リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

リケラボ 全国の大学リポジトリにある学位論文・教授論文を一括検索するならリケラボ論文検索大学・研究所にある論文を検索できる

大学・研究所にある論文を検索できる 「Seasonal Land Use Planning and Evaluation System for Food Nutrition Security Using Fuzzy Expert System, GIS, and Satellite Remote Sensing」の論文概要。リケラボ論文検索は、全国の大学リポジトリにある学位論文・教授論文を一括検索できる論文検索サービスです。

コピーが完了しました

URLをコピーしました

論文の公開元へ論文の公開元へ
書き出し

Seasonal Land Use Planning and Evaluation System for Food Nutrition Security Using Fuzzy Expert System, GIS, and Satellite Remote Sensing

MOSTAFIZ, RUBAIYA BINTE 筑波大学 DOI:10.15068/0002002142

2021.12.03

概要

Diet, nourishment and health are all intertwined. Food accessibility does not guarantee the consumption of a well- balanced food intake; a well-balanced diet is dependent on optimal consumption, purchasing power and local food customs. Local dietary habits are often influenced by agricultural practices. For this reason, the goal of this study is to create a seasonal land-use planning model combining varied crops for food nutrition security to assure a balanced caloric demand. The model is based on a fuzzy expert system. Furthermore, the findings were analyzed using a simple land fertility evaluation, based on satellite remote sensing-derived soil-vegetation indices. Satellite remote sensing technologies offer a significant potential for assessing land conditions and facilitating efficient agricultural planning. In this research, a multicriteria decision-making study was performed, as well as a multicrop land planning design was created using a geographic information system and fuzzy membership functions. Furthermore, vegetation index data were gathered in accordance with the seasonal crop cycle. To undertake spatial analysis, the environmental variables and restrictions were created in ArcGIS 10.4®. To select the best sites for agricultural production, a fuzzy expert system was used. The findings of the seasonal agricultural suitability evaluation were validated using data obtained from the Bangladesh Survey. The investigation found that 42 percent (3469 km2) of the overall land was ideal for vegetable growth during the Kharif-1 season, while 55 percent (4543 km2) was appropriate during the Kharif-2 season. Whereas current practices utilized just 12 percent and 18 percent of the area for vegetable production in the Kharif-1 and Kharif-2 seasons respectively, which is less than the regional requirement. In addition, during the Rabi season, the most suitable zones for cereals, vegetables, pulses, oilseeds and potatoes were reported as 35 percent (2891 km²), 19 percent (1569 km²), 15 percent (1239 km²), 10 percent (826 km²) and 21 percent (1734 km²) of the total land area respectively. Moreover, the land areas suitable for farming pulses and oilseeds were found to be 15 percent (1239 km²) and 10 percent (826 km²) respectively. When applying the fuzzy membership function for remote sensing-based land fertility evaluation, expert knowledge was also used, along with references and field data; as a result, 48 percent of the land (2045 km2) was identified as being highly fertile; 39 percent of the land (2045 km2) was identified as being moderately suitable and 7 percent of the land (298 km2) was identified as being marginally fertile. Additionally, 6 percent (256 km2) of the land was described as not fertile. The yield estimation using SAVI (R2 = 77.3%), ARVI (R2=68.9%), SARVI (R2=71.1%), MSAVI (R2=74.5%) and OSAVI (R2=81.2%) showed a good predictive ability. Furthermore, the combined model that used these five indices had the best accuracy (R2 = 0.839); this model was then used to create yield forecast maps for the respective years (2017-2020). This study reveals that using satellite remote sensing methodologies in GIS platforms is an efficient and simple technique for farmed land-use designers and policymakers to identify fertile cultivable land area with the prospective for improved farmed production. Additionally, using solely distant satellite datasets to determine acceptable land conditions was a cause of worry, adding a new dimension to land fertility evaluation. The integrated model provided herein may be used to manage land allocation for varied crop production, providing policymakers with additional decision-making information to achieve regional food nutrition security in the target area along with neighboring South Asian nations.

この論文で使われている画像

参考文献

Abdullah, A. B., Ito, S., & Adhana, K. (2006, March). Estimate of rice consumption in Asian countries and the world towards 2050. In Proceedings for Workshop and Conference on Rice in the World at Stake (Vol. 2, pp. 28-43).

Acharjee, T. K., van Halsema, G., Ludwig, F., & Hellegers, P. (2017). Declining trends of water requirements of dry season Boro rice in the north-west Bangladesh. Agricultural Water Management, 180, 148-159. https://doi.org/10.1016/j.agwat.2016.11.014. Aguilar-Rivera, N., Algara-Siller, M., Olvera-Vargas, L. A., & Michel-Cuello, C. (2018).

Akinci, H., Özalp, A. Y., & Turgut, B. (2013). Agricultural land use suitability analysis using GIS and AHP technique. Computers and Electronics in Agriculture, 97, 71–82. doi: 10.1016/j.compag.2013.07.006.

Alam, M. S., Quayum, M. A., & Islam, M. A. (2010). Crop production in the Haor areas of Bangladesh: insights from farm level survey. The Agriculturists, 8(2), 88-97. https://doi.org/10.3329/agric.v8i2.7582.

Alamgir, M., Mohsenipour, M., Homsi, R., Wang, X., Shahid, S., Shiru, M. S., ... & Yuzir, A. (2019). Parametric assessment of seasonal drought risk to crop production in Bangladesh. Sustainability, 11(5), 1442. https://doi.org/10.3390/su11051442.

Alamgir, M.S., Furuya, J., Kobayashi, S. et al. Farm income, inequality, and poverty among farm families of a flood-prone area in Bangladesh: climate change vulnerability assessment. GeoJournal (2020). https://doi.org/10.1007/s10708-020-10231-2.

Amin, M., Zhang, J., & Yang, M. (2015). Effects of climate change on the yield and cropping area of major food crops: A case of Bangladesh. Sustainability, 7(1), 898-915. https://doi.org/10.3390/su7010898.

