Satellite and unmanned aerial vehicle (UAV) remote sensing can be used to estimate soil properties; however, little is known regarding the effects of UAV and satellite remote sensing data integration on the estimation of soil comprehensive attributes, or how to estimate quickly and robustly. In this study, we tackled those gaps by employing UAV multispectral and Sentinel-2B data to estimate soil salinity and chemical properties over a large agricultural farm (400 ha) covered by different crops and harvest areas at the coastal saline-alkali land of the Yellow River Delta of China in 2019. Spatial information of soil salinity, organic matter, available/total nitrogen content, and pH at 0–10 cm and 10–20 cm layers were obtained via ground sampling (n = 195) and two-dimensional spatial interpolation, aiming to overlap the soil information with remote sensing information. The exploratory factor analysis was conducted to generate latent variables, which represented the salinity and chemical characteristics of the soil. A machine learning algorithm (random forest) was applied to estimate soil attributes. Our results indicated that the integration of UAV texture and Sentinel-2B spectral data as random forest model inputs improved the accuracy of latent soil variable estimation. The remote sensing-based information from cropland (crop-based) had a higher accuracy compared to estimations performed on bare soil (soil-based). Therefore, the crop-based approach, along with the integration of UAV texture and Sentinel-2B data, is recommended for the quick assessment of soil comprehensive attributes.

With the rapid development of circular agriculture in China, balancing agricultural income and environmental impact by adjusting the structure and scale of circular agriculture is becoming increasingly important. Agriculture is a major source of greenhouse gas and income earned from agriculture drives sustainable agricultural development. This paper built a multi-objective linear programming model based on greenhouse gas emission and agricultural product income and then optimized the structure and scale of circular agriculture using Beiqiu Farm as a case study. Results showed that greenhouse gas emission was mainly from manure management in livestock industry. While the agriculture income increased by 64% after optimization, GHG emission increased by only 12.3%. The optimization made full use of straw, manure and fodder, but also minimized soil nitrogen loss. The results laid a generalized guide for adjusting the structure and scale of the planting and raising industry. Measures for optimizing the management of manure were critical in achieving low agricultural carbon emissions in future agricultural development efforts.

Ecological pig-raising systems (EPRSs) differ from conventional breeding systems, focusing more on environmental consequences, human health, and food safety during production processes. Thus productions from EPRSs have undergone significant development in China. Thus far, adding plant fiber sources (e.g., sweet potato leaves, maize or wheat straw, potato, alfalfa, and vinasse) to feed has become a common practice to reduce the cost during the fattening period. Under such a context, it is necessary to choose the precision EPRS diet components and fattening period with low environmental consequences and high economic benefits. This study set up a database via pig growth models to predict environmental and economic performance based on two trials with 0%, 10%, 40%, 60%, and 80% maize silage (dry weight) added to the feed. A continuous curve about plant fiber concentration was built through the generated database. Our results showed that, with increased plant fiber concentration, the environmental performance of the EPRSs exhibited an “increase-decrease-increase” trend, and the economic performance firstly increased and then decreased. The best maize silage added percentages of emergy yield ratio (EYR), environmental loading ratio (ELR), unit emergy value (UEV), and emergy sustainability index (ESI), and the economic profits were 19.0%, 34.3%, 24.6%, 19.9%, and 18.0%, respectively. Besides, the 19.9% sun-dried maize silage added to the feed with a 360-day raising period had the best balance for environmental impact and economic performance. At the balance point, the performances of EYR, ELR, UEV, ESI, and the economic profit were only 0.04%, 3.0%, 0.8%, 0.0%, and 0.1%, respectively, lower than their maximum values. Therefore, we recommended the feed added 20% sun-dried maize silage is suitable for practical pig raising systems.