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      The evaluation of land consolidation policy in improving agricultural productivity in China

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          Abstract

          China is presently undergoing rapid economic development and unprecedented urbanization. Concerns over food security have prompted the Chinese government to implement large-scale land consolidation projects. However, no formal evaluation has been conducted on such projects. Thus, effectiveness of land consolidation policy remains uncertain. We obtained detailed geo-spatial information for 5328 land consolidation projects implemented between 2006 and 2010, and used time-series MODIS NDVI (2006–2016) data to assess effectiveness of China’s land consolidation policy in improving agricultural productivity. Our results show that the overall effectiveness of land consolidation in improving agricultural productivity is low, which lies in contrast to optimistic estimates based on regional statistical analysis and theoretical approaches. For projects (n = 560) implemented in 2006, about 29.5% showed significant ( p < 0.05) increasing trends of MODIS NDVI after implementation of land consolidation. For 2007–2010, lower percentages (e.g., 25.9% in 2007 and 13.5% in 2010) of projects showed significant increasing trends. Furthermore, we found effectiveness of land consolidation projects displayed clear regional differences and driving factors are inconsistent with policy design. We anticipate our research to be a starting point for a more comprehensive evaluation involving longer time-series and higher spatial resolution data.

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          Most cited references 35

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          Generalized linear mixed models: a practical guide for ecology and evolution.

          How should ecologists and evolutionary biologists analyze nonnormal data that involve random effects? Nonnormal data such as counts or proportions often defy classical statistical procedures. Generalized linear mixed models (GLMMs) provide a more flexible approach for analyzing nonnormal data when random effects are present. The explosion of research on GLMMs in the last decade has generated considerable uncertainty for practitioners in ecology and evolution. Despite the availability of accurate techniques for estimating GLMM parameters in simple cases, complex GLMMs are challenging to fit and statistical inference such as hypothesis testing remains difficult. We review the use (and misuse) of GLMMs in ecology and evolution, discuss estimation and inference and summarize 'best-practice' data analysis procedures for scientists facing this challenge.
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            Food security: the challenge of feeding 9 billion people.

            Continuing population and consumption growth will mean that the global demand for food will increase for at least another 40 years. Growing competition for land, water, and energy, in addition to the overexploitation of fisheries, will affect our ability to produce food, as will the urgent requirement to reduce the impact of the food system on the environment. The effects of climate change are a further threat. But the world can produce more food and can ensure that it is used more efficiently and equitably. A multifaceted and linked global strategy is needed to ensure sustainable and equitable food security, different components of which are explored here.
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              Using the satellite-derived NDVI to assess ecological responses to environmental change.

              Assessing how environmental changes affect the distribution and dynamics of vegetation and animal populations is becoming increasingly important for terrestrial ecologists to enable better predictions of the effects of global warming, biodiversity reduction or habitat degradation. The ability to predict ecological responses has often been hampered by our rather limited understanding of trophic interactions. Indeed, it has proven difficult to discern direct and indirect effects of environmental change on animal populations owing to limited information about vegetation at large temporal and spatial scales. The rapidly increasing use of the Normalized Difference Vegetation Index (NDVI) in ecological studies has recently changed this situation. Here, we review the use of the NDVI in recent ecological studies and outline its possible key role in future research of environmental change in an ecosystem context.
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                Author and article information

                Contributors
                jinxb@nju.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                5 June 2017
                5 June 2017
                2017
                : 7
                Affiliations
                [1 ]ISNI 0000 0001 2314 964X, GRID grid.41156.37, School of Geographic and Oceanographic Sciences, , Nanjing University, ; Nanjing, 210023 China
                [2 ]ISNI 0000 0001 0694 4940, GRID grid.438526.e, Department of Geography, , Virginia Polytechnic Institute and State University, ; Blacksburg, VA 24061 USA
                [3 ]GRID grid.464286.a, , China Land Surveying and Planning Institute, ; Beijing, 10029 China
                [4 ]ISNI 0000 0001 2150 1785, GRID grid.17088.36, Department of Geography, , Michigan State University, ; East Lansing, MI 48824 USA
                3026
                10.1038/s41598-017-03026-y
                5459834
                © The Author(s) 2017

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

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