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Aiming at the problems of reduced winter wheat yield and aggravation of nitrogen leaching pollution caused by the waterlogging in the Middle-Lower Yangtze Plain, China, a two-year field experiment with three farmland water levels (W40, W60, W80) and three nitrogen application rates (N150, N225, N300) as well as a non-waterlogged treatment (CK) was carried out, to investigate the coupling effects of farmland water level and nitrogen application rate on the plant growth, grain yield, crop water productivity (WPC) and nitrogen load with waterlogging conditions. Three man-made waterlogging events were applied at winter wheat jointing-booting stage, heading-flowering stage and grain filling stage, respectively. The results indicated that with the farmland water level decreased from −40 cm to −60 cm and the nitrogen application rate increased from 150 kg∙ha−1 to 225 kg∙ha−1, the plant height, aboveground dry matter, leaf area index, spike length, grain yield, effective panicles, grain number per ear, 1000-grain weight and WPC in the waterlogging field increased significantly. However, since the nitrogen application rate exceeded 225 kg∙ha−1 and farmland water level lowered more than −60 cm, the favorable effects of nitrogen application rate and farmland water level for winter wheat growth and production reduced. Additionally, both the nitrogen load and partial factor productivity of nitrogen (PFPN) increased with the decline of farmland water level, while the nitrogen load increased and the PFPN decreased with the increasing nitrogen application rate. The raise of nitrogen rate from 150 kg∙ha−1 to 225 kg∙ha−1 was beneficial to plant growth, however, the increase of nitrogen application resulted in the decrease of PFPN and increase of drainage nitrogen loads. Compared with the water farmland water level of −40 cm and nitrogen application rate of 150 kg∙ha−1, the increase of nitrogen application rate and the decrease of farmland water level in the range of 50%-100% resulted in yield raise by 5.78%-32.29% approximately and the increase of nitrogen load by 36.20%-178.44% approximately. The comprehensive evaluation with TOPSIS-Entropy method for plant growth, grain yield, WPC, PFPN and nitrogen loads suggested that, the appropriate nitrogen application rate for winter wheat in the waterlogging areas of Middle-Lower Yangtze Plain in China was 225 kg∙ha−1, and the proper farmland water level was lowering to −80 cm in wet year and −60 cm in dry year within 3 days after waterlogging.

IntroductionUnmanned aerial vehicles (UAVs) equipped with visible and multispectral cameras provide reliable and efficient methods for remote crop monitoring and above-ground biomass (AGB) estimation in rice fields. However, existing research predominantly focuses on AGB estimation based on canopy spectral features or by incorporating plant height (PH) as a parameter. Insufficient consideration has been given to the spatial structure and the phenological stages of rice in these studies. In this study, a novel method was introduced by fully considering the three-dimensional growth dynamics of rice, integrating both horizontal (canopy cover, CC) and vertical (PH) aspects of canopy development, and accounting for the growing days of rice.MethodsTo investigate the synergistic effects of combining spectral, spatial and temporal parameters, both small-scale plot experiments and large-scale field testing were conducted in Jiangsu Province, China from 2021 to 2022. Twenty vegetation indices (VIs) were used as spectral features, PH and CC as spatial parameters, and days after transplanting (DAT) as a temporal parameter. AGB estimation models were built with five regression methods (MSR, ENet, PLSR, RF and SVR), using the derived data from six feature combinations (VIs, PH+CC, PH+CC+DAT, VIs+PH +CC, VIs+DAT, VIs+PH+CC+DAT).ResultsThe results showed a strong correlation between extracted and ground-measured PH (R2 = 0.89, RMSE=5.08 cm). Furthermore, VIs, PH and CC exhibit strong correlations with AGB during the mid-tillering to flowering stages. The optimal AGB estimation results during the mid-tillering to flowering stages on plot data were from the PLSR model with VIs and DAT as inputs (R2 = 0.88, RMSE=1111kg/ha, NRMSE=9.76%), and with VIs, PH, CC, and DAT all as inputs (R2 = 0.88, RMSE=1131 kg/ha, NRMSE=9.94%). For the field sampling data, the ENet model combined with different feature inputs had the best estimation results (%error=0.6%–13.5%), demonstrating excellent practical applicability.DiscussionModel evaluation and feature importance ranking demonstrated that augmenting VIs with temporal and spatial parameters significantly enhanced the AGB estimation accuracy. In summary, the fusion of spectral and spatio-temporal features enhanced the actual physical significance of the AGB estimation models and showed great potential for accurate rice AGB estimation during the main phenological stages.

