynamic balance between organic carbon and available potassium. This study provides empirical evidence to inform the optimization of straw return practices in wheat–soybean rotation systems.

Ancient glass artifacts were susceptible to weathering from the environment, causing changes in their chemical composition, which pose significant obstacles to the identification of glass products. Analyzing the chemical composition of ancient glass has been beneficial for evaluating their weathering status and proposing measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learning techniques were used. The results indicated that a significant correlation (p < 0.05) could only observed between surface weathering and the glass types (high-potassium and lead–barium). Based on the random forest and logistic regression models, the primary chemical components that signify glass types and weathering status were determined using PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented a superior ability to identify glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.

Saline soils limit plant growth due to high salinity. Straw returning has proven effective in enhancing soil adaptability and agricultural stability on saline lands. This study evaluates the effects of different straw-returning methods—straw mulching (SM), straw incorporation (SI), and straw biochar (BC)—on soil nutrients, water dynamics, and salinity in a barley–cotton rotation system using field box experiments. SM improved soil water retention during barley’s jointing and heading stages, while SI was more effective in its filling and maturation stages. BC showed lesser water storage capacity. During cotton’s growth, SI enhanced early-stage water retention, and SM benefited the flowering and boll opening stages. Grey relational analysis pinpointed significant water relationships at 10 cm and 20 cm soil depths, with SM regulating water across layers. SM and BC notably reduced soil conductivity, primarily within the top 20 cm, and their effectiveness decreased with depth. SI significantly lowered soil conductivity at barley’s jointing stage. SM effectively reduced salinity at 10 cm and 20 cm soil depths, whereas BC decreased soil conductivity throughout barley’s jointing, filling, and heading stages. For cotton, SI lowered soil conductivity at the seedling and boll opening stages. SM consistently reduced salinity across all stages, and BC decreased conductivity in the top 30 cm of soil during all growth stages. Both SM and BC significantly enhanced the total nutrient availability for barley and cotton, especially improving soil organic carbon and available potassium, with BC showing notable improvements. At barley’s heading stage, SI maximized dry matter accumulation, while SM boosted accumulation in leaves, stems, and spikes during the filling and maturation stages. Straw returning increased barley yield, particularly with SM and BC, and improved water use efficiency by 11.60% and 5.74%, respectively. For cotton, straw returning significantly boosted yield and water use efficiency, especially with SI and SM treatments, enhancing the total bolls and yield. In conclusion, straw returning effectively improves saline soils, enhances fertility, boosts crop yields, and supports sustainable agriculture. These results provide a robust scientific foundation for adopting efficient soil improvement strategies on saline lands, with significant theoretical and practical implications for increasing agricultural productivity and crop resilience to salt stress.

