Time series analysis and forecasting of pigeonpea [Cajanus cajan (L.) Millsp.] area, production and yield in eastern Uttar Pradesh using ARIMA
DOI:
https://doi.org/10.53550/jfl.v38.i2.274Keywords:
Area, ARIMA, CAGR, Cuddy-Della Valle index, Forecasting, Instability, Production, Pigeonpea, YieldAbstract
This study examines trends, variability, and forecasting of pigeonpea cultivation in Eastern Uttar Pradesh, India, from 1960 to 2022, focusing on area, production, and yield across three periods: Period I (1960–1980), Period II (1981–2000), and Period III (2001–2022). Using descriptive statistics, the Cuddy-Della Valle instability index, trend models, ARIMA modeling, and evaluation metrics (RMSE, MAPE, R2, AIC), the analysis reveals key findings. The cultivated area declined from 206.35 thousand hectares in Period I to 155.03 thousand hectares in Period III, with a negative CAGR of -0.41%. Production peaked at 134.04 thousand tonnes in Period II but fell to 90.87 thousand tonnes in Period III, despite a marginally positive CAGR of 0.21%. Yield rose to 15.75 quintals/hectare in Period II but dropped to 13.43 quintals/hectare in Period III, showing a positive CAGR of 0.80%. Variability increased in area and production, while yield stabilized. Quadratic trend models captured non-linear patterns effectively, with R2 of 0.839, 0.675, and 0.573 for area, production, and yield, respectively. ARIMA (1,1,10) models provided accurate forecasts, emphasizing policy and technological measures for sustainable cultivation.
References
Ahmar AS, Singh PK, Ruliana R, Pandey AK and Gupta S. 2023. Comparison of Arima, SutteARIMA, and holt-winters, and NNAR models to predict food grain in India. Forecasting 5(1): 138–152. doi: 10.3390/forecast5010006.
Ajit S and Gupta RK. 2020. Pragmatic investigation for forecasting apple area and production in Himachal Pradesh. International Journal of Agricultural and Statistical Sciences 16(4): 1049-1065.
Banakara KB, Sharma N, Sahoo S, Dubey SK and Chowdary VM. 2022. Evaluation of weather parameter-based pre-harvest yield forecast models for wheat crop: a case study in Saurashtra region of Gujarat.
Environmental Monitoring and Assessment 195(1): 51. doi: 10.1007/s10661-022-10552-4.Bharati RC and Singh AK. 2019. Predicting rice production using autoregressive integrated moving average model. Journal of AgriSearch 6(4): 205–210.
Chitikela G. 2022. Prediction of mango production using machine intelligence techniques: A case study from Karnataka, India. Acta Scientific Agriculture 6(9): 16-22.
Chaudhari DJ and Tingre AS. 2014. Use of ARIMA modeling for forecasting green gram prices for Maharashtra. Journal of Food Legumes 27(2): 136-139. doi: https://doi.org/10.59797/jfl.v27i2.813.
Choudhury N, Saurav S, Kumar RR and Budhlakoti N. 2017. Modelling and forecasting of total area, irrigated area, production and productivity of important cereal crops in India towards food security. International Journal of Current Microbiology and Applied Sciences 6(10): 2591–2600. doi: 10.20546/ijcmas.2017.610.305.
Darekar A and Reddy AA. 2017. Price forecasting of pulses: case of pigeonpea. Journal of Food Legumes 30(3): 212-216.
Dharmaraja S, Jain V, Anjoy P and Chandra H. 2020. Empirical analysis for crop yield forecasting in India. Agricultural Research 9(1): 132–138. doi: 10.1007/s40003-019-00413-x.
Dheer P, Yadav P and Katiyar PK 2018. Estimation of production and yield of pulses using ARIMA-ARNN model. Journal of Food Legumes 31(4): 254-257. doi:
https://doi.org/10.59797/journaloffoodlegumes.v31i4.422.
Directorate of Economics & Statistics. 2024. Agricultural statistics at a glance. Ministry of agriculture & farmers welfare. Government of India.
Government of India. 2024. Sankhyakiya Patrika. Ministry of Statistics and Programme Implementation. Kathayat B and Dixit AK. 2021. Paddy price forecasting in India using arima model. Journal of Crop and Weed 17(1): 48-55.
Khaemba PF, Muiruri PW and Kibutu TN. 2021. Trend analysis in sugarcane growth in Mumias sugar belt, Western Kenya; for the period 1985–2015. Interdisciplinary Journal of Rural and Community Studies 3(1): 31–40. doi: 10.51986/ijrcs-2021.vol3.01.04. Kulkarni S, Mandal SN, Sharma GS and Mundada MR. 2018. Predictive analysis to improve crop yield using a neural network model. In: 2018 international conference on advances in computing, communications and informatics (ICACCI). IEEE. Pp 74–79.
Kumar H, Chandra A, Udgata AR, Hazra KK and Bhagawati K. 2025. ARIMA timeseries forecast modeling for whitefly, Bemisia tabaci (Gennadius). Journal of Food Legumes 38(1): 115-119. doi: https://doi.org/10.59797/jfl.v38i1.1565.
Kumar M, Prakash G and Rana SK. 2024. Statistical modeling for analysis of growth and trend pattern of wheat production in selected states of India. Asian Journal of Research in Crop Science 9(1): 66–75. doi:10.9734/ajrcs/2024/v9i1246.
