Wheat Production in Uttar Pradesh – A Study on the Growth and Instability Over Time

Dey, Anwesha; Anoop, M.; Gautam, Yash

doi:10.20546/ijcmas.2020.903.064

Cited by 3 publications

(2 citation statements)

References 2 publications

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“…Along with new technology, new institutional structure enabled the farmers to adopt improved methods of cultivation. These results are in agreement with work carried out by Elumalai et al (2009) [17] , Ramdas et al (2012) [29] , Anjum and Madhulika (2018) [2] , Jain (2018) [21] , and Dey et al (2020) [16] .…”

Section: Growth and Instability In Area And Production Of Rice In Indiasupporting

confidence: 92%

An analysis of growth and instability in area, production, yield and price behaviour of rice in India

SM,

Bhopala,

2024

Int. J. Stat. Appl. Math.

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To studied the growth performance of rice and instability in area and production, impact of MSP on acreage and price behavior of rice in India. Total 10 major producing states of India and 5 major producing districts of Gujarat for rice crop were selected for the study. Based on highest average arrivals of last decade, one market was taken from selected states, as well as from Gujarat state. The data of area, production and productivity were collected for the period 1990-91 to 2019-20. The data on monthly wholesale prices and arrivals of rice was compiled for the period from January 2010 to December 2019. The compound growth rate and Cuddy-Della Valle Index were used to estimate the growth and instability in area, production and yield of rice. Nerlovian Lag model was used to analyse acreage response of rice crop. ARIMA model was used for forecasting. The behaviour of prices and arrivals was examined through multiplicative model. The result revealed that the growth rate of area under rice was found negative but significant in most of the selected states. The growth rate of production and yield of rice was found positive and significant in most of the selected states as well as for India as a whole. In Gujarat, the growth rate of area, production and yield of rice was found positive and significant during overall study period. In India, the results of instability indices found to have low instability in area, production and yield of rice. In Gujarat, the instability of area and yield under rice found lower, while it was moderate in case of production during overall period. The study on impact of price and non-price factors on acreage allocation revealed that rice growers are more sensitive to non -price factors than price factors. The results of forecasting area and production of rice in Gujarat and India will increase in the next five years. The prices of rice showed less degree of irregular fluctuations, while arrivals of Vadodara market showed comparatively higher irregular fluctuation. The arrivals of Burdwan market showed less degree of irregular fluctuation. In selected markets not found any cyclical variation in the price indices.

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Section: Growth and Instability In Area And Production Of Rice In Indiasupporting

confidence: 92%

An analysis of growth and instability in area, production, yield and price behaviour of rice in India

SM,

Bhopala,

2024

Int. J. Stat. Appl. Math.

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“…India is referred to as the “powerhouse of agriculture” worldwide because it has risen to the top of the worldwide milk, spice, and pulse production rankings. Furthermore, it is the world’s second-largest wheat producer after China ( Dey, Dinesh & Rashmi, 2020 ). According to statistics released by the Population Reference Bureau, the world’s population is expected to have expanded by approximately 10 billion people by the end of 2050 ( Population Reference Bureau, 2021 ), boosting the demand for agricultural products.…”

Section: Introductionmentioning

confidence: 99%

Drought stress detection technique for wheat crop using machine learning

Gupta

Kaur

2023

PeerJ Computer Science

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The workflow of this research is based on numerous hypotheses involving the usage of pre-processing methods, wheat canopy segmentation methods, and whether the existing models from the past research can be adapted to classify wheat crop water stress. Hence, to construct an automation model for water stress detection, it was found that pre-processing operations known as total variation with L1 data fidelity term (TV-L1) denoising with a Primal-Dual algorithm and min-max contrast stretching are most useful. For wheat canopy segmentation curve fit based K-means algorithm (Cfit-kmeans) was also validated for the most accurate segmentation using intersection over union metric. For automated water stress detection, rapid prototyping of machine learning models revealed that there is a need only to explore nine models. After extensive grid search-based hyper-parameter tuning of machine learning algorithms and 10 K fold cross validation it was found that out of nine different machine algorithms tested, the random forest algorithm has the highest global diagnostic accuracy of 91.164% and is the most suitable for constructing water stress detection models.

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Synergizing Open Geospatial Data from Cloud Platform and Machine Learning (ML) Algorithms in Open Source Environment for Crop Yield Estimation

Pandey,

Frederick,

Sharon

et al. 2023

Algorithms for Intelligent Systems

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Wheat Production in Uttar Pradesh – A Study on the Growth and Instability Over Time

Cited by 3 publications

References 2 publications

An analysis of growth and instability in area, production, yield and price behaviour of rice in India

An analysis of growth and instability in area, production, yield and price behaviour of rice in India

Drought stress detection technique for wheat crop using machine learning

Synergizing Open Geospatial Data from Cloud Platform and Machine Learning (ML) Algorithms in Open Source Environment for Crop Yield Estimation

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