Vol. 117 No. 1 (2023)
Research Papers

Assessment of farmers’ preferences for growing particular crops and the correlation with land suitability

PDF
  • Risma Neswati,
  • Nurfadila Jamaluddin Suppe,
  • Sumbangan Baja,
  • Didi Rukmana
Risma Neswati
Department of Soil science, Hasanuddin University, Indonesia
Nurfadila Jamaluddin Suppe
Agricultural Science Study Program, Post Graduate School Hasanuddin University, Indonesia
Sumbangan Baja
Department of Soil science, Hasanuddin University, Indonesia
Didi Rukmana
Departments of Agriculture Socio-Economics, Hasanuddin University, Indonesia
DOI: https://doi.org/10.36253/jaeid-14182

Published 2023-06-29

Keywords

  • agricultural land management,
  • crop diversification,
  • land suitability,
  • farmer’s preferences,
  • discrete choice experiments,
  • fuzzy method,
  • , the analytic hierarchy process
    • ...More
    Less

How to Cite

Neswati, R., Jamaluddin Suppe, N., Baja, S., & Rukmana, D. (2023). Assessment of farmers’ preferences for growing particular crops and the correlation with land suitability . Journal of Agriculture and Environment for International Development (JAEID), 117(1), 85–116. https://doi.org/10.36253/jaeid-14182

Copyright (c) 2023 Risma Neswati, Nurfadila Jamaluddin Suppe, Sumbangan Baja, Didi Rukmana

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Funding data

Crossref
Scopus
Google Scholar
Europe PMC

Abstract

The success of agricultural operations is highly dependent on the site selected, which affects sustainability, and it is important to solve problems associated with activities and efficient land use. However, many researchers have selected sites based solely on climate and soil characteristics and have ignored farmer preferences, which has resulted in the failure to meet sustainable agriculture goals, and a proper strategy is therefore required to anticipate related problems. This study was conducted to: (1) analyze plantation development priorities based on the hierarchy of farmers’ preferences, (2) identify the relationship between successful plantations, climate, and soil fertility. The attributes employed to assess farmers’ preferences included price, production, and price stability over the past five years, while annual rainfall, annual temperature, and soil fertility were used to assess land suitability. Farmers’ preferences were analyzed using the discrete choice experiment (DCE) method, and land suitability was analyzed using the fuzzy method. The farmer preference analysis showed that coffee was the priority crop of farmers in most of the research areas, and cocoa was the lowest cultivation priority. Coffee had a higher land suitability index than other plants, ranging from 0.62 to 0.92, and it was dominant within the optimal suitability class. Clove, pepper, and cocoa plants belonged to the moderate land suitability class with indexes of 0.6–0.91, 0.56–0.88, and 0.4–0.86 for pepper, clove, and cocoa, respectively. A regression analysis was conducted to determine the relationship between the priority of cultivated plants based on farmers’ preference and land suitability, and a positive relationship (moderate strength) was determined. These research results show that when selecting priority crops, 21% of farmers’ decisions are influenced by land suitability.