The main purpose of this study was to assess the influence of grass planting and jujube branch mulching on soil moisture levels and jujube tree transpiration rates, with the ultimate goal of improving jujube tree production in rain-fed orchards. The study encompassed four treatments: jujube branch mulching (JBM), jujube branch mulching with white clover planting (JBM + WCP), white clover planting (WCP), and clean cultivation (CC). During a two-year experiment, it was observed that the JBM treatment exhibited the highest capacity for moisture conservation. Specifically, it resulted in an average increase of 2.69% (in 2013) and 2.23% (in 2014) in soil moisture content compared with the CC treatment. The application of statistical analysis revealed significant differences (p < 0.05) between JBM and JBM + WCP, as well as highly significant differences (p < 0.01) between JBM and WCP in the year 2013. In 2014, JBM exhibited significant differences (p < 0.01) from both JBM + WCP and WCP. Between April and August, JBM exhibited the highest soil moisture content, followed by CC, with WCP showing the lowest levels. From September to October, JBM retained its status as the treatment with the highest soil moisture content, JBM + WCP ranked second, and CC experienced a decline and recorded the lowest soil moisture content. Under sunny conditions, all treatments showed a broad peak curve in the daily variation of sap flow velocity. In cloudy weather, a multi-peak wave-like curve was observed with similar trends across treatments. Between April and August, the monthly average sap flow velocity of JBM ranked the highest, followed by CC, while WCP showed the lowest velocity. During the period of September to October, JBM maintained its lead in sap flow velocity, while JBM + WCP rose to the second position, and CC’s sap flow velocity dropped to the lowest level. JBM and WCP treatments showed significant differences (p < 0.01), and in 2014, JBM also had significant differences (p < 0.05) compared with JBM + WCP. The sap flow velocity was positively correlated with air temperature, vapor pressure deficit, wind velocity, photosynthetically active radiation, and soil temperature. Photosynthetically active radiation was identified as the main driving factor influencing jujube tree transpiration. In conclusion, the findings of this study demonstrate the effectiveness of using pruned jujube branches for coverage in rain-fed jujube orchards. This approach not only conserves mulching materials and diminishes the expenses associated with transporting pruned jujube tree branches away from the jujube orchard but also achieves multiple objectives, including increasing soil moisture, promoting jujube tree transpiration, and enhancing soil water utilization. These results have significant implications for the efficient utilization of rainwater resources in rain-fed jujube orchards and provide valuable insights for practical applications in orchard management.

In order to investigate the comprehensive effects of straw returning on soil physical and chemical properties, as well as cotton growth in Jiangsu, China, and to determine suitable high-yield and efficient straw returning measures, this study implemented three different straw returning methods: straw mulching (SM), straw incorporation (SI), and straw biochar (BC), with no straw returning served as a control (CT). The study aimed to assess the impact of these straw-returning measures on soil nutrients, soil moisture content, soil water storage, and deficit status, as well as primary indicators of cotton growth. The findings revealed that the total available nutrient storage under SM, SI, and BC showed an increase of 11.93%, 11.15%, and 32.39%, respectively, compared to CT. Among these methods, BC demonstrated a significant enhancement in soil organic carbon content, available phosphorus, and available potassium. Furthermore, SM exhibited a considerable increase in soil moisture content across all layers (0–40 cm), resulting in an average water storage increase of 7.42 mm compared to CT. Consequently, this effectively reduced the soil water deficit during the cotton development period. Moreover, the height of cotton plants was increased by SM, SI, and BC, with SM promoting the greatest growth rate of up to 66.87%. SM resulted in an 11.17 cm increase in cotton plant height compared to CT. Additionally, SM contributed to higher chlorophyll content in leaves at the end of the growth period. Overall, the indicators suggest that straw mulching is particularly effective in enhancing soil moisture and nutrient distribution, especially during dry years, and has a positive impact on promoting cotton development. Based on the results, straw mulching emerges as a recommended straw-returning measure for improving soil quality and maximizing cotton production in the study area.