Groundwater resources is not only important essential water resources but also imperative connectors within the intricate framework of the ecological environment. High nitrate concentrations in groundwater can exerting adverse impacts on human health. It is imperative to accurately delineate the distribution characteristics of groundwater nitrate concentrations. Four different machine learning models (Gradient Boosting Regression (GB), Random Forest Regression (RF), Extreme Gradient Boosting Regression (XG) and Adaptive Boosting Regression (AD)) which combine spatial environmental data and different radius contributing area was developed to predict the distribution of nitrate concentration in groundwater. The models use 595 groundwater samples and included topography, remote sensing, hydrogeological and hydrological, climate, nitrate input, and socio-economic predictor. Gradient Boosting Regression model outperforms the other models (R2 = 0.627, MAE = 0.529, RMSE = 0.705, PICP = 0.924 for test dataset) under 500 m radius contributing area. A high-resolution (1 km) groundwater nitrate concentration distribution map reveal in the majority of the study area, groundwater nitrate concentrations are below 1 mg/L and high nitrate concentration (>10 mg/L) proportion in southeast, northeast and central main urban area karst valley regions is 1.89%, 0.91%, and 0.38% respectively. In study area, hydrogeological conditions, soil parameters, nitrogen input factors, and percentage of arable land are among the most influential explanatory factors. This work, serving as the inaugural application of utilizing effective spatial methods for predicting groundwater nitrate concentrations in Chongqing city, furnish decision-making support for the prevention and control of groundwater pollution, particularly in areas primarily dependent on groundwater for water supply and holds profound significance as a milestone achievement.

Brittle deformation is prevalent in both geological processes and engineered structures, so probing its actions is an important task as much for Earth materials and engineered ones. To characterize brittle deformation, acoustic waves are especially useful in revealing deformation processes. To promote the use of acoustic techniques, we present an integrated characterization approach that includes both acoustic data collection and analysis. By customizing a rock sample and acoustic sensor assembly, we incorporate acoustic data acquisition into a core holder system that accommodates relatively small samples (2.54 cm diameter) under triaxial loading. Along with fast and high-resolution acoustic waveform recording, the compact design facilitates convenient collection of high-quality acoustic data. To meet the challenge of efficiently and accurately picking P-wave arrivals for hundreds of thousands of acoustic waveforms, we modified and implemented a deep neural network model from the seismology literature called PhaseNet. After training with an augmented dataset of manually-picked arrivals (a total of around 50,000 waveforms), the modified PhaseNet model achieved more than 88% (96%) picking accuracy within ±1 μs (±2 μs) time residual relative to manual picks. This demonstrates the potential of integrating deep learning techniques into the workflow of acoustic data analysis for rapid and accurate extraction of valuable information from a large acoustic dataset. Finally, we conducted high-resolution micro-computed tomography (micro-CT) to inform and complement acoustic characterization at micron- and centimeter-scales. Microscopic observations validate the spatial development of two macroscopic fractures, and suggest that deformation-induced changes in velocity need to be incorporated for accurately locating microcracking events. Thus, integrating acoustic monitoring, a deep neural network, and micro-CT imaging offers an effective means to understand brittle deformation from micro to centimeter scales.

