Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach

Annamalai, Niveditha; Johnson, Amala

doi:10.1007/s42979-022-01604-0

Cited by 7 publications

(2 citation statements)

References 6 publications

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“…We found very few studies that have examined the spatial and temporal changes in rice area, production, and yield dynamics. These studies were primarily focused on major ricegrowing countries such as China, India, Indonesia, Vietnam, Thailand, Myanmar, Japan, Philippines, Pakistan, and Brazil, with limited research specifically dedicated to Bangladesh [22][23][24][25][26]. To the best of our knowledge, only one study has investigated the growth and trend analysis of rice in Bangladesh, but its primary focus was on the regional context, examining 14 agricultural regions [5].…”

Section: Introductionmentioning

confidence: 99%

Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

Al Mamun,

Nihad,

Sarker

et al. 2024

PLoS ONE

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Technological advancements have long played crucial roles in rice productivity and food security in Bangladesh. Seasonal variation over time and regional differences in rice production, however, pose a threat to agricultural sustainability but remain unexplored. We performed a spatial-temporal mapping of rice cultivation area, production, and yield from 2006–2007 to 2019–2020 using secondary data for disaggregating 64 districts in Bangladesh. Growth and multivariate approaches were employed to analyze time-series data. Results showed that Mymensingh had the highest rice cultivated area and production, while Bandarban had the lowest. The 14 years highest average rice yield was found in Gopalganj and Dhaka (3.63 tons/ha), while Patuakhali (1.73 tons/ha) had the lowest. For the Aus, Aman, and Boro, the rice cultivation area in 19 districts, 11 districts, and 13 districts declined significantly. The overall rice production increased significantly in most districts. For the Aus, Aman, and Boro seasons, the rice yield in 54, 50, and 37 districts demonstrated a significant upward trend, respectively. The adoption rate of modern varieties has risen dramatically. However, there are notable variances between regions and seasons. A significant increasing trend in Aus (0.007% to 0.521%), Aman (0.004% to 0.039%), and Boro (0.013% to 0.584%) were observed in 28, 34, and 36 districts, respectively, with an increase of 1% adaptation of HYV. Predictions revealed that rice cultivation area and production of Aus, Aman, and Boro seasons will be increased in most of the regions of Bangladesh by 2030. Based on spatiotemporal cluster analysis, the five identified cluster groupings illustrated that clusters lack spatial cohesion and vary greatly seasonally. This suggests increasing rice production by expanding cultivable land, adopting high-yielding varieties, and integrating faster technological advancement in research and extension. The findings will assist scientists in developing region-specific production technologies and policymakers in designing decentral region-specific policies to ensure the future sustainability of rice production.

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Section: Introductionmentioning

confidence: 99%

Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

Al Mamun,

Nihad,

Sarker

et al. 2024

PLoS ONE

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“…Most of the scientists predicted the yields of various crops using traditional econometric models. The majority methods in the studies were linear and multiple regression (Ansarifar et al, 2021;Conradt, 2022;Murugan et al, 2020;Rai et al, 2022;Sellam & Poovammal, 2016), and exponential weighted moving average (Annamalai & Johnson, 2023;Booranawong & Booranawong, 2017;Kim et al, 2020). However, the most widespread method used by scientists in forecasting crop yields was autoregressive integrated moving average (ARIMA) (Dharmaraja et al, 2020;Fan et al, 2016;Hemavathi & Prabakaran, 2018;Alani & Alhiyali, 2021;Lwaho & Ilembo, 2023;Rathod et al, 2018;Rathod et al, 2017;Senthamarai Kannan & Karuppasamy, 2020).…”

Section: Introductionmentioning

confidence: 99%

Implications of Neural Network as a Decision-Making Tool in Managing Kazakhstan’s Agricultural Economy

KULISZ,

DUISENBEKOVA,

KUJAWSKA

et al. 2024

Appl. Comput. Sci.

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This study investigates the application of Artificial Neural Networks (ANN) in forecasting agricultural yields in Kazakhstan, highlighting its implications for economic management and policy-making. Utilizing data from the Bureau of National Statistics of the Republic of Kazakhstan (2000-2023), the research develops two ANN models using the Neural Net Fitting library in MATLAB. The first model predicts the total gross yield of main agricultural crops, while the second forecasts the share of individual crops, including cereals, oilseeds, potatoes, vegetables, melons, and sugar beets. The models demonstrate high accuracy, with the total gross yield model achieving an R-squared value of 0.98 and the individual crop model showing an R value of 0.99375. These results indicate a strong predictive capability, essential for practical agricultural and economic planning. The study extends previous research by incorporating a comprehensive range of climatic and agrochemical data, enhancing the precision of yield predictions. The findings have significant implications for Kazakhstan's economy. Accurate yield predictions can optimize agricultural planning, contribute to food security, and inform policy decisions. The successful application of ANN models showcases the potential of AI and machine learning in agriculture, suggesting a pathway towards more efficient, sustainable farming practices and improved quality management systems.

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Prediction of annual rice imports emphasizes on systematic error reduction with smoothing series and optimal parameter selection techniques

Sujjaviriyasup

2024

Neural Comput & Applic

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Analysis and Forecasting of Area Under Cultivation of Rice in India: Univariate Time Series Approach

Cited by 7 publications

References 6 publications

Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

Implications of Neural Network as a Decision-Making Tool in Managing Kazakhstan’s Agricultural Economy

Prediction of annual rice imports emphasizes on systematic error reduction with smoothing series and optimal parameter selection techniques

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