Spatiotemporal Evolution of Non-Grain Production of Cultivated Land and Its Underlying Factors in China

Abstract

Food security is the foundation of development. We comprehensively characterized the spatiotemporal patterns of non-grain production (NGP) areas in China and elucidated the underlying factors driving NGP. Our objectives were to map NGP on cultivated land (NGPCL) in China, and to quantify its spatiotemporal patterns, to investigate the factors underlying NGP spatial differentiation, and to provide a scientific basis for developing NGP management policies and reference points for protecting cultivated land in other countries. We mapped NGPCL in China from 2000 to 2018 using remote sensing and geographic information system data. The spatiotemporal evolution of the NGP rate (NGPR) was also investigated. The dominant factors driving NGP progression and associated interactions were identified using geographic detectors. From 2000 to 2018, the NGPR gradually decreased from 63.02% to 52.82%. NGPR was high in the west and low in the east, and its spatial differentiation and clustering patterns were statistically significant. Precipitation, temperature, altitude, and soil carbon content were the dominant factors affecting the spatial differentiation in NGPR. The interaction between these factors enhanced the spatial differentiation.

A Smart Application Frame of Remote Sensing in Non-grain Production Data Governance

“…3) It requires powerful computing capabilities and efficient data processing and analysis methods due to the large scale of data. 4) NGP is a dynamic process that requires timely updates and monitoring of data (Zhu et al, 2022. 5) It involves data sharing and collaboration among multiple departments and regions, and establishing effective cooperation mechanisms among policies, laws, and government departments is a challenge.…”

A non‐grain production on cropland spatiotemporal change detection method based on Landsat time‐series data

“…However, this approach may face challenges in ensuring temporal consistency due to issues such as image noise and classification algorithms, which can affect the accuracy of classification results in each period and may magnify uncertainties in land cover change trajectories, making it difficult to determine whether the observed changes are real or result from misclassification in a particular image (Xu et al, 2023). Some other scholars have used decision tree models (Zhang et al, 2023) or existing datasets of food crop planting (Zhu et al, 2022), which are all limited in their ability to distinguish specific types of NGP. None of the above-mentioned methods can accurately provide the spatiotemporal dynamics information for different categories of NGP, it is necessary to propose new perspectives and methods.…”

Estimating the Amount of the Wild Artemisia annua in China Based on the MaxEnt Model and Spatio-Temporal Kriging Interpolation

“…The analytica reflects the influencing factors with regard to species distribution and the degree to the habitat is suitable for its growth. Moreover, the spatio-temporal kriging interp method [19][20][21][22][23][24] considers the trends and spatial correlations of the data in time and which prevents the loss of important information and improves the interpolation ac when faced with missing or anomalous data problems or limited field survey d nally, GeoDetector [25][26][27][28][29][30] measures the spatial heterogeneity, detects explanato tors, and analyzes interactions between variables by calculating the q-statistic.…”