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Application of GIS in Agricultural Crisis Management

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Advancement of GI-Science and Sustainable Agriculture

Part of the book series: GIScience and Geo-environmental Modelling ((GGM))

Abstract

The application of scientific knowledge to resolve the real-world problems faced by the people of society is essential for any scientific discovery or invention. In handling any problem or crisis, whether man-made or naturally induced, a proper investigation is needed to understand the actual scenario of the matter. Based on collected information about the targeted problem from the real world, an analysis can do in a meaningful way. After that, anybody can decide on beneficial strategies to resolve the targeted issue. In this context, geographical information system (GIS) is a widely used software technology for spatial mapping data about different phenomena in nature and human society. In human society, agriculture is one of the most important economic activities adopted by human beings to sustain their livelihoods. Agriculture is an environmentally influenced and artificially controlled activity that faces several environmental and man-created problems. To overcome the said problems in the agricultural sector, visualization of the real situation in the spatiotemporal context in a more precise way GIS is a powerful and suitable technology for the geographers and planners of the different development authorities in a nation. Based on accrued real-word data, a detailed analysis can be done and hence can decide better strategies to mitigate the problems related to agricultural activities. Again, GIS helps clear visualization of strategies in spatial context and assists in implementing the strategies in more fruitful ways. In the age of globalization, agriculture is considered a high-tech industry due to commercializing agricultural productions and agro-based industries. GIS is being used to estimate and identify the potential agricultural regions for more improvement in the agricultural sector. The selection of crops for production in more profitable ways is necessary to meet the challenge of the global market by a nation. On a national level, the government can estimate the net sown area, probable production of particular crops, and amount of loss in the agricultural area due to any disaster by mapping the spatial situation through GIS. On the basis of the estimation, government can take effective measures to resolve the crisis faced by the farmers. So, applying GIS to resolve the crisis in agriculture is beyond question.

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References

  • Alemayehu ZY, Minale AS, Legesse SA (2022) Spatiotemporal rainfall and temperature variability in Suha watershed, Upper Blue Nile Basin, Northwest Ethiopia. Environ Monit Assess 194(8):1–25

    Article  Google Scholar 

  • Baban SM (2022)The suitability of satellite remote sensing and GIS technologies for mapping, monitoring and managing water resources in the Middle East. In: Satellite monitoring of water resources in the Middle East. Springer, Cham, pp 29–47

    Google Scholar 

  • Baldos ULC, Hertel TW (2015) The role of international trade in managing food security risks from climate change. Food Secur 7(2):275–290

    Article  Google Scholar 

  • Behera UK, France J (2016) Integrated farming systems and the livelihood security of small and marginal farmers in India and other developing countries. Adv Agron 138:235–282

    Article  Google Scholar 

  • Böhm S, Carrington M, Cornelius N, de Bruin B, Greenwood M, Hassan L, Shaw D et al (2022) Ethics at the centre of global and local challenges: thoughts on the future of business ethics. J Bus Ethics 180(3):835–861

    Article  Google Scholar 

  • Chakraborty D, BaÅŸaÄŸaoÄŸlu H, Alian S, Mirchi A, Moriasi DN, Starks PJ, Verser JA (2023) Multiscale extrapolative learning algorithm for predictive soil moisture modeling& applications. Expert Syst Appl 213:119056

    Article  Google Scholar 

  • Chaudhry S, Sidhu GPS (2022) Climate change regulated abiotic stress mechanisms in plants: a comprehensive review. Plant Cell Rep 41(1):1–31

    Article  CAS  Google Scholar 

  • Cho CY (1992) Feeding systems for rainbow trout and other salmonids with reference to current estimates of energy and protein requirements. Aquaculture 100(1–3):107–123

    Article  Google Scholar 

  • Cooper PJ, Dimes J, Rao KPC, Shapiro B, Shiferaw B, Twomlow S (2008) Coping better with current climatic variability in the rain-fed farming systems of sub-Saharan Africa: an essential first step in adapting to future climate change? Agr Ecosyst Environ 126(1–2):24–35

    Article  Google Scholar 

  • Couliably S, Kamsu-Foguem B, Kamissoko D, Traore D (2022) Deep learning for precision agriculture: a bibliometric analysis. Intell Syst Appl 16:200102

