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Good quality of soil nitrogen data, which is essential for the advancement of both enhanced agricultural management and ecological environment, traditionally depends on labor intensive chemical procedures. Visible near-infrared (Vis-NIR) spectroscopy, acknowledged for its efficiency, environmental compatibility and rapidity, merges as a promising alternative. However, the effectiveness of Vis-NIR measurement models are significantly compromised by soil particle size distribution (PSD), presenting a substantial challenge in improving the measurement accuracy and reliability. Here an innovative deep learning methodology that integrates PSD with Vis-NIR spectroscopy was proposed for the measurement of nitrogen content in soil samples. By leveraging the LUCAS dataset, different strategies for integrating PSD with Vis-NIR spectral data in deep learning models were explored, revealing that our proposed InSGraL framework, which incorporated mixed features of PSD and spectra as LSTM inputs achieves superior performance. Compared to models utilizing solely Vis-NIR data, InSGraL exhibits a 39.47 % reduction in RMSE and a 42.55 % decrease in MAE, and demonstrates robust performance across various land cover types, achieving an R 2 of 0.94 on grassland samples. Moreover, Shapley Additive exPlanations (SHAP) analysis revealed that incorporating PSD modifies the spectral input importance distribution, effectively mitigating spectral interference from particle size while highlighting critical wavelengths previously obscured. This study provides an innovative modeling strategy to mitigate the influence of PSD by integrating it within deep learning framework using Vis-NIR, contributing a deeper understanding of the relationship between PSD and Vis-NIR spectra for the measurement of nitrogen content and offering an effective means to attain soil nitrogen data., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Geng Leng reports financial support was provided by National Natural Science Foundation of China. Geng Leng reports financial support was provided by Sichuan Science and Technology Program. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Urban environments are thought to improve food security, by offering enhanced access to markets and income opportunities. Yet this idea is hard to test empirically due to an abundance of confounding factors and selection issues. This study leverages a resettlement program in China to provide the first quasi-experimental estimate of city life on food consumption and nutrition among low-income households. Lottery-determined timing of resettlement enables causal inference. We base our empirics on a 3-year panel and a range of difference-in-differences and matching methodologies. We find that those who were resettled to towns significantly increased both food consumption and diet variety, with increased intake of several macro- and micro-nutrients. Diet quality mostly improved, but we also found signs of over-consumption, notably of carbohydrates. Our evidence further suggests that our impacts are primarily due to improved market access. This stands in contrast to recent literature that finds little or no effect of living environments on food consumption. Instead, we reveal a significant impact of urban environments in shaping diets, bolstering the notion that supply-side channels do matter in some contexts., (© 2025 The Author(s). Health Economics published by John Wiley & Sons Ltd.)