The North China District (NCD) is one of the main grain production regions in China. The double cropping system of irrigation has been leading to the groundwater table decline at the speed of 1–2 m per year. Climate change leads to uncertainty surrounding the future of the NCD agricultural system, which will have great effects on crop yields and crop water demands. In this research, the Meteorological dataset from 54 weather station sites over the period 1960–2015 were collected to quantify the long-term spatial and temporal trends of meteorological data, including daily minimum temperature (T_min), maximum temperature (T_max), precipitation, solar radiation, reference evapotranspiration (ET₀), and aridity index (AI). The results show that the long-term wheat and maize growing season and annual average air temperatures (T_min and T_max) showed strong north to south increasing trends throughout the NCD. The average annual precipitation was 632.9 mm across the NCD, more than 70% of which was concentrated in the maize growing season. The regional average annual ET₀ was 1026.1 mm, which was 531.2 and 497.4 mm for the wheat and maize growing season, respectively. The regional precipitation decreased from northwest to southeast in each growing season and annual timescale. The funnel areas have lower precipitation and higher ET₀ than the regional average, which may lead to the mining of the groundwater funnel area. The regional average annual AI is 0.63, which lies in the humid class. For temporal analysis, the regional average trends in annual T_min, T_max, solar radiation, ET₀, precipitation, and AI were 0.37 °C/10a, 0.15 °C/10a, −0.28 MJ/day/m²/10a, −2.98 mm/10a, −12.04 mm/10a, and 0.005/10a, respectively. The increasing trend of temperature and the decreasing trend of solar radiation may have a negative effect on the regional food security. The funnel area AI showed a significant increasing trend for the winter wheat growing season and a decreasing trend for maize, which indicated that more irrigation will be needed for the maize growing season and the winter fallow policy may lead to the increasing trend precipitation being wasted. Analyzing the growing season and the annual meteorological elements of the spatiotemporal trends can help us better understand the influence of climate change on the natural resources and agricultural development in both the past and the future, and will provide us with invaluable information for the modification of cropping patterns to protect the regional and national water and food security.

The incorporation of organic fertilizer is an important practice to improve the sustainability and productivity of crop production and decrease environmental pollution from crop-livestock systems in global agriculture. However, establishing an evaluation dataset is still the main challenge for quickly and effectively assessing the effect of management measures on farmland soil health. Hereby, we developed a minimum dataset (MDS) using three methods (network analysis (NA), random forest analysis (RF), and principal component analysis (PCA)). Based on MDS and two scoring functions (nonlinear (NL) and linear scoring curve (L)), the SHI (soil health index framework) was constructed to assess soil health conditions under four fertilization treatments (no fertilization, CK; only chemical fertilizer, NPK; only cow manure, MF; 50% chemical fertilizer + 50% cow manure, NPKM) in the northern ecotone of China. The results showed that the MDS-based on SHIs were positively correlated with each other and SHI-TDS (total dataset), verifying the consistency of soil health assessment models. Higher R² was observed in the fitting of SHIs based on NA and TDS, which suggested that nMDS (minimum dataset based on network analysis) could represent most of the information in the TDS. The SHI-NL-nMDS (based on network analysis and nonlinear scoring curve) has the highest ability of sensitivity and accuracy, which indicates that compared with PCA and RF, the SHI based on NA can better embody farmland sol ecosystem functions. In addition, crop yield was significantly positive relative to SHI (soil health index). The incorporation of cow manure and chemical fertilizer improved soil health and increased crop yield. These results indicate that network analysis was a reliable technology for determining the minimum dataset in the evaluation of farmland soil health, and incorporating livestock manure could improve soil health and crop yield in this study area.