Agricultural and Biological Sciences, Statistics and Probability, Social Sciences, Animal Science and Zoology
137
Scopus Publications
Scopus Publications
Forecasting tomato production in major Asian producers: a comparative study of ARIMA, exponential smoothing, score-driven models, and XGBoost Abdullah Mohammad Ghazi Al khatib, Bayan Mohamad Alshaib, Pradeep Mishra, Shiwani Tiwari, Motee Asaad Alshalaby, et al. Scientific Reports, 2026 Tomato production is a crucial component of the agricultural sector in Asian countries. Accurate forecasting of tomato production is essential for effective agricultural planning, resource allocation, and ensuring food security in the region. This study aims to investigate the patterns and forecast tomato production in five major Asian producing countries: Bangladesh, China, India, Pakistan, and Sri Lanka, utilizing advanced time series models and machine learning techniques. A comprehensive time series dataset spanning from 1961 to 2021 was employed, partitioned into a training period (1961–2014) and a validation period (2015–2021). The study applied various modeling techniques, including ARIMA, Exponential Smoothing, Score-Driven models, and XGBoost. Model performance was evaluated using information criteria, error metrics, and diagnostic tests. Results indicate that while XGBoost yielded the lowest validation errors for several nations due to recent volatility, Exponential Smoothing was selected as the optimal practical model for forecasting Bangladesh’s production to properly account for long-term structural trend extrapolation. Score-Driven models exhibited superior performance for China, India, Pakistan, and Sri Lanka. The selected models generated forecasts up to 2028, revealing continuing upward trajectories for Bangladesh, China, India, and Pakistan, and stabilization for Sri Lanka. This study contributes to the understanding of tomato production dynamics in major Asian producers and offers guidance for agricultural planning, resource allocation, and food security policies. The findings provide valuable insights into the future trends of tomato production in the region, enabling stakeholders to make informed decisions and adapt to potential changes in the agricultural landscape.
Assessing climate variability impacts on the productivity of major crops in Krishna District, Andhra Pradesh (1997-2020) Shirish Khedikar, Swapnil Panchabhai, Santhosh kumar G, Somenath Dutta Mausam, 2026 Impacts of climate variability are already experienced across the global. The Indian agriculture mostly affects by changes in climate, it represent in the form of flood situation in some part of the country on the other hand some parts experiences severe drought conditions and that results in the regional inequality in crop production. The present study is about the effect of various weather parameters on crop productivity of major crops (Arhar/Tur, Cotton, Groundnut and Rice) cultivated in Krishna District of Andhra Pradesh State of India. The various statistical methods has been used for statistical analysis like detrending, correlation, test of significance for measuring relationship between various weather parameters (Rainfall, Temperature and wind speed) and crop productivity (Tones/Hectare). The statistical significance (at 95% and 99% level) of the various parameters was ascertained by testing significance for the recent 23 years (1997-98 to 2019-20). Crop weather calendars were used to understand crop stage-wise water requirements; favourable and unfavourable weather parameters etc.It is observed that in Arhar crop rainfall during sowing, vegetative growth, and flowering stages is favourable whereas rainfall during germination, grain formation and harvesting is not favourable for crop productivity. For the Cotton crop rainfall during initial boll maturity stages is not favourable where as rainfall during sowing, germination, vegetative growth, and flowering stage is favourable. For the groundnut crop rainfall during flowering and grain formation stage is favourable for crop productivity. For the rice crop rainfall during vegetative growth and flowering stages is favourable where as it is unfavourable during grain formation and harvesting stage. High maximum temperature during sowing and germination is not favourable whereas during grain formation and harvesting it is favourable for better crop productivity. Similar analysis is also carried out for minimum temperature and wind speed and promising results were obtained. This study can help in better crop planning, to understand crop weather relationship, for yield estimation and ultimately increasing crop productivity.
Big Data in Agriculture: Acquisition, Processing and Implications Aditya Pratap Singh, Tanushree Biswal, Umakanta Maharana, Abdullah Mohammad Ghazi Al Khatib, Pradeep Mishra Smart Farming Smarter Solutions Revolutionizing Agriculture with Artificial Intelligence, 2025
STATISTICAL ANALYSIS OF RAINFALL PATTERNS IN CHHATARPUR DISTRICT OF MADHYA PRADESH International Journal of Agricultural and Statistical Sciences, 2022
Modelling and forecasting of arhar production in India International Journal of Agricultural and Statistical Sciences, 2018
Modelling and forecasting of black pepper production in India Indian Journal of Ecology, 2017
Application of parametric and nonparametric regression models for area, production and productivity trends of tea (Camellia sinensis) in India Indian Journal of Ecology, 2017
Instability in production scenario of maize in India and forecasting using ARIMA model International Journal of Agricultural and Statistical Sciences, 2014
