Abstract
Maize is one of the potential crops can help in regional food production with self-sufficiency of foods in the drought prone areas of East Java in Indonesia. The purpose of this research is to determine the lands that are suitable for sustainable maize production in some selected areas of East Java by using various spatial and remote sensing datasets. The methodology was divided into three stages: first, the Landsat 8 operational land imagery satellite datasets were processed to create layers for the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI) and land surface temperature. For the proposed multicriteria analysis, another seven criteria: distance from roads, distance from rivers, slope, land cover, elevation levels, soil types and rainfall, were considered. Second, a spatial analysis was performed to identify highly suitable areas for maize production using a geographical extent and multicriteria analysis. Third, the criteria were determined using land suitability information for a 5-year period. The land suitability analysis with equal weight showed that 70.8% of the land (136,663 ha) was highly suitable, 26.3% of the land (50,872 ha) was moderately suitable, and 2.8% of the land (5391 ha) was marginally suitable. On the other hand, expert knowledge was also considered using the analytical hierarchy process (AHP) and indicated that 64.9% of the land (125,216 ha) was highly suitable, 30.4% of the land (58,828 ha) was moderately suitable, and 4.5% (8603 ha) of the land was marginally suitable. The yield estimation was determined for the highly suitable areas with NDVI (R2 = 77.81%) and SAVI (R2 = 72.8%). The regression analysis was incorporated to predicted yield of maize. This research recommends that satellite remote sensing, GIS and AHP-based multicriteria analysis can be extended for agricultural extension services to select suitable lands for increasing maize production.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abidin, H. Z. (2018). Strengthening basic geospatial information for sustainable development of Indonesia. The GeoICON 2018 Seminar, (July). https://doi.org/10.13140/rg.2.2.10588.08321.
Agustiani, N., Deng, N., Edreira, J. I. R., Girsang, S. S., Syafruddin, Sitaresmi, T., et al. (2018). Simulating rice and maize yield potential in the humid tropical environment of Indonesia. European Journal of Agronomy, 101(11), 10–19. https://doi.org/10.1016/j.eja.2018.08.002.
Ahamed, T., Noguchi, R., Takigawa, T., & Tian, L. (2016). Bioproduction engineering: Automation and precision agronomics for sustainable agricultural systems, (2nd ed.), Nova Science Publishers, Inc. New York, USA
Ahmadu, J., & Edeoghon, C. O. (2018). Effect of maize production on poverty alleviation of farmers in Edo state, Nigeria. Agrosearch, 18(1), 53. https://doi.org/10.4314/agrosh.v18i1.5.
Akther, A., Ahamed, T., Takigawa, T., & Noguchi, R. (2016). GIS-based multi-criteria analysis for urban waste management. Journal of the Japan Institute of Energy, 95(5), 457–467. https://doi.org/10.3775/jie.95.457.
Aldababseh, A., Temimi, M., Maghelal, P., Branch, O., & Wulfmeyer, V. (2018). Multi-criteria evaluation of irrigated agriculture suitability to achieve food security in an arid environment. Sustainability (Switzerland), 10(3), 803. https://doi.org/10.3390/su10030803.
Ali, S., Liu, Y., Ishaq, M., Shah, T., Ilyas, Abdullah, A., et al. (2017). Climate change and its impact on the yield of major food crops: Evidence from Pakistan. Foods, 6(6), 39. https://doi.org/10.3390/foods6060039.
Amara, D. M. K., Kamanda, P. J., Patil, P. L., & Kamara, A. M. (2016). Land suitability assessment for maize and paddy production in Bogur microwatershed using remote sensing and GIS techniques. International Journal of Environment, Agriculture and Biotechnology, 1(3), 505–516. https://doi.org/10.22161/ijeab/1.3.30.
Anderson, M. C., Norman, J. M., Kustas, W. P., Houborg, R., Starks, P. J., & Agam, N. (2008). A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales. Remote Sensing of Environment, 112(12), 4227–4241. https://doi.org/10.1016/j.rse.2008.07.009.