    Google Scholar 

  • Cui Y, Tang H, Jin J, Zhou Y, Jiang S, Chen M (2022) System structure–based drought disaster risk assessment using remote sensing and field experiment data. Remote Sens 14(22):5700

    Article  Google Scholar 

  • Dall’Acqua L (2021) Artificial intelligence, human intelligence, and orientism management framework: a way to improve managerial mindset and manage emergency. In: Computational thinking for problem solving and managerial mindset training. IGI Global, pp 1–30

    Google Scholar 

  • Dar ZA, Khan A, Lone AA, Bahar FA, Baghel S, Lone BA, Sultan A et al (2022) Specialty food crops: an alternate way for increasing farmers’ income. In: Secondary agriculture. Springer, Cham, pp 59–68

    Google Scholar 

  • Darnhofer I, Bellon S, Dedieu B, Milestad R (2010) Adaptiveness to enhance the sustainability of farming systems: a review. Agron Sustain Dev 30(3):545–555

    Article  Google Scholar 

  • Das J, Bhattacharya SK (2016) Profitable and viable alternative to tobacco crop in Dinhata Subdivision of Koch Behar district, West Bengal, Indian. J Landscape Syst Ecol Stud 39(2):70–88

    Google Scholar 

  • Das J, Gayen A, Saha S, Bhattacharya SK (2017) Modelling of alternative crops suitability to tobacco based on Analytical hierarchy process in Dinhata subdivision of Koch Bihar district, West Bengal. Model Earth Syst Environ 3(4):1571–1587. https://doi.org/10.1007/s40808-017-0392-y

    Article  Google Scholar 

  • Das J, Bhattacharya SK (2018) Tobacco cultivation: time to ban its production in Dinhata subdivision of Koch Bihar district, West Bengal. Res Dir 6(1):1–10

    Google Scholar 

  • Denton F (2002) Climate change vulnerability, impacts, and adaptation: why does gender matter? Gend Dev 10(2):10–20

    Article  Google Scholar 

  • Dhanya P, Geethalakshmi V, Ramanathan S, Senthilraja K, Sreeraj P, Pradipa C, Sathyamoorthy NK et al (2022) Impacts and climate change adaptation of agrometeorological services among the maize farmers of west Tamil Nadu. AgriEng 4(4):1030–1053

    Google Scholar 

  • Dhodho CG (2022) Displacement and livelihood vulnerability among the Tonga Women of Binga, 1958–1980. In: Tonga livelihoods in Rural Zimbabwe. Routledge, pp 115–130

    Google Scholar 

  • Dogan B, Gokovali U (2012) Geographical indications: the aspects of rural development and marketing through the traditional products. Procedia Soc Behav Sci 62:761–765

    Article  Google Scholar 

  • Fan S, Teng P, Chew P, Smith G, Copeland L (2021) Food system resilience and COVID-19–lessons from the Asian experience. Glob Food Sec 28:100501

    Article  Google Scholar 

  • Fatima Z, Naz S, Iqbal P, Khan A, Ullah H, Abbas G, Ahmad S et al (2022) Field crops and climate change. In: Building climate resilience in agriculture. Springer, Cham, pp 83–94

    Google Scholar 

  • Gabriele M, Brumana R, Previtali M, Cazzani A (2022) A combined GIS and remote sensing approach for monitoring climate change-related land degradation to support landscape preservation and planning tools: the Basilicata case study. Appl Geomatics, pp 1–36

    Google Scholar 

  • Gaur M K, Goyal RK (2022) Thar desert of India: application of geospatial technology for extreme weather events. In: Hydro-meteorological extremes and disasters. Springer, Singapore, pp 279–300

    Google Scholar 

  • Guillemin P (2022) Food (in) justice and social inequalities in vegetable and market garden production in Normandy, France. Rev Agric Food Environ Stud 103:1–25

    Article  Google Scholar 

  • Gumma MK, Kadiyala MDM, Panjala P, Ray SS, Akuraju VR, Dubey S, Whitbread AM et al (2022) Assimilation of remote sensing data into crop growth model for yield estimation: a case study from India. J Indian Soc Remote Sens 50(2):257–270