PDF

References

  1. Acheampong, P.P., Owusu, V., & Nurah, G. (2018). How does farmer preference matter in crop variety adoption? The case of improved cassava varieties' adoption in Ghanaians. Open Agriculture, 3, 466-477. https://doi.org/10.1515/opag-2018-0052 DOI: https://doi.org/10.1515/opag-2018-0052
  2. Aguilar, R.N., Lagunes, S.R., Michel, C., & Trujillo, M.A. (2019). Upgrading Comparative and Competitive Advantages for Ethanol Fuel Production From Agroindustrial Crops in Developing Countries: Mexico as a Case Study. Bioethanol Production from Food Crops, Sustainable Sources, Interventions, and Challenges, 20, 401-415. http://doi.org/10.1016/B978-0-12-813766-6.00020-5 DOI: https://doi.org/10.1016/B978-0-12-813766-6.00020-5
  3. Akinci, H., Ozalp, A.Y., & Turgut B. (2013). Agriculture land use suitability analysis using GIS and AHP techniques. Computers and Electronics in Agriculture, 97, 71–82. DOI: https://doi.org/10.1016/j.compag.2013.07.006
  4. https://doi.org/10.1016/j.compag.2013.07.006
  5. Alpizar, R.F., Carlsson, F., & Martinsson, P. (2003). Using Choice Experiments for Non- market valuation. Economic Issues, 8(1), 83-109.
  6. https://EconPapers.repec.org/RePEc:hhs:gunwpe:0052
  7. Antwi, E.K., Setiawati, M.D., Tetteh, J.D., Boakye-Danquah, J., Owusu, B.W., & Yohuno, P.T. (2022). Agricultural Land Suitability Assessment towards Promoting Community Crop Production in Tolon-Ghana. Research Square. https://doi.org/10.21203/rs.3.rs-1733563/v2 DOI: https://doi.org/10.21203/rs.3.rs-1733563/v2
  8. Asrat, S., Yesuf, M., Carlsson, F., & Wale E. (2010). Farmers' Preferences for Crop Variety Traits, Lessons for On-Farm Conservation and Technology Adoption. Ecological Economics, 69 (12), 2394-2401. https://doi.org/10.1016/j.ecolecon.2010.07.006. DOI: https://doi.org/10.1016/j.ecolecon.2010.07.006
  9. Blasi, C., Zavattero, L., Marignani, M., Smiraglia, D., & Copiz, R. (2008). The concept of land ecological network and its design using a land unit approach. Plant Biosystems, 142(3), 540-549. http://doi.org/10.1080/1126350080241089 DOI: https://doi.org/10.1080/11263500802410892
  10. Cartwright, J.M., Dwire, K.A., Freed, Z., Hammer, S.J., McLaughlin, B., Misztal L.W., ... & Stevens L.E. (2020). Oases of the future? Springs as potential hydrologic refugia in drying climates. Frontiers in Ecology and the Environment, 18(5), 245-253. https://doi.org/10.1002/fee.2191 DOI: https://doi.org/10.1002/fee.2191
  11. Cheng, C., Cheng, S., Wen, J., & Lee, J. (2012). Effects of Raingauge Distribution on Estimation Accuracy of Areal Rainfall. Water Resources Management, 26, 1-20. http://doi.org/10.1007/s11269-011-9898-7 DOI: https://doi.org/10.1007/s11269-011-9898-7
  12. CIAT. (2008). Bean Improvement for the Tropics, Annual Report I-P1. Centro Internacional de Agricultura Tropical, calico, Colombia.
  13. Chu, L., Zou, Y., Masiliūnas, D., Blaschke, T., & Verbesselt, J. (2021). Assessing the impact of bridge construction on the land use/cover and socioeconomic indicator time series, A case study of Hangzhou Bay Bridge. GIScience & Remote Sensing, 58(2), 199-216. DOI: https://doi.org/10.1080/15481603.2020.1868212
  14. https://doi.org/10.1080/15481603.2020.1868212
  15. Convertino, M., Baker, K.M., Vogel, J.T., Lu, C., Suedel, B., & Linkov, I. (2013). Multicriteria decision analysis to select metrics for designing and monitoring sustainable ecosystem restorations. Ecological Indicator, 26, 76-86. DOI: https://doi.org/10.1016/j.ecolind.2012.10.005
  16. https://doi.org/10.1016/j.ecolind.2012.10.005
  17. Edmeades, S., Phaneuf, D.J., Smale, M., & Renkow, M. (2008). Modeling the Crop Variety Demand of Semi-Subsistence Households, Bananas in Uganda. Jurnal of Agricultural Economics, 59, 329-349. DOI:10.1111/j.1477-9552.2007.00153.x DOI: https://doi.org/10.1111/j.1477-9552.2007.00153.x
