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      A new analytical framework of farming system and agriculture model diversities. A review

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          Farming and the fate of wild nature.

          World food demand is expected to more than double by 2050. Decisions about how to meet this challenge will have profound effects on wild species and habitats. We show that farming is already the greatest extinction threat to birds (the best known taxon), and its adverse impacts look set to increase, especially in developing countries. Two competing solutions have been proposed: wildlife-friendly farming (which boosts densities of wild populations on farmland but may decrease agricultural yields) and land sparing (which minimizes demand for farmland by increasing yield). We present a model that identifies how to resolve the trade-off between these approaches. This shows that the best type of farming for species persistence depends on the demand for agricultural products and on how the population densities of different species on farmland change with agricultural yield. Empirical data on such density-yield functions are sparse, but evidence from a range of taxa in developing countries suggests that high-yield farming may allow more species to persist.
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            Ecological intensification: harnessing ecosystem services for food security.

            Rising demands for agricultural products will increase pressure to further intensify crop production, while negative environmental impacts have to be minimized. Ecological intensification entails the environmentally friendly replacement of anthropogenic inputs and/or enhancement of crop productivity, by including regulating and supporting ecosystem services management in agricultural practices. Effective ecological intensification requires an understanding of the relations between land use at different scales and the community composition of ecosystem service-providing organisms above and below ground, and the flow, stability, contribution to yield, and management costs of the multiple services delivered by these organisms. Research efforts and investments are particularly needed to reduce existing yield gaps by integrating context-appropriate bundles of ecosystem services into crop production systems. Copyright © 2012 Elsevier Ltd. All rights reserved.
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              Functional landscape heterogeneity and animal biodiversity in agricultural landscapes.

              Biodiversity in agricultural landscapes can be increased with conversion of some production lands into 'more-natural'- unmanaged or extensively managed - lands. However, it remains unknown to what extent biodiversity can be enhanced by altering landscape pattern without reducing agricultural production. We propose a framework for this problem, considering separately compositional heterogeneity (the number and proportions of different cover types) and configurational heterogeneity (the spatial arrangement of cover types). Cover type classification and mapping is based on species requirements, such as feeding and nesting, resulting in measures of 'functional landscape heterogeneity'. We then identify three important questions: does biodiversity increase with (1) increasing heterogeneity of the more-natural areas, (2) increasing compositional heterogeneity of production cover types and (3) increasing configurational heterogeneity of production cover types? We discuss approaches for addressing these questions. Such studies should have high priority because biodiversity protection globally depends increasingly on maintaining biodiversity in human-dominated landscapes. © 2010 Blackwell Publishing Ltd/CNRS.
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                Author and article information

                Journal
                Agronomy for Sustainable Development
                Agron. Sustain. Dev.
                Springer Nature
                1774-0746
                1773-0155
                June 2017
                June 6 2017
                June 2017
                : 37
                : 3
                Article
                10.1007/s13593-017-0429-7
                13ea3054-eaca-4cfa-8787-6e6326322449
                © 2017

                http://creativecommons.org/licenses/by/4.0

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