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Arnold R. Salvacion


This study assessed the spatio-temporal changes in corn climatic suitability in the Philippines under futureclimate condition. Using extracted climatic data from WorldClim database for the country under baseline and futureclimate condition, changes in corn suitability was assessed using fuzzy logic approach and published rainfall andtemperature requirement of the crop. Based on the data, the large portion of the country will experience increase inmonthly total rainfall (88%) while increase in monthly mean and minimum temperature under future climate conditionis projected for the entire country. These increases in rainfall and temperature resulted in changes of corn climaticsuitability in the country depending on the month and location. On the average, changes in rainfall resulted in reduction(8%) and improvement (6%) in corn suitability while increase in temperature resulted in 5% and 0.4% reductionand improvement, respectively.


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  1. Alamgir M., Mukul S.A., Turton S.M., 2015. Modelling spatial distribution of critically endangered Asian elephant and Hoolock gibbon in Bangladesh forest ecosystems under a changing climate. Applied Geography 60: 10–19. doi: 10.1016/j.apgeog.2015.03.001.
  2. Antón J., Cattaneo A., Kimura S., Lankoski J., 2013. Agricultural risk management policies under climate uncertainty. Global Environmental Change 23(6): 1726–1736. doi: 10.1016/j.gloenvcha.2013.08.007.
  3. Balezentiene L., Streimikiene D., Balezentis T., 2013. Fuzzy decision support methodology for sustainable energy crop selection. Renewable and Sustainable Energy Reviews 17: 83–93. doi: 10.1016/j.rser.2012.09.016.
  4. Bonfante A., Monaco E., Alfieri S.M., De Lorenzi F., Manna P., Basile A., Bouma J., 2015. Chapter Two – Climate Change Effects on the Suitability of an Agricultural Area to Maize Cultivation: Application of a New Hybrid Land Evaluation System, in: Donald L. Sparks (Ed.), Advances in Agronomy. Academic Press, pp. 33–69.
  5. Braimoh A.K., Vlek P.L.G., Stein A., 2004. Land Evaluation for Maize Based on Fuzzy Set and Interpolation. Environmental Management 33(2): 226–238. doi: 10.1007/s00267-003-0171-6.
  6. Brown R., Rosenberg N., 1999. Climate Change Impacts on the Potential Productivity of Corn and Winter Wheat in Their Primary United States Growing Regions. Climatic Change 41(1): 73–107. doi: 10.1023/A:1005449132633.
  7. Buan R.D., Maglinao A.R., Evangelista P.P., Pajuelas B.G., 1996. Vulnerability of rice and corn to climate change in the Philippines. Water, Air, and Soil Pollution 92(1–2): 41–51. doi: 10.1007/BF00175551.
  8. Çakir R., 2004. Effect of water stress at different development stages on vegetative and reproductive growth of corn. Field Crops Research 89(1): 1–16. doi: 10.1016/j.fcr.2004.01.005.
  9. Centeno H., Balbarez A., Fabellar N., Kropff M., Matthews R., 1995. Production in the Philippines under current and future climates, in: Modeling the Impact of Climate Change on Rice Production in Asia. CAB International, pp. 237–250.
  10. Challinor A.J., Wheeler T.R., Craufurd P.Q., Slingo J.M., Grimes D.I.F., 2004. Design and optimisation of a large-area process-based model for annual crops. Agricultural and Forest Meteorology 124(1–2): 99–120. doi:10.1016/j.agrformet.2004.01.002
  11. Challinor A., Wheeler T., Garforth C., Craufurd P., Kassam A., 2007. Assessing the vulnerability of food crop systems in Africa to climate change. Climatic Change 83(3): 381–399. doi: 10.1007/s10584-007-9249-0.
  12. Chemura A., Kutywayo D., Chidoko P., Mahoya C., 2015. Bioclimatic modelling of current and projected climatic suitability of coffee (Coffea arabica) production in Zimbabwe. Regional Environmental Change 16(2): 473–485. doi: 10.1007/s10113-015-0762 9.
  13. Chen C., Lei C., Deng A., Qian C., Hoogmoed W., Zhang W., 2011. Will higher minimum temperatures increase corn production in Northeast China? An analysis of historical data over 1965–2008. Agricultural and Forest Meteorology 151(12): 1580–1588. doi: 10.1016/j.agrformet.2011.06.013.
