A Low-Cost NDIR-Based N2O Gas Detection Device for Agricultural Soils: Assembly, Calibration Model Validation, and Laboratory Testing

This research presents a low-cost, easy-to-assemble nondispersive infrared (NDIR) device for monitoring N₂O gas concentration in agricultural soils during field and laboratory experiments. The study aimed to develop a cost-effective instrument with a simple optic structure suitable for detecting a wide range of soil N₂O gas concentrations with a submerged silicone diffusion cell. A commercially available, 59 cm path-length gas cell, microelectromechanical systems (MEMS)-based infrared emitter, pyroelectric detector, two anti-reflective (AR) coated optical windows, and one convex lens were assembled into a simple instrument with secure preciseness and responsivity. Control of the IR emitter and data recording processes was achieved through a microcontroller unit (MCU). Tests on humidity tolerance and the saturation rate of the diffusion cell were carried out to test the instrument function with the soil atmosphere. The developed calibration model was validated by repeatability tests and accuracy tests. The soil N₂O gas concentration was monitored at the laboratory level by a specific experimental setup. The coefficient of determination (R²) of the repeatability tests was more than 0.9995 with a 1–2000 ppm measurability range and no impact of air humidity on the device output. The new device achieved continuous measuring of soil N₂O gas through a submerged diffusion cell.

An Ensemble Modeling Framework for Distinguishing Nitrogen, Phosphorous and Potassium Deficiencies in Winter Oilseed Rape (Brassica napus L.) Using Hyperspectral Data

Nitrogen (N), phosphorous (P), and potassium (K) are important macronutrients to crops. Deficiencies of these nutrients can change the pigment content in leaves and affect photosynthesis, resulting in the similar spectral characteristics at some wavelengths. Thus, one of the most important challenges in crop nutrient stress assessment through the canopy’s spectral reflectance is the ability to discriminate different nutrient stress conditions. This study proposes a three-layer ensemble-modeling framework to analyze N, P, and K nutrient stresses utilizing canopy hyperspectral data of crops. The framework selects spectral bands that are sensitive to N, P, and K nutrient deficiency levels, using ensembles of random forest classifiers, and then the reflectance of the selected bands is transformed into the more distinguishable probability features to diagnose the N, P, and K nutrient deficiency levels. For this study, this proposed framework was applied to winter oilseed rape (Brassica napus L.) during the overwintering stage, with 915 spectra samples collected from 14 field experiments. The analysis of nutrient deficiency levels resulting from the proposed framework was compared with that of single random forest, support vector machine, and artificial neural network classifiers, using the same reflectance features selected in the first layer of the framework. The overall accuracy of the nutrient deficiency analysis achieved by the proposed framework reached 80.76%, which was 16.55%, 18.43%, and 35.74% higher than the single random forest, support vector machine, and artificial neural network classifiers, respectively. The proposed framework demonstrated competitive advantages in differentiating the medium deficiency of N and K, and the severe deficiency of K from the normal conditions, boosting the accuracy from below 25% to above 50% because the probability features enhanced the differences among nutrient deficiency levels.

Dissection of hyperspectral reflectance to estimate nitrogen and chlorophyll contents in tea leaves based on machine learning algorithms

Nondestructive techniques for estimating nitrogen (N) status are essential tools for optimizing N fertilization input and reducing the environmental impact of agricultural N management, especially in green tea cultivation, which is notably problematic. Previously, hyperspectral indices for chlorophyll (Chl) estimation, namely a green peak and red edge in the visible region, have been identified and used for N estimation because leaf N content closely related to Chl content in green leaves. Herein, datasets of N and Chl contents, and visible and near-infrared hyperspectral reflectance, derived from green leaves under various N nutrient conditions and albino yellow leaves were obtained. A regression model was then constructed using several machine learning algorithms and preprocessing techniques. Machine learning algorithms achieved high-performance models for N and Chl content, ensuring an accuracy threshold of 1.4 or 2.0 based on the ratio of performance to deviation values. Data-based sensitivity analysis through integration of the green and yellow leaves datasets identified clear differences in reflectance to estimate N and Chl contents, especially at 1325-1575 nm, suggesting an N content-specific region. These findings will enable the nondestructive estimation of leaf N content in tea plants and contribute advanced indices for nondestructive tracking of N status in crops.

Recent Development and Challenges in Spectroscopy and Machine Vision Technologies for Crop Nitrogen Diagnosis: A Review

Recent development of non-destructive optical techniques, such as spectroscopy and machine vision technologies, have laid a good foundation for real-time monitoring and precise management of crop N status. However, their advantages and disadvantages have not been systematically summarized and evaluated. Here, we reviewed the state-of-the-art of non-destructive optical methods for monitoring the N status of crops, and summarized their advantages and disadvantages. We mainly focused on the contribution of spectral and machine vision technology to the accurate diagnosis of crop N status from three aspects: system selection, data processing, and estimation methods. Finally, we discussed the opportunities and challenges of the application of these technologies, followed by recommendations for future work to address the challenges.