Amini, S., Rohani, A., Aghkhani, M. H., Abbaspour-Fard, M. H., & Asgharipour, M. R. (2019). Assessment of land suitability and agricultural production sustainability using a combined approach (Fuzzy-AHP-GIS): A case study of Mazandaran province, Iran. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2019.10.001

Arief, U. M., & Nafi, A. Y. (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.

Arshad S, Morid S, Reza Mobasheri.M., and Agha Alikhani.M., (2008). Development of Agricultural Drought Risk Assessment Model for Kermanshah Province (Iran), using satellite data and intelligence methods. Option Mediterrianeennes, Series A, No:80.

Asai, H., Samson, B. K., Stephan, H. M., Songyikhangsuthor, K., Homma, K., Kiyono, Y., ... & Horie, T. (2009). Biochar amendment techniques for upland rice production in Northern Laos: 1. Soil physical properties, leaf SPAD and grain yield. Field crops research, 111(1-2), 81-84. https://doi.org/10.1016/j.fcr.2008.10.008.

Ashford, S. A., Sitar, N., Lysmer, J., & Deng, N. (1997). Topographic effects on the seismic response of steep slopes. Bulletin of the seismological society of America, 87(3), 701-709.

Aydi, A., Abichou, T., Nasr, I. H., Louati, M., & Zairi, M. (2016). Assessment of land suitability for olive mill wastewater disposal site selection by integrating fuzzy logic, AHP, and WLC in a GIS. Environmental monitoring and assessment, 188(1), 59. https://doi.org/10.1007/s10661015- 5076-3.

Ayehu, G. T., & Besufekad, S. A. (2015). Land suitability analysis for rice production: A GIS based multi- criteria decision approach. American Journal of Geographic Information System, 4(3), 95-104. DOI: 10.5923/j.ajgis.20150403.02

Bahrani, S., Ebadi, T., Ehsani, H., Yousefi, H., & Maknoon, R. (2016). Modeling landfill site selection by multi-criteria decision making and fuzzy functions in GIS, case study: Shabestar, Iran. Environmental Earth Sciences, 75(4), 337. DOI 10.1007/s12665-015-5146-4.

Bajgai, Y., & Sangchyoswat, C. (2018). Farmers’ knowledge of soil fertility in West-Central Bhutan. Geoderma Regional, 14, e00188. https://doi.org/10.1016/j.geodrs.2018.e00188.

Bangladesh Bureau of Statistics (BBS), (2011) Statistics and Informatics Division (SID) Ministry of Planning: Population and housing census 2011.

Bangladesh Bureau of Statistics (BBS). Small Area Atlas of Bangladesh; Ministry of Planning: Dhaka, Bangladesh, 2014.

Bangladesh Bureau of Statistics (BBS). Statistical Pocket Book Bangladesh 2016; Ministry of Planning: Dhaka, Bangladesh, 2018.

Bangladesh Bureau of Statistics (BBS). Yearbook of Agricultural Statistics-2015; Ministry of Planning: Dhaka, Bangladesh, 2016.

Bangladesh Bureau of Statistics (BBS).; Bangladesh Agricultural Statistics Yearbook 2017. Ministry of Planning: Dhaka, Bangladesh, 2018.

Bangladesh Institute of Research and Rehabilitation in Diabetes, Endocrine and Metabolic Disorders (BIRDEM). Desirable Dietary Pattern for Bangladesh; National Food Policy Capacity Strengthening Programme,2013

Barbosa, A. M. (2015). fuzzySim: applying fuzzy logic to binary similarity indices in ecology. Methods in Ecology and Evolution, 6(7), 853-858. https://doi.org/10.1111/2041-210X.12372.

Basche, A. D., Archontoulis, S. V., Kaspar, T. C., Jaynes, D. B., Parkin, T. B., & Miguez, F. E. (2016). Simulating long-term impacts of cover crops and climate change on crop production and environmental outcomes in the Midwestern United States. Agriculture, Ecosystems & Environment, 218, 95-106. https://doi.org/10.1016/j.agee.2015.11.011.

Beinat, E., & Nijkamp, P. (Eds.). (1998). Multicriteria analysis for land-use management (Vol. 9). Springer Science & Business Media.

Bellman, R. E., & Zadeh, L. A. (1970). Decision-making in a fuzzy environment. Management science, 17(4), B-141. https://doi.org/10.1287/mnsc.17.4.B141.

Bozdağ, A., Yavuz, F., & Günay, A. S. (2016). AHP and GIS based land suitability analysis for Cihanbeyli (Turkey) County. Environmental Earth Sciences, 75(9), 813. https://doi.org/10.1007/s12665- 016-5558-9.

Brian D., Wardlow, Martha C., Anderson J. and Verdin P. (2012). Remote Sensing of Drought, Taylor & Francis Group.

Buthelezi, N. N., Hughes, J. C., & Modi, A. (2013). The use of scientific and indigenous knowledge in agricultural land evaluation and soil fertility studies of two villages in KwaZulu-Natal, South Africa. African Journal of Agricultural Research, 8, 507–518.

Buthelezi‐Dube, N. N., Hughes, J. C., Muchaonyerwa, P., Caister, K. F., & Modi, A. T. (2020). Soil fertility assessment and management from the perspective of farmers in four villages of eastern South Africa. Soil Use and Management, 36(2), 250-260. https://doi.org/10.1111/sum.12551.

Campos, I., Gonzalez-Gomez, L., Villodre, J., Gonzalez-Piqueras, J., Suyker, A. E., & Calera, A. (2018). Remote sensing-based crop biomass with water or light-driven crop growth models in wheat commercial fields. Field Crops Research, 216, 175-188. https://doi.org/10.1016/j.fcr.2017.11.025.