Bai, H., & Purcell, L. C. (2018). Aerial canopy temperature differences between fast- and slow-wilting soya bean genotypes. Journal of Agronomy and Crop Science, 204(3), 243–251. https://doi.org/10.1111/jac.12259.
Bandyopadhyay, S., Jaiswal, R. K., Hegde, V. S., & Jayaraman, V. (2009). Assessment of land suitability potentials for agriculture using a remote sensing and GIS based approach. International Journal of Remote Sensing, 30(4), 879–895. https://doi.org/10.1080/01431160802395235.
Batchelor, W. D., Basso, B., & Paz, J. O. (2002). Examples of strategies to analyze spatial and temporal yield variability using crop models. European Journal of Agronomy, 18(1–2), 141–158. https://doi.org/10.1016/S1161-0301(02)00101-6.
Batjes, N. (1995). A global data set of soil pH properties. Tecnical papers Wageningen, ISRICWageningen, ISRIC (Vol. 27). http://www.isric.nl/ISRIC/webdocs/docs/isric_techpap27.pdf.
Bellvert, J., Marsal, J., Girona, J., & Zarco-Tejada, P. J. (2015). Seasonal evolution of crop water stress index in grapevine varieties determined with high-resolution remote sensing thermal imagery. Irrigation Science, 33(2), 81–93. https://doi.org/10.1007/s00271-014-0456-y.
Bera, S., Ahmad, M., & Suman, S. (2017). Land suitability analysis for agricultural crop using remote sensing and GIS—A case study of Purulia District. IJSRD-International Journal for Scientific Research & Development, 5(6), 999–1004.
BMKG (Badan Meteorologi Klimatologi dan Geofisika). (2018). Rainfall data from Meteorology of Tuban. Retrieved from: http://dataonline.bmkg.go.id/home.
Boitt, M. K., Mundia, C. N., & Pellikka, P. K. (2015). Land suitability assessment for effective crop production, a case study of Taita Hills, Kenya. Journal of Agricultural Informatics, 6(2). https://doi.org/10.17700/jai.2015.6.2.185.
BPS (Badan Pusat Statistik). Agency statistics of Tuban regency. Yield data 2013–2016. Retrieved from https://tubankab.bps.go.id/statictable/2018/11/13/300/luas-panen-jagung-kedelai-kacang-tanah-kacang-hijau-ubi-kayu-ubi-jalar-menurut-kecamatan-dikabupaten-tuban-hektar-2017-.html. Accessed 11 Sept 2019.
Borie, C., Parcero-Oubiña, C., Kwon, Y., Salazar, D., Flores, C., Olguín, L., et al. (2019). Beyond site detection: The role of satellite remote sensing in analysing archaeological problems. A case study in lithic resource procurement in the Atacama Desert, Northern Chile. Remote Sensing, 11(7), 869. https://doi.org/10.3390/rs11070869.
Bozdağ, A., Yavuz, F., & Günay, A. S. (2016). AHP and GIS based land suitability analysis for Cihanbeyli (Turkey) County. Environmental Earth Sciences, 75, 813. https://doi.org/10.1007/s12665-016-5558-9.
BPS (Badan Pusat Statistik). Agency Statistics of Tuban Regency. Yield data 2013–2016. Retrieved from: https://tubankab.bps.go.id/statictable/2018/11/13/300/luas-panen-jagung-kedelai-kacang-tanah-kacang-hijau-ubi-kayu-ubi-jalar-menurut-kecamatan-di-kabupaten-tuban-hektar-2017-.html. Retrieved 11 September 2019.
BPS (Badan Pusat Statistik). Regional Statistics East Java Province. 2007–2017. Jatim: BPS. Retrieved from https://jatim.bps.go.id/statictable/2018/10/29/1321/luas-panen-jagung-menurut-kabupaten-kota-di-jawa-timur-ha-2007-2017.html.
BPS (Badan Pusat Statistik). Regional Statistics Tuban Regency (The Central Statistics Agency of Indonesia). Population 2017. Retrieved from: https://tubankab.bps.go.id/statictable/2018/11/13/292/jumlah-penduduk-dan-rasio-jenis-kelamin-menurut-kecamatan-di-kabupaten-tuban-2017-.html. Retrieved 11 September 2019.
