Modeling and Forecasting of Rice Prices in India during the COVID-19 Lockdown Using Machine Learning Approaches

Rathod, Santosha; Chitikela, Gayatri; Bandumula, Nirmala; Ondrašek, Gabrijel; Ravichandran, Sundaram; Sundaram, R. M.

doi:10.3390/agronomy12092133

Cited by 16 publications

(8 citation statements)

References 35 publications

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“…Due to different food policy responses, fluctuations in staple food prices varied among countries during the COVID-19 pandemic. For example, while rice prices in the US and India increased differently [ 33 , 34 ], there was no significant change in China’s rice price [ 12 ]. Such variations are related to the implementation of food security policies [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

Effects of Policy Intervention on Food System Resilience to Emergency Risk Shock: Experience from China during COVID-19 Pandemic

Cui

Zhang

et al. 2023

Foods

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Achieving the goal of zero hunger within the goal of sustainable development requires improving the resilience of food systems to various types of risk shocks; food systems have shown significant vulnerability to COVID-19 outbreaks and transmission. By analyzing the impact of China’s lockdown policy and food security emergency policy in 2020 on food prices during the COVID-19 pandemic, we can clarify the role of policy intervention in enhancing the resilience of the food system, which can provide guidance, using China’s experience, for dealing with global food safety emergencies in the future. Firstly, we selected Beijing, Shanghai and Guangdong as food-consuming areas, and Shandong, Henan, and Hubei as food-producing areas. We also collected food security emergency policy data from the Chinese government website during the COVID-19 pandemic. Secondly, a difference-in-difference method was used to observe that Chinese cabbage and pork prices in the main food-producing areas and food-consuming areas rose more obviously after the adoption of lockdown policy, and Chinese cabbage and pork prices in the food-consuming areas increased more obviously than those in food-producing areas. However, staple food prices have not risen significantly. Thirdly, the response of four kinds of food prices to the food security emergency policy is analyzed quantitatively and graphically using the food price volatility index and food price increase rate; we observed that the response of food prices to the food security emergency policy is related to the food types and regions. For food types, the fluctuation degree and increase in Chinese cabbage and pork prices decreased significantly after the adoption of the food security emergency policy. For regions, when the food security emergency policy was adopted, the food prices in the main food-consuming areas fluctuated more obviously than those in food-producing areas. Finally, we found that the implementation of the transport policy and the joint supply emergency policy in the main producing and consuming areas has played a very significant and positive role in stabilizing food prices.

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Section: Discussionmentioning

confidence: 99%

Effects of Policy Intervention on Food System Resilience to Emergency Risk Shock: Experience from China during COVID-19 Pandemic

Cui

Zhang

et al. 2023

Foods

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“…Rice is a vital staple food and one of the major cash crops in Pakistan, ranking third in cultivation and contributing significantly to the country's agricultural sector. It is the primary source of calories for over four billion people globally [1,2]. However, rice yields in the Malakand Division of Khyber Pakhtunkhwa Province, Northwestern Pakistan, remain modest compared to other regions, ranging from 1633 kg ha −1 in Lower Dir to 2323 kg ha −1 in Swat [3].…”

Section: Introductionmentioning

confidence: 99%

Boosting Crop Growth Rates of Hybrid Rice (Pukhraj) through Synergistic Use of Organic Nitrogen Sources in Conjunction with Urea Nitrogen