The implementation of the “Returning Farmland to Forest” project in the loess hilly region of China has led to the establishment of large-scale economic forests, which have become the dominant industry driving local economic development. However, the region faces challenges such as drought, water shortages, and an uneven distribution of precipitation, which have a severe impact on the growth of economic forests, including jujube trees. Water stress significantly reduces yield and efficiency, posing a threat to the sustainable and healthy development of jujube ecological and economic forests. Therefore, this study aimed to address these issues by implementing straw mulching (SM) and jujube branch mulching (BM) measures in the mountainous jujube economic forests. Through long-term monitoring and statistical analysis, the study investigated the effects of different mulching treatments on soil moisture and soil temperature. The research findings reveal that both SM and BM significantly increased soil moisture in the 0–280 cm soil layer during the jujube growing season (p < 0.05). In both normal precipitation (2014) and drought (2015) years, SM increased average soil moisture content by 5.10% and 4.60%, respectively, compared to the uncovered treatment (CK). SM also had a positive impact on the soil moisture content in each layer of the soil profile. However, BM only increased soil moisture content in the 40–100 cm and 220–280 cm soil layers. Additionally, SM and BM reduced the variation of soil moisture, with SM showing a more significant effect in regulating soil moisture and achieving more stable moisture levels. During the jujube growing seasons in 2014 and 2015, SM and BM decreased soil temperature in the 0–10 cm soil layer. The temperature difference compared to CK decreased with increasing soil depth. SM had an overcooling effect, while BM reduced the temperature before the fruit expansion period and maintained warmth afterward. Both SM and BM also reduced the daily range and variation range of soil temperature, with SM having a more pronounced effect. The temperature of the 0–20 cm soil layer exhibited the strongest correlation with air temperature, and SM showed the weakest response. In conclusion, adopting straw mulching and jujube branch mulching in rain-fed jujube orchards in the loess hilly region not only saves materials and reduces costs but also contributes to water retention and temperature regulation. Straw mulching, in particular, plays a more significant role in moisture retention and temperature regulation and is advantageous for soil management in rain-fed jujube orchards. These research findings provide a scientific basis for optimizing water and heat management in orchards with limited water resources.

Abstract Glass was a significant symbol of early trade exchanges on the Silk Road. However, the glass artifacts were susceptible to environmental influences when buried underground, leading to weathering and changes in their chemical composition. Analyzing the chemical composition of ancient glass was conducive to evaluate weathering status and propose the measures to reduce glass weathering. The objective of this study was to explore the optimal machine learning algorithm for glass type classification based on chemical composition. A set of glass artifact data including color, emblazonry, weathering, and chemical composition was employed and various methods including logistic regression and machine learnings were used. Results indicated a significant correlation (p < 0.05) was only observed between surface weathering and the glass types (high-potassium and lead-barium). Based on the random forest and logistic regression, the primary chemical components that signifying glass types and weathering status were determined by PbO, K2O, BaO, SiO2, Al2O3, and P2O5. The random forest model presented superior capability for identifying glass types and weathering status, with a global accuracy of 96.3%. This study demonstrates great potential of machine learning for glass chemical component estimation and glass type and weathering status identification providing technical guidance for the appraisal of ancient glass artifacts.

The fraction of absorbed photosynthetically active radiation (FPAR), which represents the capability of vegetation-absorbed solar radiation to accumulate organic matter, is a crucial indicator of photosynthesis and vegetation growth status. Although a simplified semi-empirical FPAR estimation model was easily obtained using vegetation indices (VIs), the sensitivity and robustness of VIs and the optimal inversion method need to be further evaluated and developed for canola FPAR retrieval. The objective of this study was to identify the robust hybrid inversion model for estimating the winter canola FPAR. A field experiment with different sow dates and densities was conducted over two growing seasons to obtain canola FPARs. Moreover, 29 VIs, two machine learning algorithms and the PROSAIL model were incorporated to establish the FPAR inversion model. The results indicate that the OSAVI, WDRVI and mSR had better capability for revealing the variations of the FPAR. Three parameters of leaf area index (LAI), solar zenith angle (SZA) and average leaf inclination angle (ALA) accounted for over 95% of the total variance in the FPARs and OSAVI exhibited a greater resistance to changes in the leaf and canopy parameters of interest. The hybrid inversion model with an artificial neural network (ANN-VIs) performed the best for both datasets. The optimal hybrid inversion model of ANN-OSAVI achieved the highest performance for canola FPAR retrieval, with R2 and RMSE values of 0.65 and 0.051, respectively. Finally, the work highlights the usefulness of the radiation transfer model (RTM) in quantifying the crop canopy FPAR and demonstrates the potential of hybrid model methods for retrieving the canola FPAR at each growth stage.

