Review of wheat yield estimating methods in Morocco
DOI:
https://doi.org/10.48346/IMIST.PRSM/ajlp-gs.v5i4.33050Keywords:
Wheat yield, review, estimating wheat, remote sensing, crop modellingAbstract
Context and background:
Wheat is one of the oldest crops in the world and has always been one of the most important staple foods for millions of people around the world, especially in North Africa, where wheat is the most dominant crop. The importance of wheat yield estimation is well known in agricultural management and policy making at regional and national levels.
In semi-arid areas such as the case of Morocco, an operational cereal yield
estimating system that could assist decision makers in planning annual imports is needed.
In some developed countries, several effective tools are now available to monitor crops and optimize farm-level decisions by combining crop simulation models with seasonal forecasts. However, few tools are used to effectively manage crops at the farm level to cope with climate variability and risk.
Goal and objectives:
The following article presents an overview of current methods used for wheat yield estimation in the world and in Morocco
Methodology:
Various sections describing traditional methods, simulation models, and remote sensing. Then a section is devoted to the estimation methods used in Morocco and their efficiencies.
Results:
This article is very useful for researchers working on this subject because it brings together all the methods of estimating wheat yields worldwide and classifies them into categories and then situates Morocco, which is a relevant example of a North African country that is a leader in the use of spatial techniques and in the monitoring of crops, and wheat in particular
References
Wheat in the world - B.C. Curtis. www.fao.org/3/y4011e/y4011e04.htm..
Optimizing wheat productivity in two rain-fed environments .
New publications: The importance of wheat in the global food supply to a growing population – CIMMYT. https://www.cimmyt.org/publications/new-publications-the-importance-of-wheat-in-the-global-food-supply-to-a-growing-population/
FAO Cereal Supply and Demand Brief | World Food Situation | Food and Agriculture Organization of the United Nations. www.fao.org/worldfoodsituation/csdb/e
Araos, M., Berrang-Ford, L., Ford, J. D., Austin, S. E., Biesbroek, R., &Lesnikowski, A. (2016). Climate change adaptation planning in large cities: A systematic global assessment. Environmental Science & Policy, 66, 375–382. https://doi.org/10.1016/J.ENVSCI.2016.06.009
Araos, M., Berrang-Ford, L., Ford, J. D., Austin, S. E., Biesbroek, R., &Lesnikowski, A. (2016). Climate change adaptation planning in large cities: A systematic global assessment. Environmental Science & Policy, 66, 375–382. https://doi.org/10.1016/J.ENVSCI.2016.06.009
Atzberger, C. (2013). Advances in remote sensing of agriculture: Context description, existing operational monitoring systems and major information needs. Remote Sensing, 5(2), 949–981. https://doi.org/10.3390/rs5020949
Bahri, H., Annabi, M., CheikhM’Hamed, H., &Frija, A. (2019). Assessing the long-term impact of conservation agriculture on wheat-based systems in Tunisia using APSIM simulations under a climate change context. Science of the Total Environment, 692, 1223–1233. https://doi.org/10.1016/j.scitotenv.2019.07.307
Balaghi. Riad, jlebene Mohammed, tychon Bernard, E. H. (2012). La RedictionAgrometerologieDs Rendements Cereales Au Maroc. La PriductinAgrometeologique Des Rendements Cereales Au Maroc.
Balaghi, R. (2014). General presentation and institutional framework CGMS-MAROC : A jointly owned product , in the framework of an official agreement. November, 19–21.
Balaghi, R., Tychon, B., Eerens, H., &Jlibene, M. (2008). Empirical regression models using NDVI, rainfall and temperature data for the early prediction of wheat grain yields in Morocco. International Journal of Applied Earth Observation and Geoinformation, 10(4), 438–452. https://doi.org/10.1016/j.jag.2006.12.001
Basso, B., Cammarano, D., &Carfagna, E. (2013). Review of Crop Yield Forecasting Methods and Early Warning Systems.
Basso, B., & Liu, L. (2019). Seasonal crop yield forecast: Methods, applications, and accuracies. Advances in Agronomy, 154, 201–255. https://doi.org/10.1016/BS.AGRON.2018.11.002
Benabdelouahab, T., Balaghi, R., Hadria, R., Lionboui, H., Djaby, B., &Tychon, B. (2016). Testing Aquacrop to Simulate Durum Wheat Yield and Schedule Irrigation in a Semi-Arid Irrigated Perimeter in Morocco. Irrigation and Drainage, 65(5), 631–643. https://doi.org/10.1002/ird.1977
Campoy, J., Campos, I., Plaza, C., Calera, M., Bodas, V., & Calera, A. (2020). Estimation of harvest index in wheat crops using a remote sensing-based approach. Field Crops Research, 256(November 2019), 107910. https://doi.org/10.1016/j.fcr.2020.107910
Coupes-echantillons, M. D. E. S. (2015). L ’ estimation des rendements des cultures par la méthode des. 25–28.
Deborah et al , 2019). Predicting wheat yield by simple regression.pdf.
