Different statistical models based on weather parameters in Navsari district of Gujarat (original) (raw)
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Journal of Agriculture and Food Research
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International Journal of Environment and Climate Change, 2020
In the recent year, pre harvest crop yield forecasting has been a topic of interest for producers, policy makers, government and agricultural related organizations. Pre harvest crop forecasting is important for national food security. Construction of appropriate yield forecast promotes the output of scenario analyses of crop production at a farm level, which enables suitable tactical and strategic decision making by the farmer. Indeed, considerable benefits apply when seasonal forecasting of crop performance is applied across the whole value chain in crop production. Timely and accurate yield forecast is essential for crop production, marketing, storage and transportation decisions as well as for managing the risk associated with these activities. In present manuscript efforts were made for development of pre harvest forecast models by using different statistical approaches viz. multiple linear regression (MLR), discriminant function analysis and ordinal logistic regression. The stu...
Estimation of rice yield using multivariate analysis techniques based on meteorological parameters
Scientific reports, 2024
This study aims to develop predictive models for rice yield by applying multivariate techniques. It utilizes stepwise multiple regression, discriminant function analysis and logistic regression techniques to forecast crop yield in specific districts of Haryana. The time series data on rice crop have been divided into two and three classes based on crop yield. The yearly time series data of rice yield from 1980-81 to 2020-21 have been taken from various issues of Statistical Abstracts of Haryana. The study also utilized fortnightly meteorological data sourced from the Agrometeorology Department of CCS HAU, India. For comparing various predictive models' performance, evaluation of measures like Root Mean Square Error, Predicted Error Sum of Squares, Mean Absolute Deviation and Mean Absolute Percentage Error have been used. Results of the study indicated that discriminant function analysis emerged as the most effective to predict the rice yield accurately as compared to logistic regression. Importantly, the research highlighted that the optimum time for forecasting the rice yield is 1 month prior to the crops harvesting, offering valuable insight for agricultural planning and decision-making. This approach demonstrates the fusion of weather data and advanced statistical techniques, showcasing the potential for more precise and informed agricultural practices.
Rice is the most important staple food in India, which play crucial role in daily requisite of diet. In the Gujarat state, rice occupies about 7-8 per cent of the gross cropped area and accounts for about 14 per cent of the total food grain production. In the present study statistical forecasting models were employed to provide forecast before harvest of crop for taking timely decisions. In this paper Multiple Linear Regression (MLR) technique was utilized for estimating average rice production in Valsad district of South Gujarat. The weather indices were developed utilizing week number as weight by weekly weather parameters for the year 1975 to 2010 and for the cross-validation of the developed forecast models were tested by utilizing data from 2010 to 2014. It is observed that value of Adj. R 2 varied from 55.8 to 61.6 in different models. Based on the findings in the present study, it was observed that model-5 found to be better than all other models for pre harvest forecasting of rice crop yield.
International Journal of Enviornment and Climate Change, 2022
The pre-harvest forecasting models were developed for Kharif rice yield forecasts for Navsari, Surat and Tapi districts respectively. The data of rice yield and the weather parameters from 1995 to 2017 was used for developing statistical models for three districts of south Gujarat. The weather indices like Z41, Z50, Z231, Z241, Z341, Z351 and Time were able to forecast the yield of rice for Navsari district. Similarly, Z41, Z51, Z131, Z141, Z231, Z241, Z351 and Time were found to be most efficient predictors for Surat district. Only two variables i.e., Z241 and Z251 were found to be able to forecast the rice yield in Tapi district. Models were validated with 3 years (2018, 2019 and 2020) data. Results indicated that models explained 53 to 93 percent variations for rice yield during F 1 stage, 54 to 92 percent variation for rice yield during F 2 stage and 52 to 93 percent variations during F 3 stage for rice yield in three districts. Hence these models can be used to some extent for forecast the yield in different districts of south Gujarat a few months before harvest.
PRE-HARVEST FORECASTING OF RICE YIELD USING WEATHER INDICES IN PANTNAGAR (UTTARAKHAND
The present article deals with forecasting of rice crop yield using time series data of yield and weekly weather parameters. The association between yearly crop yields and weekly weather parameters was studied by using Karl-Pearson's correlation technique. The forecasting models utilized yearly yield and weekly weather data of University Farm at G. B. Pant University of Agriculture and Technology, Pantnagar. The techniques included development of weather indices which were used as explanatory variables (predictors) in the model. The technique was further modified by incorporating technical and statistical indicators along with developed predictors. Comparative studies of the developed models were carried out from forecast error percentage along with mean square error (MSE). The study proposed that modified model incorporating technical and statistical indicators effectively used for early pre-harvest forecasting of crop yield particularly up to two and half month before harvest.
Pre-harvest forecast of kharif rice yield using PCA and MLR technique in Navsari district of Gujarat
Journal of Agrometeorology, 2021
In this paper Principal Components (PC) and Multiple Linear Regression (MLR) Technique were used for development of pre-harvest model for rice yield in the Navsari district of south Gujarat. The weather indices were developed and utilized for development of pre-harvest forecast models. The data of rice yield and weather parameters from 1990 to 2012 were utilized. The cross validation of the developed forecast model were confirmed using data of the years 2013 to 2016. It was observed that value of Adj. R2 varied from 89 to 96. The appropriate forecast model was selected based on high value of Adj. R2. Based on the outcomes in Navsari district, MLR techniques found to be better than PCA for pre harvest forecasting of rice crop yield. The Model-2 found competent to forecast rice yield in Navsari district before eight weeks of actual harvest of crop (37th SMW) i.e during reproductive stage of the crop growth period.
Pre -harvest forecasting models for kharif rice yield in coastal Karnataka using weather indices
The data of k harif rice yield and the weather parameters from 1985 to 2009 is used for developing statistic al models for three c oastal distric ts of Karnatak a. These pre-harvest forec asting models we re developed for rice yield forecasts for Dak shin Kannada, Udupi and Uttar Kannada districts respectively. The weather indic es lik e Z 21 2 51 and Time were able to forec ast the yield of ric e for Udupi distric t. Similarly Z 120 150 and Z 241 were found to be most efficient predictors for Dakshin Kannada district. Only one variable i.e. Z 451 was found to be able to forecast the rice yield in Uttar Kannada district. The validation of the model was done for a period of three years from 2010-2012. The forecasting models were able t o explain the inter annual variation in the ric e produc tion to an extent of 86, 95 and 74% for Dak shin Kannada, Udupi and Uttar Kannada districts respec tively. Hence these models can be used to forecast rice yield two months before harvest.
International Journal of Environment and Climate Change
Rice crop yield data and weather data were considered in this study, covering the past twenty-nine years (1991-2019) in Prayagraj District, Uttar Pradesh. The data was sourced from DACNET and the College of Forestry, SHUATS Prayagraj. The analysis comprised a calibration period of 26 years (90% of the dataset) and a validation period using the remaining data (10%). In this study, 75.9% of the data were utilized for training the Artificial Neural Network (ANN) model, while the remaining 24.1% were allocated for testing and validation, ensuring comprehensive model assessment. The primary evaluation metric employed for model efficiency was the Normalized Root Mean Squared Error (nRMSE), with a focus on achieving the lowest values. Both a Stepwise Linear Regression technique and a Neural Network were employed for rice yield prediction. Notably, the regression-based model exhibited superior performance compared to the ANN model, as indicated by the nRMSE values. This conclusion was drawn...