Soil water is a major barrier to ecological restoration and sustainable land use in China’s Loess Hilly Region. For the restoration of local vegetation and the optimal use of the region’s land resources, both theoretically and practically, it is essential to comprehend the soil water regimes under various land use types. The soil water content in the 0–160 cm soil profile of slope cropland, terraced field, jujube orchard, and grassland was continuously measured using EC-5 soil moisture sensors during the growing season (May–October) in the Yuanzegou catchment in the Loess Hilly Region to characterize the changes in soil water in these four typical land use types. The results showed that in both years of normal precipitation and drought, land use patterns varied in seasonal variability, water storage characteristics, and vertical distribution of soil water. In the dry year of 2015, the terraced field effectively held water. During the growing season, the 0–60 cm soil layer’s average soil water content was 2.6%, 4.2%, and 1.8% higher than the slope cropland, jujube orchard, and grassland, respectively (p < 0.05), and the 0–160 cm soil layer’s water storage was 43.90, 32.08, and 18.69 mm higher than the slope cropland, jujube orchard, and grassland, respectively. The average soil water content of the 0–60 cm soil layer in the jujube orchard was 2.9%, 3.8%, and 4.5% lower than that of slope cropland, terraced field, and grassland, respectively, during the normal precipitation year (2014) (p < 0.05). Only 35.0% of the total soil water storage was effectively stored in the 0–160 cm soil layer of the jujube orchard during the drought year. There was a significant difference in the grey relational grade between the soil water in the top layer (0–20 cm) and the soil water in the middle layer (20–100 cm) under different land use types, with the terraced field having the highest similarity degree of soil water variation trend, followed by grassland, slope cropland, and jujube orchard. Slope croplands in the study region may be converted into terraced fields to enhance the effective use of rainfall resources and encourage the expansion of ecological agriculture. Proper water management practices must be employed to reduce jujube tree water consumption and other wasteful water usage in order to guarantee the jujube orchard’s ability to expand sustainably. This would address the issue of the acute water deficit in the rain-fed jujube orchards in the Loess Hilly Region.

The sowing date and density are considered to be the main factors affecting crop yield. The determination of the sowing date and sowing density, however, is fraught with uncertainty due to the influence of climatic conditions, topography, variety and other factors. Therefore, it is necessary to find a comprehensive consideration of these factors to guide the production of winter rapeseed. A reliable crop model could be a crucial tool to investigate the response of rapeseed growth to changes in the sowing date and density. At present, few studies related to rapeseed model simulation have been reported, especially in the comprehensive evaluation of the effects of sowing date and density factors on rapeseed development and production. This study aimed to evaluate the performance of the AquaCrop model for winter rapeseed development and yield simulation under various sowing dates and densities, and to optimize the sowing date and density for agricultural high-efficient production in the Jianghuai Plain. Two years of experiments were carried out in the rapeseed growing season in 2020 and 2021. The model parameters were fully calibrated and the simulation performances in different treatments of sowing dates and densities were evaluated. The results indicated that the capability of the AquaCrop model to interpret crop development for different sowing dates was superior to that of sowing densities. For rapeseed canopy development, the RMSE for three sowing dates and densities scenarios were 7–22% and 16–23%, respectively. The simulated biomass and grain yield for different sowing dates treatments (RMSE: 0.8–2.1 t·ha−1, Pe: 0–35.3%) were generally better than those of different densities treatments (RMSE: 0.7–3.9 t·ha−1, Pe: 8.2–90%). Compared with other sowing densities, higher overestimation errors of the biomass and yield were observed for the low-density treatment. Adequate agreement for crop evapotranspiration simulation was achieved, with an R2 of 0.79 and RMSE of 26 mm. Combining the simulation results and field data, the optimal sowing scheme for achieving a steadily high yield in the Jianghuai Plain of east China was determined to be sowing in October and a sowing density of 25.0–37.5 plant·m−2. The study demonstrates the great potential of the AquaCrop model to optimize rapeseed sowing patterns and provides a technical means guidance for the formulation of local winter rapeseed production.