BPS (Badan Pusat Statistik). Regional Statistics Tuban Regency (The Central Statistics Agency of Indonesia). Product Domestic Regional Bruto. 2012–2013. Retrieved from: https://tubankab.bps.go.id/statictable/2015/03/21/189/produk-domestik-regional-bruto-atas-dasar-harga-konstan-tahun-2000-2012-2013.html. Retrieved 11 September 2019.
BPS (Badan Pusat Statistik). Regional Statistics Tuban Regency (The Central Statistics Agency of Indonesia). Production. 2017. Retrieved from: https://tubankab.bps.go.id/statictable/2018/11/13/304/produksi-jagung-kedelai-kacang-tanah-kacang-hijau-ubi-kayu-ubi-jalar-menurut-kecamatan-di-kabupaten-tuban-ton-2017-.html. Retrieved 11 September 2019.
Brunsell, N. A., & Gillies, R. R. (2003). Length scale analysis of surface energy fluxes derived from remote sensing. Journal of Hydrometeorology, 4(6), 1212–1219. https://doi.org/10.1175/1525-7541(2003)004%3c1212:lsaose%3e2.0.co;2.
Burke, M., & Lobell, D. B. (2017). Satellite-based assessment of yield variation and its determinants in smallholder African systems. Proceedings of the National Academy of Sciences, 114(9), 2189–2194. https://doi.org/10.1073/pnas.1616919114.
Calera, A., Campos, I., Osann, A., D’Urso, G., & Menenti, M. (2017). Remote sensing for crop water management: From ET modelling to services for the end users. Sensors (Switzerland), 17(5), 1–25. https://doi.org/10.3390/s17051104.
Chen, Y. C., Yu, T. H., Tsui, P. L., & Lee, C. S. (2014). A fuzzy AHP approach to construct international hotel spa atmosphere evaluation model. Quality & Quantity, 48(2), 645–657. https://doi.org/10.1007/s11135-012-9792-2.
Crain, J., Ortiz-Monasterio, I., & Raun, B. (2012). Evaluation of a reduced cost active NDVI sensor for crop nutrient management. Sensors, 2012, 582028. https://doi.org/10.1155/2012/582028.
Demarez, V., Helen, F., Marais-Sicre, C., & Baup, F. (2019). In-season mapping of irrigated crops using landsat 8 and sentinel-1 time series. Remote Sensing, 11(2), 118. https://doi.org/10.3390/rs11020118.
Dong, J., Xiao, X., Kou, W., Qin, Y., Zhang, G., Li, L., et al. (2015). Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms. Remote Sensing of Environment,. 160, 99–113. https://doi.org/10.1016/j.rse.2015.01.004.
Dragincic, J., & Vraneševi, M. (2014). AHP based group decision making approach to supplier selection of irrigation equipment. Não projetos, 41(6), 782–791. https://doi.org/10.1134/S0097807814060050.
Du, L., Tian, Q., Yu, T., Meng, Q., Jancso, T., Udvardy, P., et al. (2013). A comprehensive drought monitoring method integrating MODIS and TRMM data. International Journal of Applied Earth Observation and Geoinformation, 23(1), 245–253. https://doi.org/10.1016/j.jag.2012.09.010.
Durão, L. F. C. S., Carvalho, M. M., Takey, S., Cauchick-Miguel, P. A., & Zancul, E. (2018). Internet of things process selection: AHP selection method. International Journal of Advanced Manufacturing Technology, 99(9–12), 2623–2634. https://doi.org/10.1007/s00170-018-2617-2.
Epule, T. E., Ford, J. D., Lwasa, S., & Lepage, L. (2017). Vulnerability of maize yields to droughts in Uganda. Water (Switzerland). https://doi.org/10.3390/w9030181.
FAO GIEWS. (2019). Available online: http://www.fao.org/giews/countrybrief/country.jsp?code=IDN. Retrieved 11 September 2019.
