journal article·2021-11-05Open access

Assessment of Agricultural Water Requirements for Semi-Arid Areas: A Case Study of the Boufakrane River Watershed (Morocco)

Mohammed El Hafyani, Ali Essahlaoui, Kimberley Fung-Loy, Jason A. Hubbart, Anton Van Rompaey

Applied Sciences· Volume 11· Issue 21

Abstract

This work was undertaken to develop a low-cost but reliable assessment method for agricultural water requirements in semi-arid locations based on remote sensing data/techniques. In semi-arid locations, water resources are often limited, and long-term water consumption may exceed the natural replenishment rates of groundwater reservoirs. Sustainable land management in these locations must include tools that facilitate assessment of the impact of potential future land use changes. Agricultural practices in the Boufakrane River watershed (Morocco) were used as a case study application. Land use practices were mapped at the thematic resolution of individual crops, using a total of 13 images generated from the Sentinel-2 satellites. Using a supervised classification scheme, crop types were identified as cereals, other crops followed by cereals, vegetables, olive trees, and fruit trees. Two classifiers were used, namely Support vector machine (SVM) and Random forest (RF). A validation of the classified parcels showed a high overall accuracy of 89.76% for SVM and 84.03% for RF. Results showed that cereal is the most represented species, covering 8870.43 ha and representing 52.42% of the total area, followed by olive trees with 4323.18 ha and a coverage rate of 25%. Vegetables and other crops followed by cereals cover 1530.06 ha and 1661.45 ha, respectively, representing 9.4% and 9.8% of the total area. In the last rank, fruit trees occupy only 3.67% of the total area, with 621.06 ha. The Food and Agriculture Organization (FAO) free software was used to overlay satellite data images with those of climate for agricultural water resources management in the region. This process facilitated estimations of irrigation water requirements for all crop types, taking into account total potential evapotranspiration, effective rainfall, and irrigation water requirements. Results showed that olive trees, fruit trees, and other crops followed by cereals are the most water demanding, with irrigation requirements exceeding 500 mm. The irrigation requirements of cereals and vegetables are lower than those of other classes, with amounts of 300 mm and 150 mm, respectively.
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Research context

OpenAlex topics: Flood Risk Assessment and Management · Water resources management and optimization · Irrigation Practices and Water Management

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

Department of Geology, Laboratory of Geoengineering and Environment, Research Group “Water Sciences and Environment Engineering”, Faculty of Sciences, Moulay Ismail University, Zitoune, Meknes BP11201, Morocco

Department of Earth and Environmental Sciences, Geography and Tourism Research Group, KU Leuven, Celestijnenlaan 200E, 3001 Heverlee, Belgium

Department of Sustainable Management of Natural Resources, Anton de Kom University of Suriname, Paramaribo, Suriname

Institute of Water Security and Science, West Virginia University, 3109 Agricultural Sciences Building, Morgantown, WV 26506, USA

Division of Plant and Soil Sciences, Davis College of Agriculture, Natural Resources and Design, West Virginia University, Agricultural Sciences Building, Morgantown, WV 26506, USA

Division of Forestry and Natural Resources, Davis College of Agriculture, Natural Resources and Design, West Virginia University, Agricultural Sciences Building, Morgantown, WV 26506, USA

References (56)

