Cereal yield forecasting in semi-arid region of Algeria using MODIS-NDVI


  • Hakima Boulaaras Plant and Animal Production Improvement Laboratory, Department of Agronomy, Faculty of Natural and Life Sciences, Ferhat Abbas University Setif 1, 1900 Setif, Algeria
  • Tarek Bouregaa Plant and Animal Production Improvement Laboratory, Department of Agronomy, Faculty of Natural and Life Sciences, Ferhat Abbas University Setif 1, 1900 Setif, Algeria




Predicting, Yield, Remote sensing, NDVI


The prediction of cereals yields today is very important for global food security and helps decision-makers in the import-export operations of countries, especially with the rise world population. The advent of remote sensing technologies in precision farming systems has made cereal yield predictions possible, providing valuable insights into the temporal and spatial variations in cereal conditions across both large and small-scale crop lands. Among the various vegetation indices used to analyze these conditions, the normalized difference of vegetation index (NDVI) has emerged as a key indicator. The main objective of this study is to evaluate the possibility of using MODIS-NDVI data to forecast the yield of cereal crops (wheat and barley) in semi-arid region of Algeria (Setif). Additionally, identify the optimal timing for reliable and accurate crop yield forecasts. The remote sensing data utilized in this study covered the growing seasons from February to June, from 2002 to 2022. The results indicated a strong correlation between cereal grain yield and NDVI from late February to mid-March, with R² values ranging from 0.55 to 0.82 for the two cereal species. The RMSE of the NDVI based prediction model ranged from 0.01 t ha-1 to 0.276 t ha-1. The approximate average increase in the grain yield of barley and wheat lies between 0.659 to 0.746 t ha-1 with an increase of 0.1 in NDVI value. These results demonstrate the effectiveness of using MODIS-NDVI data for cereal yield forecasting in semi-arid region of Algeria, offering valuable predictions two to three months before the harvest.


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Adeniyi, O. D., Szabo, A., Tamás, J., & Nagy, A. (2020). Wheat Yield Forecasting Based on Landsat NDVI and SAVI Time Series. Preprints, 2020, 2020070065. https://doi.org/10.20944/preprints202007.0065.v1

Adhab, M., & Alkuwaiti, N. A. (2022). Germiniviruses occurrence in the middle east and their impact on agriculture in Iraq. In R. K. Gaur, P. Sharma & H. Czosnek (Eds.), Geminivirus: Detection, Diagnosis and Management (pp. 171-185) Cambridge, US: Academic Press. https://doi.org/10.1016/B978-0-323-90587-9.00021-3

Al-Ani, R. A., Adhab, M. A., El-Muadhidi, M. A., & Al-Fahad, M. A. (2011). Induced systemic resistance and promotion of wheat and barley plants growth by biotic and non-biotic agents against barley yellow dwarf virus. African Journal of Biotechnology, 10(56), 12078-12084.

Algérie Eco. (2022). Cereals: Algeria imported 10.6 million tonnes during the 2021/2022 campaign. Retrieved from https://www.algerie-eco.com/2022/07/13/cereales-lalgerie-a-importe-106-millions-de-tonnes-durant-la-campagne-2021-2021/

Ammar, M. (2014). Organisation de la chaîne logistique dans la filière céréales en Algérie : état des lieux et perspectives. Master of Sciences, CIHEAMI-AMM.

Anaz, A., Kadhim, N., Sadoon, O., Alwan, G., & Adhab, M. (2023). Sustainable Utilization of Machine-Vision-Technique-Based Algorithm in Objective Evaluation of Confocal Microscope Images. Sustainability, 15(4), 3726. https://doi.org/10.3390/su15043726

Atzberger, C., Vuolo, F., Klisch, A., Rembold, F., Meroni, M., Marcio, P. M., & Formaggio, A. (2016). Agriculture. In P. S. Thenkabail (Eds.), Remote Sensing Handbook (pp. 71-103) Florida, US: CRC Press.

Becker-Reshef, I., Justice, C., Barker, B., Humber, M., Rembold, F., Bonifacio, R., Zappacosta, M., Budde, M., Magadzire, T., Shitot,e C., Pound, J., Constantino, A., Nakalembe, C., Mwangi, K., Sobue, S., Newby, T., Whitcraft, A., Jarvis, I., & Verdin, J. (2020). Strengthening agricultural decisions in countries at risk of food insecurity: The GEOGLAM Crop Monitor for Early Warning. Remote Sensing of Environment, 237, 111553. https://doi.org/10.1016/j.rse.2019.111553

Becker-Reshef, I., Vermote, E., Lindeman, M., & Justice, C. (2010). A generalized regression-based model for forecasting winter wheat yields in Kansas and Ukraine using MODIS data. Remote Sensing of Environment, 114(6), 1312-1323. https://doi.org/10.1016/j.rse.2010.01.010

Cai, Y., Guan, K., Lobell, D., Potgieter, A. B., Wang, S., Peng, J., Xu, T., Asseng, S., Zhang, Y., You, L., & Peng, B. (2019). Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agricultural and Forest Meteorology, 274, 144-459. https://doi.org/10.1016/j.agrformet.2019.03.010

