Exploring dissolved N2O characteristics and unearthing indirect N2O emission factors in the shallow groundwater of paddy and upland fields.

Indirect emissions of nitrous oxide (N2O) stemming from nitrogen (N) leaching in agricultural fields constitute a significant contributor to atmospheric N2O. Groundwater nitrate (NO3--N) pollution is severe in the Ningxia Yellow River Irrigation Area (NYRIA), coupled with high NO3--N leaching, exacerbates the risk of indirect N2O emissions from groundwater. Over two years of field observations, this study investigated the characteristics and interannual variations of dissolved N2O (dN2O) concentrations and indirect N2O emission factors (EF5g) in shallow groundwater. The research focused on three typical farmlands in the NYRIA, each subjected to six levels of N fertilizer application. The mean dN2O concentrations in the groundwater of paddy, corn and vegetable fields were 5.17, 8.40 and 16.35 µg N·L-1, respectively. Notably, the dN2O concentrations in the shallow groundwater of upland fields exceeded those in paddy fields, with maximum levels in vegetable fields nearly an order of magnitude higher. Elevated N application significantly increased dN2O concentrations across various farmlands, showing statistically significant variation. However, differences in EF5g-A and EF5g-B within the same farmland were negligible. Denitrification was the primary process contributing to N2O production in groundwater, with nitrification also played a crucial role in upland fields. Factors such as NO3--N, NH4+-N, dissolved oxygen (DO), and pH critically influenced N2O production. EF5g-B, which considers the NO3--N consumption during denitrification processes in groundwater, was deemed more appropriate than EF5g-A for assessing the indirect N2O emission in the NYRIA. The EF5g of agricultural fields exhibited minimal sensitivity to N input but was significantly affected by other factors, such as the planting pattern. The study revealed the rationality of adopting EF5g-B in assessing indirect N2O emissions, providing valuable insights for N management strategies in regions with high NO3--N leaching. Minimizing N fertilizer application while ensuring crop yield, especially in upland fields, is beneficial for reducing N2O emissions.

Improving the estimation of rice above-ground biomass based on spatio-temporal UAV imagery and phenological stages.

Introduction: Unmanned 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. Methods: To 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). Results: The 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 (R 2 = 0.88, RMSE=1111kg/ha, NRMSE=9.76%), and with VIs, PH, CC, and DAT all as inputs (R 2 = 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. Discussion: Model 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.

Mapping specific groundwater nitrate concentrations from spatial data using machine learning: A case study of chongqing, China.

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.

Coping with groundwater pollution in high-nitrate leaching areas: The efficacy of denitrification.

The Ningxia Yellow River irrigation area, characterized by an arid climate and high leaching of NO3--N, exhibits complex and unique groundwater nitrate (NO3--N) pollution, with denitrification serving as the principal mechanism for NO3--N removal. The characteristics of N leaching from paddy fields and NO3--N removal by groundwater denitrification were investigated through a two-year field observation. The leaching losses of total nitrogen (TN) and NO3--N accounted for 10.81-27.34% and 7.59-12.74%, respectively, of the N input. The linear relationship between NO3--N leaching and N input indicated that the fertilizer-induced emission factor (EF) of NO3--N leaching in direct dry seeding and seedling-raising and transplanting paddy fields was 8.2% (2021, R2 = 0.992) and 6.7% (2022, R2 = 0.994), respectively. The study highlighted that the quadratic relationship between the NO3--N leaching loss and N input (R2 = 0.999) significantly outperformed the linear relationship. Groundwater denitrification capacity was characterized by monitoring the concentrations of dinitrogen (N2) and nitrous oxide (N2O). The results revealed substantial seasonal fluctuations in excess N2 and N2O concentrations in groundwater, particularly following fertilization and irrigation events. The removal efficiency of NO3--N via groundwater denitrification ranged from 42.70% to 74.38%, varying with depth. Groundwater denitrification capacity appeared to be linked to dissolved organic carbon (DOC) concentration, redox conditions, fertilization, irrigation, and soil texture. The anthropogenic-alluvial soil with limited water retention accelerated the leaching of NO3--N into groundwater during irrigation. This process enhances the groundwater recharge capacity and alters the redox conditions of groundwater, consequently impacting groundwater denitrification activity. The DOC concentration emerged as the primary constraint on the groundwater denitrification capacity in this region. Hence, increasing carbon source concentration and enhancing soil water retention capacity are vital for improving the groundwater denitrification capacity and NO3--N removal efficiency. This study provides practical insights for managing groundwater NO3--N pollution in agricultural areas, optimizing fertilization strategies and improving groundwater quality.

Effects of Salt Tolerance Training on Multidimensional Root Distribution and Root-Shoot Characteristics of Summer Maize under Brackish Water Irrigation.

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.

Soil Moisture Regulation under Mulched Drip Irrigation Influences the Soil Salt Distribution and Growth of Cotton in Southern Xinjiang, China.

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.