  14. Chen Y., Paydar Z., 2012. Evaluation of potential irrigation expansion using a spatial fuzzy multi-criteria decision framework. Environmental Modelling & Software 38: 147–157. doi: 10.1016/j.envsoft.2012.05.010.
  15. Cinco T.A., de Guzman R.G., Hilario F.D., Wilson D., 2014. Long-term trends and extremes in observed daily precipitation and near surface air temperature in the Philippines for the period 1951–2010. Atmospheric Research 145–146: 12–26. doi: 10.1016/j.atmosres.2014.03.025.
  16. Cinco T.A., de Guzman R.G., Ortiz A.M.D., Delfino R.J.P., Lasco R.D., Hilario F.D., Juanillo E.L., Barba R., Ares E.D., 2016. Observed trends and impacts of tropical cyclones in the Philippines. International Journal of Climatology 36(14): 4638–4650. doi: 10.1002/joc.4659.
  17. Confalonieri R., Francone C., Cappelli G., Stella T., Frasso N., Carpani M., Bregaglio S., Acutis M., Tubiello F.N., Fernandes E., 2013. A multi-approach software library for estimating crop suitability to environment. Computers and Electronics in Agriculture 90: 170–175. doi: 10.1016/j.compag.2012.09.016.
  18. Cordovez J.M., Rendon L.M., Gonzalez C., Guhl F., 2014. Using the basic reproduction number to assess the effects of climate change in the risk of Chagas disease transmission in Colombia. Acta Tropica 129: 74–82. doi: 10.1016/j.actatropica.2013.10.003.
  19. Cuervo P.F., Rinaldi L., Cringoli G., 2015. Modeling the extrinsic incubation of Dirofilaria immitis in South America based on monthly and continuous climatic data. Veterinary Parasitology 209(1–2): 70–75. doi: 10.1016/j.vetpar.2015.02.010.
  20. de Carvalho Alves M., Pozza E.A., Sanches L., de Carvalho L.G., 2011. Fuzzy Logic System Modeling Soybean Rust Monocyclic Process. INTECH Open Access Publisher.
  21. elos Santos W., Lansigan F., Hansen J., 2007. Linking Corn Production, Climate Information and Farm-Level Decision-Making: A Case Study in Isabela, Philippines, in: Climate Prediction and Agriculture. pp. 157–164.
  22. Deppermann C.E., 1954. General features of Philippine Weather. Philippine Studies 2(2): 102–125.
  23. Dubey S., Pandey R.K., Gautam S.S., 2013. Literature treview on fuzzy expert system in agriculture. International Journal of Soft Computing and Engineering 2(6): 289–291.
  24. Ewert F., Rötter R.P., Bindi M., Webber H., Trnka M., Kersebaum K.C., Olesen J.E., van Ittersum M.K., Janssen S., Rivington M., Semenov M.A., Wallach D., Porter J.R., Stewart D., Verhagen J., Gaiser T., Palosuo T., Tao F., Nendel C., Roggero P.P., Bartošová L., Asseng S., 2015. Crop modelling for integrated assessment of risk to food production from climate change. Environmental Modelling & Software 72: 287–303. doi: 10.1016/j.envsoft.2014.12.003.
  25. Fand B.B., Tonnang H.E.Z., Kumar M., Bal S.K., Singh N.P., Rao D.V.K.N., Kamble A.L., Nangare D.D., Minhas P.S., 2014. Predicting the impact of climate change on regional and seasonal abundance of the mealybug Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae) using temperature-driven phenology model linked to GIS. Ecological Modelling 288: 62–78. doi: 10.1016/j.ecolmodel.2014.05.018.
  26. Fischer D., Thomas S.M., Niemitz F., Reineking B., Beierkuhnlein C., 2011. Projection of climatic suitability for Aedes albopictus Skuse (Culicidae) in Europe under climate change conditions. Global and Planetary Change 78(1–2): 54–64. doi: 10.1016/j.gloplacha.2011.05.008.
  27. Gerpacio R.V., 2004. Maize in the Philippines: production systems, constraints, and research priorities. International Fund for Agricultural Development, International Maize and Wheat Improvement Center, Mexico, D.F., Mexico.
  28. Hijmans R.J., Cameron S.E., Parra J.L., Jones P.G., Jarvis A., 2005. Very high resolution interpolated climate surfaces for global land areas. International Journal of Climatology 25(15): 1965–1978. doi: 10.1002/joc.1276. (
  29. Hijmans R.J., Condori B., Carrillo R., Kropff M., 2003. A quantitative and constraint-specific method to assess the potential impact of new agricultural technology: the case of frost resistant potato for the Altiplano (Peru and Bolivia). Agricultural Systems 76(3): 895–911. doi: 10.1016/S0308-521X(02)00081-1.
