Journals / Tarım Bilimleri Dergisi / 2020 / Volume: 26 Issue: 4

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

Fırat Arslan(KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ, BİYOSİSTEM MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-7168-226X
Hasan Değirmenci(KAHRAMANMARAŞ SÜTÇÜ İMAM ÜNİVERSİTESİ, ZİRAAT FAKÜLTESİ, BİYOSİSTEM MÜHENDİSLİĞİ BÖLÜMÜ)0000-0002-6157-816X
Sinan Kartal(AKDENİZ ÜNİVERSİTESİ, KUMLUCA MESLEK YÜKSEKOKULU, BİTKİSEL VE HAYVANSAL ÜRETİM BÖLÜMÜ)0000-0002-9600-8052
Pages
—
Publication Date
2020-12-04
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

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.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Land consolidation (LC) projects are a set of applications that improve the economics of enterprises by assembling fragmented, dispersed, and irregular parcels.
📊
Key Findings & Data: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.
💡
Conclusion & Impact: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.
🔬Design:Research Article
📍Scope:Comprehensive Sample
⚙️Variables:GIS · Kernel density analysis · Geospatial analysis

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.

🔖

Cite This Article

Arslan, F., Değirmenci, H., Kartal, S. (2020). Kernel Density Analysis of Parcel Size and Shapes Before and After Land Consolidation: A Case Study from Aşağısümenli Village in Malatya, Turkey. Tarım Bilimleri Dergisi, Volume: 26 Issue: 4. https://doi.org/10.15832/ankutbd.534337

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (23)

  1. 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
  2. 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)
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. Demetriou D, See L & Stillwell J (2013). A parcel shape index for use in land consolidation planning. Transactions in GIS, 17(6), 861-882
  11. Demirtaş E I & Sarı M (2003). Arazi Toplulaştırması (Land consolidation). Derim, 20(1): 48-58
  12. 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
  13. 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
  14. 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
  15. 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
  16. Kirmikil M, Aslan S, Gundogdu K S & Arici I (2017). The Hidden Fragmentation after Land Consolidation in Turkey. Fresenius Environmental Bulletin, 26(10): 5882
  17. 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
  18. 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
  19. 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
  20. 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
  21. Meteorological Service (2018). Malatya Long Years Climate Data. Ankara, Turkey.
  22. 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
  23. Xie Z & Yan J (2008). Kernel density estimation of traffic accidents in a network space. Computers, environment and urban systems, 32(5): 396-40