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Evaluation of weather parameter-based pre-harvest yield forecast models for wheat crop: a case study in Saurashtra region of Gujarat

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Abstract

Wheat is the important food grain and is cultivated in many Indian states: Punjab, Haryana, Uttar Pradesh, and Madhya Pradesh, which contributes to major crop production in India. In this study, popular statistical approach multiple linear regression (MLR) and time series approaches Time Delay Neural Network (TDNN) and ARIMAX models were envisaged for wheat yield forecast using weather parameters for a case study area, i.e., Junagarh district, western Gujarat region situated at the foot of Mount Girnar. Weather data corresponds to 19 weeks (42nd to 8th Standard Meteorological Week, SMW) during crop growing season was used for prediction of wheat yield using these statistical techniques and were evaluated for their predictive capability. Furthermore, trend analysis among weather parameters and crop yield was also carried out in this study using non-parametric Mann–Kendall test and Sen’s slope method. Significant negative correlation was observed between wheat yield and some of the weekly weather variables, viz., maximum temperature (48, 49, 50, 51, 52, and 4th SMW), and total rainfall (50, 51, and 1st SMW) while positive correlation was observed with morning relative humidity (49 and 3rd SMW). Study indicated that forecast error varied from 1.80 to 10.28 in MLR, 0.79 to 7.79 in ARIMAX (2,2,2), − 3.09 to 10.18 in TDNN (4,5) during model training period (1985–2014). The MAPE value shows that the time series data predicted less than 5% of variation, whereas the conventional MLR technique indicated more than 7% variation. Both ARIMAX and TDNN approaches indicated better performance during model training periods, i.e., 1985–2014 and 1985–2015, while former performed well during the forecast periods 1985–2016 and 1985–2017. Overall, the study indicated that the ARIMAX approach can be used consistently for 4 years using the same model.

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References

  • Aboukarima, A. M., Elsoury, H. A., & Menyawi, M. (2015). Artificial neural network model for the prediction of the cotton crop leaf area. International Journal of Plant & Soil Science, 8(4), 1–13. https://doi.org/10.9734/IJPSS/2015/19686

    Article  Google Scholar 

  • Aditya, F., Gusmayanti, E., & Sudrajat, J. (2021). Rainfall trend analysis using Mann-Kendall and Sen’s slope estimator test in West Kalimantan IOP Conf. Ser.: Earth Environ. Sci., 893, 1–9. https://doi.org/10.1088/1755-1315/893/1/012006

    Article  Google Scholar 

  • Agrawal, R., Chandrahas, & Aditya, K. (2012). Use of discriminant function analysis for forecasting crop yield. Mausam, 63(3), 455–458.

    Article  Google Scholar 

  • Agrawal, R., Jain, R. C., Jha, M. P., & Singh, D. (1980). Forecasting of rice yield using climatic variables. Indian Journal of Agricultural Sciences, 50(9), 680–684.

    Google Scholar 

  • Agrawal, R., & Mehta, S. C. (2007). Weather based forecasting of yield, pest and diseases-IASRI models. Journal of the Indian Society of Agricultural Statistics, 61(2), 255–263.

    Google Scholar 

  • Ankrah, S., Peiris, B. L., & Thattil, R. O. (2015). Weighted modelling and forecasting of cocoa production in Ghana: A multivariate approach. Tropical Agricultural Research, 26(3), 569–578.

    Article  Google Scholar 

  • Annu, Sisodia, B. V. S., & Rai, V. N. (2016). An application of principal component analysis for pre-harvest forecast model for rice crop based on biometrical characters. Journal of Applied and Natural Science, 8(3), 1164–1167.

    Article  Google Scholar 

  • Anonymous. (2021a). Second advance estimates of directorate of economics and statistics. Govt. of India.

  • Anonymous. (2021b). The Drought of 1987 response and management. Govt. of India, 1989, 48–50.

  • Dahikar, S. S., & Rode, S. V. (2014). Agricultural crop yield prediction using Artificial Neural Network approach. International Journal of Innovative Research In Electrical, Electronics, Instrumentation And Control Engineering, 2(1), 683–686.

    Google Scholar 

  • Dellana, S. A., & West, D. (2009). Predictive modeling for wastewater applications: Linear and nonlinear approaches. Environmental Modelling & Software, 24(1), 96–106.

    Article  Google Scholar 

  • Emami, Y. (2007). Cereal production (pp. 69–83). Shiraz University Press.pp.

    Google Scholar 

  • Garde, Y. A., Shukla, A. K., & Singh, S. (2012). Pre-harvest forecasting of rice yield using weather indices in Pantnagar. International Journal of Agricultural Statistical Science, 8(1), 233–241.