  30. Holzkämper A., Calanca P., Fuhrer J., 2011. Analyzing climate effects on agriculture in time and space. 1st Conference on Spatial Statistics 2011 – Mapping Global Change 3: 58–62. doi: 10.1016/j.proenv.2011.02.011.
  31. Holzkämper A., Fossati D., Hiltbrunner J., Fuhrer J., 2015. Spatial and temporal trends in agro-climatic limitations to production potentials for grain maize and winter wheat in Switzerland. Regional Environmental Change 15(1): 109–122. doi: 10.1007/s10113-014-0627-7.
  32. Islam A., Ahuja L.R., Garcia L.A., Ma L., Saseendran A.S., Trout T.J., 2012. Modeling the impacts of climate change on irrigated corn production in the Central Great Plains. Agricultural Water Management 110: 94–108. doi: 10.1016/j.agwat.2012.04.004.
  33. Jayathilaka P.M.S., Soni P., Perret S.R., Jayasuriya H.P.W., Salokhe V.M., 2012. Spatial assessment of climate change effects on crop suitability for major plantation crops in Sri Lanka. Regional Environmental Change 12(1): 55–68. doi: 10.1007/s10113-011-0235-8.
  34. Jose A.M., Francisco R.V., Cruz N.A., 1996. A study on impact of climate variability/change on water resources in the Philippines. Global Change: Anthropogenic Processes and Indicators for Sustainable Development 33(9): 1687–1704. doi: 10.1016/0045-6535(96)00185-3.
  35. Joss B.N., Hall R.J., Sidders D.M., Keddy T.J., 2008. Fuzzy-logic modeling of land suitability for hybrid poplar across the Prairie Provinces of Canada. Environmental Monitoring and Assessment 141(1–3): 79–96. doi: 10.1007/s10661-007-9880-2.
  36. Kampichler C., Barthel J., Wieland R., 2000. Species density of foliage-dwelling spiders in field margins: a simple, fuzzy rule-based model. Ecological Modelling 129(1): 87–99.
  37. Kim K.S., Beresford R.M., 2011. Use of a Climatic Rule and Fuzzy Sets to Model Geographic Distribution of Climatic Risk for European Canker (Neonectria galligena) of Apple. Phytopathology 102(2): 147–157. doi: 10.1094/PHYTO-01-11-0018.
  38. Kim J., Sang W., Shin P., Cho H., Seo M., Yoo B., Kim K.S., 2016. Evaluation of regional climate scenario data for impact assessment of climate change on rice productivity in Korea. Journal of Crop Science and Biotechnology 18(4): 257–264. doi: 10.1007/s12892-015-0103-z.
  39. Ko J., Ahuja L.R., 2014. Global warming likely reduces crop yield and water availability of the dryland cropping systems in the U.S. Central Great Plains. Journal of Crop Science and Biotechnology 16(4): 233–242. doi: 10.1007/s12892-013-0106-6.
  40. Ko J., Kim H.-Y., Jeong S., An J.-B., Choi G., Kang S., Tenhunen J., 2014. Potential impacts on climate change on paddy rice yield in mountainous highland terrains. Journal of Crop Science and Biotechnology 17(3): 117–126. doi: 10.1007/s12892-013-0110-x.
  41. Kroschel J., Sporleder M., Tonnang H.E.Z., Juarez H., Carhuapoma P., Gonzales J.C., Simon R., 2013. Predicting climate-change-caused changes in global temperature on potato tuber moth Phthorimaea operculella (Zeller) distribution and abundance using phenology modeling and GIS mapping. Agricultural and Forest Meteorology 170: 228–241. doi: 10.1016/j.agrformet.2012.06.017.
  42. Kuang W., Xianjiang Y., Xiuqing C., Yafeng X., 2012. Experimental Study on Water Production Function for Waterlogging Stress on Corn. 2012 International Conference on Modern Hydraulic Engineering 28: 598–603. doi: 10.1016/j.proeng.2012.01.775.
  43. Kurtener D., Torbert H.A., Krueger E., 2008. Evaluation of Agricultural Land Suitability: Application of Fuzzy Indicators, in: Gervasi, Murgante, Laganà, Taniar, Mun, Gavrilova (Eds.): Computational Science and Its Applications – ICCSA 2008, Lecture Notes in Computer Science. Springer Berlin Heidelberg, pp. 475–490.