    Article  Google Scholar 

  • Hernández-Delgado EA (2015) The emerging threats of climate change on tropical coastal ecosystem services, public health, local economies and livelihood sustainability of small islands: cumulative impacts and synergies. Mar Pollut Bull 101(1):5–28

    Article  Google Scholar 

  • Hove-Sibanda P, Motshidisi M, Igwe PA (2021)Supply chain risks, technological and digital challenges facing grocery retailers in South Africa. J Enterp Communities People Places Glob Econ 15(2):228–245

    Google Scholar 

  • Jayne TS, Sturgess C, Kopicki R, SitkoN (2014) Agricultural commodity exchanges and the development of grain markets and trade in Africa: a review of recent experience. In: Food security collaborative working papers 188568. Department of Agricultural, Food, and Resource Economics, Michigan State University

    Google Scholar 

  • Jimenez AF, Cardenas PF, Canales A, Jimenez F, Portacio A (2020) A survey on intelligent agents and multi-agents for irrigation scheduling. Comput Electron Agric 176:105474

    Article  Google Scholar 

  • Karl TR, Diamond HJ, Bojinski S, Butler JH, Dolman H, Haeberli W, Zillman J et al (2010) Observation needs for climate information, prediction and application: capabilities of existing and future observing systems. Procedia Environ Sci 1:192–205

    Article  Google Scholar 

  • Khan N, Jhariya MK, Raj A, Banerjee A, Meena RS (2021) Eco-designing for sustainability. In: Ecological intensification of natural resources for sustainable agriculture. Springer, Singapore, pp 565–595

    Google Scholar 

  • Kirilenko AP (2022) Geographic Information System (GIS). In: Applied data science in tourism. Springer, Cham, pp 513–526

    Google Scholar 

  • Knickel K, Redman M, Darnhofer I, Ashkenazy A, Chebach TC, Å Å«mane S, Rogge E et al (2018). Between aspirations and reality: making farming, food systems and rural areas more resilient, sustainable and equitable. J Rural Stud 59:197–210

    Google Scholar 

  • Kom Z, Nethengwe NS, Mpandeli NS, & Chikoore H (2020) Determinants of small-scale farmers’ choice and adaptive strategies in response to climatic shocks in Vhembe District, South Africa. GeoJournal 87:1–24

    Google Scholar 

  • MacCarthy DS, Adiku SG, Kamara AY, Freduah BS, Kugbe JX (2022) The role of crop simulation modeling in managing fertilizer use in maize production systems in northern Ghana. In: Enhancing agricultural research and precision management for subsistence farming by integrating system models with experiments. Wiley, pp 48–68

    Google Scholar 

  • Manser GM (2022) Systematizing authenticity and codifying values: the role of values, standards, and governance at farmers markets. J Rural Stud 96:154–166

    Article  Google Scholar 

  • Milesi C, Kukunuri M (2022) Crop yield estimation at gram panchayat scale by integrating field, weather and satellite data with crop simulation models. J Indian Soc Remote Sens 50(2):239–255

    Article  Google Scholar 

  • Müller C, Cramer W, Hare WL, Lotze-Campen H (2011) Climate change risks for African agriculture. Proc Natl Acad Sci 108(11):4313–4315

    Article  Google Scholar 

  • Murshed S, Griffin AL, Islam MA, Wang XH, Paull D (2022) Assessing multi-climate-hazard threat in the coastal region of Bangladesh by combining influential environmental and anthropogenic factors. Prog Disaster Sci 16:100261

    Google Scholar 

  • Nascimento N, Lazaro LLB, AmaralMH (2022) How can the water-energy-food nexus approach contribute to enhancing the resilience of Amazonian cities to climate change? In: Water-energy-food nexus and climate change in cities. Springer, Cham, pp 77–92

    Google Scholar 

  • Negese A, Worku D, Shitaye A, Getnet H (2022) Potential flood-prone area identification and mapping using GIS-based multi-criteria decision-making and analytical hierarchy process in DegaDamot district, northwestern Ethiopia. Appl Water Sci 12(12):1–21

    Article  Google Scholar 

  • Negi VS, Maikhuri RK, Pharswan D, Thakur S, Dhyani PP (2017) Climate change impact in the Western Himalaya: people’s perception and adaptive strategies. J Mt Sci 14(2):403–416

