Composting is considered to be a primary treatment method for livestock manure and rice straw, and high degree of maturity is a prerequisite for safe land application of the composting products. In this study pilot-scale experiments were carried out to characterize the co-composting process of livestock manure with rice straw, as well as to establish a maturity evaluation index system for the composts obtained. Two pilot composting piles with different feedstocks were conducted for 3 months: (1) swine manure and rice straw (SM-RS); and (2) dairy manure and rice straw (DM-RS). During the composting process, parameters including temperature, moisture, pH, total organic carbon (TOC), organic matter (OM), different forms of nitrogen (total, ammonia and nitrate), and humification index (humic acid and fulvic acid) were monitored in addition to germination index (GI), plant growth index (PGI) and Solvita maturity index. OM loss followed the first-order kinetic model in both piles, and a slightly faster OM mineralization was achieved in the SM-RS pile. Also, the SM-RS pile exhibited slightly better performance than the DM-RS according to the evolutions of temperature, OM degradation, GI and PGI. The C/N ratio, GI and PGI could be included in the maturity evaluation index system in which GI>120% and PGI>1.00 signal mature co-composts.

With the rise of large-scale crop plantation in China, inexpensive but efficient remote-sensing measures for predicting the nitrogen status of crops are needed for optimal fertilizer management. In this research, a conventional digital camera with a charge coupled device was integrated into an unmanned aerial vehicle (UAV) to capture digital aerial images of paddy rice (Oryza sativa L.) at an altitude of 50 m. The fluorescence emissions of the rice leaves under light excitation were used by Multiplex® to non-destructively assess the chlorophyll and polyphenol content. The nitrogen balance index (NBI) of the rice leaves, known as the ratio of chlorophyll to polyphenols, was used to accurately determine canopy nitrogen concentrations. The dark green colour index (DGCI) available from the aerial images was used to assess the nitrogen concentrations in the field. It was found that DGCI values predicted the nitrogen concentrations and NBI with R² (coefficient of determination) = 0.672 (p < 0.001) and R² = 0.711 (p < 0.001), respectively. The results indicated that aerial photography from UAV had the ability to assess plant nitrogen status in the field in large-scale crop plantations.