Spatiotemporal mapping of rice acreage and productivity growth in Bangladesh

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(3), P. e0300648 - e0300648

Published: March 15, 2024

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

Language: Английский

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Temperature variability and its effect on seasonal yield of rice in Bangladesh: a long-term trend assessment

Cogent Food & Agriculture, Journal Year: 2025, Volume and Issue: 11(1)

Published: Jan. 5, 2025

Bangladesh, an agriculturally reliant and climate-vulnerable country, requires real-time quantification of climatic variables, such as temperature, to sustain food security. However, the effects spatial temporal temperature variations on rice productivity across seasons remain underexplored. Our study addresses this gap by analyzing data from 35 meteorological stations (1970-2020) using parametric nonparametric methods. The Mann-Kendall test revealed a pronounced increase in minimum temperatures compared maximum temperatures, particularly during monsoon season. K-means clustering identified four seasonal station clusters, revealing that rising positively influenced Aus yields seven regions, while above 35°C negatively affected yields, especially northwest Bangladesh. Seasonal heat incidence highlighted experienced greater stress Aman Boro. Wavelet coherence analysis confirmed recent events significantly impacted Boro with high-frequency anomalies affecting Aman. Regression attributed 11–47% Aus, 4–70% Aman, 7–52% yield fluctuations. Despite localized disruptions, Bangladesh has sustained self-sufficiency through adoption stress-tolerant varieties, mechanization, improved practices. To ensure climate-resilient security, targeted regional policy planning implementation are imperative.

Language: Английский

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Integrating deep learning algorithms for forecasting evapotranspiration and assessing crop water stress in agricultural water management

Journal of Environmental Management, Journal Year: 2025, Volume and Issue: 375, P. 124363 - 124363

Published: Jan. 31, 2025

Language: Английский

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Predicting Agricultural Drought in Central Europe by Using Machine Learning Algorithms

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: unknown, P. 101783 - 101783

Published: March 1, 2025

Language: Английский

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Drought research priorities, trends, and geographic patterns

Hydrology and earth system sciences, Journal Year: 2025, Volume and Issue: 29(5), P. 1379 - 1393

Published: March 13, 2025

Abstract. Drought research addresses one of the major natural hazards that threatens progress toward Sustainable Development Goals. This study aims to map evolution and interdisciplinarity drought over time across regions, offering insights for decision-makers, researchers, funding agencies. By analysing more than 130 000 peer-reviewed articles indexed in SCOPUS from 1901 2022 using latent Dirichlet allocation (LDA) topic modelling, we identified distinct shifts priorities emerging trends. The results reveal plant genetic drought-tolerant genotypes advancements forecasting are most dominant continuously growing areas focus. In contrast, relative importance topics such as ecology, water resource management, forest has decreased. Geospatial patterns highlight a universal focus on methods, with strong secondary emphasis policy societal issues Africa Oceania. Interdisciplinarity experienced marked decline until 1983, followed by steady increase 2007 onward, suggesting integration diverse fields. Emerging recent years signal evolving future research. analysis provides comprehensive overview trends sectors strategic guidance aligning efforts resilience goals. findings crucial agencies policymakers aiming prioritize highest potential mitigate impacts effectively.

Language: Английский

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Farmers' Adoption of Newly Released Climate‐Resilient Rice Varieties in the Coastal Ecosystem of Bangladesh: Effectiveness of a Head‐To‐Head Adaptive Trial

Food and Energy Security, Journal Year: 2025, Volume and Issue: 14(2)

Published: March 1, 2025

ABSTRACT The adoption of newly released rice varieties in Bangladesh remains slow, particularly coastal ecosystems, where multiple stressors reduce productivity. Limited knowledge transfer on climate‐resilient has led farmers to favor traditional cultivars over newer ones. Head‐to‐Head Adaptive Trials (HHATs) were introduced promote the dissemination improved varieties, but their effectiveness not been fully assessed. This study evaluates farmers' trait preferences, varietal selection criteria, patterns, key determinants, and impact HHATs Bangladesh. conducted 2021–2022 2022–2023, with data collected from April June 2023. Using purposive sampling, 50 participant selected, while 150 neighboring systematically sampled based geographic proximity. Findings indicate that yield, taste, resilience salinity drought most important traits influencing selection. While valued superior grain quality concerns yield consistency climate adaptability many continue adopting older varieties. created spillover effects, encouraging broader among farmers. Education, farming as a primary occupation, income, commercial farming, extension services, training, social networks, seed access, quality, resilience, market price significantly influenced adoption, age, low soil fertility, high input costs, large landholdings barriers. Propensity score matching analysis confirmed increased rates by 11.25%–17.71%, though limited distribution hindered widespread adoption. highlights need for targeted policy measures enhance farmer support scale up

Language: Английский

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Forecasting of meteorological drought using ensemble and machine learning models

Environmental Sciences Europe, Journal Year: 2024, Volume and Issue: 36(1)

Published: Sept. 11, 2024

Language: Английский

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Delineating the drought vulnerability zones in Bangladesh

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Oct. 26, 2024

Language: Английский

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Differences in sum-of-hourly and daily reference evapotranspiration for a rice–wheat cropping system in Ganga Basin, India

Journal of Water and Climate Change, Journal Year: 2024, Volume and Issue: 15(6), P. 2845 - 2862

Published: May 11, 2024

ABSTRACT Estimating reference evapotranspiration (ETo) at 24 h timesteps has been considered sufficiently accurate for a long time. However, recent advances in weather data acquisition have made it feasible to apply hourly procedures ETo computation. Hourly can improve the accuracy of estimates, as averaged daily may misrepresent evaporative power during parts day. This study investigates differences between computations performed (ETo,d) and sum (ETo,h) rice–wheat cropping systems Ganga Basin, India. The meteorological were collected from an automatic station located experimental plot IIT Kanpur, Daily sum-of-hourly according FAO-PM equation rice wheat seasons. Diurnal variations variables resulted underestimation when timestep was considered. No significant difference observed wet periods. estimates able capture abrupt changes climate variables, while failed represent average values only. As result, sums are more reliable Plains.

Language: Английский

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A deep learning perspective on meteorological droughts prediction in the Mun River Basin, Thailand

AIP Advances, Journal Year: 2024, Volume and Issue: 14(8)

Published: Aug. 1, 2024

Accurate drought prediction is crucial for enhancing resilience and managing water resources. Developing robust forecasting models understanding the variables influencing their outcomes are essential. This study developed that integrate wavelet transformation (WT) with advanced artificial intelligence (AI) models, increasing accuracy. investigates of meteorological droughts using standalone bootstrapped random forest (BRF) bi-directional long short-term memory (Bi-LSTM) compared to wavelet-decomposed hybrid (WBRF, WBi-LSTM). These were evaluated in Mun River Basin, Thailand, utilizing monthly data (1993–2022) from Thai Meteorological Department. The predictions assessed statistical metrics (R2, MAE, RMSE, MAPE). For Standardized Precipitation Index (SPI), WBRF model consistently outperformed BRF across various timescales, demonstrating higher R2 (0.89–0.97 SPI-3) lower error (MAE: 0.144–0.21 SPI-6, RMSE: 0.2–0.3 SPI-12). Similarly, WBi-LSTM Bi-LSTM SPI predictions, exhibiting (0.87–0.91 0.19–0.23 0.27–0.81 SPI-12) all timescales. trend was also observed China Z-index, Modified Hutchinson Drought Severity Index, Rainfall Anomaly where achieved superior performance models. emerged as preferred choice different timespans. integration WT enhanced predictive accuracy making them effective tools prediction.

Language: Английский

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