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Investigating the Relationship Between Drought indices and Maize Yield Using Random Forest Method (Case Study: Qazvin Plain Irrigation Network)

Document Type : Original Article

Authors
Hadi Ramezani Etedali 1
Mojgan Ahmadi 2
1 Professor,, Department of Water Engineering, Imam Khomeini International University, Qazvin, Iran
2 PhD. in Irrigation and Drainage, Imam Khomeini International University, Qazvin, Iran
10.30467/nivar.2024.467444.1299
Abstract
Pre-harvest crop yield prediction is crucial for food security, grain trade, and policy making. In this research, the use of random forest method in simulating maize yield in ten selected fields in Qazvin plain irrigation network during 2019-2020 period using NDVI, MSAVI, and EVI drought indices has been investigated. Sentinel 2 satellite was used for drought indices. The results were evaluated using R2, NRMSE, and MBE statistics. To investigate the relationship between drought indices and maize yield, seven scenarios were defined. The results showed that the random forest model in scenarios one, three, and four in the test stage with a significant probability of 95% respectively (P-value=0.00) and an explanation coefficient of more than 0.8 and a small amount of NRMSE is a suitable estimate of the maize yield.
Keywords
Maize Yield
Drought Indicators
Sentinel 2
Machine Learning
Subjects
  1. امینی، س.، علیزاده، ا.، فرید حسینی، ع.، اکبری، م. (1401). پیش بینی مقدار تولید محصول ذرت علوفه ای با استفاده از اطلاعات ماهواره ای. آبیاری و زهکشی ایران، دوره 16، شماره، 2، صفحات 386-371.
  2. گنجی خرم دل، ن.، خالقی، غ. (1399). تخمین عملکرد و زیست توده ذرت علوفه ای در هر بازدید ماهواره ای در طی فصل رشد (مطالعه موردی: دشت فراهان). تولید و فرآوری محصولات زراعی و باغی، دوره 10، شماره 1، صفحات 126-113.
  3. Azzari, G., Jain, M., & Lobell, D. B. (2017). Towards fine resolution global maps of crop yields: Testing multiple methods and satellites in three countries. Remote Sensing of Environment, 202, 129-141.
  4. Cai, Y.; Guan, K.; Lobell, D.; Potgieter, A.B.; Wang, S.; Peng, J.; Xu, T.; Asseng, S.; Zhang, Y.; You, L. Integrating satellite and climate data to predict wheat yield in Australia using machine learning approaches. Agric. For. Meteorol. 274, 144–159.
  5. Chen, X., Feng, L., Yao, R., Wu, X., Sun, J., & Gong, W. 2021. Prediction of maize yield at the city level in China using multi-source data. Remote Sensing, 13(1), 146.
  6. Córdoba, M. A., Bruno, C. I., Costa, J. L., Peralta, N. R., & Balzarini, M. G. (2016). Protocol for multivariate homogeneous zone delineation in precision agriculture. Biosystems engineering, 143, 95-107.
  7. DiRienzo, C., Fackler, P. L., & Goodwin, B. K. (2000). Modeling spatial dependence and spatial heterogeneity in county yield forecasting models.
  8. Erenstein, O., Jaleta, M., Sonder, K., Mottaleb, K., & Prasanna, B. M. (2022). Global maize production, consumption and trade: trends and R&D implications. Food security, 14(5), 1295-1319.
  9. Feng, L.; Wang, Y.; Zhang, Z.; Du, Q. 2021. Geographically and temporally weighted neural network for winter wheat yield prediction. Remote Sens. Environ. 262, 112514.
  10. Fróna, D., Szenderák, J., & Harangi-Rákos, M. (2019). The challenge of feeding the world. Sustainability, 11(20), 5816.
  11. Grote, U., Fasse, A., Nguyen, T. T., & Erenstein, O. (2021). Food security and the dynamics of wheat and maize value chains in Africa and Asia. Frontiers in Sustainable Food Systems, 4, 617009.
  12. Hunt, M.L.; Blackburn, G.A.; Carrasco, L.; Redhead, J.W.; Rowland, C.S. High resolution wheat yield mapping using Sentinel-2. Remote Sens. Environ.233, 111410.
  13. Idso, S.B.; Jackson, R.D.; Reginato, R.J.1977. Remote sensing for agricultural water management and crop yield prediction. Water Manag., 1, 299–310.
  14. Khaki, S.; Pham, H.; Wang, L. 2021. Simultaneous corn and soybean yield prediction from remote sensing data using deep transfer learning. Rep.11, 11132.
  15. Ma, Y.; Zhang, Z.; Kang, Y.; Özdo˘gan, M. Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach. Remote Sens. Environ. 259, 112408.
  16. Ma, Y.; Zhang, Z.; Kang, Y.; Özdo˘gan, M. 2021. Corn yield prediction and uncertainty analysis based on remotely sensed variables using a Bayesian neural network approach. Remote Sens. Environ. 259, 112408.
  17. Neupane, D., Adhikari, P., Bhattarai, D., Rana, B., Ahmed, Z., Sharma, U., & Adhikari, D. (2022). Does climate change affect the yield of the top three cereals and food security in the world?. Earth, 3(1), 45-71.
  18. Ranum, P., Peña‐Rosas, J. P., & Garcia‐Casal, M. N. (2014). Global maize production, utilization, and consumption. Annals of New York academy of sciences, 1312(1), 105-112.
  19. Shahhosseini, M.; Hu, G.; Huber, I.; Archontoulis, S.V. Coupling machine learning and crop modeling improves crop yield prediction in the US Corn Belt. Sci. Rep. 11, 1606.
  20. Sun, J.; Di, L.; Sun, Z.; Shen, Y.; Lai, Z. County-level soybean yield prediction using deep CNN-LSTM model. Sensors 2019, 19, 4363.
  21. Tigchelaar, M., Battisti, D. S., Naylor, R. L., & Ray, D. K. (2018). Future warming increases probability of globally synchronized maize production shocks. Proceedings of the National Academy of Sciences, 115(26), 6644-6649.
  22. Wang, Y.; Zhang, Z.; Feng, L.; Du, Q.; Runge, T. 2020. Combining multi-source data and machine learning approaches to predict winter wheat yield in the conterminous united states. Remote Sens. 12, 1232.
  23. Yuan, W.; Chen, Y.; Xia, J.; Dong, W.; Magliulo, V.; Moors, E.; Olesen, J.E.; Zhang, H. Estimating crop yield using a satellite-based light use efficiency model. Ecol. Indic60, 702–709.
  24. Erenstein, O., Jaleta, M., Sonder, K., Mottaleb, K., & Prasanna, B. M. 2022. Global maize production, consumption and trade: trends and R&D implications. Food security, 14(5), 1295-1319.
  25. Joinior W, Loireau M, Fargette M, Filho B, Wele A.2017. Correlation between soil erodibility and satellite data on areas of current desertification: a case study in Senegal. Ci Trop. Recife. 42 (2):51-66.

 

 

Nivar
Volume 48, 126-127 - Serial Number 126
October 2024
Pages 127-137

  • Receive Date 11 July 2024
  • Revise Date 25 September 2024
  • Accept Date 22 October 2024
  • Publish Date 22 September 2024