  18. Eitzinger, A., Binder, C., & Meyer, M. (2018). Risk perception and decision-making, do farmers consider risks from climate change?. Climatic Change, 151, 507-524. https://doi.org/10.1007/s10584-018-2320-1 DOI: https://doi.org/10.1007/s10584-018-2320-1
  19. FAO. (1976). A Framework for Land Evaluation. Food and Agriculture Organization, Soil Bulletin 32, Rome, Italy. https://www.fao.org/3/x5310e/x5310e00.htm
  20. Feizizadeh, B., & Blaschke, T., (2013). Land suitability analysis for Tabriz County, Iran, a multicriteria evaluation approach using GIS. Journal of Environmental Planning and Management, 56 (1), 1-23. https://doi.org/10.1080/09640568.2011.646964 DOI: https://doi.org/10.1080/09640568.2011.646964
  21. Feizizadeh, B., Jankowski, P., & Blaschke, T. (2014). A GIS based spatially-explicit sensitivity and uncertainty analysis approach for multicriteria decision analysis. Computer Geoscience, 64, 81–95. https://doi.org/10.1016/j.cageo.2013.11.009 DOI: https://doi.org/10.1016/j.cageo.2013.11.009
  22. Fernandez, C., Leon, C.J., Steel, M.F.J., & Polo F.J.V. (2004). Bayesian analysis of interval data contingent valuation models and pricing policies. Journal of Business & Economic Statistics, 22 (4), 431-442. https://www.jstor.org/stable/1392049 DOI: https://doi.org/10.1198/073500104000000415
  23. Friedrich, T., Derpsch, R., & Kassam, A. (2012). Overview of the global spread of conservation agriculture. Field Actions Science Reports, The journal of field actions, Special Issue 6. http://journals.openedition.org/factsreports/1941
  24. Garcia, J.L., Alvarado, A., Blanco, J., Jimenez, E., Maldonado, A.A., & Cortés, G. (2014). Multi-attribute evaluation and selection of sites for agricultural product warehouses based on an analytic hierarchy process. Computers and Electronics Agriculture, 100, 60-69. https://doi.org/10.1016/j.compag.2013.10.009 DOI: https://doi.org/10.1016/j.compag.2013.10.009
  25. Gay, L.R. & Diehl, P.L. (1992). Research Methods for Business and Management. Macmillan Publishing Company, New York, Macmillan.
  26. Gebre, S.L., Cattrysse, D., Alemayehu, E., & Orshoven, J.V. (2021). Multicriteria decision-making methods to address rural land allocation problems, A systematic review. International Soil and Water Conservation Research, 9, 490-501. DOI: https://doi.org/10.1016/j.iswcr.2021.04.005
  27. https://doi.org/10.1016/j.iswcr.2021.04.005
  28. Geo, L., & Saediman, H. (2019). Assessing Factors Affecting Cocoa Development in Southeast Sulawesi. Pakistan Journal of Nutrition, 18(5), 479-490. DOI: https://doi.org/10.3923/pjn.2019.479.490
  29. http://dx.doi.org/10.3923/pjn.2019.479.490
  30. Ginige, A., Walisadeera, A.I., Ginige, T., Silva, L.D., Di Giovanni P., Mathai M., ... & Jain R. (2016). Digital Knowledge Ecosystem for Achieving Sustainable Agriculture Production, A Case Study from Sri Lanka. International Conference on Data Science and Advanced Analytics (DSAA), pp. 602-611, DOI: https://doi.org/10.1109/DSAA.2016.82
  31. http://doi.org/10.1109/DSAA.2016.82.
  32. Giordano, R., & Liersch, S., (2012). A fuzzy GIS-based system to integrate local and technical knowledge in soil salinity monitoring. Environmental Modelling & Software, 36,49-63. https://doi.org/10.1016/j.envsoft.2011.09.004 DOI: https://doi.org/10.1016/j.envsoft.2011.09.004
  33. Gong, J., Liu, Y., & Chen, W., (2012). Land suitability evaluation for development using a matter-elemnt model, A case study in Zengcheng, Guang zhou, China. Land Use Policy, 29, 464-472. https://doi.org/10.1016/j.landusepol.2011.09.005 DOI: https://doi.org/10.1016/j.landusepol.2011.09.005
  34. Hanemann, W. M. (1991). Willingness to pay and willingness to accept, How much can they differ?. The American Economic Review, 81(3), 635-647.
