查看更多>>摘要：Land use influences soil biota community composition and diversity,and then belowground ecosystem processes and functions.To characterize the effect of land use on soil biota,soil nematode communities in crop land,forest land and fallow land were investigated in six regions of northern China.Generic richness,diversity,abundance and biomass of soil nematodes was the lowest in crop land.The richness and diversity of soil nematodes were 28.8 and 15.1％higher in fallow land than in crop land,respectively.No significant differences in soil nematode indices were found between forest land and fallow land,but their network keystone genera composition was different.Among the keystone genera,50％of forest land genera were omnivores-predators and 36％of fallow land genera were bacterivores.The proportion of fungivores in forest land was 20.8％lower than in fallow land.The network complexity and the stability were lower in crop land than forest land and fallow land.Soil pH,NH4+-N and NO3--N were the major factors influencing the soil nematode community in crop land while soil organic carbon and moisture were the major factors in forest land.Soil nematode communities in crop land influenced by artificial management practices were more dependent on the soil environment than communities in forest land and fallow land.Land use induced soil environment variation and altered network relationships by influencing trophic group proportions among keystone nematode genera.
查看更多>>摘要：Faced with increasing global soil degradation,spatially explicit data on cropland soil organic matter(SOM)provides crucial data for soil carbon pool accounting,cropland quality assessment and the formulation of effective management policies.As a spatial information prediction technique,digital soil mapping(DSM)has been widely used to spatially map soil information at different scales.However,the accuracy of digital SOM maps for cropland is typically lower than for other land cover types due to the inherent difficulty in precisely quantifying human disturbance.To overcome this limitation,this study systematically assessed a framework of"information extraction-feature selection-model averaging"for improving model performance in mapping cropland SOM using 462 cropland soil samples collected in Guangzhou,China in 2021.The results showed that using the framework of dynamic information extraction,feature selection and model averaging could efficiently improve the accuracy of the final predictions(R2:0.48 to 0.53)without having obviously negative impacts on uncertainty.Quantifying the dynamic information of the environment was an efficient way to generate covariates that are linearly and nonlinearly related to SOM,which improved the R2 of random forest from 0.44 to 0.48 and the R2 of extreme gradient boosting from 0.37 to 0.43.Forward recursive feature selection(FRFS)is recommended when there are relatively few environmental covariates(＜200),whereas Boruta is recommended when there are many environmental covariates(＞500).The Granger-Ramanathan model averaging approach could improve the prediction accuracy and average uncertainty.When the structures of initial prediction models are similar,increasing in the number of averaging models did not have significantly positive effects on the final predictions.Given the advantages of these selected strategies over information extraction,feature selection and model averaging have a great potential for high-accuracy soil mapping at any scales,so this approach can provide more reliable references for soil conservation policy-making.
查看更多>>摘要：Excessive consumption of refined grains harms human health and ecosystem viability.Whole grains,as a healthy and sustainable alternative to refined grains,can benefit individual health by providing dietary fiber,B vitamins,and bioactive substances.Additionally,they aid in improving the environment due to their higher extraction rate and lower carbon emission during the processing stage.However,few studies have attempted to evaluate the economic and social benefits of increasing the amount of whole grain in grain intake.This paper estimates the potential savings in healthcare costs and reduced food carbon footprints(CFs)that could result from a shift toward whole grain consumption following the Chinese Dietary Guidelines(CDG).We investigate hypothetical scenarios where a certain proportion(5-100％)of Chinese adults could increase their whole grain intakes as proposed by CDG to meet the average shortfall of 30.2 g.In that case,the healthcare costs for associated diseases(e.g.,type 2 diabetes mellitus(T2DM),cardiovascular disease(CVD),and colorectal cancer(CRC))are expected to reduce by a substantial amount,from USD 2.82 to 56.37 billion;the carbon emission levels are also projected to decrease by 0.24-5.72 million tons.This study provides compelling evidence that advocating for the transition towards greater consumption of whole grain products could benefit individual health,the environment,and society,by reducing both healthcare costs and carbon emissions.
查看更多>>摘要：Food security has been long understudied in the context of Central Asia.We present an analysis examining household-level food demand for Tajikistan and assessing the magnitude of its food security changes during the COVID-19 pandemic.Based on an extensive household survey data set from Tajikistan,we estimate the expenditure,income,and price elasticities for nine food categories using the QUAIDS model.Then,we develop a microsimulation model using the estimated elasticities to assess the dual impact of declining remittance income and rising food prices stemming from the pandemic shock.There are significant differences in demand elasticities across food groups,with high elasticities observed for nutritious foods,such as meat,fruit,eggs,and milk,in rural households.Moreover,our findings show that changes in remittance income and food prices significantly negatively affected food security for rural households during the COVID-19 pandemic.These findings have important implications for policymakers concerned about rural livelihoods and food security in remittance-receiving economies during the post-pandemic period.
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