    Google Scholar 

  • Hemavathi, M., & Prabakaran, K. (2018). ARIMA model for forecasting of area, production and productivity of rice and its growth status in Thanjavur District of Tamil Nadu, India. International Journal of Current Microbiology and Applied Sciences, 7(02), 149–156. https://doi.org/10.20546/ijcmas.2018.702.019

    Article  Google Scholar 

  • Hyndman, R., Athanasopoulos, G., Bergmeir, C., Caceres, G., Chhay, L., O'Hara-Wild, M., Petropoulos, F., Razbash, S., Wang, E., & Yasmeen, F. (2019). Forecast: Forecasting functions for time series and linear models. R package version 8.7.

  • Jebb, A. T., Tay, L., Wang, W., & Huang, Q. (2015). Time series analysis for psychological research: Examining and forecasting change. Frontiers in Psychology, 6, 1–24. https://doi.org/10.3389/fpsyg.2015.00727

    Article  Google Scholar 

  • Kendall, M. G. (1975). Rank correlation methods. New York, NY: Oxford University Press.

    Google Scholar 

  • Kumar, J., Devi, M., DeepikaVerma, D. P. M., & Sharma, A. (2021). Pre-harvest forecast of rice yield based on meteorological parameters using discriminant function analysis. Journal of Agriculture and Food Research, 5. https://doi.org/10.1016/j.jafr.2021.100194

  • Kumar, N., Pisal, R. R., Shukla, S. P., & Pandey, K. K. (2014). Crop yield forecasting of paddy, sugarcane and wheat through regression technique for south Gujarat. MAUSAM, 65(3), 361–364. https://mausamjournal.imd.gov.in/index.php/MAUSAM/article/view/1041

    Article  Google Scholar 

  • Kumari, P., Mishra, G. C., & Srivastava, C. P. (2013). Forecasting of productivity and pod damage by Helicoverpaarmigera using artificial neural network model in pigeonpea (CajanusCajan). International Journal of Agriculture Environment and Biotechnology, 6(2), 335–340.

    Google Scholar 

  • Kumari, P., Mishra, G. C., & Srivastava, C. P. (2014a). Time series forecasting of losses due to pod borer, pod fly and productivity of pigeonpea (Cajanuscajan) for North West Plain Zone (NWPZ) by using artificial neural network (ANN). International Journal Agricultural and Statistical Science, 10(1), 15–21.

    Google Scholar 

  • Kumari, P., Mishra, G. C., Pant, A. K., Shukla, G., & Kujur, S. N. (2014b). Comparison of forecasting ability of different statistical models for productivity of rice (Oryzasativa l.) in India. The Ecoscan., 8(3&4), 193–198.

    CAS  Google Scholar 

  • Kumari, P., Mishra, G. C., & Srivastava, C. P. (2016). Statistical models for forecasting pigeon pea yield in Varanasi region. Journal of Agrometeorology, 18(2), 306–310.

    Article  Google Scholar 

  • Lee, B.-H., Kenkel, P., & Brorsen, B. W. (2013). Pre-harvest forecasting of county wheat yield and wheat quality using weather information. Agricultural and Forest meteorology., 168, 25–35. https://doi.org/10.1016/j.agrformet.2012.08.010

    Article  Google Scholar 

  • Mann, H. B. (1945). Nonparametric tests against trend. Econometrica, 13, 245–259. https://doi.org/10.2307/1907187

    Article  Google Scholar 

  • Mcculloch, W. S., & Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. Bulletin of Mathematical Biophysics, 5, 115–133. https://doi.org/10.2307/2268029

    Article  Google Scholar 

  • Pandey, K. K., Rai, V. N., Sisodia, B. V. S., Bharti, A. K., & Gairola, K. C. (2013). Pre-harvest forecast models based on weather variable and weather indices for eastern U.P. Advances in Bioresearch, 4(2), 118–122.

    Google Scholar 

  • Parihar, J. S., & Oza, S. R. (2006). FASAL: An integrated approach for crop assessment and production forecasting. Proceedings of SPIE - The International Society for Optical Engineering, 6411, 1–13. https://doi.org/10.1117/12.713157

    Article  Google Scholar 

  • Sen, P. K. (1968). Estimates of the regression coefficient based on Kendall’s tau. Journal of the American Statistical Association, 63, 1379–1389. https://doi.org/10.1080/01621459.1968.10480934

    Article  Google Scholar 

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Authors and Affiliations

  1. Mahalanobis National Crop Forecast Centre (MNCFC), Ministry of Agriculture and Farmers Welfare, Govt. of India, Pusa Campus, New Delhi, 110012, India

    K. B. Banakara, Neha Sharma, Soham Sahoo, Sunil Kumar Dubey & V. M. Chowdary

  2. National Remote Sensing Centre, Hyderabad, 500037, India

    V. M. Chowdary

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  1. K. B. Banakara
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  2. Neha Sharma
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  3. Soham Sahoo
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  4. Sunil Kumar Dubey
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  5. V. M. Chowdary
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Banakara, K.B., Sharma, N., Sahoo, S. et al. Evaluation of weather parameter-based pre-harvest yield forecast models for wheat crop: a case study in Saurashtra region of Gujarat. Environ Monit Assess 195, 51 (2023). https://doi.org/10.1007/s10661-022-10552-4

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