FAO STAT. (2017). Cited August 30 2019. http://www.fao.org/faostat/en/#data/QC/visualize. Retrieved 11 September 2019.
Ferretti, V., & Pomarico, S. (2013). An integrated approach for studying the land suitability for ecological corridors through spatial multicriteria evaluations. Environment, Development and Sustainability, 15(3), 859–885. https://doi.org/10.1007/s10668-012-9400-6.
Forestry, Ministry. of Environment. INDONESIAN LAND COVER MAP 2011. Available online: http://appgis.dephut.go.id/appgis/petapl2011.html.
Gašparović, I., & Gašparović, M. (2019). Determining optimal solar power plant locations based on remote sensing and GIS methods: A case study from Croatia. Remote Sensing, 11(12), 1481. https://doi.org/10.3390/rs11121481.
Gerhards, M., Schlerf, M., Rascher, U., Udelhoven, T., Juszczak, R., Alberti, G., et al. (2018). Analysis of airborne optical and thermal imagery for detection of water stress symptoms. Remote Sensing, 10(7). https://doi.org/10.3390/rs10071139.
Gerpacio, R. V., & Pingali, P. L. (2007). Tropical and subtropical maize in Asia: Production systems, constraints, and research priorities. CIMMYT.
Haboudane, D., Miller, J. R., Pattey, E., Zarco-Tejada, P. J., & Strachan, I. B. (2004). Hyperspectral vegetation indices and novel algorithms for predicting green LAI of crop canopies: Modeling and validation in the context of precision agriculture. Remote Sensing of Environment, 90(3), 337–352. https://doi.org/10.1016/j.rse.2003.12.013.
Harini, R., Susilo, B., & Nurjani, E. (2015). Geographic information system-based spatial analysis of agricultural land suitability in Yogyakarta. Indonesian Journal of Geography, 47(2), 171–179.
Hatfield, J. L., Ort, D., Wolfe, D., Ziska, L. H., Kimball, B. A., Boote, K. J., et al. (2011). Climate impacts on agriculture: Implications for crop production. Agronomy Journal, 103(2), 351–370.
He, M., Kimball, J. S., Maneta, M. P., Maxwell, B. D., Moreno, A., Beguería, S., et al. (2018). Regional crop gross primary productivity and yield estimation using fused landsat-MODIS data. Remote Sensing, 10(3), 372. https://doi.org/10.3390/rs10030372.
Helman, D., Bahat, I., Netzer, Y., Ben-Gal, A., Alchanatis, V., Peeters, A., et al. (2018). Using time series of high-resolution planet satellite images to monitor grapevine stem water potential in commercial vineyards. Remote Sensing, 10(10), 1615. https://doi.org/10.3390/rs10101615.
Holzkämper, A., Calanca, P., & Fuhrer, J. (2013). Identifying climatic limitations to grain maize yield potentials using a suitability evaluation approach. Agricultural and Forest Meteorology. https://doi.org/10.1016/j.agrformet.2012.09.004.
Huete, A. R. (1988). A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment, 25(3), 295–309. https://doi.org/10.1016/0034-4257(88)90106-X.
Islam, M. M., Ahamed, T., & Noguchi, R. (2018). Land suitability and insurance premiums: A GIS-based multicriteria analysis approach for sustainable rice production. Sustainability (Switzerland), 10(6), 1759. https://doi.org/10.3390/su10061759.
Jeevalakshmi, D., Narayana Reddy, S., & Manikiam, B. (2017). Land surface temperature retrieval from LANDSAT data using emissivity estimation. International Journal of Applied Engineering Research, 12(20), 9679–9687.
Jesus, J. B. De, & Santana, I. D. M. (2017). Estimation of land surface temperature in caatinga area using Landsat 8 data, 7, 150–157. Retrieved from https://periodicos.ufpe.br/revistas/jhrs/article/view/22766.
Jiang, Z., Huete, A. R., Chen, J., Chen, Y., Li, J., Yan, G., et al. (2006). Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment, 101(3), 366–378. https://doi.org/10.1016/j.rse.2006.01.003.