  35. https://www.jstor.org/stable/2006525
  36. Isaac, S., & Michael, W.B. (1995). Handbook in research and evaluation, A collection of principles, methods, and strategies useful in the planning, design, and evaluation of studies in education and the behavioral sciences, 3rd ed. San Diego. EdITS Publishers.
  37. Johnson, R., & Orme, B. (2003). Getting the Most from CBC. Sawtooth Software Research Paper Series. https://sawtoothsoftware.com/resources/technical-papers/getting-the-most-from-cbc
  38. Khan, D. (2008). Managing Risk In Farming, Farm Management Extension Guide 3. Food and Agriculture Organization, Rome, Italy. https://www.fao.org/uploads/media/3-ManagingRiskInternLores.pdf
  39. Keshavarzi, A., Tuffour, H.O., Bagherzadeh, A., Tattrah, L.P., Kumar, V., Gholizadeh, A., & Comino, J.R. (2020). Using fuzzy-AHP and parametric technique to assess soil fertility status in Northeast of Iran. Journal of Mountain Science, 17(4), 931-948. https://doi.org/10.1007/s11629-019-5666-6 DOI: https://doi.org/10.1007/s11629-019-5666-6
  40. Leake, C., & Malczewski J. (2000). GIS and multicriteria decision analysis. Journal of the Operational Research Society, 51(2), 247. http://www.jstor.org/stable/254268 DOI: https://doi.org/10.2307/254268
  41. Lee, J., Kim, S., & Jun, H. (2018). A Study of the Influence of the Spatial Distribution of Rain Gauge Networks on Areal Average Rainfall Calculation. Water, 10(11), 1635. https://doi.org/10.3390/w10111635 DOI: https://doi.org/10.3390/w10111635
  42. Liu, Y., Jiao, L., Liu, Y., & He, J. (2013). A self-adapting fuzzy inference system for the evaluation of agricultural land. Environmental modelling & software, 40, 226-234. DOI: https://doi.org/10.1016/j.envsoft.2012.09.013
  43. https://doi.org/10.1016/j.envsoft.2012.09.013
  44. Louviere, J.J., Hensher, D.A., Swait, J.D. & Adamowicz, W.L. (2000). Stated Choice Methods, Analysis and Applications. Cambridge University Press, 8, 227-251. DOI: https://doi.org/10.1017/CBO9780511753831
  45. http://dx.doi.org/10.1017/CBO9780511753831.008
  46. Malczewski, J. (2006). GIS-based multicriteria decision analysis, a survey of the literature. International Journal of Geographical Information Science, 20(7), 703-726. DOI: https://doi.org/10.1080/13658810600661508
  47. https://doi.org/10.1080/13658810600661508
  48. McFadden, D. (1973). Conditionial logit analysis of qualitative choice behavior. Institute of Urban and Regional Development, University of California.
  49. https://eml.berkeley.edu/reprints/mcfadden/zarembka.pdf
  50. Moranco, A.B., Eugenio, A.M.F., & Salazar, S.D.S. (2005). A comparison of empirical models used to infer the willingness to pay in contingent valuation. Empirical Economics, 30, 235-244. http://dx.doi.org/10.1007/s00181-005-0236-x DOI: https://doi.org/10.1007/s00181-005-0236-x
  51. Mosadeghi, R., Warnken, J., Tomlinson, R., & Mirfenderesk, H. (2015). Comparison of Fuzzy-AHP and AHP in a spatial multicriteria decision making model for urban land-use planning. Computers, Environment and Urban Systems, Vol 49, 54-65. https://doi.org/10.1016/j.compenvurbsys.2014.10.001 DOI: https://doi.org/10.1016/j.compenvurbsys.2014.10.001
  52. Mosleh, Z., Salehi, M.H., Fasakhodi, A.A., Jafari, A., Mehnatkesh, A. & Borujeni, I.E. (2017). Sustainable allocation of agricultural lands and water resources using suitability analysis and mathematical multi-objective programming. Geoderma, 303, 52–59. https://doi.org/10.1016/j.geoderma.2017.05.015 DOI: https://doi.org/10.1016/j.geoderma.2017.05.015
  53. Murphy, J.J., Allen, P.G., Stevens, T.H. and Weatherhead D., (2005). A meta-analysis of hypothetical bias in stated preference valuation. Environmental and Resources Economics, 30, 313-325. https://doi.org/10.1007/s10640-004-3332-z DOI: https://doi.org/10.1007/s10640-004-3332-z