Ceglar, A., Toreti, A., Prodhomme, C., Zampieri, M., Turco, M., & Doblas-Reyes, F. J. (2018). Land- surface initialisation improves seasonal climate prediction skill for maize yield forecast. Scientific reports, 8(1), 1-9. https://doi.org/10.1038/s41598-018-19586-6.

Chaignon, V., Bedin, F. & Hinsinger (2002). P. Copper bioavailability and rhizosphere pH changes as affected by nitrogen supply for tomato and oilseed rape cropped on an acidic and a calcareous soil. Plant and Soil243, 219–228. https://doi.org/10.1023/A:1019942924985.

Chakraborthy A., Sehgal V. K., (2010), Assessment of Agricultural Drought Using MODIS Derived Normalized Difference Water Index, Journal of Agricultural Physics, Vol. 10, pp. 28-36.

Chapin F.; Sturm M.; Serreze M.; McFadden J.; Key J.; Lloyd A.; McGuire A.; Rupp T.; Lynch A.; Schimel J. (2005). Role of Land-Surface Changes in Arctic Summer Warming. Science, 310, 657–660.

Chauhan, B. S., Jabran, K., & Mahajan, G. (Eds.). (2017). Rice production worldwide (Vol. 247). Springer International Publishing. Basel, Switzerland, 2017; pp. 255–277. DOI: 10.1007/978- 3-319-47516-5.

Cho, M. A., & Skidmore, A. K. (2009). Hyperspectral predictors for monitoring biomass production in Mediterranean mountain grasslands: Majella National Park, Italy. International Journal of Remote Sensing, 30(2), 499-515. https://doi.org/10.1080/01431160802392596.

Chow, T. E., & Sadler, R. (2010). The consensus of local stakeholders and outside experts in suitability modeling for future camp development. Landscape and urban planning, 94(1), 9-19. https://doi.org/10.1016/j.landurbplan.2009.07.013

Cosgrove, W. J., Rijsberman, F. R., & Rijsberman, F. (2000a). World water vision: making water everybody's business. World Water Council, Publications, London, UK (2000).

Dabrowska-Zielinska K., Kogan F., Ciolkosz A., Gruszczynska M. & Kowalik W. (2002): Modelling of crop growth conditions and crop yield in Poland using AVHRR based indices, International Journal of Remote Sensing, 23:6, 1109-1123.

Das, A. C., Noguchi, R., & Ahamed, T. (2020). Integrating an Expert System, GIS, and Satellite Remote Sensing to Evaluate Land Suitability for Sustainable Tea Production in Bangladesh. Remote Sensing, 12(24), 4136. https://doi.org/10.3390/rs12244136.

Datta A., Ullah H., Ferdous Z. (2017) Water Management in Rice. In: Chauhan B., Jabran K., Mahajan G. (eds) Rice Production Worldwide. (pp. 255-277). Springer, Cham.

de Lima, T. M., Weindorf, D. C., Curi, N., Guilherme, L. R., Lana, R. M., & Ribeiro, B. T. (2019). Elemental analysis of Cerrado agricultural soils via portable X-ray fluorescence spectrometry: Inferences for soil fertility assessment. Geoderma, 353, 264-272. https://doi.org/10.1016/j.geoderma.2019.06.045.

Dexter, A. R. (2004). Soil physical quality: Part I. Theory, effects of soil texture, density, and organic matter, and effects on root growth. Geoderma, 120(3-4), 201-214. https://doi.org/10.1016/j.geoderma.2003.09.004.

Dharumarajan, S., & Singh, S. K. (2014). GIS based soil site suitability analysis for potato-a case study in lower indogangetic alluvial plain. Potato Journal, 41(2), 113-121.

Dou, F.; Soriano, J.; Tabien, R.E.; Chen, 2016 K. Soil Texture and Cultivar Effects on Rice (Oryza Sativa, L.) Grain Yield, Yield Components and Water Productivity in Three Water Regimes. PLoS ONE 2016, 11, e0150549.https://doi.org/10.1007/978-3-319-47516-5_11.

Egamberdieva, D., Jabborova, D. & Berg, G (2016). Synergistic interactions between Bradyrhizobium japonicum and the endophyte Stenotrophomonas rhizophila and their effects on growth, and nodulation of soybean under salt stress. Plant Soil 405, 35–45. https://doi.org/10.1007/s11104- 015-2661-8.

El Bilali, H., Callenius, C., Strassner, C., & Probst, L. (2019). Food and nutrition security and sustainability transitions in food systems. Food and Energy Security, 8(2), e00154. https://doi.org/10.1002/fes3.154.

Elsheikh, R., Shariff, A. R. B. M., Amiri, F., Ahmad, N. B., Balasundram, S. K., & Soom, M. A. M. (2013). Agriculture Land Suitability Evaluator (ALSE): A decision and planning support tool for tropical and subtropical crops. Computers and electronics in agriculture, 93, 98-110. https://doi.org/10.1016/j.compag.2013.02.003.

Ennouri, K., & Kallel, A. (2019). Remote sensing: an advanced technique for crop condition assessment. Mathematical Problems in Engineering, 2019. https://doi.org/10.1155/2019/9404565.

Feizizadeh, B., Blaschke, T., (2013). GIS-multicriteria decision analysis for landslide susceptibility mapping: comparing three methods for the Urmia lake basin, Iran. Nat. Hazards 65 (3), 2105– 2128. https://doi.org/10.1007/s11069-012-0463-3.

Feizizadeh, B., Blaschke, T., Nazmfar, H., & Rezaei Moghaddam, M. H. (2013). Landslide susceptibility mapping for the Urmia Lake basin, Iran: a multi-criteria evaluation approach using GIS. International Journal of Environmental Research, 7(2), 319-336. https://doi.org/10.1007/s11069-012-0463-3.

Fern, R. R., Foxley, E. A., Bruno, A., & Morrison, M. L. (2018). Suitability of NDVI and OSAVI as estimators of green biomass and coverage in a semi-arid rangeland. Ecological Indicators, 94, 16-21. https://doi.org/10.1016/j.ecolind.2018.06.029.

