DOI: 10.57760/sciencedb.j00001.01657
title: Dataset of Maize Yield, Soil Moisture, and Nitrate Nitrogen in the Long-Term Nitrogen Fertilizer Management on the Loess Plateau (2017-2020)
title: 2017-2020年黄土旱塬长期氮肥管理试验玉米产量、土壤水分及硝态氮数据集

description: Compared to short-term monitoring, high-quality long-term observational data are particularly valuable as they can capture the cumulative effects of environmental evolution and agricultural management practices, thereby providing precise data support for subsequent research. Initiated in 2013 at the Changwu Agro-Ecological Experiment Station on the Loess Plateau, the nitrogen fertilizer management experiment has conducted long-term, stable monitoring of soil water distribution dynamics, soil nitrate nitrogen residue, and spring maize biomass under different nitrogen application patterns in the rain-fed agricultural area of the Loess Plateau. This effort is crucial for elucidating the response mechanisms and processes of the dryland spring maize system to nitrogen management. This dataset comprises data on soil water content and nitrate nitrogen content across different soil layers, as well as spring maize biomass at harvest, under four nitrogen management patterns: reduced nitrogen application (CK, N1, N2, N3, N4), straw deep incorporation (CON1, CON2, CR1, CR2), nitrification inhibitor application (CRN1, CRN2, CRN3, CRN4), and slow/controlled-release fertilizer application (CRS1, CRS2, CRS3). Covering the period from 2017 to 2020, the data were collected by different researchers at various times following unified protocols. The process encompassed the entire workflow from field surveys and laboratory analyses to multiple rounds of verification and review, resulting in a relatively complete and accurate dataset that includes comprehensive background site information and data observation methodologies. This dataset provides fundamental data for understanding the response patterns of the spring maize system to long-term differentiated nitrogen management in the Loess Plateau region. It also serves as a data foundation for subsequent regional nitrogen fertilizer optimization, efficient utilization of water and fertilizer resources, and the promotion of sustainable agricultural development.
description: Compared to short-term monitoring, high-quality long-term observational data are particularly valuable as they can capture the cumulative effects of environmental evolution and agricultural management practices, thereby providing precise data support for subsequent research. Initiated in 2013 at the Changwu Agro-Ecological Experiment Station on the Loess Plateau, the nitrogen fertilizer management experiment has conducted long-term, stable monitoring of soil water distribution dynamics, soil nitrate nitrogen residue, and spring maize biomass under different nitrogen application patterns in the rain-fed agricultural area of the Loess Plateau. This effort is crucial for elucidating the response mechanisms and processes of the dryland spring maize system to nitrogen management. This dataset comprises data on soil water content and nitrate nitrogen content across different soil layers, as well as spring maize biomass at harvest, under four nitrogen management patterns: reduced nitrogen application (CK, N1, N2, N3, N4), straw deep incorporation (CON1, CON2, CR1, CR2), nitrification inhibitor application (CRN1, CRN2, CRN3, CRN4), and slow/controlled-release fertilizer application (CRS1, CRS2, CRS3). Covering the period from 2017 to 2020, the data were collected by different researchers at various times following unified protocols. The process encompassed the entire workflow from field surveys and laboratory analyses to multiple rounds of verification and review, resulting in a relatively complete and accurate dataset that includes comprehensive background site information and data observation methodologies. This dataset provides fundamental data for understanding the response patterns of the spring maize system to long-term differentiated nitrogen management in the Loess Plateau region. It also serves as a data foundation for subsequent regional nitrogen fertilizer optimization, efficient utilization of water and fertilizer resources, and the promotion of sustainable agricultural development.