Devkota, M., &Yigezu, Y. A. (2020). Explaining yield and gross margin gaps for sustainable intensification of the wheat-based systems in a Mediterranean climate. Agricultural Systems, 185(September). https://doi.org/10.1016/j.agsy.2020.102946
Dong, J., Lu, H., Wang, Y., Ye, T., & Yuan, W. (2020). Estimating winter wheat yield based on a light use efficiency model and wheat variety data. ISPRS Journal of Photogrammetry and Remote Sensing, 160(August 2019), 18–32. https://doi.org/10.1016/j.isprsjprs.2019.12.005
Dong, J., Lu, H., Wang, Y., Ye, T., & Yuan, W. (2020). Estimating winter wheat yield based on a light use efficiency model and wheat variety data. ISPRS Journal of Photogrammetry and Remote Sensing, 160(August 2019), 18–32. https://doi.org/10.1016/j.isprsjprs.2019.12.005
Durgun, Y. Ö., Gobin, A., Duveiller, G., &Tychon, B. (2020). A study on trade-offs between spatial resolution and temporal sampling density for wheat yield estimation using both thermal and calendar time. International Journal of Applied Earth Observation and Geoinformation, 86(October 2019), 101988. https://doi.org/10.1016/j.jag.2019.101988
Epule, T., Chehbouni, A., Chfadi, T., Ongoma, V., Martínez-cruz, L., Lytia, E., Er-raki, S., Khabba, S., Etongo, D., Achli, S., Salih, W., Chuwah, C., Jemo, M., &Chairi, I. (2022). A Systematic National Stocktake of Crop Models in Morocco. 470(May). https://doi.org/10.1016/j.ecolmodel.2022.110036
Gómez, D., Salvador, P., Sanz, J., & Casanova, J. L. (2021). Modelling wheat yield with antecedent information, satellite and climate data using machine learning methods in Mexico. Agricultural and Forest Meteorology, 300(October 2020), 1–8. https://doi.org/10.1016/j.agrformet.2020.108317
Htitiou, A., Boudhar, A., Lebrini, Y., Hadria, R., Lionboui, H., Elmansouri, L., Tychon, B., &Benabdelouahab, T. (2019). The Performance of Random Forest Classification Based on Phenological Metrics Derived from Sentinel-2 and Landsat 8 to Map Crop Cover in an Irrigated Semi-arid Region. Remote Sensing in Earth Systems Sciences, 2(4), 208–224. https://doi.org/10.1007/s41976-019-00023-9
Huang, J., Tian, L., Liang, S., Ma, H., Becker-Reshef, I., Huang, Y., Su, W., Zhang, X., Zhu, D., & Wu, W. (2015). Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model. Agricultural and Forest Meteorology, 204, 106–121. https://doi.org/10.1016/j.agrformet.2015.02.001
Hunt, M. L., Blackburn, G. A., Carrasco, L., Redhead, J. W., & Rowland, C. S. (2019). High resolution wheat yield mapping using Sentinel-2. Remote Sensing of Environment, 233(August), 111410. https://doi.org/10.1016/j.rse.2019.111410
Kamir, E., Waldner, F., & Hochman, Z. (2020). Estimating wheat yields in Australia using climate records, satellite image time series and machine learning methods. ISPRS Journal of Photogrammetry and Remote Sensing, 160(December 2019), 124–135. https://doi.org/10.1016/j.isprsjprs.2019.11.008
Kowalik, W., Dabrowska-Zielinska, K., Meroni, M., Raczka, T. U., & de Wit, A. (2014). Yield estimation using SPOT-VEGETATION products: A case study of wheat in European countries. International Journal of Applied Earth Observation and Geoinformation, 32(1), 228–239. https://doi.org/10.1016/j.jag.2014.03.011
Le Mouël, C., &Forslund, A. (2017). How can we feed the world in 2050? A review of the responses from global scenario studies. European Review of Agricultural Economics, 44(4), 541–591. https://doi.org/10.1093/erae/jbx006
Lionboui, H., Benabdelouahab, T., Htitiou, A., Lebrini, Y., Boudhar, A., Hadria, R., &Elame, F. (2020). Spatial assessment of losses in wheat production value: A need for an innovative approach to guide risk management policies. Remote Sensing Applications: Society and Environment, 18(February), 100300. https://doi.org/10.1016/j.rsase.2020.100300
Mann, M. L., & Warner, J. M. (2017). Ethiopian wheat yield and yield gap estimation: A spatially explicit small area integrated data approach. Field Crops Research, 201, 60–74. https://doi.org/10.1016/j.fcr.2016.10.014
Ouaadi, N., Jarlan, L., Ezzahar, J., Zribi, M., Khabba, S., Bouras, E., Bousbih, S., &Frison, P. L. (2020). Monitoring of wheat crops using the backscattering coefficient and the interferometric coherence derived from Sentinel-1 in semi-arid areas. Remote Sensing of Environment, 251(April). https://doi.org/10.1016/j.rse.2020.112050
Paul c. Doraiswamy, S. M. (2003). crop yield assesment from Romte sensing.pdf.
