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Penki Ramu
ramu.p@gmrit.edu.in
https://orcid.org/0000-0001-7551-9630
Penki Ramu
GMRIT, Department of Civil Engineering, Rajam, Vizianagaram, AP , India
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B. Sai Santosh
B. Sai Santosh
GMRIT, Department of Civil Engineering, Rajam, Vizianagaram, AP , India
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K. Chalapathi
K. Chalapathi
GMRIT, Department of Civil Engineering, Rajam, Vizianagaram, AP , India
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Abstract
Food, water, and energy scarcity threaten India's future, and they must be addressed first. To meet the country's ever-increasing population needs, agricultural productivity must be expanded. For the crop-land suitability, we have studied an area of about 6,539 km2 in Vizianagaram district. The majority of the land is used for paddy agriculture (Kharif). The crop-land suitability has been evaluated based on the different parameters identified in that study area. “Remote sensing (RS)” and “geographic information system (GIS)” were combined for the crop-land suitability using nine parameters. The slope, elevation, rainfall, soil texture, lithology, groundwater, land use–land cover (LULC), TWI, and land surface temperature are the primary criteria used to determine the crop-land suitability in the Vizianagaram district (AP). Thematic maps were created using Landsat 8 images and SRTM DEM images from USGS Earth Explorer. Based on these maps and the influence of these parameters, we may assign weights to the parameters and then rank them, the Analytic Hierarchy Process (AHP) allowing us to identify which area is more suitable for good crop productivity and which is not. In this study, the soils are divided into four categories: low suitability, moderate suitability, high suitability, and extremely high suitability. The suitability index is found to be in the range of 0–55.2%, which indicates the lack of outstanding agricultural lands in the sudy region.
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-
Aguilar-Rivera, N., Serna-Lagunes, R., Michel-Cuello, C., and Trujillo-Mata, A. (2019). Chapter 20 - upgrading comparative and competitive advantages for ethanol fuel production from agroindustrial crops in developing countries: Mexico as a case study. In: Ray, R.C. and Ramachandran, S. (Eds.), Bioethanol production from food crops. Academic Press, pp. 401–415. https://doi.org/10.1016/B978-0-12-813766-6.00020-5.
-
Akıncı, H., Özalp, A.Y., and Turgut, B. (2013). Agricultural land use suitability analysis using GIS and AHP technique. Computers and Electronics in Agriculture, 97: 71–82. https://doi.org/10.1016/j.compag.2013.07.006.
-
Bera, S., Ahmad, M., and 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 and Development, 5(6): 999–1004.
-
Bozdağ, A., Yavuz, F., and Dönertaş, A. (2016). AHP and GIS based land suitability analysis for Cihanbeyli (Turkey) County. Environmental Earth Sciences, 75. https://doi.org/10.1007/s12665-016-5558-9.
-
Debesa, G., Gebre, S., Abraham, A., Regassa, A., and Teka, S. (2020). GIS and remote sensing-based physical land suitability analysis for major cereal crops in Dabo Hana district, South-West Ethiopia. Cogent Food & Agriculture, 6. https://doi.org/10.1080/23311932.2020.1780100.
-
Doula, M.K., Moreno-Ortego, J.L., Tinivella, F., Inglezakis, V.J., Sarris, A., and Komnitsas, K. (2017). Chapter 2 - olive mill waste: recent advances for the sustainable development of olive oil industry. In: Galanakis, C.M. (Ed.), Olive Mill waste. Academic Press, pp. 29–56. https://doi.org/10.1016/B978-0-12-805314-0.00002-9.
-
Everest, T., Sungur, A., and Özcan, H. (2020). Determination of agricultural land suitability with a multiple-criteria decision-making method in Northwestern Turkey. International Journal of Environmental Science and Technology, 18. https://doi.org/10.1007/s13762-020-02869-9.
-
Girmay, G., Sebnie, W., and Reda, Y. (2018). Land capability classification and suitability assessment for selected crops in Gateno watershed, Ethiopia. Cogent Food & Agriculture, 4, 1532863. https://doi.org/10.1080/23311932.2018.1532863.
-
Hassan, I., Javed, M., Asif, M., Luqman, M., Ahmad, S., Ahmad, A., Akhtar, S., and Hussain, B. (2020). Weighted overlay based land suitability analysis of agriculture land in Azad Jammu and Kashmir using GIS and AHP. Pakistan Journal of Agricultural Sciences, 57: 1509–1519. https://doi.org/10.21162/PAKJAS/20.9507.