  54. Nasery, S., Matcı, D.K. & Avdan, U., (2021). GIS-based wind farm suitability assessment using fuzzy AHP multicriteria approach: the case of Herat, Afghanistan. Arabian Journal of Geosciences, 14, 1091. https://doi.org/10.1007/s12517-021-07478-5 DOI: https://doi.org/10.1007/s12517-021-07478-5
  55. Ochoa, P., Chamba, Y.M., Arteaga, J. & Capa, E.D. (2017). Estimation of Suitable Areas for Coffee Growth Using a GIS Approach and Multicriteria Evaluation in Regions with Scarce Data. Applied Engineering in Agriculture, 33, 841-848. http://dx.doi.org/10.13031/aea.12354 DOI: https://doi.org/10.13031/aea.12354
  56. Haider, P.U., Mostegl, N.M., Finan, K.J., Haider, W., Formayer, H., Kantelhardt, J., Moser, T., Kapfer, M., & Trenholm, R., (2016). Farmers' Preferences for Future Agriculture Land Use Under the Consideration of Climate Change. Environment Management, 58(3), 446-64. https://doi.org/10.1007/s00267-016-0720-4 DOI: https://doi.org/10.1007/s00267-016-0720-4
  57. Rindra, Y., Marimin, S., & Hardjomodjojo, H., (2020). Spatial Analysis for Crop Land Suitability Evaluation, A Case Study of Potatoes Cultivation in Wonosobo, Indonesia. International Seminar on Application for Technology of Information and Communication (iSemantic), pp. 313-319.
  58. https://doi.org/10.1109/iSemantic50169.2020.9234284
  59. Ritung, S., Nugroho, K., Mulyani, A., & Suryani E. (2011). Badan Penelitian dan Pengembangan Pertanian. Petunjuk Teknis Evaluasi Lahan untuk Komoditas Pertanian (in Indonesia). Balai Besar Penelitian dan Pengembangan Sumberdaya Lahan Pertanian. Bogor, Indonesia.
  60. Saaty, T.L. (1977). A scaling method for priorities in hierarchical structures. Journal of mathematical psychology, 15(3), 234-281. DOI: https://doi.org/10.1016/0022-2496(77)90033-5
  61. https://doi.org/10.1016/0022-2496(77)90033-5
  62. Saaty, T.L. (1980). The analytic hierarchy process. McGrawhill, Juc, New York. DOI: https://doi.org/10.21236/ADA214804
  63. Sengupta, S., Mohinuddin, S.K., Arif, M., Sengupta, B., & Zhang, W. (2022). Assessment of agricultural land suitability using GIS and fuzzy analytical hierarchy process approach in Ranchi District, India. Geocarto International, 37,26, 13337-13368. https://doi.org/10.1080/10106049.2022.2076925 DOI: https://doi.org/10.1080/10106049.2022.2076925
  64. Sichilima, T., Mapemba, L., & Tembo, G. (2016). Drivers of dry common beans trade in Lusaka, Zambia, a trader's perspective. Sustainable Agricultural Research, 5(2), 15-26. https://doi.org/10.22004/ag.econ.234990 DOI: https://doi.org/10.5539/sar.v5n2p15
  65. Sidhoum, A.A., Dakpo, K.H., & Latruffe, L. (2022). Trade-offs between economic, environmental and social sustainability on farms using a latent class frontier efficiency model, Evidence for Spanish crop farms. PloS one, 17(1), e0261190. https://doi.org/10.1371/journal.pone.0261190 DOI: https://doi.org/10.1371/journal.pone.0261190
  66. Smith, H.W., Ashworth, A.J., & Owens, P.R., (2022). GIS-Based Evaluation of Soil Suitability for Optimized Production on US Tribal Lands. Agriculture, 12(9), 1307. https://doi.org/10.3390/agriculture12091307 DOI: https://doi.org/10.3390/agriculture12091307
  67. Steiner, F., McSherry, L., & Cohen, J. (2000). Land suitability analysis for the Upper Gila River Watershed. Landscape Urban Planning, 50, 199–214. DOI: https://doi.org/10.1016/S0169-2046(00)00093-1
  68. https://doi.org/10.1016/S0169-2046(00)00093-1
  69. Stoms, D., McDonald, J. M. & Davis, F. W. (2002). Fuzzy assessment of land suitability for scientific research reserves. Environmental Management, 29, 545–558. https://doi.org/10.1007/s00267-001-0004-4 DOI: https://doi.org/10.1007/s00267-001-0004-4