Temperature compensatory effect, which quantifies the increase in cumulative air temperature from soil temperature increase caused by mulching, provides an effective method for incorporating soil temperature into crop models. In this study, compensated temperature was integrated into the AquaCrop model to investigate the capability of the compensatory effect to improve assessment of the promotion of maize growth and development by plastic film mulching (PM). A three-year experiment was conducted from 2014 to 2016 with two maize varieties (spring and summer) and two mulching conditions (PM and non-mulching (NM)), and the AquaCrop model was employed to reproduce crop growth and yield responses to changes in NM, PM, and compensated PM. A marked difference in soil temperature between NM and PM was observed before 50 days after sowing (DAS) during three growing seasons. During sowing–emergence and emergence–tasseling, the increase in air temperature was proportional to the compensatory coefficient, with spring maize showing a higher compensatory temperature than summer maize. Simulation results for canopy cover (CC) were generally in good agreement with the measurements, whereas predictions of aboveground biomass and grain yield under PM indicated large underestimates from 60 DAS to the end of maturity. Simulations of spring maize biomass and yield showed general increase based on temperature compensation, accompanied by improvement in modeling accuracy, with RMSEs decreasing from 2.5 to 1.6 t ha−1 and from 4.1 t to 3.4 t ha−1. Improvement in biomass and yield simulation was less pronounced for summer than for spring maize, implying that crops grown during low-temperature periods would benefit more from the compensatory effect. This study demonstrated the effectiveness of the temperature compensatory effect to improve the performance of the AquaCrop model in simulating maize growth under PM practices.

Various land use types have been implemented by the government in the loess hilly region of China to facilitate sustainable land use. Understanding the variability in soil moisture and temperature under various sloping land use types can aid the ecological restoration and sustainable utilization of sloping land resources. The objective of this study was to use approximate entropy (ApEn) to reveal the variations in soil moisture and temperature under different land use types, because ApEn only requires a short data series to obtain robust estimates, with a strong anti-interference ability. An experiment was conducted with four typical land use scenarios (i.e., soybean sloping field, maize terraced field, jujube orchard, and grassland) over two consecutive plant growing seasons (2014 and 2015), and the time series of soil moisture and temperature within different soil depth layers of each land use type were measured in both seasons. The results showed that the changing amplitude, degree of variation, and active layer of soil moisture in the 0-160 cm soil depth layer, as well as the changing amplitude and degree of variation of soil temperature in the 0-100 cm soil layer increased in the jujube orchard over the two growing seasons. The changing amplitude, degree of variation, and active layer of soil moisture all decreased in the maize terraced field, as did the changing amplitude and degree of variation of soil temperature. The ApEn of the soil moisture series was the lowest in the 0-160 cm soil layer in the maize terraced field, and the ApEn of the soil temperature series was the highest in the 0-100 cm layer in the jujube orchard in the two growing seasons. Finally, the jujube orchard soil moisture and temperature change process were more variable, whereas the changes in the maize terraced field were more stable, with a stable soil moisture and temperature. This work highlights the usefulness of ApEn for revealing soil moisture and temperature changes and to guide the management and development of sloping fields.

由于在我国湖泊富营养化现象普遍,已成为生态环境修复的重难点问题.减少水体表层沉积物磷的释放是控制水体富营养化的关键.研究底泥磷释放的基本规律及控制底泥磷释放有重要的理论和实践意义.分析了目前底泥磷形态提取方法、各形态磷特征、影响磷形态的各物理、化学、生物因素的作用规律,以及底泥磷释放对生态环境及水质的影响.梳理了底泥磷的不同治理方法,并得到了以下结论:①底泥磷形态主流提取方法是SMT,但该方法分离出来的磷形态少,不便于后续细致研究.②被沉积物中的氧化物、氢氧化物以及粘土矿物颗粒表面等吸附的弱吸附态磷,在外界条件变化的情况下可以重新释放至上覆水,是水体磷二次释放的重要来源.温度、扰动、混浊度、pH值、溶解氧含量、盐度、水体生物等因子均可影响底泥磷的释放.③底泥磷的修复有物理、化学和生物方法,目前主流是生物方法(微生物+植物)和化学方法(钝化剂等),物理方法虽成熟,但其所耗大,不易广泛使用.最后提出了镧改性膨润土锁磷剂、镧铝改性凹凸棒锁磷剂是较为理想的磷钝化剂,但需要深入开展生态风险的研究.在磷形态中需要开展Zr-oxide DGT有效磷与SMT方法的对比研究.未来需要深入开展沉积物有机磷形态及生态风险研究.

