ABSTRACT
Stable growth in grain production is a critical challenge to ensure food security in North China Plain (NCP), an area dominated by smallholder farming. Food production and security of NCP largely depend on how smallholders farm their land. This study took Ningjin County of the NCP as an example to describe the characteristics of crop planting structure and the changes in crop production based on household surveys, statistics, various documents, and literature by descriptive statistics, calculation of crop self-sufficiency, and curve fitting, and aimed to reveal crop security and the influencing factors of crop production at the household level. The results were as follows: (1) Wheat and maize sown area accounted for 61.69% and 47.96% of the total sown area of crops during 2000-2020, increasing at a rate of 3.42% and 5.93%, respectively. Their planted areas increased from 27.52% and 15.54% in 2000 to 47.82% and 44.75% in 2020, respectively. (2) The self-sufficiency rate of maize showed a significant upward trend and reached its peak in 2019. the self-sufficiency rate of wheat also showed an increasing trend, from 192.87% to 617.37%, which indicates that wheat and maize can meet food self-sufficiency and the per capita grain yield is in a safe state. (3) The trends on wheat yield and fertilizer initially grew, then decreased, closely resembling an inverted "U", while the maize yield showed a pattern of increasing first and then basically remaining stable, similar to an "S" shape. A turning point for fertilizer use (550 kg/ha) was identified, indicating the limits of fertilizer use to increase yield. The national agricultural production and environmental protection policies, continuous improvement of crop varieties, as well as the farmers' traditional practices have significant impacts on crop production. This study will enhance management practices for improved yield, which can support the integrated management of agricultural production in intensive agricultural areas.
ABSTRACT
The outbreak of the 2019 novel coronavirus disease (COVID-19) has caused more than 100,000 people infected and thousands of deaths. Currently, the number of infections and deaths is still increasing rapidly. COVID-19 seriously threatens human health, production, life, social functioning and international relations. In the fight against COVID-19, Geographic Information Systems (GIS) and big data technologies have played an important role in many aspects, including the rapid aggregation of multi-source big data, rapid visualization of epidemic information, spatial tracking of confirmed cases, prediction of regional transmission, spatial segmentation of the epidemic risk and prevention level, balancing and management of the supply and demand of material resources, and social-emotional guidance and panic elimination, which provided solid spatial information support for decision-making, measures formulation, and effectiveness assessment of COVID-19 prevention and control. GIS has developed and matured relatively quickly and has a complete technological route for data preparation, platform construction, model construction, and map production. However, for the struggle against the widespread epidemic, the main challenge is finding strategies to adjust traditional technical methods and improve speed and accuracy of information provision for social management. At the data level, in the era of big data, data no longer come mainly from the government but are gathered from more diverse enterprises. As a result, the use of GIS faces difficulties in data acquisition and the integration of heterogeneous data, which requires governments, businesses, and academic institutions to jointly promote the formulation of relevant policies. At the technical level, spatial analysis methods for big data are in the ascendancy. Currently and for a long time in the future, the development of GIS should be strengthened to form a data-driven system for rapid knowledge acquisition, which signifies that GIS should be used to reinforce the social operation parameterization of models and methods, especially when providing support for social management.