Improving nitrogen (N) management of small-scale farming systems in developing countries is crucially important for food security and sustainable development of world agriculture, but it is also very challenging. The N Nutrition Index (NNI) is a reliable indicator for crop N status, and there is an urgent need to develop an effective method to non-destructively estimate crop NNI in different smallholder farmer fields to guide in-season N management. The eBee fixed-wing unmanned aerial vehicle (UAV)-based remote sensing system, a ready-to-deploy aircraft with a Parrot Sequoia+ multispectral camera onboard, has been used for applications in precision agriculture. The objectives of this study were to (i) determine the potential of using fixed-wing UAV-based multispectral remote sensing for non-destructive estimation of winter wheat NNI in different smallholder farmer fields across the study village in the North China Plain (NCP) and (ii) develop a practical strategy for village-scale winter wheat N status diagnosis in small scale farming systems. Four plot experiments were conducted within farmer fields in 2016 and 2017 in a village of Laoling County, Shandong Province in the NCP for evaluation of a published critical N dilution curve and for serving as reference plots. UAV remote sensing images were collected from all the fields across the village in 2017 and 2018. About 150 plant samples were collected from farmer fields and plot experiments each year for ground truthing. Two indirect and two direct approaches were evaluated for estimating NNI using vegetation indices (VIs). To facilitate practical applications, the performance of three commonly used normalized difference VIs were compared with the top performing VIs selected from 59 tested indices. The most practical and stable method was using VIs to calculate N sufficiency index (NSI) and then to estimate NNI non-destructively (R² = 0.53–0.56). Using NSI thresholds to diagnose N status directly was quite stable, with a 57–59% diagnostic accuracy rate. This strategy is practical and least affected by the choice of VIs across fields, varieties, and years. This study demonstrates that fixed-wing UAV–based remote sensing is a promising technology for in-season diagnosis of winter wheat N status in smallholder farmer fields at village scale. The considerable variability in local soil conditions and crop management practices influenced the overall accuracy of N diagnosis, so more studies are needed to further validate and optimize the reported strategy and consecutively develop practical UAV remote sensing–based in-season N recommendation methods.

Identifying the current status of the heavy metal(loid) input of agricultural soils is vital for the soil ecological environment of agricultural-producing areas. Most previous studies have typically carried been out in small regions with limited sampling sites, which is insufficient to reveal the overall status of China. This study reviewed publications from over the past 20 years and calculated the input fluxes of heavy metal(loid)s in agricultural soil via atmospheric deposition, fertilizer, manure, and irrigation in different regions of China based on spatiotemporal heterogeneity using a meta-analysis, providing more accurate and reliable results. It was found that the heavy metal(loid) input flux of atmospheric deposition in China is large, while that of fertilizer and manure is relatively low compared to Europe. The major sources of As, Cd, Cr, Ni, and Pb entering the soil was atmospheric deposition, which accounted for 12% to 92% of the total input. Manure was responsible for 19% to 75% of the Cu and Zn input. Cd is the element presenting the most significant risk to the environment of agricultural soils in China and its safety limit will be reached within 100 years for most regions. The region we need to be concerned about is Huang-Huai-Hai due to its comprehensive pollution.