Changes in soil moisture and soil temperature result from the combined effects of several environmental factors. Scientific determination of the response characteristics of soil moisture and soil temperature to environmental factors is critical for adjusting the sloping land use structure and improving the ecological environment in China's loess hilly region. Soybean sloping fields, maize terraced fields, jujube orchards, and grasslands in the loess hilly region were selected as the research areas. The change in characteristics of soil moisture and soil temperature, as well as their interactions and statistical relationships with meteorological factors, were analyzed using continuously measured soil moisture, soil temperature, and meteorological factors. The results revealed that air temperature and humidity were the main controlling factors affecting soil moisture changes in the 0-60 cm soil layer of soybean sloping fields and grasslands in the normal precipitation year (2014) and the dry year (2015). Humidity and wind speed were the main meteorological factors affecting soil moisture changes in the maize terraced field. Air temperature had a significant negative effect on soil moisture in the jujube orchard. Soil moisture and soil temperature were all negatively correlated under the four sloping land use types. In normal precipitation years, atmospheric humidity had the greatest direct and comprehensive effect on soil moisture in soybean sloping fields, maize terraced fields, and grasslands; soil temperature had a relatively large impact on soil moisture in jujube orchards. The direct and comprehensive effects of soil temperature on soil moisture under all sloping land use types were the largest and most negative in the dry year. Air temperature had a high correlation with soil temperature in the 0-60 cm soil layer under the four sloping land use types, and the grey relational grade decreased as the soil layer deepened. The coefficient of determination between the 0-20 cm soil temperature and air temperature in the maize terraced field was low, indicating a weak response to air temperature. The above findings can serve as a scientific foundation for optimizing sloping land use structures and maximizing the efficient and sustainable utilization of sloping land resources in China's loess hilly region.

The influence of different mulching measures on soil moisture, soil temperature, and crop growth was investigated during the jujube growing season in rain-fed jujube orchards using micro-plot experiments. The mulching treatments included clean tillage (CT, control treatment), jujube branches mulching (JBM), and white clover planting (WCP). The results revealed that: (1) The average soil moisture content of JBM was greater than that of CT by 3.76% and 2.34%, respectively, during the 2013 and 2014 jujube growth periods, and its soil water deficit was minimal in each soil layer from 0 to 70 cm. WCP had the greatest soil water deficit. The average soil moisture content of the 0–70 cm soil layer in WCP was 3.88% and 5.55% lower than that in CT during the 2013 and 2014 jujube growth seasons, respectively (p < 0.05). (2) JBM had the highest annual average soil moisture content in each soil layer from 0 to 70 cm, followed by CT, while WCP had the lowest. White clover and jujube competed for water in the 20–40 cm soil layer, and JBM had the lowest variation in soil moisture. (3) Mulching with jujube branches and planting white clover could both control the temperature of the 0–25 cm soil layer and narrow the daily temperature range, with JBM being the least affected by air temperature. (4) Jujube’s leaf area index and stem diameter increase in JBM were both significantly greater than in CT and WCP. In conclusion, using pruned jujube branches as surface mulch is appropriate for rain-fed jujube orchards because it can preserve soil moisture, regulate soil temperature, and promote jujube growth.