Kernel Density Analysis of Parcel Size and Shapes Before and After Land Consolidation: A Case Study from Aşağısümenli Village in Malatya, Turkey

Year 2020, Volume: 26 Issue: 4, 388 - 394, 04.12.2020

Fırat Arslan , Hasan Değirmenci , Sinan Kartal

https://doi.org/10.15832/ankutbd.534337

Cited By: 3

Abstract

Land consolidation (LC) projects are a set of applications that improve the economics of enterprises by assembling fragmented, dispersed, and irregular parcels. As the parcel densities coalesce around the village centre, the operation becomes easier, and fuel costs are reduced. Besides, the size of the parcel is one of the most important factors that increase the income of the enterprises, as well as the plant pattern, agricultural production form, soil quality, talents, labour force and technology features. The aim of the current study conducted within Aşağısümenli LC project in Malatya, Turkey, was to assess the density of small parcels around the village centre by using kernel density analysis as one of the geospatial analyses and to investigate the spatial distribution of irregular parcels with shape index. To identify the smallest parcels spatial distribution, 50%, 75% and 90% bandwidths were determined. Before LC, the average parcel area within 50%, 75% and 90% bandwidth was 0.69, 0.93 and 1.07 ha; after LC was 0.89, 1.45 and 1.63 ha, respectively. The area averages of parcels between 50% and 75% bandwidths before LC were 1.79 between 75% and 90% bandwidths and 4.77 ha out of 90% bandwidth; after LC, 1.60, 2.47 and 3.13 ha, respectively. As a result, the small parcels after LC were more concentrated around the village centre than before LC. Moreover, it can be said that the density of the small rectangular shaped parcels around the centre of the village is a positive result in terms of reducing the operation cost. 

Keywords

GIS, Kernel density analysis, Geospatial analysis, Land consolidation

References

• Alp A, Akyüz A, Özcan M & Yerlі S V (2018). Assessment of movements and habitat use of Salmo opimus in Fırnız stream, river Ceyhan of Turkey using radio telemetry techniques. Environmental Biology of Fishes, 11: 1-12
• ArcGIS (2018). Kernel Density. http://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/how-kernel-density-works.htm (Last access date: 15.10.2018)
• Aslan T, Gündoğdu K & Arıcı İ (2007). Some Metric Indices for the Assessment of Land Consolidation Projects. Pakistan Journal of Biological Sciences, 10(9): 1390-1397
• Assuncao J J & Ghatak M (2003). Can unobserved heterogeneity in farmer ability explain theinverse relationship between farm size and productivity?. Economics Letters, 80(2): 189-194
• Barrett C B, Bellemare M F & Hou J Y (2010). Reconsidering conventional explanations of the inverse productivity–size relationship. World Development, 38(1): 88-97 Bayram R & Değirmenci H (2018). Analysis of Parsel Shapes in Land Consolidation Projects: A Case Study of Niğde Misli Plain 2. Kısım. KSU J. Agric Nat., 21(4): 500-510
• Boztoprak T, Demir O, Çoruhlu Y E & Nişancı R (2015). Investigating the Land Consolidations’ Effects on Agricultural Enterprises. Selcuk Univ. J. Eng. Sci. Tech., 3(3): 1-9
• Cai X, Wu Z & Cheng J (2013). Using kernel density estimation to assess the spatial pattern of road density and its impact on landscape fragmentation. International Journal of Geographical Information Science, 27(2): 222-230
• Carmona A, Nahuelhual L, Echeverría C & Báez A (2010). Linking farming systems to landscape change: an empirical and spatially explicit study in southern Chile. Agriculture, Ecosystems & Environment, 139(1): 40-50
• Değirmenci H, Arslan F, Tonçer R & Yoğun E (2017). Evaluation of Land Fragmentation Parcel Shapes before Land Consolidation Project: A Case Study of Tırhan Village in Niğde Misli Plain. Journal of Agricultural Faculty of Gaziosmanpasa University, 34(3): 182-189
• Demetriou D, See L & Stillwell J (2013). A parcel shape index for use in land consolidation planning. Transactions in GIS, 17(6), 861-882
• Demirtaş E I & Sarı M (2003). Arazi Toplulaştırması (Land consolidation). Derim, 20(1): 48-58
• ESRI 2019. How Kernel Density Works. Retreived in February, 27, 2019 from https://pro.arcgis.com/en/pro-app/tool-reference/spatial-analyst/how-kernel-density-works.htm
• Gökçe O & Adanacıoğlu H (2002). An Investigation on Determining of Optimum Farm Size in Agriculture. Processding of the Turkey 5. Agricultural Economics Congress, 12-18 September, Erzurum, pp. 97-101
• Jiao L, Liu Y & Li H (2012). Characterizing land-use classes in remote sensing imagery by shape metrics. ISPRS, Journal of Photogrammetry and Remote Sensing, 72: 46-55
• Kakwagh V V, Aderonmu J A & Ikwuba A (2011). Land Fragmentation and Agricultural Development in Tivland of Benue State, Nigeria. Current Research Journal of Social Sciences, 3(2): 54-58
• Kirmikil M, Aslan S, Gundogdu K S & Arici I (2017). The Hidden Fragmentation after Land Consolidation in Turkey. Fresenius Environmental Bulletin, 26(10): 5882
• Kumbasaroğlu H & Dağdemir V (2007). Economic Analysis of Farms With Respect to Land Fragmentation in Central District of Erzurum Province. Atatürk University Journal of the Agricultural Faculty, 38(1): 49-58
• Kwinta A & Gniadek J (2017). The description of parcel geometry and its application in terms of land consolidation planning. Computers and Electronics in Agriculture, 136: 117-124
• Looga J, Jürgenson E, Sikk K, Matveev E & Maasikamäe S (2018). Land fragmentation and other determinants of agricultural farm productivity: The case of Estonia. Land Use Policy, 79: 285-292
• Lu H, Xie H, He Y, Wu Z & Zhang X (2018). Assessing the impacts of land fragmentation and plot size on yields and costs: A translog production model and cost function approach. Agricultural Systems, 161: 81-88
• Meteorological Service (2018). Malatya Long Years Climate Data. Ankara, Turkey.
• Sirirwardane M S, Samanmali M A D & Rathnayake R N (2015). Cloud Based GIS Approach for Monitoring Environmental Pollution in the Coastal Zone of Kalutara, Sri Lanka. Journal of Tropical Forestry and Environment, 5(1): 9-18
• Xie Z & Yan J (2008). Kernel density estimation of traffic accidents in a network space. Computers, environment and urban systems, 32(5): 396-40