11
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Evaluating and predicting the effectiveness of farmland consolidation on improving agricultural productivity in China

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Food security has always been a focus issue in China. Farmland consolidation (FC) was regarded as a critical way to increase the quantity and improve the quality of farmland to ensure food security by Chinese government. FC projects have been nationwide launched, however few studies focused on evaluating the effectiveness of FC at a national scale. As such, an efficient way to evaluate the effectiveness of FC on improving agricultural productivity in China will be needed and it is critical for future national land consolidation planning. In this study, we selected 7505 FC projects completed between 2006 and 2013 with good quality Normalized Difference Vegetation Index (NDVI) as samples to evaluate the effectiveness of FC. We used time-series Moderate Resolution Imaging Spectroradiometer NDVI from 2001 to 2013, to extract four indicators to characterize agricultural productivity change of 4442 FC projects completed between 2006 and 2010, i.e., productivity level (PL), productivity variation (PV), productivity potential (PP), and multi-cropping index (MI). On this basis, we further predicted the same four characteristics for 3063 FC projects completed between 2011 and 2013, respectively, using Support Vector Machines (SVM). We found FC showed an overall effective status on improving agricultural productivity between 2006 and 2013 in China, especially on upgrading PL and improving PP. The positive effect was more prominent in the southeast and eastern China. It is noteworthy that 27.30% of all the 7505 projects were still ineffective on upgrading PL, the elementary improvement of agricultural productivity. Finally, we proposed that location-specific factors should be taken into consideration for launching FC projects and diverse financial sources are also needed for supporting FC. The results provide a reference for government to arrange FC projects reasonably and to formulate land consolidation planning in a proper way that better improve the effectiveness of FC.

          Related collections

          Most cited references61

          • Record: found
          • Abstract: not found
          • Article: not found

          Support vector machines in remote sensing: A review

            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            Constraints and potentials of future irrigation water availability on agricultural production under climate change.

            We compare ensembles of water supply and demand projections from 10 global hydrological models and six global gridded crop models. These are produced as part of the Inter-Sectoral Impacts Model Intercomparison Project, with coordination from the Agricultural Model Intercomparison and Improvement Project, and driven by outputs of general circulation models run under representative concentration pathway 8.5 as part of the Fifth Coupled Model Intercomparison Project. Models project that direct climate impacts to maize, soybean, wheat, and rice involve losses of 400-1,400 Pcal (8-24% of present-day total) when CO2 fertilization effects are accounted for or 1,400-2,600 Pcal (24-43%) otherwise. Freshwater limitations in some irrigated regions (western United States; China; and West, South, and Central Asia) could necessitate the reversion of 20-60 Mha of cropland from irrigated to rainfed management by end-of-century, and a further loss of 600-2,900 Pcal of food production. In other regions (northern/eastern United States, parts of South America, much of Europe, and South East Asia) surplus water supply could in principle support a net increase in irrigation, although substantial investments in irrigation infrastructure would be required.
              Bookmark
              • Record: found
              • Abstract: not found
              • Article: not found

              Seasonality extraction by function fitting to time-series of satellite sensor data

                Bookmark

                Author and article information

                Contributors
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ResourcesRole: SoftwareRole: ValidationRole: VisualizationRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Formal analysisRole: Funding acquisitionRole: MethodologyRole: Project administrationRole: ResourcesRole: SupervisionRole: ValidationRole: Writing – review & editing
                Role: Data curationRole: InvestigationRole: ResourcesRole: Supervision
                Role: MethodologyRole: SoftwareRole: ValidationRole: Visualization
                Role: InvestigationRole: ResourcesRole: Software
                Role: Data curationRole: ResourcesRole: Supervision
                Role: Funding acquisitionRole: Project administrationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                6 June 2018
                2018
                : 13
                : 6
                : e0198171
                Affiliations
                [1 ] School of Geographic and Oceanographic Sciences, Nanjing University, Nanjing, Jiangsu, China
                [2 ] Key Laboratory of Coastal Zone Exploitation and Protection, Ministry of Land and Resources, Nanjing, Jiangsu, China
                [3 ] Natural Resources Research Center, Nanjing University, Nanjing, Jiangsu, China
                [4 ] China Land Surveying and Planning Institute, Ministry of land and resources, Beijing, China
                University of Bonn, GERMANY
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0003-0001-1642
                http://orcid.org/0000-0003-1862-5837
                Article
                PONE-D-17-17128
                10.1371/journal.pone.0198171
                5991407
                29874258
                d3d7045f-1db4-4887-a45c-992ca47efcb2
                © 2018 Fan et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 4 May 2017
                : 15 May 2018
                Page count
                Figures: 9, Tables: 3, Pages: 20
                Funding
                Funded by: National Science Technology Support Plan Projects of China
                Award ID: 2015BAD06B02
                Award Recipient :
                This work was supported by a grant from the National Science Technology Support Plan Projects of China (2015BAD06B02).
                Categories
                Research Article
                People and Places
                Geographical Locations
                Asia
                China
                Computer and Information Sciences
                Artificial Intelligence
                Machine Learning
                Support Vector Machines
                Biology and Life Sciences
                Agriculture
                Agricultural Soil Science
                Ecology and Environmental Sciences
                Soil Science
                Agricultural Soil Science
                Biology and Life Sciences
                Agriculture
                Crop Science
                Crops
                Biology and Life Sciences
                Agriculture
                Biology and Life Sciences
                Agriculture
                Agricultural Production
                Engineering and Technology
                Biology and Life Sciences
                Population Biology
                Population Metrics
                Population Density
                Custom metadata
                All relevant data are within the paper and its Supporting Information file.

                Uncategorized
                Uncategorized

                Comments

                Comment on this article