Sichuan snub-nosed monkeys were observed for 197 days from 2000 to 2003 in the Qinling Mountains, Central China. The study group was provisioned in 2001 allowing detailed observations of social organization based on individual identification. The group was composed of 45-82 monkeys, all of which belonged to one of 6-8 one-male units (OMU) that foraged to form one big group. The average unit size was 9.0+/-2.3, 8+/-1.5 in the winter and 11.1+/-2.0 in the spring. Immigration or emigration of one-male units to or from the foraging group was observed, as was migration of individuals in and out of OMU, especially for by subadult females and juveniles. Group size therefore tended to fluctuate with the number of OMU and the number of young monkeys present in the group. The OMU in the study area were smaller than those in Shennongjia area. The factors influencing the size of these OMU and the entire group are discussed.

Climate change, increasing populations, competing demands on land for production of biofuels, and declining soil quality are challenging global food security. Finding sustainable solutions requires bold new approaches and integration of knowledge from diverse fields, such as materials science and informatics. The convergence of precision agriculture, whereby farmers respond in real-time to changes in crop growth, with nanotechnology and artificial intelligence offers exciting opportunities for sustainable food production. Coupling existing models for nutrient cycling and crop productivity with nanoinformatics approaches to optimize targeting, uptake, delivery nutrient capture and long term impacts on soil microbial communities will allow design of nanoscale agrochemcials that combine optimal safety and functionality profiles. bacterial community structure after just 90 days of exposure to a realistic concentration of NPs (1 mg kg −1 dry soil) 10 , while studies with Ag NMs, which are well-known for their antimicrobial activity have shown that the extent of impact on soil community composition over 90 days are affected by exposure time and physicochemical composition of soil as well as the type and coating of the NMs 11 . Thus, an important caveat at the outset of this review is that NMs represent a very broad spectrum of chemistries, compositions and physicochemical properties, which are dynamic and evolving as the NMs interact with their surroundings, and as such generalisations regarding their applications in agriculture are difficult, and predictions of long-term effects are challenging currently.However, as noted in the aforementioned reviews 3, 4, 5 , the development of nanotechnology for agricultural applications is still at an early stage and is moving forward quite slowly. Significant differences may exist between nanotechnology-based pesticides and conventional pesticides, including altered bioavailability, sensitivity, dosimetry, and pharmacokinetics 12, 13 . Challenges and barriers include limited understanding of plant-NMs interactions, limited methods for efficient delivery of NMs to plants and soil, risks of potentially hazardous effects of NMs to human health from accumulation of NMs and active ingredient residues in edible portions of plants 4 , and to long term soil quality and soil health from accumulation of NMs and their degradation products in soil and resultant potential alterations in microbial biodiversity 14 . There is an urgent need to address these barriers and achieve a true win-win scenario, whereby improved agricultural production, reduced environmental pollution from agriculture and lower costs for farmers can be achieved synergistically. A one-health approach to nano-agriculture was proposed by Lombi et al., that requires interdisciplinarity and the bridging of human and environmental health research 15 . Computational approaches including artificial intelligence (A.I.) and machine learning (M.L.) modelling will undoubtedly play critical roles in the progess of nano-enabled agric...

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