    Article  Google Scholar 

  • Newburn D, Reed S, Berck P, Merenlender A (2005) Economics and land-use change in prioritizing private land conservation. Conserv Biol 19(5):1411–1420

    Article  Google Scholar 

  • Ngigi MW, Muange EN (2022) Access to climate information services and climate-smart agriculture in Kenya: a gender-based analysis. Clim Change 174(3):1–23

    Google Scholar 

  • Ozegin KO, Salufu SO (2022) Electrical geophysical method and GIS in agricultural crop productivity in a typical sedimentary environment. NRIAG J Astron Geophys 11(1):69–80

    Article  Google Scholar 

  • Patra S (2022) Uncertainty and chaos in farming: an interpretation on production, livelihood and income. Doctoral dissertation, Bidhan Chandra Krishi Viswavidyalaya

    Google Scholar 

  • Pettit CJ (2005) Use of a collaborative GIS-based planning-support system to assist in formulating a sustainable-development scenario for Hervey Bay, Australia. Environ Plann B Plann Des 32(4):523–545

    Article  Google Scholar 

  • Prasad NR, Patel NR, Danodia A, Manjunath KR (2022) Comparative performance of semi-empirical based remote sensing and crop simulation model for cotton yield prediction. Model Earth Syst Environ 8(2):1733–1747

    Article  Google Scholar 

  • Reddy GP, Ramamurthy V, Singh SK (2018) Integrated remote sensing, GIS, and GPS applications in agricultural land use planning. In: Geospatial technologies in land resources mapping, monitoring and management. Springer, Cham, pp 489–515

    Google Scholar 

  • Rizzo G, Mazzilli SR, Ernst O, Baethgen WE, Berger AG (2022) Season-specific management strategies for rainfed soybean in the South American Pampas based on a seasonal precipitation forecast. Agric Syst 196:103331

    Article  Google Scholar 

  • Rosenzweig C, Parry ML (1994) Potential impact of climate change on world food supply. Nature 367(6459):133–138

    Article  Google Scholar 

  • Sakamoto T, vanNguyen N, Ohno H, Ishitsuka N, Yokozawa M (2006) Spatio–temporal distribution of rice phenology and cropping systems in the Mekong Delta with special reference to the seasonal water flow of the Mekong and Bassac rivers. Remote Sens Environ 100(1):1–16

    Article  Google Scholar 

  • Saraf SA, Ali J, Bahar FA, Sheraz Mahdi S (2022) Marketing of agricultural produce in India: problems and prospects. In Secondary agriculture. Springer, Cham, pp 85–95

    Google Scholar 

  • Sarkar AN (2013) Promoting eco-innovations to leverage sustainable development of eco-industry and green growth. Eur J Sustain Dev 2(1):171–171

    Google Scholar 

  • Schipper L, Pelling M (2006) Disaster risk, climate change and international development: scope for, and challenges to, integration. Disasters 30(1):19–38

    Article  Google Scholar 

  • Sengupta S, Mohinuddin S, Arif M, Sengupta B, Zhang W (2022) Assessment of agricultural land suitability using GIS and Fuzzy Analytical Hierarchy Process approach in Ranchi District, India. Geocarto Int 37(3):1–34

    Google Scholar 

  • Sgroi F (2022) Cooperation and innovation in Italian agribusiness between theoretical analysis and empirical evidence. J Agric Food Res 10:100406

    Google Scholar 

  • Sharma M, Bangotra P, Gautam AS, Gautam S (2022) Sensitivity of normalized difference vegetation index (NDVI) to land surface temperature, soil moisture and precipitation over district Gautam Buddh Nagar, UP, India. Stoch Env Res Risk Assess 36(6):1779–1789

    Article  Google Scholar 

  • Shiferaw B, Tesfaye K, Kassie M, Abate T, Prasanna BM, Menkir A (2014) Managing vulnerability to drought and enhancing livelihood resilience in sub-Saharan Africa: technological, institutional and policy options. Weather Clim Extremes 3:67–79

    Article  Google Scholar 

  • Shuai G, Basso B (2022) Subfield maize yield prediction improves when in-season crop water deficit is included in remote sensing imagery-based models. Remote Sens Environ 272:112938

