Modelling and Forecasting of Area, Production and Productivity of Tomatoes in Haryana and India

Kumar, Parveen; Jaslam, P. K. Muhammed; Chandanshive, Aniket

doi:10.48165/ijee.2022.58231

Cited by 4 publications

(4 citation statements)

References 6 publications

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“…It is calculated using the ratio of standard deviation to mean. A distribution or data collection is considered to be symmetric (Skewness value around zero) if it appears the same to the left and right of the centre point [6,8,9]. Kurtosis is a metric used to determine how heavily or thinly tailed the data are in relation to a normal distribution.…”

Section: Resultsmentioning

confidence: 99%

Forecasting Groundnut Area, Production and Productivity in Rajasthan, India using ARIMA Model

Bhusanar¹,

Meena

2023

AJAEES

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This paper presents an analysis of the area, production and productivity of groundnut in Rajasthan over the last thirty years and a forecast of these variables using the auto regressing integrated moving average (ARIMA) model. Descriptive statistics show that there was a large fluctuation in the lowest and maximum values of area, production, and productivity of groundnut in Rajasthan over the period of last thirty years. The ARIMA model was used to forecast the area, production, and productivity of groundnut in Rajasthan. The parameter estimates of the ARIMA model were used to determine the model fit statistics, including the R-squared value, which indicates how well the model fits the data. The Ljung-Box Q Statistics and the corresponding Sig. indicate that there is no significant autocorrelation in the residuals of the model. Finally, forecasts for 2021, 2022, 2023, 2024, and 2025 are presented, along with their corresponding upper and lower confidence limits. The results indicate that there is a considerable upward trend in area, production, and productivity of groundnut in Rajasthan over the last thirty years. The ARIMA model was found to be successful in forecasting the area, production, and productivity of ground. The findings of this paper can help in the formulation of better policies for groundnut production in Rajasthan.

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Section: Resultsmentioning

confidence: 99%

Forecasting Groundnut Area, Production and Productivity in Rajasthan, India using ARIMA Model

Bhusanar¹,

Meena

2023

AJAEES

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“…The fit statistics shown include the R-squared value, the Mean Absolute Percentage Error (MAPE), the Root Mean Square Error (RMSE), the Bayesian Information Criterion (BIC), and the Ljung-Box Q statistic. The ARIMA model with parameters (1, 1, 1) had the best fit, as indicated by the highest R-squared value, the lowest MAPE and RMSE, and the lowest BIC and Ljung-Box Q statistic [8,9].…”

Section: Obj1 Forecasting Arrival and Price Of Groundnut At Apmc In R...mentioning

confidence: 99%

Forecasting of Arrival and Price of Groundnut in Rajasthan by ARIMA Model for Livelihood of Farmers

Bhusanar

Meena

Beniwal

2023

AJAEES

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This paper studies the forecasting of arrival and price of groundnut at the APMC in Rajasthan. ARIMA models was used to model the time series data of arrival and price of groundnut at APMC from 2005 to 2021. Groundnut arrival was at its highest in the months of November, December, and January of the 2018–19 growing season, with 16.3%, 32.3%, and 33.6%, respectively. In the year 2019-20, the highest arrival of groundnuts was seen in October, November, and December with 28.08%, 29.79%, and 28.51% respectively. The best fitting model for the arrival of groundnut is ARIMA (1, 1, 1), and for price is ARIMA (2, 1, 1) with R-squared values of 0.931 and 0.852 respectively. The results indicate that the arrival of groundnut at APMC will increase from 1015.48 thousand metric tonnes in 2022 to 1180.68 thousand metric tonnes in 2026, while the price of groundnut will increase from Rs 5261.08/qtl in 2022 to Rs 6057.07/qtl in 2026. The results study provides valuable insights into the seasonal pattern of groundnut arrivals at APMCs and the forecasts can be used to make decisions about future supply and demand of the product to stakeholders and helps them plan for the future.

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“…Tomato is the second-largest crop of the world in terms of its demand and productivity [1]. Unfortunately, it is among one of the most vulnerable to disease causing pathogen crops as well [2].…”

Section: Introductionmentioning

confidence: 99%

Impacts of Different Sources of Carbonates on Growth of Fusarium oxysporum f. sp. lycopersici in Different Growth Media

Akram,

Hussain,

Butt

2023

Adv Environ Eng Res

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Fusarium oxysporum f. sp. lycopersici (FOL) is the most common soil-borne pathogen that causes wilt in many plant crops, among which the tomato is one of the most susceptible crops. This experiment is conducted to analyse the impacts of inorganic carbon compounds i.e., calcium carbonate CaCO3, sodium carbonate Na2CO3 and potassium bicarbonate KHCO3 on the growth of FOL causing wilt in tomato plant in different culture media including Malt Extract Agar (MEA), Potato Dextrose Agar (PDA) and V8. Three concentrations viz 0.5 g/L, 1 g/L and 1.5 g/L of each carbonate are applied. The study indicates that the efficiency of carbon compounds is related to the nature of the media. Among various carbon compounds tested in the present study, maximum growth is obtained with KHCO3 while FOL showed least growth with Na2CO3 in each of the three media. MEA has more profound effect on limiting the growth and biomass of the fungus than compared to V8 and PDA. This shows Fusarium growth is restricted in the chemical environment containing Na2CO3 and MEA. In addition, the study reflects that Fusarium thrives well in PDA containing KHCO3 in each of its three concentrations which may be involved in vegetative and reproductive growth. This study paves a path for further research on how the growth behavior of FOL can be controlled under the influence of inorganic carbon compounds in the soil and yet in the face of environmental changes.

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Modelling and Forecasting of Area, Production and Productivity of Tomatoes in Haryana and India

Cited by 4 publications

References 6 publications

Forecasting Groundnut Area, Production and Productivity in Rajasthan, India using ARIMA Model

Forecasting Groundnut Area, Production and Productivity in Rajasthan, India using ARIMA Model

Forecasting of Arrival and Price of Groundnut in Rajasthan by ARIMA Model for Livelihood of Farmers

Impacts of Different Sources of Carbonates on Growth of <i>Fusarium oxysporum</i> f. sp. <i>lycopersici</i> in Different Growth Media

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