To investigate the impact of brackish water irrigation on the multidimensional root distribution and root-shoot characteristics of summer maize under different salt-tolerance-training modes, a micro-plot experiment was conducted from June to October in 2022 at the experimental station in Hohai University, China. Freshwater irrigation was used as the control (CK), and different concentrations of brackish water (S0: 0.08 g·L−1, S1: 2.0 g·L−1, S2: 4.0 g·L−1, S3: 6.0 g·L−1) were irrigated at six-leaf stage, ten-leaf stage, and tasseling stage, constituting different salt tolerance training modes, referred to as S0-2-3, S0-3-3, S1-2-3, S1-3-3, S2-2-3, and S2-3-3. The results showed that although their fine root length density (FRLD) increased, the S0-2-3 and S0-3-3 treatments reduced the limit of root extension in the horizontal direction, causing the roots to be mainly distributed near the plants. This resulted in decreased leaf area and biomass accumulation, ultimately leading to significant yield reduction. Additionally, the S2-2-3 and S2-3-3 treatments stimulated the adaptive mechanism of maize roots, resulting in boosted fine root growth to increase the FRLD and develop into deeper soil layers. However, due to the prolonged exposure to a high level of salinity, their roots below 30 cm depth senesced prematurely, leading to an inhibition in shoot growth and also resulting in yield reduction of 10.99% and 11.75%, compared to CK, respectively. Furthermore, the S1-2-3 and S1-3-3 treatments produced more reasonable distributions of FRLD, which did not boost fine root growth but established fewer weak areas (FLRD < 0.66 cm−3) in their root systems. Moreover, the S1-2-3 treatment contributed to increasing leaf development and biomass accumulation, compared to CK, whereas it allowed for minimizing yield reduction. Therefore, our study proposed the S1-2-3 treatment as the recommended training mode for summer maize while utilizing brackish water resources.

Water deficiency, together with soil salinization, has been seriously restricting sustainable agriculture around the globe for a long time. Optimal soil moisture regulation contributes to the amelioration of soil water and salinity for crops, which is favorable for plant production. A field experiment with five soil water lower limit levels (T1: 85% FC, T2: 75% FC, T3: 65% FC, T4: 55% FC, and T5: 45% FC, where FC is the field capacity) was conducted in southern Xinjiang in 2018 to investigate the responses of soil water–salt dynamics and cotton performance to soil moisture regulation strategies. The results indicated that in the horizontal direction, the farther away the drip irrigation belt, the lower the soil moisture content and the greater the soil salinity. In the vertical direction, the soil moisture and soil salinity increased first and then decreased with an increase in soil depth after irrigation, and the distribution was similar to an ellipse. Moreover, the humid perimeter of soil water and the leaching range of soil salt increased with a decrease in the soil moisture lower limit. Though more soil salt was leached out for the T5 treatment at the flowering stage due to the higher single irrigation amount, soil salinity increased again at the boll setting stage owing to the long irrigation interval. After the cotton was harvested, soil salt accumulated in the 0–100 cm layer and the accumulation amount followed T3 > T5 > T1 > T2 > T4. Moreover, with a decline of soil moisture lower limit, both plant height and nitrogen uptake decreased significantly while the shoot–root ratio increased. Compared with the yield (7233.2 kg·hm−2) and water use efficiency (WUE, 1.27 kg·m−3) of the T1 treatment, the yield for the T2 treatment only decreased by 1.21%, while the WUE increased by 10.24%. Synthetically, considering the cotton yield, water–nitrogen use efficiency, and soil salt accumulation, the soil moisture lower limit of 75% FC is recommended for cotton cultivation in southern Xinjiang, China.