    Article  Google Scholar 

  • Sinha BB, Dhanalakshmi R (2022) Recent advancements and challenges of internet of things in smart agriculture: a survey. Futur Gener Comput Syst 126:169–184

    Article  Google Scholar 

  • Souissi D, Souie A, Sebei A, Mahfoudhi R, Zghibi A, Zouhri L, Ghanmi M et al (2022) Flood hazard mapping and assessment using Fuzzy Analytic Hierarchy process and GIS techniques in Takelsa, Northeast Tunisia. Arab J Geosci 15(16):1–24

    Google Scholar 

  • Southworth J, Randolph JC, Habeck M, Doering OC, Pfeifer RA, Rao DG, Johnston JJ (2000) Consequences of future climate change and changing climate variability on maize yields in the midwestern United States. Agr Ecosyst Environ 82(1–3):139–158

    Article  Google Scholar 

  • Stephens EC, Timsina J, Snow V, Martin G, van Wijk M, Klerkx L, Reidsma P(2022) The immediate impact of the first waves of the global COVID-19 pandemic on agricultural systems worldwide: reflections on the COVID-19 special issue for agricultural systems. Agric Syst 201:103436

    Google Scholar 

  • Sziroczak D, Rohacs D, Rohacs J (2022) Review of using small UAV based meteorological measurements for road weather management. Prog Aerosp Sci 134:100859

    Article  Google Scholar 

  • Talukdar S, Naikoo MW, Mallick J, Praveen B, Sharma P, Islam ARMT, Rahman A et al (2022) Coupling geographic information system integrated fuzzy logic-analytical hierarchy process with global and machine learning based sensitivity analysis for agricultural suitability mapping. Agric Syst 196:103343

    Article  Google Scholar 

  • Taylor M (2018) Climate-smart agriculture: what is it good for? J Peasant Stud 45(1):89–107

    Article  Google Scholar 

  • Timmer CP (1988) The agricultural transformation. Handb Dev Econ 1:275–331

    Google Scholar 

  • van Tulder R, van Mil E (2022) Principles of sustainable business: frameworks for corporate action on the SDGs. Taylor & Francis

    Book  Google Scholar 

  • Van Wart J, van Bussel LG, Wolf J, Licker R, Grassini P, Nelson A, Cassman KG et al (2013) Use of agro-climatic zones to upscale simulated crop yield potential. Field Crop Res 143:44–55

    Article  Google Scholar 

  • Wilson JP (1999) Local, national, and global applications of GIS in agriculture. In: Geographical information systems: principles, techniques, management, and applications, pp 981–998

    Google Scholar 

  • Wuebbles DJ, Hayhoe K (2004) Climate change projections for the United States Midwest. Mitig Adapt Strat Glob Change 9(4):335–363

    Article  Google Scholar 

  • Yin L, Tao F, Chen Y, Wang Y (2022) Reducing agriculture irrigation water consumption through reshaping cropping systems across China. Agric for Meteorol 312:108707

    Article  Google Scholar 

  • Yousefi MR, Razdari AM (2015) Application of GIS and GPS in precision agriculture (a review). Int J.adv Biol Biom Res 3(1):7–9

    Google Scholar 

  • Zadeh AH, Zolbanin HM, Sharda R, Delen D (2019) Social media for nowcasting flu activity: spatio-temporal big data analysis. Inf Syst Front 21(4):743–760

    Article  Google Scholar 

  • Zhang P, Howell K, Li Y, Li L, Wang J, Eckard R, Barlow EWR (2023) Using historical weather data and a novel season temperature index to classify winegrape growing zones in Australia. Sci Hortic 307:111516

    Article  Google Scholar 

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  1. Department of Geography, Kaliachak College, Malda, West Bengal, India

    Sanjoy Saha

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Correspondence to Sanjoy Saha .

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  1. Geography, Rampurhat College, Rampurhat, West Bengal, India

    Jayanta Das

  2. Kaliachak College/Dpt. of Geography, Kaliachak College, Kaliachak, West Bengal, India

    Somenath Halder

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Saha, S. (2023). Application of GIS in Agricultural Crisis Management. In: Das, J., Halder, S. (eds) Advancement of GI-Science and Sustainable Agriculture. GIScience and Geo-environmental Modelling. Springer, Cham. https://doi.org/10.1007/978-3-031-36825-7_2

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