Show source-provided references
1. The Impact of Urbanization on Agricultural Dynamics: A Case Study in Belgium10.1080/1747423X.2020.1769211 ↗
2. Mazoyer, M., and Roudart, L. (2006). A History of World Agriculture: From the Neolithic Age to the Current Crisis, NYU Press.10.4324/9781849773812 ↗
3. Crop Type Mapping from Pansharpened Landsat 8 NDVI Data: A Case of a Highly Fragmented and Intensive Agricultural System
4. Assessment of Uncertainties in Projected Temperature and Precipitation over the Arabian Peninsula Using Three Categories of Cmip5 Multimodel Ensembles
5. Weather Regimes—Moroccan Precipitation Link in a Regional Climate Change Simulation10.1016/j.gloplacha.2010.03.004 ↗
6. El Hafyani, M., Essahlaoui, A., Van Rompaey, A., Mohajane, M., El Hmaidi, A., El Ouali, A., Moudden, F., and Serrhini, N.-E. (2020). Assessing Regional Scale Water Balances through Remote Sensing Techniques: A Case Study of Boufakrane River Watershed, Meknes Region, Morocco. Water, 12.10.3390/w12020320 ↗
7. Climate Change Impacts on the Water Highway Project in Morocco10.5194/hess-24-1467-2020 ↗
8. Identifying Agricultural Systems Using SVM Classification Approach Based on Phenological Metrics in a Semi-Arid Region of Morocco10.1007/s41748-019-00106-z ↗
9. Land Use/Land Cover Change and Environmental Impact Assessment in Béni-Mellal District (Morocco) Using Remote Sensing and GIS10.1007/s41748-019-00088-y ↗
10. Mohajane, M., Essahlaoui, A., Oudija, F., El Hafyani, M., Hmaidi, A.E., El Ouali, A., Randazzo, G., and Teodoro, A.C. (2018). Land Use/Land Cover (LULC) Using Landsat Data Series (MSS, TM, ETM+ and OLI) in Azrou Forest, in the Central Middle Atlas of Morocco. Environments, 5.10.3390/environments5120131 ↗
11. Remote Sensing Technology for Mapping and Monitoring Land-Cover and Land-Use Change10.1016/S0305-9006(03)00066-7 ↗
12. Remote Sensing for Monitoring and Mapping Land Productivity in Italy: A Rapid Assessment Methodology10.1016/j.catena.2019.104375 ↗
13. Monitoring and Predicting Land Use Change in Beijing Using Remote Sensing and GIS10.1016/j.landurbplan.2005.10.002 ↗
14. Spatial and Temporal Variations in Vegetation Coverage Observed Using AVHRR GIMMS and Terra MODIS Data in the Mainland of China10.1080/01431161.2020.1714781 ↗
15. Randazzo, G., Cascio, M., Fontana, M., Gregorio, F., Lanza, S., and Muzirafuti, A. (2021). Mapping of Sicilian Pocket Beaches Land Use/Land Cover with Sentinel-2 Imagery: A Case Study of Messina Province. Land, 10.10.3390/land10070678 ↗
16. Land Cover Change in Europe between 1950 and 2000 Determined Employing Aerial Photography10.1177/0309133309360141 ↗
17. Tavares, P.A., Beltrão, N., Guimarães, U.S., Teodoro, A., and Gonçalves, P. (2019). Urban Ecosystem Services Quantification through Remote Sensing Approach: A Systematic Review. Environments, 6.10.3390/environments6050051 ↗
18. Multi-Data Approach for Remote Sensing-Based Regional Crop Rotation Mapping: A Case Study for the Rur Catchment, Germany
19. Fine Crop Mapping by Combining High Spectral and High Spatial Resolution Remote Sensing Data in Complex Heterogeneous Areas10.1016/j.compag.2017.05.003 ↗
20. Estimating Fractional Cover of Crop, Crop Residue, and Soil in Cropland Using Broadband Remote Sensing Data and Machine Learning
21. Boualoul, M., Randazzo, G., Lanza, S., Allaoui, A., Ouardi, H.E., Habibi, H., and Ouhaddach, H. (December, January 30). The use of remote sensing for water protection in the karst environment of the Tabular Middle Atlas/the causse of El Hajeb/Morocco. Proceedings of the IX Conference of the Italian Society of Remote Sensing, Firenze, Italy.
22. Muzirafuti, A., Boualoul, M., Barreca, G., Allaoui, A., Bouikbane, H., Lanza, S., Crupi, A., and Randazzo, G. (2020). Fusion of Remote Sensing and Applied Geophysics for Sinkholes Identification in Tabular Middle Atlas of Morocco (the Causse of El Hajeb): Impact on the Protection of Water Resource. Resources, 9.10.3390/resources9040051 ↗
23. Fault Analysis and Its Relationship with Karst Structures Affecting Lower Jurassic Limestones in the Agourai Plateau (Middle Atlas, Morocco)
24. Large-Scale Surface Water Change Observed by Sentinel-2 during the 2018 Drought in Germany10.1080/01431161.2020.1723817 ↗
25. Bachri, I., Hakdaoui, M., Raji, M., Teodoro, A.C., and Benbouziane, A. (2019). Machine Learning Algorithms for Automatic Lithological Mapping Using Remote Sensing Data: A Case Study from Souk Arbaa Sahel, Sidi Ifni Inlier, Western Anti-Atlas, Morocco. ISPRS Int. J. Geo-Inf., 8.10.3390/ijgi8060248 ↗
26. Wavelet SVM in Reproducing Kernel Hilbert Space for Hyperspectral Remote Sensing Image Classification10.1016/j.optcom.2010.08.009 ↗
