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Responsibility Of Regression Analysis To Prophesy Of Crop Yield

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Abstract

Prophecy of yield gives the benefit to the farmer in increasing the prices and reducing the losses. The objective of this work is to analyze the surrounding the parameter like area under Irrigation (AUI), Annual rainfall and Food Price index (FPI)that effect the yield of crop and create a relation between the parameter. In this research Regression analysis (RA) is use to examine the surrounding their factor dealing on crop yield .Regression analysis is multivariate analysis technique which is help to obtained decision on groups of factor into explanatory and response variables. A sample of surrounding factor like annual Rainfall, Area under cultivation and Food Price Index are taken for 9 year period from 2010 to 2018. Linear Regression are use to create the bonding between the surrounding factor and crop yield as response variable. This research can be extended by considering the factors like minimum support price, cost price index ,wholesale price index etc. and there bonding with the crop yield.

Keyword: Regression Analysis, Yield of crop.

Introduction

Agriculture gives the support to Indian economy. Agriculture consist large share of country’s national income though the share has declined by 33%.

More than 2/3 manpower is working in agriculture in our country. Recent census data for the year 2011 indicate that agriculture workers/cultivators and agricultural laborers) account for 53.3% of workforce in India.

Growth of other sector and overall economy depends on the performance of agriculture to considerable extent – Agriculture plays role in foreign exchange earner. Table 1 present the contribution of agriculture to the national income and its shares in export for of 9 years. An examination of table-1 make clear that the share in the national income and in the export is declining consistently yield prediction is one of the issue is facing in agriculture sector, lack of knowledge in agriculture is can be decrease the production of crop.

Regression Analysis can be defined as structured approached which stresses on the analysis of data the research purpose on decision making and problem solving.

We consider Area Rainfall, Area under cultivation, Food Price Index (F.P.I) that are the contribute to the crop yield. To identify the influence of crop yield using Regression analysis also establishes the relationship among three factors. Regression analysis is used to identify the effect on crop yield. Regression analysis uses the strength between dependent and independent variable. Impact on AUI on yield. AR on yield and FPI on yield.

An on AUI and FPI the crop considered for analysis is rice because it is the most cultivated crop in so many area in India.

Agriculture in India

Agriculture in India is main stream of production. Production of agriculture can improve storage of grain infrastructure and farm productivity. In order to get more crop the quality of the soil is to be improved while using a proportionate fertilizer and demand of soil. We have to improve the percentage of production of yield crop for not only growing population in India but also to export globally.

India ranks first globally with highest net cropped area, followed by US and China. Economic contribution of agriculture’s to India’s GDP is steadily declining with the country’s broad based economic growth. Indian agricultural and processed food is exported to more than 120 countries.

India is now one among the world’s largest suppliers of wheat, rice, cotton and sugar. It has exported over 2 million metric tons of wheat. In the production of raw material, roots products, rubber crop pulses, farmed fishes, sugarcane, eggs, coconut and vegetables India stand 2nd or 3rd in the world largest of over 80% agricultural product like coffee and cotton.

Regression Analysis

Regression analysis is used to analyze and determine the relationship between the Response and Explanatory variable. The variable considered for analysis in this research work the Annual Rainfall (AR) Area under irrigation (AUI), Food Price Index (FPI) crop is a dependent variable which depend on ecological factor.

Linear Regression

Linear Regression is a technique which used to analysis a response and explanatory variables which changes with the value of intervention variable (X), predicting the value of response variable (Y)from the given value of explanatory variable is also used for the prediction. Fitting of the polynomial relationship is obtained by using least square file. A linear regression model is computed to analyze the relation between AR, AUI, FPI and yield.

Computation of linear regression model linear Regression is the most widely used statistical technique; it is a way to model a relationship between variables. The result of the regression equation that can be used to make prediction about the crop. For this we can use the scatter plot so the data roughly fit to a line. The response variable and explanatory variable are explained linearity of the model. That model using prediction value. Nearly normal residual states that the residual should be distributed centered around ‘0’. There occur many instances during which there may be unusual observation. This condition can be checked using histogram or normal probability plot of the residuals. If the histogram or normal probability plot of residuals histogram are to symmetric the interpretation can be normally distributed. In case of the plot of residuals, if the plot is closer to the normality then the symmetry condition is satisfied.

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Compute the Residual values:

Residual value is one of the constituents of calculating operation. It describes the, the prediction value of good in terms. The difference between the observed value of the dependent and the predicted variable (y) and ŷ respectively. ŷ is called as rectitude

Each data point has one residual

Residual = observed value – predicted value

e = y – ŷ

Both the sum and the mean of the residuals are equal to zero that is

∑▒〖e=0〗 And e ̅=0

Methodology:

The data is used to find the relationship between the variables AUI and yield. Annual rainfall and yield and food price index and yield. 8 year data is used for analyzing the relationship and its impact on yield.

AUI, AR, FPI is taken as explanatory variables and yield of the crop is considered as response variable. Linear regression method is applied three times for wheat crop to find relationship between the three variables (AR, AUI, FPI and yield)

Regression analysis is used to implement linear regression for the data analyzed over a period of 8 years. If the value of R8 obtained is greater than 0.5 then the relation between the response variable and explanatory variable is some high.

The prophecy of yield from AUI is some high because the value of R2 is 0.997871 so it is clearly informed that the irrigation area increases the yield of crop increases. The result R2 clearly indicate that the crop yield is totally depend on annual rainfall yield is the response variable for the explanatory variable food price index in Figure yield is depend on Area under irrigation. Annual rainfall and Food Price Index.

From the particular linear relationship is obtained.

Such a way the impact of AR, AUI, FPI are influence the specific crop of yield.

Conclusion

As shown above there is steady growth or small difference in Annual rainfall, Area under cultivation and Food Price Index which shows the effect on yield of crop. The R2 = 0.997871 is obtained by applying the Regression analysis.

Average value of AR, AUI, and FPI is influence in the yield of crop. In this work Regression Analysis is for find the relationship between the variable like AR, AUI, FPI and their impact on yield of wheat. This work can be extended by using more variables like quality of soil, fertilizer; weather condition etc happens the impact on yield of crop and by using various statistical models to analyze the variable influencing the crop yield.

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Responsibility Of Regression Analysis To Prophesy Of Crop Yield. (2022, February 27). Edubirdie. Retrieved August 12, 2022, from https://edubirdie.com/examples/responsibility-of-regression-analysis-to-prophesy-of-crop-yield/
“Responsibility Of Regression Analysis To Prophesy Of Crop Yield.” Edubirdie, 27 Feb. 2022, edubirdie.com/examples/responsibility-of-regression-analysis-to-prophesy-of-crop-yield/
Responsibility Of Regression Analysis To Prophesy Of Crop Yield. [online]. Available at: <https://edubirdie.com/examples/responsibility-of-regression-analysis-to-prophesy-of-crop-yield/> [Accessed 12 Aug. 2022].
Responsibility Of Regression Analysis To Prophesy Of Crop Yield [Internet]. Edubirdie. 2022 Feb 27 [cited 2022 Aug 12]. Available from: https://edubirdie.com/examples/responsibility-of-regression-analysis-to-prophesy-of-crop-yield/
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