Qader, S. H., Dash, J., & Atkinson, P. M. (2018). Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq. Science of the Total Environment, 613–614, 250–262. https://doi.org/10.1016/j.scitotenv.2017.09.057
Qader, S. H., Dash, J., & Atkinson, P. M. (2018). Forecasting wheat and barley crop production in arid and semi-arid regions using remotely sensed primary productivity and crop phenology: A case study in Iraq. Science of the Total Environment, 613–614, 250–262. https://doi.org/10.1016/j.scitotenv.2017.09.057
Ren, J., Chen, Z., Zhou, Q., & Tang, H. (2008). Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China. International Journal of Applied Earth Observation and Geoinformation, 10(4), 403–413. https://doi.org/10.1016/j.jag.2007.11.003
Salehnia, N., Salehnia, N., SaradariTorshizi, A., &Kolsoumi, S. (2020). Rainfed wheat (Triticumaestivum L.) yield prediction using economical, meteorological, and drought indicators through pooled panel data and statistical downscaling. Ecological Indicators, 111(May 2019), 105991. https://doi.org/10.1016/j.ecolind.2019.105991
Satir, O., &Berberoglu, S. (2016). Crop yield prediction under soil salinity using satellite derived vegetation indices. Field Crops Research, 192, 134–143. https://doi.org/10.1016/j.fcr.2016.04.028
Schauberger, B., Jägermeyr, J., &Gornott, C. (2020). A systematic review of local to regional yield forecasting approaches and frequently used data resources. In European Journal of Agronomy (Vol. 120). Elsevier B.V. https://doi.org/10.1016/j.eja.2020.126153
Shanmugapriya, P., Rathika, S., Ramesh, T., & Janaki, P. (2019). Applications of Remote Sensing in Agriculture - A Review. International Journal of Current Microbiology and Applied Sciences, 8(01), 2270–2283. https://doi.org/10.20546/ijcmas.2019.801.238
Statistiques, D. (2018). Système Statistique Agricole du Maroc.
Tian, H., Wang, P., Tansey, K., Zhang, S., Zhang, J., & Li, H. (2020). An IPSO-BP neural network for estimating wheat yield using two remotely sensed variables in the Guanzhong Plain, PR China. Computers and Electronics in Agriculture, 169(January), 105180. https://doi.org/10.1016/j.compag.2019.105180
Tian, L., Wang, C., Li, H., & Sun, H. (2020). Yield prediction model of rice and wheat crops based on ecological distance algorithm. Environmental Technology and Innovation, 20, 101132. https://doi.org/10.1016/j.eti.2020.101132
Wahab, I., Hall, O., &Jirström, M. (2018). Remote sensing of yields: Application of UAV imagery-derived ndvi for estimating maize vigor and yields in complex farming systems in Sub-Saharan Africa. Drones, 2(3), 1–16. https://doi.org/10.3390/drones2030028
Wiegand, C. L., Richardson, A. J., Escobar, D. E., &Gerbermann, A. H. (1991). Vegetation indices in crop assessments. Remote Sensing of Environment, 35(2–3), 105–119. https://doi.org/10.1016/0034-4257(91)90004-P
Wu, S., Yang, P., Ren, J., Chen, Z., & Li, H. (2021). Regional winter wheat yield estimation based on the WOFOST model and a novel VW-4DEnSRF assimilation algorithm. Remote Sensing of Environment, 255(April 2020). https://doi.org/10.1016/j.rse.2020.112276
Zhang, T., Su, J., Liu, C., & Chen, W. H. (2021). State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter. Computers and Electronics in Agriculture, 180(June 2020), 105909. https://doi.org/10.1016/j.compag.2020.105909
Zhang, T., Su, J., Liu, C., & Chen, W. H. (2021). State and parameter estimation of the AquaCrop model for winter wheat using sensitivity informed particle filter. Computers and Electronics in Agriculture, 180(June 2020), 105909. https://doi.org/10.1016/j.compag.2020.105909
Zhao, Y., Potgieter, A. B., Zhang, M., Wu, B., & Hammer, G. L. (2020). Predicting wheat yield at the field scale by combining high-resolution Sentinel-2 satellite imagery and crop modelling. Remote Sensing, 12(6). https://doi.org/10.3390/rs12061024
Zhou, X., Wang, P., Tansey, K., Zhang, S., Li, H., & Tian, H. (2020). Reconstruction of time series leaf area index for improving wheat yield estimates at field scales by fusion of Sentinel-2, -3 and MODIS imagery. Computers and Electronics in Agriculture, 177(17), 105692. https://doi.org/10.1016/j.compag.2020.105692
Zhou, X., Wang, P., Tansey, K., Zhang, S., Li, H., & Tian, H. (2020). Reconstruction of time series leaf area index for improving wheat yield estimates at field scales by fusion of Sentinel-2, -3 and MODIS imagery. Computers and Electronics in Agriculture, 177(17), 105692. https://doi.org/10.1016/j.compag.2020.105692