-
Hossen, B., Yabar, H., and Mizunoya, T. (2021). Land suitability assessment for pulse (green gram) production through remote sensing, GIS and multicriteria analysis in the coastal region of Bangladesh. Sustainability, 13: 12360. https://doi.org/10.3390/su132212360.
-
Jamil, M., Sahana, M., and Sajjad, H. (2018). Crop suitability analysis in the Bijnor district, UP, using geospatial tools and fuzzy analytical hierarchy process. Agricultural Research, 7. https://doi.org/10.1007/s40003-018-0335-5.
-
Khan, M.A., Ahmad, R., and Khan, H.H. (2022). Multi-criteria land suitability analysis for agriculture using AHP and remote sensing data of northern region India. IntechOpen. https://doi.org/10.5772/intechopen.102432.
-
Kumar, A., Pramanik, M., Chaudhary, S., and Negi, M.S. (2021). Land evaluation for sustainable development of Himalayan agriculture using RS-GIS in conjunction with analytic hierarchy process and frequency ratio. Journal of the Saudi Society of Agricultural Sciences, 20: 1–17. https://doi.org/10.1016/j.jssas.2020.10.001.
-
Loc, N., Chou, T.-Y., Hoang, T.V., and Tran, T. (2021). A GIS-based multicriteria analysis of land suitability for groundnut crop in Nghe an Province, Vietnam. International Journal of Geoinformatics, 7: 85–95. https://doi.org/10.52939/ijg.v17i6.2071.
-
Penki, R., Basina, S.S., and Tanniru, S.R. (2022a). Application of geographical information system-based analytical hierarchy process modeling for flood susceptibility mapping of Krishna district in Andhra Pradesh. https://doi.org/10.21203/rs.3.rs-1399020/v1.
-
Penki, R., Srinivasa Rao, T., Vinod Naik, G., and Aparna, R. (2022b). Identification of parking sites in the Kukatpally region using GIS and AHP. In: Gupta, A.K., Shukla, S.K., and Azamathulla, H. (Eds.), Advances in construction materials and sustainable environment. Springer, Singapore, pp. 447–456. https://doi.org/10.1007/978-981-16-6557-8_36.
-
Pramanik, M. (2016). Site suitability analysis for agricultural land use of Darjeeling district using AHP and GIS techniques. Modeling Earth Systems and Environment, 2. https://doi.org/10.1007/s40808-016-0116-8.
-
Ramu, P., Srinivasarao, T., Lalith Nandan, K., and Keshav, M. (2020). Fuzzy AHP, RS & GIS based hybrid approach for airport site selection-Kothagudem district. International Journal of Advanced Science and Technology, 29: 1645–1653.
-
Rao, T.S., Babu, I.N., and Chandana, N. (2018). MSW landfill site selection for Hyderabad city using GIS and AHP. Journal of Remote Sensing GIS, 9: 15–25. https://doi.org/10.37591/.v9i1.81.
-
Ravinder, R. and Ramu, P. (2020). Flood risk assessment using remote sensing and GIS for Anigunta watershed, Telangana. International Journal of Scientific & Technology Research, 9: 10.
-
Romeijn, H., Faggian, R., Diogo, V., and Sposito, V. (2016). Evaluation of deterministic and complex analytical hierarchy process methods for agricultural land suitability analysis in a changing climate. ISPRS International Journal of Geo-Information, 5: 99. https://doi.org/10.3390/ijgi5060099.
-
Saaty, T.L. (1990). How to make a decision: the analytic hierarchy process. European Journal of Operational Research, Desicion making by the analytic hierarchy process: Theory and applications, 48: 9–26. https://doi.org/10.1016/0377-2217(90)90057-I.
-
Saha, S., Sarkar, D., Mondal, P., and Goswami, S. (2021). GIS and multi-criteria decision-making assessment of sites suitability for agriculture in an anabranching site of sooin river, India. Modeling Earth Systems and Environment, 7. https://doi.org/10.1007/s40808-020-00936-1.
-
Singh, R. (2019). Where deep learning meets GIS. Esri. URL https://www.esri.com/about/newsroom/arcwatch/where-deep-learning-meets-gis/ (accessed 8 January 2022).
-
Singh, P., Upadhyay, R.K., Bhatt, H.P., Oza, M.P., and Vyas, S.P. (2018). Crop suitability analysis for cereal crops of Uttar Pradesh, India. In: The International archives of the photogrammetry, remote sensing and spatial information sciences. Copernicus GmbH, pp. 353–360. https://doi.org/10.5194/isprs-archives-XLII-5-353-2018.