Food and Agriculture Organization of the United Nations (FAO) (2016c). Soils and pulses: symbiosis for life. FAO, Rome.

Food and Agriculture Organization of the United Nations (FAO) (2003). Bruinsma, Jelle, ed. World agriculture: towards 2015/2030: an FAO perspective. Earthscan, 2003

Food and Agriculture Organization of the United Nations (FAO) (2004). Cereals and other starch‐based staples: are consumption patterns changing? FAO 2004 Rome, Italy, 10-11 February 2004. Joint meeting of the intergovernmental group on grains (30th session) and the intergovernmental group on rice (41st session) Rome, Italy, 10-11 February 2004.

Food and Agriculture Organization of the United Nations (FAO), 1976. A framework for land evaluation. Food and Agriculture Organization of the United Nations, Soils Bulletin 32. FAO, Rome.

Food and Agriculture Organization of the United Nations (FAO), 2014. W. Country nutrition paper Bangladesh. In Joint FAO/WHO International Conference on Nutrition (Vol. 21, p. 47).

Food and Agriculture Organization of the United Nations (FAO), 2001. The State of Food and Agriculture 2001. No. 33. Food & Agriculture Org., 2001.

Food and Agriculture Organization of the United Nations (FAO). (1995). FAO Quarterly Bulletin of Statistics. 18: 1-2.

Food and Agriculture Organization of the United Nations (FAO). (2004). The State of Food Security in The World. pp. 30-31.

Food and Agriculture Organization. A Framework for Land Evaluation; FAO: Rome, Italy, 1976.

Gerpacio, R. V., & Pingali, P. L. (2007). Tropical and Subtropical Maize in Asia: Production Systems, Constraints, and Research Priorities. CIMMYT.

Gilabert, M. A., González-Piqueras, J., Garcıa-Haro, F. J., & Meliá, J. (2002). A generalized soil-adjusted vegetation index. Remote Sensing of environment, 82(2-3), 303-310. https://doi.org/10.1016/S0034-4257(02)00048-2.

Gitari, H.I., Gachene, C.K.K., Karanja, N.N. et al. (2019). Potato-legume intercropping on a sloping terrain and its effects on soil physico-chemical properties. Plant Soil 438, 447–460. https://doi.org/10.1007/s11104-019-04036-7.

GRiSP, G. R. S. P. (2013). Rice Almanac (4th editio). Los Baños, Philippines: Global Rice Science Partnership.

Guo, Xi, Hongyi Li, Huimin Yu, Weifeng Li, Yingcong Ye, and Asim Biswas. "Drivers of spatio-temporal changes in paddy soil pH in Jiangxi Province, China from 1980 to 2010." Scientific reports 8, no. 1 (2018): 1-11. https://doi.org/10.1038/s41598-018-20873-5.

Habibie, M. I., Noguchi, R., Shusuke, M., & Ahamed, T. (2019). Land suitability analysis for maize production in Indonesia using satellite remote sensing and GIS-based multicriteria decision support system. GeoJournal, 1-31. https://doi.org/10.1007/s10708-019-10091-5.

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.

Hamdy, A., Ragab, R., & Scarascia‐Mugnozza, E. (2003). Coping with water scarcity: water saving and increasing water productivity. Irrigation and Drainage: The Journal of the International Commission on Irrigation and Drainage, 52(1), 3-20. https://doi.org/10.1002/ird.73.

Hassan, N., Huda, N., & Ahmad, K. (1985). Seasonal patterns of food intake in rural Bangladesh: its impact on nutritional status. Ecology of Food and Nutrition, 17(2), 175-186. https://doi.org/10.1080/03670244.1985.9990891.

HIES. Preliminary Report on Household Income and Expenditure Survey 2016; Bangladesh Bureau of Statistics (BBS), Statistics and Informatics Division (SID), Ministry of Planning: Dhaka, Bangladesh, 2016.https://doi.org/10.5897/AJAR2014.9248.

Hu, W., Huang, B., Borggaard, O. K., Ye, M., Tian, K., Zhang, H., & Holm, P. E. (2018). Soil threshold values for cadmium based on paired soil-vegetable content analyses of greenhouse vegetable production systems in China: implications for safe food production. Environmental Pollution, 241, 922-929. https://doi.org/10.1016/j.envpol.2018.06.034.

Huete, A. (1988). Huete, AR A soil-adjusted vegetation index (SAVI). Remote Sensing of Environment. Remote sensing of environment, 25, 295-309. https://doi.org/10.1016/0034- 4257(88)90106-X.

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, 9679-9687. Retrieved from https://www.ripublication.com/ijaer17/ijaerv12n20_57.pdf.

Jesus, J. B. De, & Santana, I. D. M. (2017). Estimation of land surface temperature in caatinga area using Landsat 8 data. Journal of Hyperspectral Remote Sensing. 7(3), 150–157. Retrieved from https://periodicos.ufpe.br/revistas/jhrs/article/viewFile/22766/pdf.

Jhariya, D. C., Kumar, T., Dewangan, R., Pal, D., & Dewangan, P. K. (2017). Assessment of groundwater quality index for drinking purpose in the Durg district, Chhattisgarh using geographical information system (GIS) and multi-criteria decision analysis (MCDA) techniques. Journal of the Geological Society of India, 89(4), 453-459. https://doi.org/10.1007/s12594-017-0628-5.

Jiang, Z., Huete, A. R., Chen, J., Chen, Y., Li, J., Yan, G., & Zhang, X. (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.

Johnston, A. M., Tanaka, D. L., Miller, P. R., Brandt, S. A., Nielsen, D. C., Lafond, G. P., & Riveland, N. R. (2002). Oilseed crops for semiarid cropping systems in the northern Great Plains. Agronomy Journal, 94(2), 231-240. https://doi.org/10.2134/agronj2002.2310.