27. A Self-Trained Semisupervised SVM Approach to the Remote Sensing Land Cover Classification10.1016/j.cageo.2013.03.024 ↗
28. A Self-Trained Ensemble with Semisupervised SVM: An Application to Pixel Classification of Remote Sensing Imagery10.1016/j.patcog.2010.09.021 ↗
29. Application of Remote Sensing and Machine Learning Algorithms for Forest Fire Mapping in a Mediterranean Area10.1016/j.ecolind.2021.107869 ↗
30. Investigation of the Spatial and Temporal Variation of Soil Salinity Using Random Forests in the Central Desert of Iran10.1016/j.geoderma.2020.114233 ↗
31. Poppy Crop Capsule Volume Estimation Using UAS Remote Sensing and Random Forest Regression
32. An Evaluation of Guided Regularized Random Forest for Classification and Regression Tasks in Remote Sensing
33. Impact of Climate Change on Surface Water Availability and Crop Water Demand for the Sub-Watershed of Abbay Basin, Ethiopia10.1007/s40899-019-00339-w ↗
34. GIS-Based Surface Irrigation Potential Assessment for Ethiopian River Basin10.1002/ird.2346 ↗
35. Economic Evaluation of Green Water in Cereal Crop Production: A Production Function Approach10.1016/j.wre.2019.100148 ↗
36. Predicting the Water Use-Demand as a Climate Change Adaptation Strategy for Rice Planting Crops in the Long Xuyen Quadrangle Delta10.1007/s10333-018-00686-y ↗
37. Projecting Climate Change Impacts on Rainfed Wheat Yield, Water Demand, and Water Use Efficiency in Northeast Iran10.1007/s00704-019-02896-8 ↗
38. Increased Crop Water Requirements Have Exacerbated Water Stress in the Arid Transboundary Rivers of Central Asia10.1016/j.scitotenv.2020.136585 ↗
39. Spatial and Temporal Dynamics of Water Footprint for Soybean Production in Areas of Recent Agricultural Expansion of the Brazilian Savannah (Cerrado)10.1016/j.jclepro.2019.119482 ↗
40. Surendran, U., Sushanth, C.M., Joseph, E.J., Al-Ansari, N., and Yaseen, Z.M. (2019). FAO CROPWAT Model-Based Irrigation Requirements for Coconut to Improve Crop and Water Productivity in Kerala, India. Sustainability, 11.10.3390/su11185132 ↗
41. Modelling the Crop Water Requirement Using FAO-CROPWAT and Assessment of Water Resources for Sustainable Water Resource Management: A Case Study in Palakkad District of Humid Tropical Kerala, India10.1016/j.aqpro.2015.02.154 ↗
42. Crop Water and Irrigation Requirements of Jatropha curcas L. in Semi-Arid Conditions of Botswana: Applying the CROPWAT Model10.1016/j.agwat.2019.105754 ↗
43. Brouwer, C., and Heibloem, M. (1986). Irrigation Water Management: Irrigation Water Needs. Train. Man., 3, Available online: https://www.fao.org/3/s2022e/s2022e00.htm.
44. Vapnik, V. (1998). The Support Vector Method of Function Estimation. Nonlinear Modeling, Springer.10.7551/mitpress/1130.003.0006 ↗
45. Application of Support Vector Machine for Classification of Multispectral Data10.1088/1755-1315/20/1/012038 ↗
46. Random Forests10.1023/A:1010933404324 ↗
47. Comparative Assessment of the Flash-Flood Potential within Small Mountain Catchments Using Bivariate Statistics and Their Novel Hybrid Integration with Machine Learning Models10.1016/j.scitotenv.2019.134514 ↗
48. Assessment of Landslide Susceptibility by Decision Trees in the Metropolitan Area of Istanbul, Turkey10.1155/2010/901095 ↗
49. Rouse, J.W. (2021, August 22). Monitoring the Vernal Advancement and Retrogradation of Natural Vegetation. NASA/GSFCT Type II Report, Greenbelt, MD, USA, Available online: https://ntrs.nasa.gov/citations/19730020508.
50. Rouse, J.W. (2021, August 22). Monitoring the Vernal Advancement and Retrogradation (Greenwave Effect) of Natural Vegetatioa NASA/GSFCT Type III Final Report, Greenbelt, MD, USA, Available online: https://ntrs.nasa.gov/citations/19740022555.
51. A Review of Assessing the Accuracy of Classifications of Remotely Sensed Data10.1016/0034-4257(91)90048-B ↗
52. Estimating the Kappa Coefficient and Its Variance under Stratified Random Sampling
53. Making Better Use of Accuracy Data in Land Change Studies: Estimating Accuracy and Area and Quantifying Uncertainty Using Stratified Estimation10.1016/j.rse.2012.10.031 ↗
54. Explaining the Unsuitability of the Kappa Coefficient in the Assessment and Comparison of the Accuracy of Thematic Maps Obtained by Image Classification10.1016/j.rse.2019.111630 ↗
55. Doorenbos, J., and Pruitt, W.O. (1977). Guidelines for Predicting Crop Water Requirements, Food and Agriculture Organization of the United Nations. FAO irrigation and drainage paper.
56. ABHS (2011). Etude D’actualisation du Plan Directeurd’Aménagements Intégrés des Ressources en Eau (PDAIRE) du Bassin Hydraulique de Sebou.