Smart agriculture is based on latest technologies in communication, big data analysis, high-performance sensors, and automatic machinery to improve the effectiveness of agriculture industry. China is looking forward to achieve modernization of its agriculture industry through smart agriculture. But its unique climate, landscape, diversity in crops, and large population make this task more challenging compared to other counties. Herein, we present the current achievements regarding policies made to guide and support the development of smart agriculture, the promotion of smart techniques and smart equipment, and the cultivation of specialists in smart agriculture. Then we discussed the existing challenges that hinder further development of smart agriculture industry and proposed several feasible solutions to solve or ease the problem, including the pertinent development of smart agriculture in provinces with different resources, enrich modes of online technical support and specialist cultivation, and set a series of standards to guide research and development of novel smart systems.

The recent surge of data-driven methods in social policy have created new opportunities to assess existing poverty programs. The expectation is that the combination of advanced methods and more data can calculate the effectiveness of public interventions more accurately and tailor local initiatives accordingly. Specifically, nonmonetary indicators are increasingly being measured at micro levels in order to target social exclusion in combination with poverty. However, the multidimensional character of poverty, local context, and data matching pose challenges to data-driven analyses. By linking Dutch household-level data with policy-initiative-specific data at local level, we present an explorative study on the uptake of a local poverty pass. The goal is to unravel pass usage in terms of household income and location as well as the age of users. We find that income and age play a role in whether the pass is used, and usage differs per neighborhood. With this, the paper feeds into the discourse on how to operationalize and design data matching work in the multidimensional space of poverty and nonmonetary government initiatives.