Joerin, F., Thérialult, M., & Musy, A. (2001). Using GIS and outranking multicriteia analysis for land-use suitability assesment. International Journal of Geographical Information Science, 15(2), 153–174. https://doi.org/10.1080/13658810051030487.
Joesidawati, M. K., Suwarsih, & Sari, L. K. (2019). Analysis of water availability in tuban regency watershed area. Journal of Environment and Earth Science., 9(3), 1–9. https://doi.org/10.7176/JEES.
Kaiser, H. M., Riha, S. J., Wilks, D. S., Rossiter, D. G., & Sampath, R. (1993). A farm-level analysis of economic and agronomic impacts of gradual climate warming. American Journal of Agricultural Economics, 75(2), 387. https://doi.org/10.2307/1242923.
Karnieli, A., Agam, N., Pinker, R. T., Anderson, M., Imhoff, M. L., Gutman, G. G., et al. (2010). Use of NDVI and land surface temperature for drought assessment: Merits and limitations. Journal of Climate, 23(3), 618–633. https://doi.org/10.1175/2009JCLI2900.1.
Kastner, T., Rivas, M. J. I., Koch, W., & Nonhebel, S. (2012). Global changes in diets and the consequences for land requirements for food. Proceedings of the National Academy of Sciences, 109(18), 6868–6872. https://doi.org/10.1073/pnas.1117054109.
Koulouri, M., & Giourga, C. (2007). Land abandonment and slope gradient as key factors of soil erosion in Mediterranean terraced lands. CATENA, 69(3), 274–281. https://doi.org/10.1016/j.catena.2006.07.001.
Kussul, N., Lemoine, G., Gallego, F. J., Skakun, S. V., Lavreniuk, M., & Shelestov, A. Y. (2016). Parcel-based crop classification in ukraine using landsat-8 data and sentinel-1A data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(6), 2500–2508. https://doi.org/10.1109/JSTARS.2016.2560141.
Li, L., Zhao, J., & Yuan, T. (2011). Study on approaches of land suitability evaluation for crop production using GIS. IFIP Advances in Information and Communication Technology. IFIP AICT 345, pp. 587–596. https://doi.org/10.1007/978-3-642-18336-2_72.
Linda, A., Oluwatola, A., & Opeyemi, T. (2015). Land suitability analysis for maize production in Egbeda Local Government Area of Oyo State using GIS techniques. International Journal of Biological, Biomolecular, Agricultural, Food and Biotechnological Engineering, 9(3), 276–281.
Liu, J., Xu, Z., Chen, F., Chen, F., & Zhang, L. (2019). Flood hazard mapping and assessment on the Angkor world heritage site. Cambodia: Remote Sensing. https://doi.org/10.3390/rs11010098.
Malczewski, J. (2006). GIS-based multicriteria decision analysis: A survey of the literature. International Journal of Geographical Information Science, 20(7), 703–726. https://doi.org/10.1080/13658810600661508.
Mardero, S., Schmook, B., López-Martínez, J., Cicero, L., Radel, C., & Christman, Z. (2018). The uneven influence of climate trends and agricultural policies on maize production in the Yucatan Peninsula. Mexico: Land. https://doi.org/10.3390/land7030080.
Mathewos, M., Dananto, M., Erkossa, T., & Mulugeta, G. (2018). Parametric land suitability assessment for rainfed agriculture: The Case of Bilate Alaba Sub-watershed. Southern Ethiopia. Agrotechnology, 7(183), 2. https://doi.org/10.4172/2168-9881.1000183.
Meshram, S. G., Alvandi, E., Singh, V. P., & Meshram, C. (2019). Comparison of AHP and fuzzy AHP models for prioritization of watersheds. Soft Computing, 1–11. https://doi.org/10.1007/s00500-019-03900-z.