  44. Lane A., Jarvis A., 2007. Changes in Climate will modify the Geography of Crop Suitability: Agricultural Biodiversity can help with Adaptation. Journal of Semi-arid Tropical Agricultural Research 4(1): 1–12.
  45. Lansigan F.P., de los Santos W.L., Hansen J., 2007. Delivering Climate Forecast Products to Farmers: Ex Post Assessment of Impacts of Climate Information on Corn Production Systems in Isabela, Philippines, in: Sivakumar, Hansen (Eds.), Climate Prediction and Agriculture. Springer Berlin Heidelberg, pp. 41–48.
  46. Lansigan F.P., Salvacion A.R., 2007. Assessing the effect of climate change on rice and corn yields in selected provinces in the Philippines, in: 10th National Convention on Statistics (NCS). Presented at the 10th National Convention on Statistics (NCS), Mandaluyong City.
  47. Lewis S.M., Fitts G., Kelly M., Dale L., 2014. A fuzzy logic-based spatial suitability model for drought-tolerant switchgrass in the United States. Computers and Electronics in Agriculture 103: 39–47. doi: 10.1016/j.compag.2014.02.006.
  48. Meza F.J., Silva D., Vigil H., 2008. Climate change impacts on irrigated maize in Mediterranean climates: Evaluation of double cropping as an emerging adaptation alternative. Agricultural Systems 98(1): 21–30. doi: 10.1016/j.agsy.2008.03.005.
  49. Mighty M.A., 2015. Site suitability and the analytic hierarchy process: How GIS analysis can improve the competitive advantage of the Jamaican coffee industry. Applied Geography 58: 84–93. doi: 10.1016/j.apgeog.2015.01.010.
  50. Naughton C.C., Lovett P.N., Mihelcic J.R., 2015. Land suitability modeling of shea (Vitellaria paradoxa) distribution across sub-Saharan Africa. Applied Geography 58: 217–227. doi: 10.1016/j.apgeog.2015.02.007.
  51. Orlandini S., Marta A.D., D’Angelo I., Genesio R., 2003. Application of fuzzy logic for the simulation of Plasmopara viticola using agrometeorological variables*. EPPO Bulletin 33(3): 415–420. doi: 10.1111/j.1365-2338.2003.00666.x.
  52. Ovalle-Rivera O., Läderach P., Bunn C., Obersteiner M., Schroth G., 2015. Projected Shifts in Coffea arabica Suitability among Major Global Producing Regions Due to Climate Change. PLoS ONE 10(4): e0124155. doi: 10.1371/journal.pone.0124155.
  53. PAGASA [Philippine Atmospheric Geophysical and Astronomical Services Administration], 2017. Online: (accessed 8 February 2017).
  54. Papageorgiou E.I., Markinos A., Gemptos T., 2009. Application of fuzzy cognitive maps for cotton yield management in precision farming. Expert Systems with Applications 36(10): 12399–12413. doi: 10.1016/j.eswa.2009.04.046.
  55. Parthasarathy U., Jayarajan K., Johny A.L., Parthasarathy V.A., 2008. Identification of suitable areas and effect of climate change on ginger – a GIS study. Journal of Spices and Aromatic Crops 17(2): 61–68.
  56. PSA-BAS [Philippine Statistics Authority-Bureau of Agricultural Statistics], 2015. CountrySTAT Philippines. Online: (accessed 9 December 2015).
  57. Ramirez-Cabral N.Y.Z., Kumar L., Taylor S., 2016. Crop niche modeling projects major shifts in common bean growing areas. Agricultural and Forest Meteorology 218–219: 102–113. doi: 10.1016/j.agrformet.2015.12.002.
  58. Ramirez-Villegas J., Jarvis A., Läderach P., 2013. Empirical approaches for assessing impacts of climate change on agriculture: The EcoCrop model and a case study with grain sorghum. Agricultural and Forest Meteorology 170: 67–78. doi: 10.1016/j.agrformet.2011.09.005.
  59. Ray D.K., Gerber J.S., MacDonald G.K., West P.C., 2015. Climate variation explains a third of global crop yield variability. Nat Commun 6.
  60. Reshmidevi T.V., Eldho T.I., Jana R., 2009. A GIS-integrated fuzzy rule-based inference system for land suitability evaluation in agricultural watersheds. Agricultural Systems 101(1–2): 101–109. doi: 10.1016/j.agsy.2009.04.001.
