As a staple food, rice has an enormous market in Bangladesh in terms of market participants and the volume of the product. As the price of rice is always a sensitive factor for producers, poor consumers and policy makers, this paper investigates market integration and price transmission along the vertical supply chain of rice. Johansen’s test of co-integration confirmed that farm, wholesale and retail prices are co-integrated in the long-run. A causality test revealed that prices were found to be at wholesale levels for both the upstream and downstream markets. The asymmetry error correction model (ECM) has discovered short-run and long-run asymmetry in price transmission in the vertical supply chain where both producers and consumers were being affected due to positive and negative asymmetry. Threshold autoregressive (TAR) and momentum threshold autoregressive (M-TAR) models have confirmed threshold co-integration as well as threshold effect on asymmetry in price transmission. The results highlight the inevitability of policy implementations and increased public interventions to reduce asymmetry for engendering greater pricing efficiency in Bangladesh rice markets.

Drought prediction is the most effective way to mitigate drought impacts. The current study examined the ability of three renowned machine learning models, namely additive regression (AR), random subspace (RSS), and M5P tree, and their hybridized versions (AR-RSS, AR-M5P, RSS-M5P, and AR-RSS-M5P) in predicting the standardized precipitation evapotranspiration index (SPEI) in multiple time scales. The SPEIs were calculated using monthly rainfall and temperature data over 39 years (1980–2018). The best subset regression model and sensitivity analysis were used to determine the most appropriate input variables from a series of input combinations involving up to eight SPEI lags. The models were built at Rajshahi station and validated at four other sites (Mymensingh, Rangpur, Bogra, and Khulna) in drought-prone northern Bangladesh. The findings indicated that the proposed models can accurately forecast droughts at the Rajshahi station. The M5P model predicted the SPEIs better than the other models, with the lowest mean absolute error (27.89–62.92%), relative absolute error (0.39–0.67), mean absolute error (0.208–0.49), root mean square error (0.39–0.67) and highest correlation coefficient (0.75–0.98). Moreover, the M5P model could accurately forecast droughts with different time scales at validation locations. The prediction accuracy was better for droughts with longer periods.

This study aimed to investigate the cost-benefit per hectare of rice production in the haor region in Bangladesh. For doing so, the multi-stage sampling technique was used to collect cross-sectional data during 2018 from four haor districts producing rice in Bangladesh. To achieve the purpose, a total of 368 randomly selected farming households from Habiganj, Sunamganj, Moulvibazar, and Sylhet districts were surveyed using a structured questionnaire. Data analysis was done utilizing descriptive statistics and cost and return analysis. The result revealed that labor costs constituted the largest proportion of gross operating expenses, followed by fertilizer, irrigation, tillage, insecticides and herbicides, and threshing cost. The cost-benefit analysis finding also shows that rice is a profitable enterprise in the haor areas as the lower production cost compared to return. Because of the amount of input used and the price of output, the profitability differs between different seasons, however. The model shows that cost of seed, human labor cost, cost of TSP, cost of MoP, and cost of irrigation were the key factors that influenced rice production. This study also identified some of the problems related to rice production in haor areas. Lower output price, higher input price, unavailability of short growth duration high yielding varieties, and embankment damages, etc., are key obstacles to rice production. Therefore, this study provides the government's concerned authority with appropriate suggestions and policy recommendations to solve the farmers' issues that could boost rice productivity in the haor areas and contribute to food security and self-sufficiency in rice cultivation.

Declining natural resources, rising production costs, and diminishing returns from input‐intensive practices threaten the sustainability of smallholder rice systems in Bangladesh. Using a primal system approach and survey data from 1869 rice‐producing households, this study evaluates technical and allocative inefficiency across major agro‐ecological zones and examines factors associated with inefficiency‐driven cost increases. Results reveal substantial technical inefficiency, equivalent to a loss of one quarter of potential output. Allocative inefficiency is also pronounced, with seeds, fertilisers, and labour overused while machinery remains underutilised. Together, these inefficiencies inflate production costs by 48%, with technical inefficiency being the primary driver. Cost increases from technical inefficiency are shaped primarily by environmental and managerial conditions, whereas those from allocative inefficiency reflect market access constraints and institutional arrangements. Notably, plot size generates trade‐offs between productivity gains and optimal resource allocation. Closing these gaps requires targeted technological, institutional, and market reforms under increasing climatic and resource pressures.