Kamkar, B., Dorri, M. A., & da Silva, J. A. T. (2014). Assessment of land suitability and the possibility and performance of a canola (Brassica napus L.)–soybean (Glycine max L.) rotation in four basins of Golestan province, Iran. The Egyptian Journal of Remote Sensing and Space Science, 17(1), 95-104. https://doi.org/10.1016/j.ejrs.2013.12.001.

Kaufman, Y. J., & Tanre, D. (1992). Atmospherically resistant vegetation index (ARVI) for EOS- MODIS. IEEE transactions on Geoscience and Remote Sensing, 30(2), 261-270. https://doi.org/10.1109/36.134076.

Kawasaki, K., & Uchida, S. (2016). Quality Matters more than quantity: asymmetric temperature effects on crop yield and quality grade. American Journal of Agricultural Economics, 98(4), 1195- 1209. https://doi.org/10.1093/ajae/aaw036.

Kazemi, H., & Akinci, H. (2018). A land use suitability model for rainfed farming by Multi-criteria Decision-making Analysis (MCDA) and Geographic Information System (GIS). Ecological Engineering, 116, 1-6. https://doi.org/10.1016/j.ecoleng.2018.02.021.

Kenfack Essougong, U.P., Slingerland, M., Mathé, S. et al. Farmers’ Perceptions as a Driver of Agricultural Practices: Understanding Soil Fertility Management Practices in Cocoa Agroforestry Systems in Cameroon. Hum Ecol 48, 709–720 (2020). https://doi.org/10.1007/s10745-020-00190-0.

Kennedy, C. M., Hawthorne, P. L., Miteva, D. A., Baumgarten, L., Sochi, K., Matsumoto, M., ... & Kiesecker, J. (2016). Optimizing land use decision-making to sustain Brazilian agricultural profits, biodiversity and ecosystem services. Biological Conservation, 204, 221-230. https://doi.org/10.1016/j.biocon.2016.10.039.

Kihoro, J., Bosco, N. J., & Murage, H. (2013). Suitability analysis for rice growing sites using a multicriteria evaluation and GIS approach in great Mwea region, Kenya. SpringerPlus, 2(1), 265. https://doi.org/10.1186/2193-1801-2-265.

Kim, M. S., Daughtry, C. S. T., Chappelle, E. W., McMurtrey, J. E., & Walthall, C. L. (1994). The use of high spectral resolution bands for estimating absorbed photosynthetically active radiation. Proceedings of the 6th Symp. on Physical Measurements and Signatures in Remote Sensing, Jan. 17–21, 1994, Val D'Isere, France (1994), pp. 299-306.

Kladivko, E. J., Griffith, D. R., & Mannering, J. V. (1986). Conservation tillage effects on soil properties and yield of corn and soya beans in Indiana. Soil and Tillage Research, 8, 277-287. https://doi.org/10.1016/0167-1987(86)90340-5.

Koohafkan, P., & Stewart, B. A. (2008). Water and cereals in drylands. chapter 2 - Cereal production in drylands. Earthscan. FAO, Rome (2008).

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.

Land management in Mexican sugarcane crop fields. Land Use Policy, 78, 763-780. https://doi.org/10.1016/j.landusepol.2018.07.034.

Lobry de Bruyn, L. A., & Abbey, J. A. (2003). Characterisation of farmers’ soil sense and the implications for on-farm monitoring of soil health. Australian Journal of Experimental Agriculture, 43, 285–305. https://doi.org/10.1071/EA00176.

Lobry de Bruyn, L., & Ingram, J. (2019). Soil information sharing and knowledge building for sustainable soil, use and management: Insights and implications for the 21st Century. Soil Use and Management, 35, 1–5. https://doi.org/10.1111/sum.12493 Mäder, P., Fliessbach, A., Dubois, D., Gunst, L., Fried, P., & Niggli, U. (2002). Soil fertility and biodiversity in organic farming. Science, 296, 1694–1697. https://doi.org/10.1126/science.1071148.

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 .

Mapanda, F., Mangwayana, E. N., Nyamangara, J., & Giller, K. E. (2005). The effect of long-term irrigation using wastewater on heavy metal contents of soils under vegetables in Harare, Zimbabwe. Agriculture, Ecosystems & Environment, 107(2-3), 151-165. https://doi.org/10.1016/j.agee.2004.11.005.

Marklein, A., Elias, E., Nico, P., & Steenwerth, K. (2020). Projected temperature increases may require shifts in the growing season of cool-season crops and the growing locations of warm-season crops. Science of The Total Environment, 746, 140918. https://doi.org/10.1016/j.scitotenv.2020.140918.

McCormick, J. I., Virgona, J. M., & Kirkegaard, J. A. (2012). Growth, recovery, and yield of dual-purpose canola (Brassica napus) in the medium-rainfall zone of south-eastern Australia. Crop and Pasture Science, 63(7), 635-646. https://doi.org/10.1071/CP12078.

Melillos, G., Themistocleous, K., & Hadjimitsis, D. G. (2020, August). Detecting underground structures in vegetation indices: MSR, RDVI, OSAVI, IRG, time series using histograms. In Eighth International Conference on Remote Sensing and Geoinformation of the Environment (RSCy2020)(Vol. 11524, p. 115241P). International Society for Optics and Photonics. https://doi.org/10.1016/j.jag.2020.102198.

Meng, X. D., Ma, H., Wei, M., & Xing, Y. X. (1997, May). Breeding of vegetable crops for protected growing conditions. In International Symposium on Growing Media and Hydroponics 481 (pp. 695-700). https://doi.org/10.17660/ActaHortic.1999.481.83.

Miller, P. R., McConkey, B. G., Clayton, G. W., Brandt, S. A., Staricka, J. A., Johnston, A. M., ... & Neill, K. E. (2002). Pulse crop adaptation in the northern Great Plains. Agronomy journal, 94(2), 261-272. https://doi.org/10.2134/agronj2002.2610.