  61. Riahi K., Rao S., Krey V., Cho C., Chirkov V., Fischer G., Kindermann G., Nakicenovic N., Rafaj P., 2011. RCP 8.5—A scenario of comparatively high greenhouse gas emissions. Climatic Change 109(1–2): 33–57. doi: 10.1007/s10584-011-0149-y.
  62. Rübbelke D., Vögele S., 2011. Impacts of climate change on European critical infrastructures: The case of the power sector. Environmental Science & Policy 14(1): 53–63. doi: 10.1016/j.envsci.2010.10.007.
  63. Salvacion A.R., 2009. Assessing Potential Impact of Changing Climate on Agricultural Crop Production in the Philippines (MSc Thesis). University of the Philippines Los Baños, College, Laguna.
  64. Salvacion A.R., 2015. Climatic Change Impact on Corn Productivity in the Philippines. International Journal of Sciences: Basic and Applied Research (IJSBAR) 23(1): 54–68.
  65. 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. doi: 10.1007/s12892-016-0019-2.
  66. Salvacion A.R., Pangga I.B., Cumagun C.J.R., 2015. Assessment of mycotoxin risk on corn in the Philippines under current and future climate change conditions. Reviews on Environmental Health 30(3): 135–142. doi: 10.1515/reveh-2015-0019.
  67. Scherm H., 2000. Simulating uncertainty in climate-pest models with fuzzy numbers. Environmental Pollution 108(3): 373–379. doi: 10.1016/S0269-7491(99)00216-X.
  68. Sicat R.S., Carranza E.J.M., Nidumolu U.B., 2005. Fuzzy modeling of farmers’ knowledge for land suitability classification. Agricultural Systems 83(1): 49–75. doi: 10.1016/j.agsy.2004.03.002.
  69. Sivakumar M.V.K., Hansen J., 2007. Climate Prediction and Agriculture: Summary and the Way Forward, in: Sivakumar, Hansen (Eds.), Climate Prediction and Agriculture. Springer Berlin Heidelberg, pp. 1–13.
  70. Sys C., Van Ranst E., Debaveye J., Beernaert F., 1993. Land Evaluation: Part III – Crop Requirments. Agricultural Publications, Brussels, Belgium.
  71. Triantafilis J., Ward W.T., McBratney A.B., 2001. Land suitability assessment in the Namoi Valley of Australia, using a continuous model. Soil Research 39(2): 273–289.
  72. Van Ranst E., Tang H., Groenemam R., Sinthurahat S., 1996. Application of fuzzy logic to land suitability for rubber production in peninsular Thailand. Geoderma 70(1): 1–19. doi: 10.1016/0016-7061(95)00061-5.
  73. Wang Y., Tan Z., Sun G., 2015. The Impact of Climate Change on the Potential Suitable Distribution of Major Crops in Zambia and the Countermeasures, in: Li, Chen (Eds.), Computer and Computing Technologies in Agriculture VIII: 8th IFIP WG 5.14 International Conference, CCTA 2014, Beijing, China, September 16–19, 2014, Revised Selected Papers. Springer International Publishing, Cham, pp. 460–472.
  74. Yan-Ling S., De-Liang C., Yan-Ju L., Ying X., 2012. The Influence of Climate Change on Winter Wheat during 2012–2100 under A2 and A1B Scenarios in China. Advances in Climate Change Research 3(3): 138–146. doi: 10.3724/SP.J.1248.2012.00138.
  75. Zabel F., Putzenlechner B., Mauser W., 2014. Global Agricultural Land Resources – A High Resolution Suitability Evaluation and Its Perspectives until 2100 under Climate Change Conditions. PLoS ONE 9(9): e107522. doi: 10.1371/journal.pone.0107522.
  76. Zaidi P.H., Rafique S., Rai P.K., Singh N.N., Srinivasan G., 2004. Tolerance to excess moisture in maize (Zea mays L.): susceptible crop stages and identification of tolerant genotypes. Field Crops Research 90(2–3): 189–202. doi: 10.1016/j.fcr.2004.03.002.
  77. Zhang X., Cai X., 2011. Climate change impacts on global agricultural land availability. Environmental Research Letters 6(1): 14014.
  78. Zhao J., Guo J., Xu Y., Mu J., 2015. Effects of climate change on cultivation patterns of spring maize and its climatic suitability in Northeast China. Agriculture, Ecosystems & Environment 202: 178–187. doi: 10.1016/j.agee.2015.01.013.