Changes in organic C stability within soil aggregates under different fertilization patterns in a greenhouse vegetable field

Knowledge of the stability of soil organic C(SOC)is vital for assessing SOC dynamics and cycling in agroecosystems.Studies have documented the regulatory effect of fertilization on SOC stability in bulk soils.However,how fertilization alters organic C stability at the aggregate scale in agroecosystems remains largely unclear.This study aimed to appraise the changes of organic C stability within soil aggregates after eight years of fertilization(chemical vs.organic fertilization)in a greenhouse vegetable field in Tianjin,China.Changes in the stability of organic C in soil aggregates were evaluated by four methods,i.e.,the modified Walkley-Black method(chemical method),13C NMR spectroscopy(spectroscopic method),extracellular enzyme assay(biological method),and thermogravimetric analysis(thermogravimetric method).The aggregates were isolated and separated by a wet-sieving method into four fractions:large macroaggregates(>2 mm),small macroaggregates(0.25–2 mm),microaggregates(0.053–0.25 mm),and silt/clay fractions(<0.053 mm).The results showed that organic amendments increased the organic C content and reduced the chemical,spectroscopic,thermogravimetric,and biological stability of organic C within soil aggregates relative to chemical fertilization alone.Within soil aggregates,the content of organic C was the highest in microaggregates and decreased in the order microaggregates>macroaggregates>silt/clay fractions.Meanwhile,organic C spectroscopic,thermogravimetric,and biological stability were the highest in silt/clay fractions,followed by macroaggregates and microaggregates.Moreover,the modified Walkley-Black method was not suitable for interpreting organic C stability at the aggregate scale due to the weak correlation between organic C chemical properties and other stability characteristics within the soil aggregates.These findings provide scientific insights at the aggregate scale into the changes of organic C properties under fertilization in greenhouse vegetable fields in China.

Blockchain-based prosumer incentivization for peak mitigation through temporal aggregation and contextual clustering

Peak mitigation is of interest to power companies as peak periods may require the operator to over provision supply in order to meet the peak demand.Flattening the usage curve can result in cost savings,both for the power companies and the end users.Integration of renewable energy into the energy infrastructure presents an opportunity to use excess renewable generation to supplement supply and alleviate peaks.In addition,demand side management can shift the usage from peak to off-peak times and reduce the magnitude of peaks.In this work,we present a data driven approach for incentive-based peak mitigation.Understanding user energy profiles is an essential step in this process.We begin by analysing a popular energy research dataset published by the Ausgrid corporation.Extracting aggregated user energy behavior in temporal contexts and semantic linking and contextual clustering give us insight into consumption and rooftop solar generation patterns.We implement,and performance test a blockchain-based prosumer incentivization system.The smart contract logic is based on our analysis of the Ausgrid dataset.Our implementation is capable of supporting 792,540 customers with a reasonably low infrastructure footprint.

AN EXTENDED AGGREGATED RATIO ANALYSIS IN DEA

Data Envelopment Analysis（DEA） and Ratio Analysis（RA） are two widely used methods for measuring units＇ productivity and any other criteria that could be assessed based on the available input and output variables.A number of researchers have studied DEA and RA and noted the positive and negative differences between them.Aggregated ratio analysis（ARA） model,which provide an important linkage between DEA and RA theory,is equivalent to the CCR DEA model,and this equivalence property offers a great deal of opportunities for DEA to be interpreted and applied in different ways.This paper extends the results of ARA model and proposes an extended aggregated ratio analysis（EARA） model,similar as the development from CCR model to BCC model in DEA context.The proposed model can offer an insight into the characteristic of returns to scale,playing the corresponding role as BCC model does.The numerical example is revisited in the paper and the results are compared.

China's agricultural land use change and its underlying drivers:A literature review

Understanding the manifestations and underlying drivers of agricultural land use change in China is of great importance for both domestic and global food security. However, little is known about the holistic pattern of agricultural land use change across China, especially from the perspective of intensity since the evidence has been gathered mainly through case studies at local levels. This study conducts a systemic review of agricultural land use change and its underlying drivers in China by aggregating 169 relevant case studies from 123 publications. The cases related to intensification and disintensification, which are the two types of agricultural land use change, are generally equal, accounting for 50% of the total number of cases. Intensification and disintensification can be further divided into the same three categories: expansion/contraction of agricultural land, changes in agricultural land use activities and changes in land management intensity. Demographic, economic, technological, and institutional drivers, together with location factors, are frequently noted as significant underlying drivers, while sociocultural drivers and farm(er) characteristics are less frequently recognized. Finally, three major land use change trajectories are summarized mainly concerning rising labor costs and the concomitant increase in off-farm employment, the ecological improvement policy, and advances in agricultural technology.