Ministry of Environment and Forests (MoEF). Bangladesh Climate Change Strategy and Action Plan 2008; Government of the People’s Republic of Bangladesh: Dhaka, Bangladesh, 2008.

Mitchell, S., & Cohen, K. (2014, October). Fuzzy logic decision making for autonomous robotic applications. In 2014 IEEE 6th International Conference on Awareness Science and Technology (iCAST) (pp. 1-6). IEEE. https://doi.org/10.1109/ICAwST.2014.6981843.

Mottaleb, K. A., Kruseman, G., & Erenstein, O. (2018). Determinants of maize cultivation in a land- scarce rice-based economy: The case of Bangladesh. Journal of Crop Improvement, 32(4), 453-476. https://doi.org/10.1080/15427528.2018.1446375.

Mwinuka, P. R., Mbilinyi, B. P., Mbungu, W. B., Mourice, S. K., Mahoo, H. F., & Schmitter, P. (2020). The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L). Agricultural Water Management, 106584. https://doi.org/10.1016/j.agwat.2020.106584.

Nahar, Q., Choudhury, S., Faruque, M., Sultana, S., & Siddiquee, M. (2013). Desirable Dietary Pattern for Bangladesh. Final Research Results, 226.

Nahusenay, A., & Kibebew, K. (2015). Land suitability evaluation in Wadla Delanta Massif of north central highlands of Ethiopia for rainfed crop production. African Journal of Agricultural Research, 10(13), 1595-1611. https://doi.org/10.5897/AJAR2014.9248.

Narasimhan B., and R. Srinivasan. (2005). Development and evaluation of soil moisture deficit index and evapotranspiration deficit index for agricultural drought monitoring, Agricultural and Forest Meteorology 133: 69-88.

Nasim, M., Shahidullah, S. M., Saha, A., Muttaleb, M. A., Aditya, T. L., Ali, M. A., & Kabir, M. S. (2017). Distribution of crops and cropping patterns in Bangladesh. Bangladesh Rice Journal, 21(2), 1-55. https://doi.org/10.3329/brj.v21i2.38195

Nath, J. A., Lal, R., & Das, A. K. (2015). Ethnopedology and soil quality of bamboo (Bambusa sp.) based agroforestry system. Science of the Total Environment, 521, 372–379.

Ngoy, K. I., & Shebitz, D. (2020). Potential Impacts of Climate Change on Areas Suitable to Grow Some Key Crops in New Jersey, USA. Environments, 7(10), 76. https://doi.org/10.3390/environments7100076.

Nguyen, T. T., Verdoodt, A., Van Y, T., Delbecque, N., Tran, T. C., & Van Ranst, E. (2015). Design of a GIS and multi-criteriabased land evaluation procedure for sustainable land-use planning at the regional level. Agriculture, Ecosystems & Environment, 200, 1-11. https://doi.org/10.1016/j.agee.2014.10.015.

Niemeijer, D., & Mazzucato, V. (2003). Moving beyond indigenous soil taxonomies: Local theories of soils for sustainable development. Geoderma, 111, 403–424. https://doi.org/10.1016/ S0016- 7061(02)00274-4.

Noorollahi, E., Fadai, D., Akbarpour Shirazi, M., & Ghodsipour, S. H. (2016). Land suitability analysis for solar farms exploitation using GIS and fuzzy analytic hierarchy process (FAHP)—a case study of Iran. Energies, 9(8), 643. https://doi.org/10.3390/en9080643.

Novara, A., Gristina, L., Sala, G., Galati, A., Crescimanno, M., Cerdà, A., ... & La Mantia, T. (2017). Agricultural land abandonment in Mediterranean environment provides ecosystem services via soil carbon sequestration. Science of the Total Environment, 576, 420-429. https://doi.org/10.1016/j.scitotenv.2016.10.123.

Novara, A., Minacapilli, M., Santoro, A., Rodrigo-Comino, J., Carrubba, A., Sarno, M., ... & Gristina, L. (2019). Real cover crops contribution to soil organic carbon sequestration in sloping vineyard. Science of The Total Environment, 652, 300-306. https://doi.org/10.1016/j.scitotenv.2018.10.247.

Olivero, J., Real, R., & Marquez, A. L. (2011). Fuzzy chorotypes as a conceptual tool to improve insight into biogeographic patterns. Systematic Biology, 60(5), 645-660. https://doi.org/10.1093/sysbio/syr026.

Ostovari, Y., Honarbakhsh, A., Sangoony, H., Zolfaghari, F., Maleki, K., & Ingram, B. (2019). GIS and multi-criteria decision-making analysis assessment of land suitability for rapeseed farming in calcareous soils of semi-arid regions. Ecological indicators, 103, 479-487. https://doi.org/10.1016/j.ecolind.2019.04.051

Pandey, V. L., Dev, S. M., & Jayachandran, U. (2016). Impact of agricultural interventions on the nutritional status in South Asia: A review. Food policy, 62, 28-40. https://doi.org/10.1016/j.foodpol.2016.05.002

Paul, B.; Rashid, H. Climatic Hazards in Coastal Bangladesh: Non-Structural and Structural Solution; Butterworth-Heinemann: Oxford, UK, 2016; pp. 121–152.

Pelosi, C., Baudry, E. & Schmidt, O. Comparison of the mustard oil and electrical methods for sampling earthworm communities in rural and urban soils. Urban Ecosyst (2020). https://doi.org/10.1007/s11252-020-01023-0.

Pilevar, A. R., Matinfar, H. R., Sohrabi, A., & Sarmadian, F. (2020). Integrated fuzzy, AHP and GIS techniques for land suitability assessment in semi-arid regions for wheat and maize farming. Ecological Indicators, 110, 105887. https://doi.org/10.1016/j.ecolind.2019.105887.

Pimentel, D., & Burgess, M. (2013). Soil erosion threatens food production. Agriculture, 3(3), 443-463. https://doi.org/10.3390/agriculture3030443.

