Minimalizing Non-point Source Pollution Using a Cooperative Ion-Selective Electrode System for Estimating Nitrate Nitrogen in Soil

Simulation analysis of the preventative effects of planting sweet corn on nitrate leaching in a cherry greenhouse soil

Introduction

To ensure higher productivity, fertilizers have been excessively applied to the fruit greenhouse soil yearly, thus resulting in the increasing risks of residual nitrate leaching in the North China Plain.

Methods

In this study, a water and solute transport HYDRUS-1D model was used to evaluate the effects of using sweet corn as a catch crop on deep water drainage and nitrate leaching in a sweet cherry greenhouse soil. A three-year (2019-2021) field experiment was conducted during the rainfall season from July to September in the post-harvest of sweet cherry, when the plastic cover was removed each year. In the experiment, the five treatments were designed. The three nitrate residue levels denoted by CKR, N1R, and N2R, represented nitrate residue amounts in the soil profile of three nitrogen fertilizer levels(0, 280 and 420kg ha-1) before the harvest of sweet cherry(March to June). Two other treatments with and without sweet corn as a catch crop based on the treatments of N1R and N2R were denoted by N1RC and N2RC, respectively. The data of both the spatial and temporal distribution of water and nitrate content during the rainy seasons of 2019, 2020 and 2021 in the field experiment were collected to calibrate and validate the model.

Results

The simulated results have showed that using sweet corn as a catch crop increased the evapotranspiration rate, the upward flux of water and nitrate at a 100 cm soil depth reached a maximum of 1.5 mm d-1 and 1.0 kg N ha-1d-1, respectively, and the downward movement of water and nitrate leached to deeper soil layers was reduced. Compared with CKR, the treatments with catch crops (N1RC and N2RC) reduced the amount of water drainage by 16.4% -47.7% in the 0-180cm soil profile. The average amounts of nitrate leaching in the 1.8 m soil profile during the three-year experiment were 88.1, 113.3, and 58.2 kg N ha-1 for the treatment without catch crop (N1R and N2R) and 32.3, 54.8, and 31.4 kg N ha-1 for the treatment with catch crop (N1RC and N2RC), respectively. The treatments (N1RC and N2RC) with catch crops decreased the amount of nitrate leaching by 29.6%-69.1% compared with the treatments without catch crops (N1R and N2R).

Discussion

Sweet corn as summer catch crop can reduce nitrate leaching in the sweet cherry greenhouses. Our study has provided an effective method to reduce the risk of nitrate leaching for sweet cherry greenhouses in the North China Plain.

Evaluation of agricultural non-point source pollution using an in-situ and automated photochemical flow analysis system

Off-line leachate collection from agricultural landscapes cannot guarantee precise evaluation of agricultural non-point source (ANPS) due to geospatial variations, time, and transportation from the field to the laboratory. Implementing an in-situ nitrogen and phosphorous monitoring system with a robust photochemical flow analysis is imperative for precision agriculture, enabling real-time intervention to minimize non-point source pollution and overcome the limitations posed by conventional analysis in laboratory. A reliable, robust and in-situ approach was proposed to monitor nitrogen and phosphorous for determining ANPS pollution. In this study, a home-made porous ceramic probe and the frequency domain reflectometer (FDR) based water content sensors were strategically placed at different soil depths to facilitate the collection of leachates. These solutions were subsequently analyzed by in-situ photochemical flow analysis monitoring system built across the field to estimate the concentrations of phosphorus and nitrogen. After applying both natural and artificial irrigation to the agricultural landscape, at least 10 mL of soil leachates was consistently collected using the porous ceramic probe within 20 min, regardless of the depth of the soil layers when the volumetric soil water contents are greater than 19%. The experimental results showed that under different weather conditions and irrigation conditions, the soil water content of 50 cm and 90 cm below the soil surface was 19.58% and 26.08%, respectively. The average concentrations of NH4+-N, NO3--N, PO43- are 0.584 mg/L, 15.7 mg/L, 0.844 mg/L, and 0.562 mg/L, 16.828 mg/L and 0.878 mg/L at depths of 50 cm and 90 cm below the soil surface, respectively. Moreover, the comparison with conventional laboratory spectroscopic analysis confirmed R2 values of 0.9951, 0.9943, 0.9947 average concentration ranges of NH4+-N, NO3--N, and PO43-, showcasing the accuracy and reliability of robust photochemical flow analysis in-situ monitoring system. The suggested monitoring system can be helpful in the assessment of soil nutrition for precision agriculture.

Research on the Effects of Drying Temperature for the Detection of Soil Nitrogen by Near-Infrared Spectroscopy

Nitrogen nitrates play a significant role in the soil's nutrient cycle, and near-infrared spectroscopy can efficiently and accurately detect the content of nitrate-nitrogen in the soil. Accordingly, it can provide a scientific basis for soil improvement and agricultural productivity by deeply examining the cycle and transformation pattern of nutrients in the soil. To investigate the impact of drying temperature on NIR soil nitrogen detection, soil samples with different N concentrations were dried at temperatures of 50 °C, 65 °C, 80 °C, and 95 °C, respectively. Additionally, soil samples naturally air-dried at room temperature (25 °C) were used as a control group. Different drying times were modified based on the drying temperature to completely eliminate the impact of moisture. Following data collection with an NIR spectrometer, the best preprocessing method was chosen to handle the raw data. Based on the feature bands chosen by the RFFS, CARS, and SPA methods, two linear models, PLSR and SVM, and a nonlinear ANN model were then established for analysis and comparison. It was found that the drying temperature had a great effect on the detection of soil nitrogen by near-infrared spectroscopy. In the meantime, the SPA-ANN model simultaneously yielded the best and most stable accuracy, with Rc2 = 0.998, Rp2 = 0.989, RMSEC = 0.178 g/kg, and RMSEP = 0.257 g/kg. The results showed that NIR spectroscopy had the least effect and the highest accuracy in detecting nitrogen at 80 °C soil drying temperature. This work provides a theoretical foundation for agricultural production in the future.

Soil Nitrogen Content Detection Based on Near-Infrared Spectroscopy

Traditional soil nitrogen detection methods have the characteristics of being time-consuming and having an environmental pollution effect. We urgently need a rapid, easy-to-operate, and non-polluting soil nitrogen detection technology. In order to quickly measure the nitrogen content in soil, a new method for detecting the nitrogen content in soil is presented by using a near-infrared spectrum technique and random forest regression (RF). Firstly, the experiment took the soil by the Xunsi River in the area of Hubei University of Technology as the research object, and a total of 143 soil samples were collected. Secondly, NIR spectral data from 143 soil samples were acquired, and chemical and physical methods were used to determine the content of nitrogen in the soil. Thirdly, the raw spectral data of soil samples were denoised by preprocessing. Finally, a forecast model for the soil nitrogen content was developed by using the measured values of components and modeling algorithms. The model was optimized by adjusting the changes in the model parameters and Gini coefficient (∆Gini), and the model was compared with the back propagation (BP) and support vector machine (SVM) models. The results show that: the RF model modeling set prediction R²_C is 0.921, the RMSEC is 0.115, the test set R²_P is 0.83, and the RMSEP is 0.141; the detection of the soil nitrogen content can be realized by using a near-infrared spectrum technique and random forest algorithm, and its prediction accuracy is better than that of the BP and SVM models; using ∆ Gini to optimize the RF modeling data, the spectral information of the soil nitrogen content can be extracted, and the data redundancy can be reduced effectively.