Milad Aburas, M., Ho Abdullah, S., Firuz Ramli, M., & Hanan Ash, Z. (2015). A review of land suitability analysis for Urban growth by using the GIS-based analytic hierarchy process. Asian Journal of Applied Sciences, 3(6), 2015. Retrieved from https://ajouronline.com/index.php/AJAS/article/view/3480/1853.
Mohajane, M., Essahlaoui, A., Oudija, F., El Hafyani, M., Hmaidi, A. El, El Ouali, A., et al. (2018). Land use/land cover (LULC) using landsat data series (MSS, TM, ETM + and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments, 5(12), 131. https://doi.org/10.3390/environments5120131.
Morelli, F. (2011). Importance of road proximity for the nest site selection of the Red-backed shrike (Lanius collurio) in an agricultural environment in central Italy. Journal of Mediterranean Ecology, 11, 21–29.
Muhsin, N., Ahamed, T., & Noguchi, R. (2017). GIS-based multi-criteria analysis modeling used to locate suitable sites for industries in suburban areas in Bangladesh to ensure the sustainability of agricultural lands. Asia-Pacific Journal of Regional Science, 2(1), 35–64. https://doi.org/10.1007/s41685-017-0046-0.
Nadler, A. J., & Bullock, P. R. (2011). Long-term changes in heat and moisture related to corn production on the Canadian Prairies. Climatic Change, 104(2), 339–352. https://doi.org/10.1007/s10584-010-9881-y.
Neswati, R., Asrul, L., Molla, A., Widiayani, N., & Nurqadri, S. (2019). Land suitability for cocoa development in South Sulawesi: An analysis using GIS and parametric approach. IOP Conference Series: Earth and Environmental Science, 280(1), 012014. https://doi.org/10.1088/1755-1315/280/1/012014.
Nuarsa, I. W., Dibia, I. N., Wikantika, K., Suwardhi, D., & Rai, I. N. (2018). Gis based analysis of agroclimate land suitability for Banana plants in Bali Province. Indonesia. HAYATI Journal of Biosciences, 25(1), 11–17. https://doi.org/10.4308/hjb.25.1.11.
Nurdin, (2012). Land suitability and farmer perceptionon maize cultivation in Limboto Basin Gorontalo. Jurnal Perkebunan & Lahan Tropika, 2(1), 35–44.
Nurmiaty, & Baja, S. (2013). Spatial based assessment of land suitability and availability for maize (Zea mays L.) development in Maros Region, South Sulawesi, Indonesia. Open Journal of Soil Science, 03(05), 244–251. https://doi.org/10.4236/ojss.2013.35029.
Oktavia, H., Hanani, N., & Suhartini, S. (2016). The role of agricultural sector on economic development in East Java Province (Input–Output Approach). Habitat, 27(2), 72–84. https://doi.org/10.21776/ub.habitat.2016.027.2.9.
Pandey, V., & Srivastava, P. K. (2019). Integration of microwave and optical/infrared derived datasets for a drought hazard inventory in a sub-tropical region of India. Remote Sensing, 11(4), 439. https://doi.org/10.3390/rs11040439.
Pearson, C. J., Bucknell, D., & Laughlin, G. P. (2008). Modelling crop productivity and variability for policy and impacts of climate change in eastern Canada. Environmental Modelling and Software, 23(12), 1345–1355. https://doi.org/10.1016/j.envsoft.2008.02.008.
Perry, C. (2011). Accounting for water use: Terminology and implications for saving water and increasing production. Agricultural Water Management, 98(12), 1840–1846. https://doi.org/10.1016/j.agwat.2010.10.002.
Purnamasari, R. A., Ahamed, T., & Noguchi, R. (2018). Land suitability assessment for cassava production in Indonesia using GIS, remote sensing and multi-criteria analysis. Asia-Pacific Journal of Regional Science, 3(1), 1–32. https://doi.org/10.1007/s41685-018-0079-z.
Rashid, M. F. A. (2018). Capabilities of a GIS-based multi-criteria decision analysis approach in modelling migration. GeoJournal, 84(2), 1–14. https://doi.org/10.1007/s10708-018-9872-5.