Kumar N, Kushwaha RR, Meena NR, Mishra H and Yadav APS. 2023. A study on costs and returns of paddy cultivation in Ambedkar Nagar district of Uttar Pradesh. International Journal of Statistics and Applied Mathematics 8(3): 107–111.
Kumari P, Mishra GC and Srivastava CP. 2013. Forecasting of productivity and pod damage by Helicoverpa armigera using artificial neural network model in pigeonpea (Cajanus cajan). International Journal of Agriculture Environment & Biotechnology 6(2): 335–340.
Kumari P, Mishra GC and Srivastava CP. 2018. Predictive models for pod damage by pod fly in pigeonpea for North East Plain Zone of India. International Journal of Agricultural and Statistical Sciences 14(2): 465–468.
Kumari P, Mishra GC and Srivastava CP. 2021. Forecasting of losses due to pod borer, pod fly and yield of pigeonpea (Cajanus cajan) for central Zone (CZ) of India by using artificial neural network. Current Trends in Agricultural Science 5: 68–78.
Mishra D, Singh KK, Mishra H and Srivastava AB. 2023. Resource use efficiency (RUE) of lentil cultivation in Sultanpur District of Uttar Pradesh. Environment and Ecology 41(2B): 1209–1216.
Mishra H, Supriya, Kushwaha RR, Kumar M, and Gautam S 2025. Modelling and Forecasting of Sugarcane (Saccharum officinarum L.) Area, Production and Yield in Eastern Uttar Pradesh using ARIMA: A Data- Driven Approach to Sustainability. Environment and Ecology 43(1A): 210-221. doi: https://doi.org/10.60151/envec/NSRV4438.
Ndanguza D and Uwamariya D. 2018. Bayesian inference approach in modeling and forecasting maize production in Rwanda. African Journal of AppliedStatistics 5(2): 503–517. doi: 10.16929/ajas/503.227.
Pandit P, Bakshi B and Gangadhar V. 2021. Hybrid time series models for forecasting maize production in India. Current Journal of Applied Science and Technology 40(23): 49–57. doi: 10.9734/cjast/2021/v40i2331492.
Pandit P, Krishnamurthy KN and Bakshi B. 2022. Prediction of crop yield and pest-disease infestation. In: AI, edge and IoT-based smart agriculture. Academic Press. Pp 375–393. doi: 10.1016/B978-0-12-823694-9.00021-9.
Paul RK, Vennila S, Yadav SK, Bhat MN, Kumar M, Chandra P, Paul AK, Prabhakar M and Prabhakar M. 2020. Weather based forecasting of sterility mosaic disease in pigeonpea (Cajanu cajan) using machine learning techniques and hybrid models. Indian Journal of Agricultural Sciences 90(10): 1952–1958. doi: 10.56093/ijas.v90i10.107971.
Paul RK, Vennila S, Yeasin M, Yadav SK, Nisar S, Paul AK, Gupta A, Malathi S, Jyosthna MK and Kavitha Z. 2022. Wavelet decomposition and machine learning technique for predicting occurrence of spiders in pigeonpea. Agronomy 12(6): 1429. doi: 10.3390/agronomy12061429.
Prabakaran K, Sivapragasam C, Jeevapriya C and Narmatha A. 2013. Forecasting cultivated areas and production of wheat in India using Arima model. Golden Res Thoughts III: 1–7. doi: 10.9780/2231-5063/332013/2831.
Rahman MM, Islam MA, Mahboob MG, Mohammad N and Ahmed I. 2022. Forecasting of potato production in Bangladesh using Arima and mixed model approach. Scholars Journal of Agriculture and Veterinary Sciences 9(10): 136–145. doi: 10.36347/sjavs.2022.v09i10.001.
Sayiprathap BR, Patibanda AK, Mantesh M, Hiremath SS, Sagar N, Reddy CNL, Jahir Basha CR, Diwakar Reddy SE, Kasi Rao M and Nair RM. 2024. Sterility mosaic disease of pigeonpea (Cajanus cajan (L.) Huth): current status, disease management strategies, and future prospects. Plants (Basel) 13(15): 2146. doi: 10.3390/plants13152146.
Shukla G, Mishra GC and Singh SK. 2015. Kriging approach for estimating deficient micronutrients in the soil: a case study. International Journal of Agriculture Environment & Biotechnology 8(2): 309–314. doi:10.5958/2230-732X.2015.00038.8.
Sridhara S, Manoj KN, Gopakkali P, Kashyap GR, Das B, Singh KK and Srivastava AK. 2023. Evaluation of machine learning approaches for prediction of pigeonpea yield based on weather parameters in India. International Journal of Biometeorology 67(1): 165–180. doi: 10.1007/s00484-022-02396-x.
Srivastava AB, Singh KK, Supriya SK, Mishra H and Ahmad R. 2023. Production and export dynamics of wheat in India. Mathematics 8(3): 206–209. doi: 10.22271/maths.2023.v8.i3Sc.1023.
Umesh KB, Hamsa KR and Vijayalaxmi DK. 2023. Trends and prospects of crop production in Karnataka. In: Agricultural sector in India. Routledge India.Pp 89– 106. doi: 10.4324/9781003434672-9.