Quantum cascade laser based absorption spectroscopy for direct monitoring of atmospheric N2O isotopes

A compact high-resolution spectroscopic sensor using a thermoelectrically (TE) cooled continuous-wave (CW) room temperature (RT) quantum cascade laser (QCL) operating at 4.6 μm, is employed for simultaneous detection of three main isotopic species (¹⁴N¹⁵N¹⁶O, ¹⁵N¹⁴N¹⁶O and ¹⁴N¹⁴N¹⁶O). To enable a high-precision analysis of N₂O isotopic species at ambient mixing ratios, a liquid nitrogen-free preconcentration unit is built to trap and load atmospheric N₂O. The absorption spectra of ¹⁴N¹⁵N¹⁶O, ¹⁵N¹⁴N¹⁶O, and ¹⁴N¹⁴N¹⁶O between 2188.6 cm^-1 and 2189 cm^-1 are measured, and the respective ratios of the rare to the abundant isotopologues abundances are demonstrated. Moreover, spectroscopic parameters of pressure-broadening coefficient for selected absorption lines have been determined, and a good agreement is obtained by comparing with HITRAN database.

From the ground up: global nitrous oxide sources are constrained by stable isotope values

Rising concentrations of nitrous oxide (N₂O) in the atmosphere are causing widespread concern because this trace gas plays a key role in the destruction of stratospheric ozone and it is a strong greenhouse gas. The successful mitigation of N₂O emissions requires a solid understanding of the relative importance of all N₂O sources and sinks. Stable isotope ratio measurements (δ¹⁵N-N₂O and δ¹⁸O-N₂O), including the intramolecular distribution of ¹⁵N (site preference), are one way to track different sources if they are isotopically distinct. ‘Top-down’ isotope mass-balance studies have had limited success balancing the global N₂O budget thus far because the isotopic signatures of soil, freshwater, and marine sources are poorly constrained and a comprehensive analysis of global N₂O stable isotope measurements has not been done. Here we used a robust analysis of all available in situ measurements to define key global N₂O sources. We showed that the marine source is isotopically distinct from soil and freshwater N₂O (the continental source). Further, the global average source (sum of all natural and anthropogenic sources) is largely controlled by soils and freshwaters. These findings substantiate past modelling studies that relied on several assumptions about the global N₂O cycle. Finally, a two-box-model and a Bayesian isotope mixing model revealed marine and continental N₂O sources have relative contributions of 24–26% and 74–76% to the total, respectively. Further, the Bayesian modeling exercise indicated the N₂O flux from freshwaters may be much larger than currently thought.

Isotopic constraints on marine and terrestrial N2O emissions during the last deglaciation

Nitrous oxide (N2O) is an important greenhouse gas and ozone-depleting substance that has anthropogenic as well as natural marine and terrestrial sources. The tropospheric N2O concentrations have varied substantially in the past in concert with changing climate on glacial-interglacial and millennial timescales. It is not well understood, however, how N2O emissions from marine and terrestrial sources change in response to varying environmental conditions. The distinct isotopic compositions of marine and terrestrial N2O sources can help disentangle the relative changes in marine and terrestrial N2O emissions during past climate variations. Here we present N2O concentration and isotopic data for the last deglaciation, from 16,000 to 10,000 years before present, retrieved from air bubbles trapped in polar ice at Taylor Glacier, Antarctica. With the help of our data and a box model of the N2O cycle, we find a 30 per cent increase in total N2O emissions from the late glacial to the interglacial, with terrestrial and marine emissions contributing equally to the overall increase and generally evolving in parallel over the last deglaciation, even though there is no a priori connection between the drivers of the two sources. However, we find that terrestrial emissions dominated on centennial timescales, consistent with a state-of-the-art dynamic global vegetation and land surface process model that suggests that during the last deglaciation emission changes were strongly influenced by temperature and precipitation patterns over land surfaces. The results improve our understanding of the drivers of natural N2O emissions and are consistent with the idea that natural N2O emissions will probably increase in response to anthropogenic warming.

Methane and nitrous oxide in the ice core record

Polar ice cores contain, in trapped air bubbles, an archive of the concentrations of stable atmospheric gases. Of the major non-CO2 greenhouse gases, methane is measured quite routinely, while nitrous oxide is more challenging, with some artefacts occurring in the ice and so far limited interpretation. In the recent past, the ice cores provide the only direct measure of the changes that have occurred during the industrial period; they show that the current concentration of methane in the atmosphere is far outside the range experienced in the last 650,000 years; nitrous oxide is also elevated above its natural levels. There is controversy about whether changes in the pre-industrial Holocene are natural or anthropogenic in origin. Changes in wetland emissions are generally cited as the main cause of the large glacial-interglacial change in methane. However, changing sinks must also be considered, and the impact of possible newly described sources evaluated. Recent isotopic data appear to finally rule out any major impact of clathrate releases on methane at these time-scales. Any explanation must take into account that, at the rapid Dansgaard-Oeschger warmings of the last glacial period, methane rose by around half its glacial-interglacial range in only a few decades. The recent EPICA Dome C (Antarctica) record shows that methane tracked climate over the last 650,000 years, with lower methane concentrations in glacials than interglacials, and lower concentrations in cooler interglacials than in warmer ones. Nitrous oxide also shows Dansgaard-Oeschger and glacial-interglacial periodicity, but the pattern is less clear.