  70. Subramanian, V., Semenzin, E., Hristozov, D., Marcomini, A. & Linkov, I. (2014). Sustainable nanotechnology, defining, measuring and teaching. Nano Today, 9(1), 6-9. https://doi.org/10.1016/j.nantod.2014.01.001 DOI: https://doi.org/10.1016/j.nantod.2014.01.001
  71. Swapan, P. & Ghosh, S. (2022). Identification of solid waste dumping site suitability of Kolkata Metropolitan Area using Fuzzy-AHP model. Cleaner Logistics and Supply Chain, 3, 100030. https://doi.org/10.1016/j.clscn.2022.100030 DOI: https://doi.org/10.1016/j.clscn.2022.100030
  72. Sys, C., Van, R.E., Debaveye, I.J. & Beernaert, F. (1993). Land evaluation. Part III, crop requirements. Agricultural Publication, Brussels, Belgium.
  73. Mehrjardi, T.R., Nabiollahi, K., Rasoli, L., Kerry, R., & Scholten, T. (2020). Land Suitability Assessment and Agricultural Production Sustainability Using Machine Learning Models. Agronomy, 10(4), 573. https://doi.org/10.3390/agronomy10040573 DOI: https://doi.org/10.3390/agronomy10040573
  74. Tatham, E., Eisenberg, D.A., & Linkov, I. (2014). Sustainable urban systems, a review of how sustainability indicators inform decisions. In Linkov I., Linkov I. (Eds.), Sustainable Cities and Military Installations, Springer, Netherlands, pp. 3–20. https://doi.org/10.1007/978-94-007-7161-1_1 DOI: https://doi.org/10.1007/978-94-007-7161-1_1
  75. Wale, E., & Mburu, J. (2006). An attribute-based index of Coffee Diversity and Implications for on- farm conservation in Ethiopia. Valuing Crop Biodiversity, On-Farm Genetic Resources and Economic Change, 42-62. DOI: https://doi.org/10.1079/9780851990835.0048
  76. https://doi.org/10.1079/9780851990835.0048
  77. Wang, H., & Whittington, D. (2005). Measuring Individuals’ Valuation Distributions Using a Stochastic Payment Card Approach. Journal of Ecological Economics, 55, 143–54. https://doi.org/10.1016/j.ecolecon.2004.11.011 DOI: https://doi.org/10.1016/j.ecolecon.2004.11.011
  78. Willock, J., Deary, I., Jones, E.G., Gibson, G., McGrego R.M., & Sutherland A., (1999). The role of attitudes and objectives in farmer decision making, business and environmentally-oriented behavior in Scotland. Journal of Agricultural Economics, 50(2), 286–303. https://doi.org/10.1111/j.1477-9552.1999.tb00814.x DOI: https://doi.org/10.1111/j.1477-9552.1999.tb00814.x
  79. Yang, J.S., Chung, E.S., Kim, S.U. and Kim, T.W. (2012). Prioritization of water management under climate change and urbanization using multicriteria decision-making methods. Hydrology and Earth System Sciences, 16, 801-814. https://doi.org/10.5194/hess-16-801-2012 DOI: https://doi.org/10.5194/hess-16-801-2012
  80. Yang, L., Meng, X., & Zhang, X. (2011). SRTM DEM and its application advances. International Journal of Remote Sensing, 32(14), 3875-3896. DOI: https://doi.org/10.1080/01431161003786016
  81. https://doi.org/10.1080/01431161003786016
  82. Zadeh, L. (1965). Fuzzy sets. Information and control, 8 (3), 338-353. https://doi.org/10.1016/S0019-9958(65)90241-X DOI: https://doi.org/10.1016/S0019-9958(65)90241-X
  83. Zalhaf, A.S., Elboshy, B., Kotb, K.M., Han, Y., Almaliki, A.H., Aly, R.M. & Elkadeem, M.R. (2021). A High-Resolution Wind Farms Suitability Mapping Using GIS and Fuzzy AHP Approach, A National-Level Case Study in Sudan. Sustainability, 14 (1), 358. https://doi.org/10.3390/su14010358 DOI: https://doi.org/10.3390/su14010358
  84. Zhao, J., & Kling, C.L. (2004). Willingness to pay, compensating variations, and the cost of commitment. Economic inquiry, 42 (3), 503-517. https://doi.org/10.1093/ei/cbh077 DOI: https://doi.org/10.1093/ei/cbh077
  85. Zonneveld, I.S. (1989). The land unit, A fundamental concept in landscape ecology and its applications. Landscape Ecology, 3, 67-86. http://doi.org/10.1007/BF00131171 DOI: https://doi.org/10.1007/BF00131171