Regency, T. (2014). Memorandum program sanitasi (MPS). 5–6. Retrieved from http://ppsp.nawasis.info/dokumen/perencanaan/sanitasi/pokja/mp/kab.tuban/Bab_1%20MPS%20Kab.%20Tuban.docx.
Regional Statistics East Java Regency. (2017). 2007–2017. Jatim: BPS. https://jatim.bps.go.id/statictable/2018/10/29/1321/luas-panen-jagung-menurut-kabupaten-kota-di-jawa-timur-ha-2007-2017.html.
Reilly, J., Tubiello, F., McCarl, B., Abler, D., Darwin, R., Fuglie, K., et al. (2003). U.S. Agriculture and climate change: New results. Climatic Change, 57(1–2), 43–69. https://doi.org/10.1023/A:1022103315424.
Riha, S. J., Wilks, D. S., & Simoens, P. (1996). Impact of temperature and precipitation variability on crop model predictions. Climatic Change, 32(3), 293–311. https://doi.org/10.1007/BF00142466.
Rossiter, D. G. (1996). A theoretical framework for land evaluation. Geoderma, 72(3–4), 165–190. https://doi.org/10.1016/0016-7061(96)00031-6.
Rouse, W., Haas, R. H., Schell, J. A., & Deering, D. W. (1973). Monitoring vegetation systems in the Great Plains with ERTS. In 3rd ERTS Symposium.
Saaty, T. L. (1989). Group decision making and the AHP. In B. L. Golden, E. A. Wasil, & P. T. Harker (Eds.), The analytic hierarchy process. Berlin, Heidelberg: Springer.
Saaty, T. L. (1990). How to make a decision: The analytic hierarchy process. European Journal of Operational Research, 48(1), 9–26. https://doi.org/10.1016/0377-2217(90)90057-I.
Salvacion, A. R., & Martin, A. A. (2016). Climate change impact on corn suitability in Isabela province, Philippines. Journal of Crop Science and Biotechnology, 19(3), 223–229. https://doi.org/10.1007/s12892-016-0019-2.
Silva, C., Ferreira, J. G., Bricker, S. B., DelValls, T. A., Martín-Díaz, M. L., & Yáñez, E. (2011). Site selection for shellfish aquaculture by means of GIS and farm-scale models, with an emphasis on data-poor environments. Aquaculture, 318(3–4), 444–457. https://doi.org/10.1016/j.aquaculture.2011.05.033.
Subiyanto, H., Arief, U. M., & Nafi, A. Z. (2018). An accurate assessment tool based on intelligent technique for suitability of soybean cropland: case study in Kebumen Regency. Indonesia. Heliyon, 4(7), e00684. https://doi.org/10.1016/j.heliyon.2018.e00684.
Sulaiman, A. A., Sulaeman, Y., Mustikasari, N., & Nursyamsi, D. (2019). Increasing sugar production in Indonesia through land suitability analysis and sugar mill restructuring. Land, 8(4), 1–18. https://doi.org/10.3390/land8040061.
Suroyo, S., Suntoro, S., & dan, S. (2013). Sistem Tumpangsari Dan Integrasi Ternak Terhadap Perubahan Sifat Fisik Dan Kimia Tanah Litosol (Intercropping and Livestock Integration System: Changes in Physical and Chemical Properties of Litosol). Sains Tanah—Jurnal Ilmu Tanah dan Agroklimatologi, 10(1), 71–80. https://doi.org/10.15608/stjssa.v10i1.142.
Tao, F., Yokozawa, M., Xu, Y., Hayashi, Y., & Zhang, Z. (2006). Climate changes and trends in phenology and yields of field crops in China, 1981-2000. Agricultural and Forest Meteorology, 138(1–4), 82–92. https://doi.org/10.1159/000092636.
Utami, N. H. (2009). Kajian Sifat Fisik, Sifat Kimia Dan Sifat Biologi Tanah Paska Tambang Galian C Pada Tiga Penutupan Lahan (Studi Kasus Pertambangan Pasir (Galian C) di Desa Gumulung Tonggoh, Kecamatan Astanajapura, Kabupaten Cirebon, Provinsi Jawa Barat), (Galian C), p. 112.