Chennafi, H., Bouzerzour, H., Aidaoui, A., & Saci, A. (2006). Yield response of durum wheat (Triticum durum Desf.) cultivar Waha to deficit irrigation under semi arid growth conditions. Asian Journal of Plant Sciences, 5(5), 854-860. https://doi.org/10.3923/ajps.2006.854.860

Climate Data. (2022). Climate Setif (Algeria). Retrieved from https://fr.climate-data.org/afrique/algerie/setif/setif-3595/

Dorosh, P., & Salam, A. (2006). Wheat markets and price stabilization in Pakistan: An analysis of policy options. The Pakistan Development Review, 47(1), 71-87.

Fang, H., Liang, S., Hoogenboom, G., Teasdale, J., & Cavigelli, M. (2008). Corn-yield estimation through assimilation of remotely sensed data into the CSM-CERES-Maize model. International Journal of Remote Sensing, 29(10), 3011-3032. https://doi.org/10.1080/01431160701408386

Fang, J., Piao, S., He, J., & Ma, W. ( 2004). Increasing terrestrial vegetation activity in China, 1982-1999. Science in China Series C: Life Sciences, 47, 229-240. https://doi.org/10.1007/BF03182768

FAO. (2018). The State of Food Security and Nutrition in the World 2018: Building climate resilience for food security and nutrition. Rome, Italy: FAO.

Geng, L., Mengdi, L., Zhang, G., & Ye, L. (2022). Barley: a potential cereal for producing healthy and functional foods. Food Quality and Safety, 6, fyac012. https://doi.org/10.1093/fqsafe/fyac012

GIMMS. (2021). Global Agricultural Monitoring System. Retrieved January 12, 2021 from https://glam1.gsfc.nasa.gov/

Google Earth Pro. (2023). Google Earth Pro [Computer Software]. https://earth.google.com/intl/earth/versions/#download-pro

Gop, N. V., & Savenkov, O. A. (2019). Relationships between the NDVI, Yield of Spring Wheat, and Properties of the Plow Horizon of Eluviated Clay-Illuvial Chernozems and Dark Gray Soils. Eurasian Soil Science, 52, 339-347. https://doi.org/10.1134/S1064229319030050

Huang, J., Wang, X., Li, X., Tian, H., & Pan, Z. (2013). Remotely sensed rice yield prediction using multi-temporal NDVI data derived from NOAA’sAVHRR. PloS One, 8(8), e70816. https://doi.org/10.1371/journal.pone.0070816

Khalaf, L. K., Adhab, M., Aguirre-Rojas, L. M., & Timm, A. E. (2023). Occurrences of wheat curl mite aceria tosichella keifer 1969 (eriophydae) and the associated viruses, (WSMV, HPWMoV, TriMV) in IRAQ. Iraqi Journal of Agricultural Sciences, 54(3), 837-849. https://doi.org/10.36103/ijas.v54i3.1767

Khalaf, L., Chuang, W.-P., Aguirre-Rojas, L. M., Klein, P., & Smith, C. M. (2019). Differences in Aceria tosichella population responses to wheat resistance genes and wheat virus transmission. Arthropod-Plant Interactions, 13, 807-818. https://doi.org/10.1007/s11829-019-09717-9

Kouadio, L., Newlands, N. K., Davidson, A., Zhang, Y., & Chipanshi, A. (2014). Assessing the performance of MODIS NDVI and EVI for seasonal crop yield forecasting at the ecodistrict scale. Remote Sensing, 6(10), 10193-10214. https://doi.org/10.3390/rs61010193

Li, C., Qi, J., Yang, L., Wang, S., Yang, W., Zhu, G., Zou, S., & Zhang, F. (2014). Regional vegetation dynamics and its response to climate change—a case study in the Tao River Basinin Northwestern China. Environmental Research Letters, 9(12), 125003. https://doi.org/10.1088/1748-9326/9/12/125003

Liu, H., Zhang, X., Xu, Y., Ma, F., Zhang, J., Cao, Y., Li, L., & An, D. (2020). Identification and validation of quantitative trait loci for kernel traits in common wheat (Triticum Aestivum L.). BMC Plant Biology, 20, 529. https://doi.org/10.1186/s12870-020-02661-4

Lykhovyd, P. V. (2020). Sweet corn yield simulation using normalized difference vegetation index and leaf area index. Journal of Ecological Engineering, 21(3), 228-236. https://doi.org/10.12911/22998993/118274

Mashaba, Z., Chirima, G., Botai, J. O., Combrinck, L., Munghemezulu C., & Dube, E. (2017). Forecasting winter wheat yields using MODIS NDVI data for the Central Free State region. South African Journal of Science, 113(11/12), 1-6. https://doi.org/10.17159/sajs.2017/20160201

Mekhlouf, A., Dehbi, F., Hanachi, A., & Harbi, M. (2012). Réponses de blé dur aux basses températures en relation avec la capacité de production. Agriculture, 3(1), 13-23.

