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High yields and low carbon emissions are new challenges for modern crop production. Balancing the crop yield and reducing greenhouse gas (GHG) emissions has become a new field of agronomic technology innovation. Cereal–legume intercropping is a typical diversification planting system, which has been expected to achieve the dual goals of high production and low GHG emissions. However, the synergistic effect of integrating various technologies in an intercropping system on GHG emissions and whether it will achieve the high yield and low emissions goal remains to be determined. Therefore, bibliometric analysis has investigated the worldwide development trend of cereal–legume intercropping designs. The literature on the GHG emissions of the cereal–legume intercropping system was summarized. Additionally, the effects and mechanisms of different agricultural management methods regarding soil nitrous oxide and carbon dioxide emissions in the cereal–legume intercropping system were summarized. The research on GHG emissions of cereal–legume intercropping systems in non-growing seasons must be revised. In situ observations of GHG emissions from intercropping systems in different regions should be strengthened. This work is valuable in supporting and evaluating the potential of GHG reduction in a cereal–legume intercropping system in various farming areas. [ABSTRACT FROM AUTHOR]

Genetically modified (GM) technology is of great significance for increasing crop production, protecting biodiversity, and reducing environmental pollution. However, with the frequent occurrence of safety events regarding GM foods, more and more disputes have arisen over the potential safety of transgenic technology. It is particularly necessary to find a fast and accurate method for transgenic product identification. In this research, mid-infrared spectroscopy, coupled with chemometric methods, was applied to discriminate GM maize from its non-GM parent. A total of 120 GM maize and 120 non-GM maize samples were prepared, and the spectral information in the range of 400–4000 cm−1 was collected. After acquiring the spectra, wavelet transform (WT) was used to preprocess the data, and k-means was carried out to split all samples into calibration and prediction sets in the ratio of 2:1. Principal component analysis (PCA) was then conducted to qualitatively distinguish the two types of samples, and an apparent cluster was observed. Since the full spectrum covered a large amount of data and redundant information, we adopted the successive projections algorithm (SPA) to select optimal wavelengths for further analysis. Chemometrics, including partial least squares-discriminant analysis (PLS-DA), the k-nearest neighbor algorithm (KNN), and the extreme learning machine (ELM), were performed to establish classification models based on full spectra and optimal wavelengths. The overall results indicated that ELM models based on full spectra and optimal spectra showed better accuracy and reliability, with a 100% recognition rate in the calibration set and a 98.75% recognition rate in the prediction set. It has been confirmed that mid-infrared spectroscopy, combined with chemometric methods, can be a novel approach to identify transgenic maize. [ABSTRACT FROM AUTHOR]

Rapid detection of soil nutrient elements is beneficial to the evaluation of crop yield, and it’s of great significance in agricultural production. The aim of this work was to compare the detection ability of single-pulse (SP) and collinear double-pulse (DP) laser-induced breakdown spectroscopy (LIBS) for soil nutrient elements and obtain an accurate and reliable method for rapid detection of soil nutrient elements. 63 soil samples were collected for SP and collinear DP signal acquisition, respectively. Macro-nutrients (K, Ca, Mg) and micro-nutrients (Fe, Mn, Na) were analyzed. Three main aspects of all elements were investigated, including spectral intensity, signal stability, and detection sensitivity. Signal-to-noise ratio (SNR) and relative standard deviation (RSD) of elemental spectra were applied to evaluate the stability of SP and collinear DP signals. In terms of detection sensitivity, the performance of chemometrics models (univariate and multivariate analysis models) and the limit of detection (LOD) of elements were analyzed, and the results indicated that the DP-LIBS technique coupled with PLSR could be an accurate and reliable method in the quantitative determination of soil nutrient elements. [ABSTRACT FROM AUTHOR]