Wang, L., Dong, Q., Yang, L., Gao, J., & Liu, J. (2019). Crop classification based on a novel feature filtering and enhancement method. Remote Sensing, 11(4), 455. https://doi.org/10.3390/rs11040455.
Wetzel, P. J., Atlas, D., & Woodward, R. H. (2002). Determining soil moisture from geosynchronous satellite infrared data: A feasibility study. Journal of Climate and Applied Meteorology, 23(3), 375–391. https://doi.org/10.1175/1520-0450(1984)023%3c0375:dsmfgs%3e2.0.co;2.
Widiatmaka, A. W., Setiawan, Y., & Walter, C. (2016). Assessing the suitability and availability of land for agriculture in tuban regency, East Java, Indonesia. Applied and Environmental Soil Science, 7302148,13. https://doi.org/10.1155/2016/7302148.
Widiatmaka, W., Ambarwulan, W., Purwanto, M. Y. J., Setiawan, Y., & Effendi, H. (2017). Land capability based environmental carrying capacity in Tuban, East Java. Jurnal Manusia dan Lingkungan, 22(2), 247–259. https://doi.org/10.22146/jml.18749.
Xu, H., Twine, T. E., & Girvetz, E. (2016). Climate change and maize yield in Iowa. PLoS ONE, 11(5), 1–20. https://doi.org/10.1371/journal.pone.0156083.
Yan, J., Warburton, M., & Crouch, J. (2011). Association mapping for enhancing maize (Zea mays L.) genetic improvement. Crop Science, 51(2), 433–449. https://doi.org/10.2135/cropsci2010.04.0233.
Yang, J. Y., Mei, X. R., Huo, Z. G., Yan, C. R., Ju, H., Zhao, F. H., et al. (2015). Water consumption in summer maize and winter wheat cropping system based on SEBAL model in Huang-Huai-Hai Plain, China. Journal of Integrative Agriculture., 14(10), 2065–2076. https://doi.org/10.1016/S2095-3119(14)60951-5.
Zhang, H. H., Han, M., Chávez, J. L., & Lan, Y. Bin. (2017). Improvement in estimation of soil water deficit by integrating airborne imagery data into a soil water balance model. International Journal of Agricultural and Biological Engineering, 10(3), 37–46. https://doi.org/10.3965/j.ijabe.20171003.3081.
Zhou, T., Pan, J., Zhang, P., Wei, S., & Han, T. (2017). Mapping winter wheat with multi-temporal SAR and optical images in an urban agricultural region. Sensors (Switzerland), 17(6), 1210. https://doi.org/10.3390/s17061210.
Zolekar, R. B., & Bhagat, V. S. (2015). Multi-criteria land suitability analysis for agriculture in hilly zone: Remote sensing and GIS approach. Computers and Electronics in Agriculture, 118, 300–321. https://doi.org/10.1016/j.compag.2015.09.016.
Acknowledgements
We would like to thank the University of Tsukuba for supporting this research to develop the multicriteria land suitability analysis for maize production in Indonesia. We also express our sincere thanks to the United States Geological Survey (USGS) and to Indonesian agencies, such as the Indonesia Geospatial Agency (BIG), Indonesia Statistics (BPS), and Indonesia Meteorology and Climatology Agency (BMKG) for geographical, satellite data and climate data information. We sincerely thank the Indonesia Endowment Fund for Education (LPDP) for providing a scholarship for this research to be continued in Japan. We also expressed our gratitude to Indonesian and international experts and field surveyors who participated in this research.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Ethical approval
All publication ethics have been followed.
Informed consent
Acknowledgements and proper permission were collected for analyzing and preparing digital maps.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Habibie, M.I., Noguchi, R., Shusuke, M. et al. Land suitability analysis for maize production in Indonesia using satellite remote sensing and GIS-based multicriteria decision support system. GeoJournal 86, 777–807 (2021). https://doi.org/10.1007/s10708-019-10091-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10708-019-10091-5