The sustainability of rice (Oryza sativa L.) cultivation has been threatened by water deficit and nitrogen (N)-fertilizer abuse. Straw return combined with N-fertilizer reduction could be an effective agronomic practice to improve N-use efficiency in rice production, but the interaction with water-saving irrigation regimes remains largely unknown. Here, a 2-year paddy field experiment was conducted to elucidate the effects of irrigation regime (continuously flooded, CF; controlled irrigation and drainage, CID) and straw return with N reduction (conventional farmers’ fertilization practice of 300 kg N ha−1 without straw return, N300; straw return with 25% N reduction, SN225; straw return with 50% N reduction, SN150) on rice growth dynamics, grain yield and water–nitrogen utilization. The results showed that CID significantly affected photosynthesis and fluorescence indicators, and increased grain yield and water productivity of rice. Straw return with N reduction reduced most rice growth traits, exhibiting lower plant height, tillers, leaf photosynthesis, chlorophyll fluorescence and dry matter accumulation, especially in vegetative growth under CF. In contrast, SN225 under CID showed compensatory effects on photosynthetic and fluorescence traits, thus improving N uptake during the reproductive growth stage. Despite a 6.6–7.1% yield reduction in SN225, 25% of N-fertilizer input was saved, with a corresponding increase in internal N-use efficiency and N-partial factor productivity. Overall, the present study indicates that straw return combined with moderate N deficiency might be a more eco-friendly and sustainable agronomic practice in water-saving irrigated rice fields.

Rice production involves abundant water and fertilizer inputs and is prone to nitrogen (N) loss via surface runoff and leaching, resulting in agricultural diffuse pollution. Based on a two-season paddy field experiment in Jiangsu Province, China, field water and N dynamics and their balances were determined with the well-calibrated HYDRUS-1D model. Then, scenarios of different controlled drainage and N fertilizer applications were simulated using the HYDRUS-1D model to analyze the features and factors of N loss from paddy fields. Evapotranspiration and deep percolation were the two dominant losses of total water input over the two seasons, with an average loss of 50.9% and 38.8%, respectively. Additionally, gaseous loss of N from the whole soil column accounted for more than half of total N input on average, i.e., ammonia volatilization (17.5% on average for two seasons) and denitrification (39.7%), while the N uptake by rice accounted for 37.1% on average. The ratio of N loss via surface runoff to total N input exceeded 20% when the N fertilizer rate reached 300 kg ha−1. More and longer rainwater storage in rice fields under controlled drainage reduced surface runoff losses but increased the risk of groundwater contamination by N leaching. Therefore, compared with raising the maximum ponding rainwater depth for controlled drainage, optimizing N fertilizer inputs may be more beneficial for controlling agricultural diffuse pollution by reducing N loss via surface runoff and leaching. The HYDRUS-1D model provides an approach for the quantitative decision-making process of sustainable agricultural water and N management.

To optimize suitable water-saving and soil salt-controlling irrigation needed for the high yield and good quality of cotton in southern Xinjiang, a field experiment was carried out to study the effects of soil water lower limits on water consumption, water use efficiency (WUE), yield, cotton fiber quality and soil salt accumulation under mulched drip irrigation in Korla, Xinjiang. The field capacity (FC) was regarded as the upper limit of soil moisture, and five soil water lower limits (85% FC, 75% FC, 65% FC, 55% FC, 45% FC, referred as T1~T5, respectively) were designed during the cotton growth period. The results indicated that the irrigation frequency and irrigation quota of cotton were gradually increased with the increase in the soil water lower limit, while the water consumption modulus for T2 treatment during the critical period of water demand arrived at the maximum value. Moreover, with the decrease in the soil water lower limit, the WUE, fiber micronaire value and fiber maturity index of cotton increased, whereas the yield, nitrogen partial factor productivity (PFPN) and fiber breaking elongation of cotton decreased. However, when the soil water lower limit exceeded 75% FC, the increase had little effect on the cotton yield increase and PFPN improvement, and the yield and PFPN for T2 treatment were 7146.4 kg∙hm−2 and 23.82 kg∙kg−1, respectively, In addition, the decrease in the soil water lower limit was unfavorable for an increase in fiber length, but it was conducive to the enhancement of fiber strength. Furthermore, soil salt accumulated inside and outside the film for the designed soil water lower limits, and the amount of accumulated salt in 0~100 cm followed T3 > T5 > T1 > T2 > T4. Based on a comprehensive analysis with the entropy TOPSIS method, the findings of the present study suggested that the suitable soil water lower limit for cotton under mulched drip irrigation was 75% FC in southern Xinjiang, China.