Data-Driven Projections Demonstrate Non-Farming Use of Cropland in Non-Major Grain-Producing Areas: A Case Study of Shaanxi Province, China

Abstract

The non-farming use of cropland has led to food insecurity in China due to drastic land use (LU) changes under the stresses of ecological restoration and urbanization, particularly in non-major grain-producing areas. Questions were raised about spatiotemporal cropland losses/gains and their drivers in these areas in the future for sustainable development of the agriculture sector. However, the answers to these questions have not been well acknowledged. This study, therefore, presents analyses of cropland area change from 1990 to 2018 and from 2018 to 2051 in Shaanxi province based on the Future Land Use Simulation (FLUS) model that follows the integration of the Shared Socioeconomic Pathway 2 and the Representative Concentration Pathway 4.5 (SSP245) within the International Coupled Model Intercomparison Project 6 (CMIP6). The results highlight that ecological restoration and fast-paced urbanization mainly drove the alarming non-farming use of cropland. The per capita cropland area is projected to increase, but the cropland loss will still occur, which potentially causes food insecurity. Thus, food security will be a challenging issue in the near future. The quantitative findings call for careful designs of LU policies, taking into account cropland protection, socio-economic development, and ecological restoration.

Impact of high-standard farmland construction on farmers’ income growth—quasi-natural experiments from China

“…If the majority of households engage in agricultural production, it is often difficult to obtain substantial wages. Instead, specialized family farms and large-scale grain farmers are the main agricultural operators in these regions (Linghu et al, 2023). Most households transfer their land and shift to non-agricultural industries, promoting the cultivation of new agricultural management entities and obtaining wage and property income.…”

Evaluation of the Potential of Using Machine Learning and the Savitzky–Golay Filter to Estimate the Daily Soil Temperature in Gully Regions of the Chinese Loess Plateau

“…Promoting the sustainable development of agriculture is one of the United Nations Sustainable Development Goals (SDGs) [1]. However, an extraordinary challenge in achieving Sustainable Development Goal 2 (SDG2) is the food problem [2]. Reasonable and effective agricultural production activities can help to meet this challenge.…”

“…Therefore, the objectives of this study were: (1) to simulate the shallow soil temperature by using environmental factors as input and evaluate the performance difference between RF and MLP; (2) to construct deep soil temperature prediction models based on the simulated shallow soil temperature and the air temperature; and (3) to evaluate the performance of deep soil temperature prediction models.…”