-
Taani, A., Al-Husban, Y., and Farhan, I. (2020). Land suitability evaluation for agricultural use using GIS and remote sensing techniques: the case study of Ma’an Governorate, Jordan. The Egyptian Journal of Remote Sensing and Space Science, 24(1): 109–117. https://doi.org/10.1016/j.ejrs.2020.01.001.
-
Taghizadeh-Mehrjardi, R., Nabiollahi, K., Rasoli, L., Kerry, R., and Scholten, T. (2020). Land suitability assessment and agricultural production sustainability using machine learning models. Agronomy, 10: 573. https://doi.org/10.3390/agronomy10040573.
-
Talaviya, T., Shah, D., Patel, N., Yagnik, H., and Shah, M. (2020). Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides. Artificial Intelligence in Agriculture, 4: 58–73. https://doi.org/10.1016/j.aiia.2020.04.002.
-
Tashayo, B., Honarbakhsh, A., Akbari, M., and 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. https://doi.org/10.1016/j.jssas.2020.03.003.
-
Yalew, S., Griensven, A., Mul, M.L., and Zaag, P. (2016). Land suitability analysis for agriculture in the Abbay basin using remote sensing, GIS and AHP techniques. Modeling Earth Systems and Environment, 101(2). https://doi.org/10.1007/s40808-016-0167-x.
Prof. Felföldi, József
Institute: MATE - Hungarian University of Agriculture and Life Sciences, Institute of Food Science and Technology, Department of Measurements and Process Control
Address: 1118 Budapest Somlói út 14-16
E-mail: felfoldi.jozsef@uni-mate.hu
Indexing and Abstracting Services:
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- SCOPUS
| 2022 | ||
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| Scimago | ||
| Scimago H-index |
9 | |
| Scimago Journal Rank |
0.191 | |
| Scimago Quartile Score |
Environmental Engineering (Q4) |
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| Scopus | ||
| Scopus Cite Score |
1.1 | |
| Scopus CIte Score Rank |
General Agricultural and Biological Sciences 141/213 (34th PCTL) Agricultural and Biological Sciences 104/147 (29th PCTL) Industrial and Manufacturing Engineering 261/355 (26th PCTL) Mechanical Engineering 494/631 (21st PCTL) Environmental Engineering 145/184 (21st PCTL) |
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| Scopus SNIP |
0.222 |
| 2021 | |
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not indexed |
| Scimago | |
| Scimago H-index |
8 |
| Scimago Journal Rank |
0,141 |
| Scimago Quartile Score | Environmental Engineering (Q4) Industrial and Manufacturing Engineering (Q4) Mechanical Engineering (Q4) |
| Scopus | |
| Scopus Cite Score |
0,8 |
| Scopus CIte Score Rank |
Industrial and Manufacturing Engineering 261/338 (Q4) Environmental Engineering 138/173 (Q4) Mechanical Engineering 495/601 (Q4) |
| Scopus SNIP |
0,381 |
| 2020 | |
|---|---|
| Scimago H-index |
8 |
| Scimago Journal Rank |
0,197 |
| Scimago Quartile Score |
Environmental Engineering Q4 Industrial and Manufacturing Engineering Q3 Mechanical Engineering Q4 |
| Scopus Cite Score |
33/69=0,5 |
| Scopus Cite Score Rank |
Environmental Engineering 126/146 (Q4) Industrial and Manufacturing Engineering 269/336 (Q3) Mechanical Engineering 512/596 (Q4) |
| Scopus SNIP |
0,211 |
| Scopus Cites |
53 |
| Scopus Documents |
41 |
| Days from submission to acceptance | 122 |
| Days from acceptance to publication | 40 |
| Acceptance rate | 86% |
| 2019 | |
|---|---|
| Scimago H-index |
6 |
| Scimago Journal Rank |
0,123 |
| Scimago Quartile Score |
Environmental Engineering Q4 Industrial and Manufacturing Engineering Q4 Mechanical Engineering Q4 |
| Scopus Cite Score |
18/33=0,5 |
| Scopus Cite Score Rank |
Environmental Engineering 108/132 (Q4) Industrial and Manufacturing Engineering 242/340 (Q3) Mechanical Engineering 481/585 (Q4) |
| Scopus SNIP |
0,211 |
| Scopus Cites |
13 |
| Scopus Documents |
5 |
| Progress in Agricultural Engineering Sciences | |
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