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Weather Forecast Based Rice Advisory Services in Bangladesh

N M F Rahman
Agricultural Statistics Division, Bangladesh Rice Research Institute (BRRI), Gazipur-1701, Bangladesh

M C Rahman
Agricultural Economics Division, BRRI, Gazipur-1701, Bangladesh.

M A Baten
Professor
Department of Statistics, Shahjalal University of Science and Technology, Sylhet, Bangladesh

M I Hossain
Agricultural Statistics Division, Bangladesh Rice Research Institute (BRRI), Gazipur-1701, Bangladesh

S M Q Hassan
Meteorologist, Storm Warning Centre, Bangladesh Meteorological Department, Dhaka, Bangladesh

R Ahmed
Agricultural Statistics Division, Bangladesh Rice Research Institute (BRRI), Gazipur-1701, Bangladesh

M M Hossain
Entomology Division, BRRI, Gazipur-1701, Bangladesh.

ABM Z Hossain
Irrigation and Water Management Division, BRRI, Gazipur-1701, Bangladesh.

M A Aziz
Agricultural Statistics Division, Bangladesh Rice Research Institute (BRRI), Gazipur-1701, Bangladesh

M M Haque
Soil Science Division, BRRI, Gazipur-1701, Bangladesh.

Abstract/ Executive Summary:

Strengthening the early warning system to forecast extreme weather and provide action-oriented advisories may increase rice yield as well as enhance the income of the farmers through minimizing risks and losses if proper use of the generated advisory is ensured. This study assessed the importance and impact of weather forecast-based advisory service (WFBAS) in Bangladesh. Literature review and field experimental data were used in a sensitivity analysis to show the impact of WFBAS on rice production. Available literature suggested that the weather-based advisory service would enhance rice yield by around 6.7-10%, but the experimental findings revealed that yield advantage could be 21.48%. Besides, the adoption of this technology would reduce the cost of cultivation by 12%, whereas the aggregate impact would increase the farmers’ profit by 25%. In the sensitivity study, we considered the production and price of Aman and Boro seasons (actual and government procured prices for 2018-19) and assumed only a 6.70% yield increase with 5% of farmers adopting WFBAS. As such, additional 0.172 million tons will be added to the national rough rice production, and thus the nation will be benefited by 3143 million Bangladeshi takas (BDT) at the actual farmgate price and it would be 4478 million BDT at the government procured price. The return on one BDT investment in this technology would be 51-73 BDT based on actual and procured prices. The projection showed enhanced production of the rough rice at 0.119 million tons and 0.214 million tons by 2025 and 2030, on which the additional return would be BDT 2441 and BDT 5223 million at the projected actual farmgate price and BDT 3522 and BDT 6979 million at the projected Government procured price, respectively. Hence, the successful implementation of the WFBAS will help to develop resilient rice farming communities, minimize livelihood risk, reduce the cost of production, utilize resources efficiently, and enable the farmers to take maximum benefit from favorable weather conditions through improved agricultural extension services. Finally, the enhanced yield and loss reduction will help to achieve the target of Sustainable Development Goal (SDG) by 2030 through sustaining rice production in Bangladesh under changing climate.

Keywords: Climate-resilience, Farmers’ income, Future prospects, Model verification, Sensitivity analysis, Sustainable production

Location: Plant Physiology Division of BRRI.

Start Date: 00-05-2016

End Date: 00-08-2016

Program Area: Knowledge Management

Commodity: Rice

Objectives:

This study has been designed to (i) assess the weather forecast based rice advisory system for increasing productivity and show its importance on rice production; (ii) develop and mapping the research, development, and extension (RDE) activities for three decades on disseminating the WFBAS in the rice farming in Bangladesh.

Methodology:

The Weather Research and Forecasting (WRF) Model is an atmospheric model designed for both research and numerical weather prediction (Power et al., 2017). It is being used for weather forecasts on micro-levels. For the development of the WRF model, a partnership was formed among the National Center for Atmospheric Research (NCAR), the National Oceanic and Atmospheric Administration (NOAA), the U.S. Air Force, the Naval Research Laboratory, the University of Oklahoma, and the Federal Aviation Administration. This model is a numerical weather prediction method of next-generation mesoscale designed to serve both the needs of atmospheric science and operational forecasting. This model allows scientists to make real-time predictions of the atmosphere based on real data (observations, analyses). By developing a broader research community, the WRF model performs operational prediction in a flexible and computationally efficient way with the advancement in physics, numeric, and data assimilation.

The WRF model predictions were validated with the observed data collected from the Plant Physiology Division of BRRI. The seven days lead time approach was followed in the forecast validation. The plot, normalized root-mean-square error (NRMSE), and relative bias (relBias) were estimated for the verification of prediction performance. The parameter Êi - Ei represents the error between the predicted and observed values for the ith sample. The normalized root means squared error (NRMSE) is the root mean squared error (RMSE) divided by the mean of the observed data. The level of prediction accuracy was assessed by using RMSE, a good predictor that reflects the consistency of the model and its ability to explain the actual behavior of the system. An experiment was conducted by the agromet lab of BRRI in Aus seasons at the locations. The locations represent the West, Northwest, South, and central part of Bangladesh. The Randomized Control Block (RCB) design has been used as a field experimental design with three replications. The experiment was aimed to assess the effectiveness of WFBAS regarding the yield enhancement of rice varieties. The WRF model was used from May to August 2016 for localized daily weather forecasting of minimum and maximum temperature, rainfall, and relative humidity. After that, the weather forecast-based advisory bulletin was prepared once a week for a specific location. The weather forecast was done 7 days ahead to generate advisories for crop management and pest protection. That advisory was followed into the WFBAS experimental plots at four locations and other plots were managed through usual practices. The advisory bulletins were sent directly to the scientific assistant of the respective locations every Sunday/Monday during the study.

Source of Information: Bangladesh Rice J. 25 (1) : 51-74, 2021

Article Link (Online Journal): DOI: https://doi.org/10.3329/brj.v25i1.55179

Funding Source:

1.

Budget:

Total Budget (Tk.) :

Achievement/Findings:

The implementation of a weather-based advisory service (WFBAS) has the potentiality to boost rough rice production by enhancing productivity in Bangladesh. The findings of this study show that the adoption of weather-based management practices can increase grain yield by 6.7-21.48% and reduces the cost of production by about 12%. If only 5% of rice farmers would adopt the WFBAS, the minimum probability of yield increase (6.70%) will add 0.172 million tons of rough rice to the national food basket, the value of which will add about 4478 million BDT to the national GDP. If WFBAS is materialized properly at the entire production system (such as research, extension, and farm), one BDT investment will return 51-73 BDT for the nation. The WFBAS is found very much effective for the farmers as it provides not only the weather forecast information but also technical guidance on the cultivation aspects including timely transplanting and application of fertilizer/ water/pesticides/herbicides/ insecticides. It also saves inputs such as water, manpower, electricity, and fuel, through proper irrigation scheduling. The study has targeted to adopt the WFBAS by 5% farmers at 2030 and projected the outcomes as additional 0.214 million tons national rough rice production that will add BDT 5223 million as the contribution of rice sector to the national GDP. Therefore, the adoption of WFBAS will increase the national agricultural income significantly through increasing rice productivity. Moreover, the projected returns from adopting WFBAS in rice farming will guide the policymakers to take necessary decisions and actions for the establishment, upgrading, and dissemination of this technology to the stakeholders.

Knowledge Management: Journal