Browsing by Author "Abha Goyal"

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Climate change and its effects on maize yield in Nepal: An empirical analysis using the ARDL model
(Association of Agrometeorologists, 2025) Aashma Aryal; Abha Goyal; Ankit Yadav; Bharath Kumar Mannepalli; Prakhar Deep; Virendra Kamalvanshi; Saket Kushwaha
This study analyzes the impact of climate change on maize yield in Nepal’s Gulmi (hilly) and Rupandehi (Terai) districts using climatic data from 1981 to 2023 on rainfall, relative humidity, maximum temperature, and minimum temperature applying the Autoregressive Distributed Lag (ARDL) model. The findings obtained ARDL model shows that rainfall positively influences yield in both regions. Relative humidity has a positive long-term effect in Gulmi but a negative impact in Rupandehi. Maximum temperature increases yield in Gulmi but significantly reduces it in Rupandehi, indicating regional sensitivity. Minimum temperature negatively affects Gulmi yields but has a negligible positive effect in Rupandehi. The ARDL models demonstrate strong explanatory power, with adjusted R² values of 0.86 (Gulmi) and 0.80 (Rupandehi), confirming a significant long-term relationship between climate variables and yield. Error correction terms suggest that 28% (Gulmi) and 30% (Rupandehi) of short-term yield deviations adjust back to long-run equilibrium annually. These results highlight the importance of localized climate adaptation strategies in agriculture. © 2025, Association of Agrometeorologists. All rights reserved.
Forecasting Potato Prices: Application of ARIMA Model
(AESSRA, 2022) Prakash Singh Badal; V. Kamalvanshi; Abha Goyal; Pramod Kumar; Biswajit Mondal
Price fluctuations in potatoes (Solanum tuberosum L.) concern consumers, farmers, and policymakers, and its accurate price prediction is important for all the stakeholders. In India, out of a total of 5.34 million ha of land under vegetables, potato occupies nearly 20.8 per cent of area. India produces 12.3 per cent of world potato production (around 45.34 million tons) and is next only to China. The major potato-producing states are highly concentrated in the Indo-gangetic plains of the country. Uttar Pradesh, West Bengal and Bihar account for 32.4, 26.9 and 14.6 per cent of national production of potato. The present study was designed to forecast the prices of potato in these three major potato-producing states of the country. Autoregressive Integrated Moving Average forecasting models - ARIMA (1,0,1) for Varanasi market, ARIMA (2,0,1) for Kolkata market, and the ARIMA (3,0,1) for Patna market were applied. The performance of the ARIMA models produced reliable forecast of prices of potatoes for all three major producing states. © 2022 AESSRA. All Rights Reserved.
Unveiling trends in forecasting models for crop pest and disease outbreaks: A systematic and scientometric analysis
(Springer Nature, 2025) Abha Goyal; Abhishek Kumar Singh; M. Raghuraman; Pritha Ghosh; Aaditya Jadhav
The intensifying impact of global climate change has led to a significant rise in crop pests and disease outbreaks, resulting in substantial agricultural losses worldwide. Weather-driven forecasting models are essential tools in predicting these outbreaks, enabling timely and effective intervention strategies. This study presents a comprehensive systematic review of existing literature to assess the comparative strengths and limitations of statistical versus machine learning pest forecasting models. In parallel, we conducted a detailed bibliometric analysis of 1,215 peer-reviewed studies spanning 2000–2023, sourced from the Web of Science Core Collection database. This dataset reveals a marked surge in research activity, particularly after 2019. The most significant growth is evident in key disciplines such as entomology, plant pathology, and agronomy. Geographically, the highest research output originates from Asia, Europe, and North America, where China, the United States, and India are emerging as the foremost contributors in terms of publication volume. Chinese researchers lead the list of top contributing authors, while high-impact journals such as PLOS ONE, Insects, Computers and Electronics in Agriculture, etc., emerge as central publication platforms in this domain. The analysis identifies major thematic focuses in the literature, including deep learning, convolutional neural networks, artificial neural networks, and diverse forecasting models. Frequently occurring keywords such as “regression,” “prediction,” “insects,” and “population dynamics” reflect the evolving research priorities in this field. Collectively, these studies highlight significant progress in developing predictive models for crop pests and disease outbreaks. This study provides useful insights into the field of agriculture & pest management and serves as a framework for future research. © African Association of Insect Scientists 2025.