Wheat yield forecast using detrended yield over a sub-humid climatic environment in five districts of Uttar Pradesh, India

Gurung, Bishal; Panwar, Sanjeev; Singh, K. N.; Banerjee, Rahul; Gurung, Sarad; Rathore, Abhishek

doi:10.56093/ijas.v87i1.67047

Cited by 4 publications

(2 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This state covers a wide geographical region, and the plains differ from the hilly areas. Further, there is a broad diversity in the climate change of a state (Gurung et al, 2017). The temperature is about 47°C in summer and −1°C during winter.…”

Section: Introductionmentioning

confidence: 99%

Severity of natural calamities and crop yield prediction using hybrid deep learning model in Uttar Pradesh

Kumar,

Mahapatra

2024

World Water Policy

View full text Add to dashboard Cite

Crop yield prediction has gained major potential for global food production. Predicting crop yields based on specific parameters like soil, environment, crop, and water has been an interesting research topic in recent decades. To accurately predict crop yields, measuring the severities of natural calamities including water level is mainly required. However, the existing studies failed to predict crop yields accurately because of various issues like overfitting problems, difficulty in training, inability to handle large data, and reduced learning capability. Thus, the proposed study develops an efficient mechanism for accurately predicting crop yields by analyzing several natural calamities. Here, the input samples are initially pre‐processed to remove unwanted noises using data normalization and standardization. To enhance the performance of crop yield prediction, natural calamities are computed by using an Extreme Gradient Boosting (XGBoost) model based on parameters like the Palmer Drought Severity Index (PDSI), Severe Hail Index (SHI), and Storm Severity Index (SSI). Also, the hyperparameters of XGBoost model are tuned by utilizing Sheep Flock Optimization Algorithm (SFOA). Finally, the crop yield is predicted by proposing a new one‐dimensional convolutional gated recurrent unit neural network (1D‐CGRU). The proposed classifier predicts the crop yields with reduced error rates like mean square error (MSE) of 0.4363, root mean square error (RMSE) of 0.1904, normalized root mean squared error (NRMSE) of 0.00101, mean absolute error (MAE) of 0.2437, and R‐squared (R2) of .2756. Also, the significant findings of the proposed study positively indicate that this study can be applicable to real‐time agricultural practices and is highly suitable for water quality predictions. Also, it can assist farmers and farming businesses in predicting the yield of crops in a specific season when to harvest and crop a plant for attaining improved crop yields.

show abstract

Section: Introductionmentioning

confidence: 99%

Severity of natural calamities and crop yield prediction using hybrid deep learning model in Uttar Pradesh

Kumar,

Mahapatra

2024

World Water Policy

View full text Add to dashboard Cite

show abstract

“…Sahu (2006) used the ARIMA method for forecasting harvests of rice, wheat, potato and mustard. Similarly, many other authors have also used the ARIMA methodology for forecasting the production of various crops in India (Celik et al, 2017; Gurung et al, 2017; Mishra et al, 2015; Singh et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Statistical modelling and forecasting annual sugarcane production in India: Using various time series models

Tyagi

Chandra

Tyagi

2023

Annals of Applied Biology

View full text Add to dashboard Cite

This paper proposes a comparison of various time series forecasting models to forecast annual data on sugarcane production over 63 years from 1960 to 2022. In this research, the Mean Forecast Model, the Naive Model, the Simple Exponential Smoothing Model, Holt's model, and the Autoregressive Integrated Moving Average time series models have all been used to make effective and accurate predictions for sugarcane. Different scale-dependent error forecasting techniques and residual analysis have been used to examine the forecasting accuracy of these time series models. SE of Residuals, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE) andAkaike's Information Criterion (AIC) are used to analyse the forecast's accuracy.The best model has been selected based on the predictions with the lowest value, according to the three-performance metrics of RMSE, MAE, and AIC. The estimated sugarcane production shows an increasing trend for the next 10 years and is projected to be 37,763.38 million tonnes in the year 2032. Further, empirical results support the plan and execution of viable strategies to advance sugarcane production in India to fulfil the utilisation need of the increasing population and further improve food security.

show abstract

Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model

Wawale

Jawarneh²,

et al. 2022

Journal of Food Quality

View full text Add to dashboard Cite

Agribusiness employs more than 66 percent of India’s rural population and is the country’s economic backbone. Beat crop growth is essential for practical farming since it increases soil diversity and actual design, and it may be grown in blended frameworks. Crop growth rates, applicability, and yields have not improved significantly over time in the United States. Crops are defined by their seasonality, derived nature of demand, and relatively inelastic pricing. The general purpose of this research is to demonstrate the usefulness of price forecasting for agricultural prices and validate it for rice, which is consumed more in Indian states, for the year 2022, using time series data from 2016 to 2021. Every year, data for 50 days is collected and multiplied. The range of ten and its multiple is used for predicting. The results were obtained through the use of univariate analysis. To develop grain price estimates, researchers used Autoregressive Integrated Moving Average (ARIMA) methods, and the precision of the forecasts was examined using conventional mean square error (MSE) and mean absolute percentage error (MAPE) standards. As proven by the outcomes of ARIMA price predictions, the ARIMA model’s efficacy as a tool for price forecasting was effectively demonstrated by realistic models of projected prices for 2020. Because the MSA and MAPE values were lower, the forecast was more accurate. In addition, the price forecasting in this model is dependent on government incentives.

show abstract

Wheat yield forecast using detrended yield over a sub-humid climatic environment in five districts of Uttar Pradesh, India

Cited by 4 publications

References 5 publications

Severity of natural calamities and crop yield prediction using hybrid deep learning model in Uttar Pradesh

Severity of natural calamities and crop yield prediction using hybrid deep learning model in Uttar Pradesh

Statistical modelling and forecasting annual sugarcane production in India: Using various time series models

Minimizing the Error Gap in Smart Framing by Forecasting Production and Demand Using ARIMA Model

Contact Info

Product

Resources

About