Purnamasari, R. A., Noguchi, R., & Ahamed, T. (2019). Land suitability assessments for yield prediction of cassava using geospatial fuzzy expert systems and remote sensing. Computers and Electronics in Agriculture, 166, 105018. https://doi.org/10.1016/j.compag.2019.105018.

Qin, S., Li, L., Wang, D., Zhang, J., & Pu, Y. (2013). Effects of limited supplemental irrigation with catchment rainfall on rain-fed potato in semi-arid areas on the Western Loess Plateau, China. American journal of potato research, 90(1), 33-42. https://doi.org/10.1007/s12230-012- 9267-y.

Ramanathan V.; Crutzen P.; Kiehl J.; Rosenfeld D. (2001). Aerosols, Climate, and the Hydrological Cycle. Science, 294, 2119–2124.

Redulla, C. A., Davenport, J. R., Evans, R. G., Hattendorf, M. J., Alva, A. K., & Boydston, R. A. (2002). Relating potato yield and quality to field scale variability in soil characteristics. American Journal of Potato Research, 79(5), 317-323. https://doi.org/10.1007/BF02870168

Ren, H., & Feng, G. (2015). Are soil‐adjusted vegetation indices better than soil‐unadjusted vegetation indices for above‐ground green biomass estimation in arid and semi‐arid grasslands?. Grass and Forage Science, 70(4), 611-619. https://doi.org/10.1111/gfs.12152.

Richards, J., Madramootoo, C. A., & Goyal, M. K. (2014). Determining irrigation requirements for vegetables and sugarcane in Jamaica. Irrigation and Drainage, 63(3), 340-348. https://doi.org/10.1002/ird.1811.

Richardson A.J., Wiegand C.L. (1977) - Distinguishing vegetation from soil background information. Photogrammetric Engineering & Remote Sensing, 43 (2): 1541-1552.

Romano, G., Dal Sasso, P., Liuzzi, G. T., & Gentile, F. (2015). Multi-criteria decision analysis for land suitability mapping in a rural area of Southern Italy. Land Use Policy, 48, 131-143. https://doi.org/10.1016/j.landusepol.2015.05.013.

Rondeaux, G., Steven, M., & Baret, F. (1996). Optimization of soil-adjusted vegetation indices. Remote sensing of environment, 55(2), 95-107. https://doi.org/10.1016/0034-4257(95)00186-7.

Saini, G. R., & Grant, W. J. (1980). Long-term effects of intensive cultivation on soil quality in the potato- growing areas of New Brunswick (Canada) and Maine (USA). Canadian Journal of Soil Science, 60(3), 421-428. https://doi.org/10.4141/cjss80-047.

Salman, S.M.; Mahul, O.; Bagazonzya, H.K. Agricultural Insurance in Bangladesh: Promoting Access to Small and Marginal Farmers (No. 53081, pp. 1-146). The World Bank: Washington, DC, USA, 2010; Available online: http://documents.worldbank.org/curated/en/482331468013812662/Agricultural- insurance-in-Bangladesh-promoting-access-to-small-and-marginal-farmers (accessed on 1 October 2020).

Samanta, S.; Pal, B.; Pal, D.K. (2011). Land Suitability Analysis for Rice Cultivation Based on Multi- Criteria Decision Approach through GIS. Data Base, 12–20.

Sarker, R. A., Talukdar, S., & Haque, A. A. (1997). Determination of optimum crop mix for crop cultivation in Bangladesh. Applied Mathematical Modelling, 21(10), 621-632. https://doi.org/10.1016/S0307-904X(97)00083-8.

Schutter, M., Sandeno, J., & Dick, R. (2001). Seasonal, soil type, and alternative management influences on microbial communities of vegetable cropping systems. Biology and Fertility of Soils, 34(6), 397-410. https://doi.org/10.1007/s00374-001-0423-7.

Serio, F., Miglietta, P. P., Lamastra, L., Ficocelli, S., Intini, F., De Leo, F., & De Donno, A. (2018). Groundwater nitrate contamination and agricultural land use: A grey water footprint perspective in Southern Apulia Region (Italy). Science of the Total Environment, 645, 1425-1431. https://doi.org/10.1016/j.scitotenv.2018.07.241.

Seyedmohammadi, J., Sarmadian, F., Jafarzadeh, A. A., & McDowell, R. W. (2019). Development of a model using matter element, AHP and GIS techniques to assess the suitability of land for agriculture. Geoderma, 352, 80-95. https://doi.org/10.1016/j.geoderma.2019.05.046.

Shimoda, S., Kanno, H., & Hirota, T. (2018). Time series analysis of temperature and rainfall-based weather aggregation reveals significant correlations between climate turning points and potato (Solanum tuberosum L) yield trends in Japan. Agricultural and Forest Meteorology, 263, 147-155. https://doi.org/10.1016/j.agrformet.2018.08.005.

Somvanshi, S. S., & Kumari, M. (2020). Comparative analysis of different vegetation indices with respect to atmospheric particulate pollution using sentinel data. Applied Computing and Geosciences, 7, 100032. https://doi.org/10.1016/j.acags.2020.100032.

Sonobe, R., Yamaya, Y., Tani, H., Wang, X., Kobayashi, N., & Mochizuki, K. I. (2018). Crop classification from Sentinel-2-derived vegetation indices using ensemble learning. Journal of Applied Remote Sensing, 12(2), 026019. https://doi.org/10.1117/1.JRS.12.026019.

Stark, J. C., Thornton, M., & Nolte, P. (Eds.). (2020). Potato production systems. Springer Nature.Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. DOI:10.1016/S0019- 9958(65)90241-X.

Sulaiman, A. A., Sulaeman, Y., Mustikasari, N., Nursyamsi, D., & Syakir, A. M. (2019). Increasing sugar production in Indonesia through land suitability analysis and sugar mill restructuring. Land, 8(4), 61. https://doi.org/10.3390/land8040061.

