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Impacts of Climate Change on Rice Yield and Variability; an Analysis of Disaggregate Level in the Southwestern Part of Bangladesh Especially Jessore and Sathkhira Districts

Humayun Kabir MD
Department of Urban and regional Planning, International Islamic University Malaysia, Kulliyyah of Architecture and Environment Design (KAED), Jalan Gombak, 53100 Kuala Lumpur, Selangor, Malaysia.

Abstract/ Executive Summary:

Climate Change is a hot issue in the world. Effect of climate change on yield of two varieties of boro rice has been assessed using the CERES Rice model of the DSSAT modeling system. The yield of BR3 and BR14 boro varieties for the years 2008, 2030, 2050 and 2070 have been simulated for 12 locations (districts) of Bangladesh, which were selected from among the major rice growing areas in different regions of Bangladesh. The CERES-Rice model uses a detailed set of crop specific genetic coefficients for predicting yield (BR3 and BR14) because “genetic coefficients” for these varieties are available in the DSSAT modeling system. Available data on soil and hydrologic characteristics of these locations, and typical crop management practice for boro rice were used in the simulations. The weather data required for the model (daily maximum and minimum temperatures, daily solar radiation and daily precipitation) were generated for the selected years and for the selected locations using the regional climate model PRECIS. The model predicted significant reduction in yield of both varieties of boro rice due to climate change; yield reductions of over 20% and 50% have been predicted for both rice varieties for the years 2050 and 2070, respectively. Increases in daily maximum and minimum temperatures have been found to be primarily responsible for reduction in yield. Increases in incoming solar radiation and atmospheric carbon-di-oxide concentration increases rice yield to some extent, but their effect is not significant compared to the negative effects of temperature. Variations in rainfall pattern over the growing period have also been found to affect rice yield. Increasing temperatures and solar radiation have been found to reduce the duration of physiological maturity of the rice varieties. Model results also suggest that in addition to reducing yield, climate change may also make rice yield more vulnerable to transplanting date, predicting significant reduction in yield as transplanting date is delayed, especially beyond 15 January. DSSAT modeling system could be a useful for assessing possible impacts of climate change and management practices on different varieties rice and other crops.

Keywords: Climate change, Boro rice, Rice yield, CERES-Rice model

Location: Department of Urban and regional Planning, International Islamic University Malaysia

Start Date: 00-00-1972

End Date: 00-00-2009

Program Area: Risk Management in Agriculture

Commodity: Rice

Objectives:

To estimate the economic impact of climate change on rice yields

Methodology:

This section has used pooled cross-sectional time series data for the three major rice crops (Aus, Aman and Boro) for 13 out of 19 greater districts in Bangladesh. The selection of the districts was based on the availability of consistent data both on climate and rice yield. District level data on climate and rice yield for the 1972-2009 period are grouped into seven climatic sub-zones of Bangladesh. Four (the time period for analysis matches available climate and rice production data). The summary statistics of data are described. These data reveal that there is a correlation between higher mean values and higher standard deviation values. Both maximum and minimum temperature is higher during Aus rice period and lowest during Boro rice period. But absolute variability in temperature is higher for Boro rice. The variability in rainfall varies considerably among the three rice varieties which in large part correspond to their growing seasons. Descriptive statistics such as mean, standard deviation and CV are used to examine the district level variability for climate variables. Since the first objective of this chapter is primarily to examine the inter-district or inter-region variations in climate, the relative variability expressed by the CV is a more appropriate measure than the standard error. It is essential to investigate the presence of unit roots for each potential variable before we estimate the model either using the FGLS or MLE method. One important assumption of the Just and Pope model is that the variables under estimation are stationary. Therefore, variables having the properties of I must be differenced before panel estimation occurs. Otherwise, using anon-stationary data set directly might yield spurious results. However, the time series properties of one variable comprising many regions in a panel data setting are hard to characterize. This study uses the Fisher-type test as proposed in Maddala and Wu. The Fisher test obtains more precise results and achieves higher power compared to other tests such as LLC. In order to determine the effects of climate variables on the level of yield and its variability, the stochastic production function approach pioneered by Just and Pope is applied here. The fundamental concept underpinning this approach is that the production function can be decomposed into two segments: the first segment is linked to the mean output level while the second segment is associated with the variability of that output. Three functional forms of production functions (Cobb-Douglas, quadratic and translog) are used for the estimation of the Just and Pope Production function. Because of the multiplicative interaction between the mean and variance, a translog functional form violates the Just and Pope postulates. Therefore, Cobb-Douglas and linear quadratic forms are selected for the mean yield function estimation. Only the linear functional (CD) form is considered for the variability function because the variance function has a non-linear form and the inclusion of quadratic terms for explanatory variables makes the analysis more difficult. Fixed effects and random effects models are usually used for a panel model. This study has used the fixed effects model purposely for two reasons. First, it allows region or district specific characteristics to be included which are one of the motivations of using a panel model. To take into account the regional differences in the mean yield function, regional dummies are included in the model. Second, the fixed effects model is appropriate in situations where there is a possibility of correlation between regressors and time-invariant distinctiveness. In contrast, the random effects model necessitates the assumption of no correlation between unobserved time-invariant characteristics and the explanatory variables. Therefore, the fixed effects model is selected for this study and this selection is consistent with past studies. Both MLE and a three-step FGLS were prescribed in Just and Pope for estimating both functional forms. However, FGLS estimation has been employed in most quantitative studies, although MLE is more efficient and unbiased than FGLS for small samples. Given the large sample here, FGLS was used in this study as described to estimate a form of fixed effects panel model for the above equations. Moreover, both FGLS and MLE were used in the preliminary analyzes but FGLS was found to produce better results. This is another reason for the choice of FGLS as an estimation method. Furthermore, panel model estimation involving both cross-section and time series data may encounter the problems of heteroscedasticity and auto-co-relation. These two problems are better addressed in FGLS since it assumes that panels are homoscedastic and there is no auto-correlation.

Source of Information: J Geogr Nat Disast, 2015, Vol 5(3), ISSN: 2167-0587

Article Link (Online Journal): http://dx.doi.org/10.4172/2167-0587.1000148

Funding Source:

Budget:

Total Budget (Tk.) :

Achievement/Findings:

The results revealed that the impacts of climate variables vary among the three rice varieties. Maximum temperature is positively related to Aus and Aman mean rice yield in the linear model while the relationship is negative in the quadratic model. Finally, the impacts of rainfall on yield variability were positive for Aman rice and negative for Aus and Boro rice. This confirms that rainfall is risk increasing for Aman rice while risk decreasing for Aus and Aman rice. Three time scenarios (2030, 2050 and 2100) were developed to model potential climate changes on rice yield and its variability. The results revealed that future climate change is expected to increase the variability of rice yield for Aman and Boro varieties

Knowledge Management: Journal