Amanullah,

Ullah

2024

Nitrogen

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In Northwestern Pakistan’s rice-based cropping systems, the prevalent reliance on inorganic nitrogen fertilizers (INF) has led to insufficient nitrogen (N) contributions from soil organic manures (OM). This study aims to evaluate the impact of organic sources (OS), including animal manures (AM) and crop residues (CR), on crop growth rates (CGR) in a rice-wheat rotation. A two-year field experiment involving hybrid rice (Oryza sativa L., Pukhraj) was conducted in Batkhela, Khyber Pakhtunkhwa. Various OS and inorganic-N (urea) combinations were applied, emphasizing their influence on CGR. The findings highlight poultry manure (PM) application as the most impactful on CGR, while wheat straw (WS) application resulted in the lowest CGR among the six OS investigated. Additionally, the use of AM showcased superior CGR compared to CR. In the initial year, the highest CGR occurred, with 75% of N sourced from urea and 25% from OS. In the second year, a balanced 50% N application from each source yielded the highest CGR. Urea and PM demonstrated the most robust CGR among OS combinations, while urea and WS yielded the lowest. Notably, onion leaves, a cost-effective option, delivered promising results comparable to berseem residues, indicating their potential as organic manure, especially in sulfur-deficient soils. These findings underscore the viability of onion residue management as a cost-effective alternative to ammonium sulfate fertilizers with global applicability. The abstract recommends promoting organic sources, particularly poultry manure and onion leaves, alongside inorganic-N fertilizers to enhance CGR and reduce dependence on costly alternatives. However, further research and field trials are necessary to explore the long-term impacts of these organic sources on soil health, nutrient cycling, and the sustainability of rice-based cropping systems in Northwestern Pakistan and beyond. In conclusion, this study investigates the influence of organic sources on CGR in rice-wheat rotations, emphasizing the superiority of poultry manure and onion leaves. The findings highlight cost-effective alternatives to conventional fertilizers, emphasizing the need for further research to validate long-term sustainability and applicability beyond the study area.

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“…Another study addressed NN, MRA (multiple regression analysis), and CNDA (chaotic nonlinear dynamic algorithms) models using solar radiation and air temperature data to predict stream water temperature from available sources [42]. Food price prediction data were obtained by analysing the factors affecting the price of a food product by using ML algorithms from different fields [43,44]. ML algorithms significantly contribute to getting concrete and validated predictive data, especially in analyses based on real data.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Postal Service Delivery Time and Energy Cost with E-Scooter by Machine Learning Algorithms

İnaç¹,

Ayözen²,

Atalan

et al. 2022

Applied Sciences

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This research aims to estimate the delivery time and energy cost of e-scooter vehicles for distributing mail or packages and to show the usage efficiency of e-scooter sharing services in postal service delivery in Turkey. The machine learning (ML) methods used to implement the prediction of delivery time and energy cost as output variables include random forest (RF), gradient boosting (GB), k-nearest neighbour (kNN), and neural network (NN) algorithms. Fifteen input variables under demographic, environmental, geographical, time, and meta-features are utilised in the ML algorithms. The correlation coefficient (R2) values of RF, GB, NN, and kNN algorithms were computed for delivery time as 0.816, 0.845, 0.821, and 0.786, respectively. The GB algorithm, which has a high R2 and the slightest margin of error, exhibited the best prediction performance for delivery time and energy cost. Regarding delivery time, the GB algorithm’s MSE, RMSE, and MAE values were calculated as 149.32, 12.22, and 6.08, respectively. The R2 values of RF, GB, NN, and kNN algorithms were computed for energy cost as 0.917, 0.953, 0.400, and 0.365, respectively. The MSE, RMSE, and MAE values of the GB algorithm were calculated as 0.001, 0.019, and 0.009, respectively. The average energy cost to complete a package or mail delivery process with e-scooter vehicles is calculated as 0.125 TL, and the required time is approximately computed as 11.21 min. The scientific innovation of the study shows that e-scooter delivery vehicles are better for the environment, cost, and energy than traditional delivery vehicles. At the same time, using e-scooters as the preferred way to deliver packages or mail has shown how well the delivery service works. Because of this, the results of this study will help in the development of ways to make the use of e-scooters in delivery service even more efficient.

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Modeling and Forecasting of Rice Prices in India during the COVID-19 Lockdown Using Machine Learning Approaches

Cited by 16 publications

References 35 publications

Effects of Policy Intervention on Food System Resilience to Emergency Risk Shock: Experience from China during COVID-19 Pandemic

Effects of Policy Intervention on Food System Resilience to Emergency Risk Shock: Experience from China during COVID-19 Pandemic

Boosting Crop Growth Rates of Hybrid Rice (Pukhraj) through Synergistic Use of Organic Nitrogen Sources in Conjunction with Urea Nitrogen

Estimation of Postal Service Delivery Time and Energy Cost with E-Scooter by Machine Learning Algorithms

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