Environmental impacts and production performances of organic agriculture in China: A monetary valuation.

Organic agriculture has developed rapidly in China since the 1990s, driven by the increasing domestic and international demand for organic products. Quantification of the environmental benefits and production performances of organic agriculture on a national scale helps to develop sustainable high yielding agricultural production systems with minimum impacts on the environment. Data of organic production for 2013 were obtained from a national survey organized by the Certification and Accreditation Administration of China. Farming performance and environmental impact indicators were screened and indicator values were defined based on an intensive literature review and were validated by national statistics. The economic (monetary) values of farming inputs, crop production and individual environmental benefits were then quantified and integrated to compare the overall performances of organic vs. conventional agriculture. In 2013, organically managed farmland accounted for approximately 0.97% of national arable land, covering 1.158 million ha. If organic crop yields were assumed to be 10%-15% lower than conventional yields, the environmental benefits of organic agriculture (i.e., a decrease in nitrate leaching, an increase in farmland biodiversity, an increase in carbon sequestration and a decrease in greenhouse gas emissions) were valued at 1921 million RMB (320.2 million USD), or 1659 RMB (276.5 USD) per ha. By reducing the farming inputs, the costs saved was 3110 million RMB (518.3 million USD), or 2686 RMB (447.7 USD) per ha. The economic loss associated with the decrease in crop yields from organic agriculture was valued at 6115 million RMB (1019.2 million USD), or 5280 RMB (880 USD) per ha. Although they were likely underestimated because of the complex relationships among farming operations, ecosystems and humans, the production costs saved and environmental benefits of organic agriculture that were quantified in our study compensated substantially for the economic losses associated with the decrease in crop production. This suggests that payment for the environmental benefits of organic agriculture should be incorporated into public policies. Most of the environmental impacts of organic farming were related to N fluxes within agroecosystems, which is a call for the better management of N fertilizer in regions or countries with low levels of N-use efficiency. Issues such as higher external inputs and lack of integration cropping with animal husbandry should be addressed during the quantification of change of conventional to organic agriculture, and the quantification of this change is challenging.

Decision-tree-model identification of nitrate pollution activities in groundwater: A combination of a dual isotope approach and chemical ions.

To develop management practices for agricultural crops to protect against NO3(-) contamination in groundwater, dominant pollution activities require reliable classification. In this study, we (1) classified potential NO3(-) pollution activities via an unsupervised learning algorithm based on δ(15)N- and δ(18)O-NO3(-) and physico-chemical properties of groundwater at 55 sampling locations; and (2) determined which water quality parameters could be used to identify the sources of NO3(-) contamination via a decision tree model. When a combination of δ(15)N-, δ(18)O-NO3(-) and physico-chemical properties of groundwater was used as an input for the k-means clustering algorithm, it allowed for a reliable clustering of the 55 sampling locations into 4 corresponding agricultural activities: well irrigated agriculture (28 sampling locations), sewage irrigated agriculture (16 sampling locations), a combination of sewage irrigated agriculture, farm and industry (5 sampling locations) and a combination of well irrigated agriculture and farm (6 sampling locations). A decision tree model with 97.5% classification success was developed based on SO4(2-) and Cl(-) variables. The NO3(-) and the δ(15)N- and δ(18)O-NO3(-) variables demonstrated limitation in developing a decision tree model as multiple N sources and fractionation processes both resulted in difficulties of discriminating NO3(-) concentrations and isotopic values. Although only the SO4(2-) and Cl(-) were selected as important discriminating variables, concentration data alone could not identify the specific NO3(-) sources responsible for groundwater contamination. This is a result of comprehensive analysis. To further reduce NO3(-) contamination, an integrated approach should be set-up by combining N and O isotopes of NO3(-) with land-uses and physico-chemical properties, especially in areas with complex agricultural activities.