Svinurai, W., Hassen, A., Tesfamariam, E., & Ramoelo, A. (2018). Performance of ratio‐based, soil‐ adjusted and atmospherically corrected multispectral vegetation indices in predicting herbaceous aboveground biomass in a Colophospermum mopane tree–shrub savanna. Grass and Forage Science, 73(3), 727-739. https://doi.org/10.1111/gfs.12367.

Tashayo, B., Honarbakhsh, A., Akbari, M., & Eftekhari, M. (2020). Land suitability assessment for maize farming using a GIS-AHP method for a semi-arid region, Iran. Journal of the Saudi Society of Agricultural Sciences, 19(5), 332-338. https://doi.org/10.1016/j.jssas.2020.03.003.

Thaker, S., & Nagori, V. (2018). Analysis of fuzzification process in fuzzy expert system. Procedia computer science, 132, 1308-1316. https://doi.org/10.1016/j.procs.2018.05.047.

Timsina, J., Wolf, J., Guilpart, N., Van Bussel, L. G. J., Grassini, P., Van Wart, J., ... & Van Ittersum, M. K. (2018). Can Bangladesh produce enough cereals to meet future demand?. Agricultural systems, 163, 36-44. https://doi.org/10.1016/j.agsy.2016.11.003.

Todmal, R. S., Korade, M. S., Dhorde, A. G., & Zolekar, R. B. (2018). Hydro-meteorological and agricultural trends in water-scarce Karha Basin, western India: Current and future scenario. Arabian Journal of Geosciences. https://doi.org/10.1007/s12517-018-3655-7.

Tucker, C.J., 1979. Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens. Environ. 8 (2), 127–150. https://doi.org/10.1016/0034-4257(79)90013-0.

United Nations Development Program (UNDP), 2004.Reducing Disaster Risk: A Challenge for Development-A Global Report; UNDP: New York, NY, USA.

Ustaoglu, E., & Aydınoglu, A. C. (2020). Suitability evaluation of urban construction land in Pendik district of Istanbul, Turkey. Land Use Policy, 99, 104783. https://doi.org/10.3390/rs12091463.

Venancio, L. P., Mantovani, E. C., do Amaral, C. H., Neale, C. M. U., Gonçalves, I. Z., Filgueiras, R., & Campos, I. (2019). Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI). Agricultural Water Management, 225, 105779. https://doi.org/10.1016/j.agwat.2019.105779.

Venancio, L. P., Mantovani, E. C., do Amaral, C. H., Neale, C. M. U., Gonçalves, I. Z., Filgueiras, R., & Campos, I. (2019). Forecasting corn yield at the farm level in Brazil based on the FAO-66 approach and soil-adjusted vegetation index (SAVI). Agricultural Water Management, 225, 105779. https://doi.org/10.1016/j.agwat.2019.105779.

Wang, Y.H., and Li, J.Y. (2005). The plant architecture of rice (Oryza sativa). Plant Mol. Biol. 59: 75-84. WDI, Washington, World Bank, DC (2014), Doi: 10.1596/978-1-4648-0163-1.

World Health Organization. (2019). Healthy diet (No. WHO-EM/NUT/282/E). World Health Organization. Regional Office for the Eastern Mediterranean. https://apps.who.int/iris/handle/10665/325828.

Xing, Z., Chow, L., W. Rees, H., Meng, F., Monteith, J., & Stevens, L. (2011). A comparison of effects of one-pass and conventional potato hilling on water runoff and soil erosion under simulated rainfall. Canadian Journal of Soil Science, 91(2), 279-290. https://doi.org/10.4141/cjss10099.

Y.J. Kaufman, D. Tanre Atmospherically resistant vegetation index (ARVI). IEEE Trans. Geosci. Remote Sens., 30 (1992), pp. 261-270. Doi: 10.1109/36.134076.

Yalew, S. G., van Griensven, A., Mul, M. L., & van der Zaag, P. (2016). Land suitability analysis for agriculture in the Abbay basin using remote sensing, GIS and AHP techniques. Modeling Earth Systems and Environment, 2(2), 101.101. https://doi.org/10.1007/s40808-016-0167-x.

Yalew, S. G., van Griensven, A., Mul, M. L., & van der Zaag, P. (2016). Land suitability analysis for agriculture in the Abbay basin using remote sensing, GIS and AHP techniques. Modeling Earth Systems and Environment, 2(2), 101.101. https://doi.org/10.1007/s40808-016-0167-x.

Zadeh, L. A. (1965). Fuzzy sets. Information and control, 8(3), 338-353. https://doi.org/10.1016/S0019- 9958(65)90241-X. Zhao, H., Xiong, Y. C., Li, F. M., Wang, R. Y., Qiang, S. C., Yao, T. F., & Mo, F. (2012). Plastic film mulch for half growing-season maximized WUE and yield of potato via moisture-temperature improvement in a semi-arid agroecosystem. Agricultural Water Management, 104, 68-78. https://doi.org/10.1016/j.agwat.2011.11.016.

Zhu, K. W., Chen, Y. C., Zhang, S., Yang, Z. M., Huang, L., Li, L., ... & Li, Y. C. (2020). Output risk evolution analysis of agricultural non-point source pollution under different scenarios based on multi-model. Global Ecology and Conservation, e01144. https://doi.org/10.1016/j.gecco.2020.e01144.

Zinat, M.R.M., Salam, R., Badhan, M.A. et al. Appraising drought hazard during Boro rice growing period in western Bangladesh. Int J Biometeorol 64, 1687–1697 (2020). https://doi.org/10.1007/s00484-020-01949-2.

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.

Zolekar, R. B., & Bhagat, V. S. (2018). Multi-criteria land suitability analysis for plantation in Upper Mula and Pravara basin: Remote sensing and GIS approach. Journal of Geographical Studies,2(1), 12-20.

参考文献をもっと見る

全国の大学の
卒論・修論・学位論文

一発検索!

この論文の関連論文を見る