Mkhabela, M., Bullock, P., Gervais, M., Finlay, G., & Sapirstein, H. (2010). Assessing indi-cators of agricultural drought impacts on spring wheat yield and quality on the Canadian Prairies. Agricultural and Forest Meteorology, 150(3), 399-410. https://doi.org/10.1016/j.agrformet.2010.01.001

Mulianga, B., Bégué, A., Simoes, M., & Todoroff, P. (2013). Forecasting regional sugarcane yield based on time integral and spatial aggregation of MODIS NDVI. Remote Sensing, 5(5), 2184-2199. https://doi.org/10.3390/rs5052184

Nagy, A., Fehér, J., & Tamás, J. (2018). Wheat and maize yield forecasting for the Tisza river catchment using MODIS NDVI time series and reported crop statistics. Computers and Electronics in Agriculture, 151, 41-49. https://doi.org/10.1016/j.compag.2018.05.035

Nagy, A., Szabó, A., Adeniyi, O. D., & Tamás J. (2021). Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics. Agronomy, 11(4), 652. https://doi.org/10.3390/agronomy11040652

Panek, E., & Gozdowski, D. (2021). Relationship between MODIS Derived NDVI and Yield of Cereals for Selected European Countries. Agronomy, 11(2), 340. https://doi.org/10.3390/agronomy11020340

Phiri, D., Simwanda, M., Salekin, S., Nyirenda V. R., Murayama, Y., & Ranagalage, M. (2020). Sentinel-2 data for land cover/use mapping: A review. Remote Sensing, 12(14), 2291. https://doi.org/10.3390/rs12142291

Pismennaya, E. V., Azarova1, M. Y., Golosnoy, E.V., Odintsov, S.V., & Kipa, L.V. (2021). Relationship between NDVI index obtained from MODIS and winter wheat yield. Earth and Environmental Science, 848, 012110. https://doi.org/10.1088/1755-1315/848/1/012110

Rouabhi, A. (2017). Spatiotemporal characterization of the annual Rrainfall in Setif region-Algeria. Revue Agriculture, 4(1), 31-38.

Rouse, J. W., Haas, R. H., Schell, J. A., & Deering, D. W. (1974). Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 351, 309-317.

Sabins, F. F. Jr. (1987). Remote sensing – principles and interpretation. (2nd ed.). New York, UK: W. H. Freeman and Company.

Tuğaç, M. G., Özbayoğlu, A. M., Torunlar, H., & Karakurt, E. (2022). Wheat Yield Prediction with Machine Learning based on MODIS and Landsat NDVI Data at Field Scale. International Journal of Environment and Geoinformatics, 9(4), 172-184. https://doi.org/10.30897/ijegeo.1128985

USGS. (2021). Global Croplands. Retrieved January 12, 2021 form https://croplands.org/app/map?lat=0&lng=0&zoom=2

Vannoppen, A., Gobin, A., Kotova, L., Top, S., Cruz, L., Vıksna, A., Aniskevich, S., Bobylev, L., Buntemeyer, L., Caluwaerts, S., Troch, R. D., Gnatiuk, N., Hamdi, R., Remedio, A. R., Sakalli, A., Vyver, H. V. D., Schaeybroeck, B. V., & Termonia, P. (2020). Wheat Yield Estimation from NDVI and Regional Climate Models in Latvia. Remote Sensing, 12(14), 2206. https://doi.org/10.3390/rs12142206

Vozhehova, R., Maliarchuk, M., Biliaieva, I., Lykhovyd, P. V., Maliarchuk, A., & Tomnytskyi, A., (2020). Spring row crops productivity prediction using normalized difference vegetation index. Journal of Ecological Engineering, 21(6), 176-182. https://doi.org/10.12911/22998993/123473

Wang, Y., Xu, X., Huang, L., Yang, G., Fan, L., Wei, P., & Chen, G. (2019). An Improved CASA Model for Estimating winter wheat yield from remote sensing images. Remote Sensing, 11(9), 1088. https://doi.org/10.3390/rs11091088

Yin, Z., & Williams, T. H. L. (1997). Obtaining spatial and temporal vegetation data from Landsat MSS and AVHRR/NOAA satellite mages for a hydrologic model, Photogrammetric Engineering and Remote Sensing, 63(1), 69-77.

Zhang, P.-P., Zhou, X.-X., Wang, Z.-X., Mao, W., Li, W.-X., Yun, F., Guo, W.-S., & Tan, C.-W. (2020). Using HJ-CCD image and PLS algorithm to estimate the yield of field-grown winter wheat. Scientific Reports, 10, 5173. https://doi.org/10.1038/s41598-020-62125-5



How to Cite

Boulaaras, H., & Bouregaa, T. (2024). Cereal yield forecasting in semi-arid region of Algeria using MODIS-NDVI. Journal of Aridland Agriculture, 10, 7–14. https://doi.org/10.25081/jaa.2024.v10.8562