Ecosystem Services of Grazed Grasslands in the Flooding Pampa

The Flooding Pampa grasslands are the last remnant of the Rio de la Plata grasslands in Argentina.Anthropo-genic interventions have led to severe degradation and,as a result,the ecosystem services provided by the grass-lands are declining,in terms of provisioning,regulating,and supporting services.We synthesized the existing literature on the ecosystem goods and services provided by these grasslands under grazing in different conditions and conservation status.We found that plant and animal diversity and primary production are the most studied ecosystem services,while climate regulation,water supply,nutrient cycling,meat production and erosion control,in that order,are less studied.Cultural services are under-researched.Continuous grazing and glyphosate spraying are the main drivers of grassland degradation.Controlled grazing and conservative stocking rates have been shown to reverse degradation and demonstrate that livestock production is compatible with ecosystem conserva-tion by maintaining regulating and provisioning services.As these management strategies are poorly integrated,improving their implementation will require important changes in farmers’decisions and the development of policies that create the economic conditions for this to happen.Research is needed to understand the conditions that prevent the knowledge generated from being transferred to producers and translated into practices that would improve the provision of ecosystem services.

Prediction of Compressive Strength of Self-Compacting Concrete Using Intelligent Computational Modeling

In the present scenario,computational modeling has gained much importance for the prediction of the properties of concrete.This paper depicts that how computational intelligence can be applied for the prediction of compressive strength of Self Compacting Concrete(SCC).Three models,namely,Extreme Learning Machine(ELM),Adaptive Neuro Fuzzy Inference System(ANFIS)and Multi Adaptive Regression Spline(MARS)have been employed in the present study for the prediction of compressive strength of self compacting concrete.The contents of cement(c),sand(s),coarse aggregate(a),fly ash(f),water/powder(w/p)ratio and superplasticizer(sp)dosage have been taken as inputs and 28 days compressive strength(fck)as output for ELM,ANFIS and MARS models.A relatively large set of data including 80 normalized data available in the literature has been taken for the study.A comparison is made between the results obtained from all the above-mentioned models and the model which provides best fit is established.The experimental results demonstrate that proposed models are robust for determination of compressive strength of self-compacting concrete.