Impact of COVID-19 lockdown on prices of potato and onion in metropolitan cities of India

Abstract

PurposeThis study focuses on accessing the impact of lockdown implemented to curb the pandemic of coronavirus disease 2019 (COVID-19) on prices of potato and onion crops using the time series analysis techniques.Design/methodology/approachThe present study uses secondary price series data for both crops. Along with the study of percent increase or decrease, the time series analysis techniques of autoregressive integrated moving average (ARIMA) and generalized autoregressive conditional heteroskedasticity (GARCH), as well as machine learning; neural network autoregressive (NNAR) models were used to model the prices. For the purpose of comparison, the data from past years were taken as the period of normalcy. The behaviour of the forecasts for the normal periods and during the pandemic based on respective datasets was compared.FindingsThe results show that there was an unprecedented rise in prices during the months of lockdown. It could be attributed to the decline in arrivals due to several reasons like issues with transportation and labour availability. Also, towards the end of lockdown (May 2020), the prices seemed to decrease. Such a drop could be attributed to the relaxations in lockdown and reduced demand. The study also discusses that how some unique approaches like e-marketing, localized resource development for attaining self-sufficiency and developing transport chain, especially, for agriculture could help in such a situation of emergency.Research limitations/implicationsA more extensive study could be conducted to mark the factors specifically that caused the increase in price.Originality/valueThe study clearly marks that the prices of the crops increased more than expectations using time series methods. Also, it surveys the prevailing situation through available resources to link up the reasons behind it.

Predicting the Potential Impact of Emergency on Global Grain Security: A Case of the Russia–Ukraine Conflict

“…The autoregressive integrated moving average (ARIMA) model is a commonly used model in time series analysis and prediction methods [ 48 , 49 ]. The model is widely used to analyse time series data in various fields [ 50 , 51 , 52 ]. The model is based on the law and past and present historical data to estimate and infer the state of something at some point in the future [ 53 , 54 ].…”

“…The model consists of three parts: the autoregressive model (AR model), the differential and the moving average model (MA model), denoted as ARIMA ( p , d , q ), where p , d and q represent the order of the autoregressive, differential and moving average, respectively. Using this model for prediction generally involves a data unit root test and stationary processing, model identification, model parameter estimation, and testing steps [ 52 , 54 , 55 ]. In the ARIMA model, the future value of the sequence is expressed as a linear function of the current and lag periods of the lag term and the random disturbance term.…”

Impactos de la pandemia del COVID-19 en la producción, distribución y consumo de productos agropecuarios en el departamento Norte de Santander, Colombia

“…El incremento de los costos de producción fue influenciado por la disminución de los rendimientos, mengua en las superficies cultivadas o en el número de animales en producción, dificultades en la disponibilidad de la mano de obra e insumos, deterioro de productos por perecebilidad y perdidas económicas para los agricultores. En relación a estos hallazgos Rajpoot et al [32] en la India reportan aumentos sobre los precios de los cultivos de papa y cebolla, originados por problemas de transporte y disponibilidad de mano de obra y; Hirvonen et al [33], en Etiopia, observaron incrementos mayores en los precios de los productos vegetales a nivel de finca en comparación con los precios a nivel de consumidores.…”

Gendered impact of COVID-19 on consumption of perishable and nonperishable food commodities in Pakistan

“…In addition to the health consequences, economic fallouts of the pandemic have started to emerge in the form of GDP shrinkage, rising unemployment and an increase in food prices worldwide (Akber and Paltasingh, 2022; Rajpoot et al. , 2021; Torero, 2020; Wieser et al.…”

“…In addition to the health consequences, economic fallouts of the pandemic have started to emerge in the form of GDP shrinkage, rising unemployment and an increase in food prices worldwide (Akber and Paltasingh, 2022;Rajpoot et al, 2021;Torero, 2020;Wieser et al, 2020), which have a direct link to food consumption as food intakes are determined by the income level of the people and the prices of the food commodities. Low-income people choose to consume cheaper food commodities rather than the consumption of high-priced food commodities (Kettings et al, 2009;Lo et al, 2012).…”