Impacts of seasonal climate variation on rice yield: Evidence from the Central Coast of Vietnam

Nguyen, Phuong Thi Minh; Ho, Phuc Trong; Pham, Hung Xuan Article Impacts of seasonal climate variation on rice yield: Evidence from the Central Coast of Vietnam Cogent Economics & Finance Provided in Cooperation with: Taylor & Francis Group Suggested Citation: Nguyen, Phuong Thi Minh; Ho, Phuc Trong; Pham, Hung Xuan (2024) : Impacts of seasonal climate variation on rice yield: Evidence from the Central Coast of Vietnam, Cogent Economics & Finance, ISSN 2332-2039, Taylor & Francis, Abingdon, Vol. 12, Iss. 1, pp. 1-15, https://doi.org/10.1080/23322039.2024.2421894 This Version is available at: https://hdl.handle.net/10419/321650 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Cogent Economics & Finance ISSN: 2332-2039 (Online) Journal homepage: www.tandfonline.com/journals/oaef20 Impacts of seasonal climate variation on rice yield: Evidence from the Central Coast of Vietnam Phuong Thi Minh Nguyen, Phuc Trong Ho & Hung Xuan Pham To cite this article: Phuong Thi Minh Nguyen, Phuc Trong Ho & Hung Xuan Pham (2024) Impacts of seasonal climate variation on rice yield: Evidence from the Central Coast of Vietnam, Cogent Economics & Finance, 12:1, 2421894, DOI: 10.1080/23322039.2024.2421894 To link to this article: https://doi.org/10.1080/23322039.2024.2421894 © 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group Published online: 29 Oct 2024. Submit your article to this journal Article views: 1338 View related articles View Crossmark data Full Terms & Conditions of access and use can be found at https://www.tandfonline.com/action/journalInformation?journalCode=oaef20 DEVELOPMENT ECONOMICS | RESEARCH ARTICLE Impacts of seasonal climate variation on rice yield: Evidence from the Central Coast of Vietnam Phuong Thi Minh Nguyen , Phuc Trong Ho and Hung Xuan Pham Faculty of Economics and Development Studies, University of Economics, Hue University, Hue City, Vietnam ABSTRACT This study investigates the impact of seasonal climate change on rice productivity in Vietnam’s Central Coast, using 26years of data from 1996 to 2021. To achieve this objective, the study applies a Feasible Generalized Least Squares (FGLS) model to obtain robust estimates for the panel analysis. The findings reveal the consequences of climate variation on rice productivity throughout different seasons. Notably, increases in maximum temperature during the winter–spring season and minimum temperature during the summer–autumn season boost rice yields, while higher maximum temperatures in summer–autumn and minimum temperatures in winter–spring reduce yields. Specifically, a 1% increase in maximum temperature improves winter– spring yields by 1.66% but reduces summer–autumn yields by 1.01%, while a 1% rise in minimum temperature decreases winter–spring yields by 0.30% but enhances summer–autumn yields by 3.32%. In addition, increases in both maximum and minimum relative humidity positively impact yields. The study also finds that a 1% increase in maximum precipitation slightly reduces summer–autumn yields. These findings provide important insights for developing strategies to improve the resilience of rice production to climate change. IMPACT STATEMENT This study aims to provide reliable scientific evidence on the impacts of seasonal climate change on rice productivity over an extended period of time in the Central Coast of Vietnam. Its findings can assist policymakers in proposing climate-adaptive farming measures to mitigate adverse effects on rice production and may also serve as a reference for regions with similar climates in other rice-producing countries. ARTICLE HISTORY Received 18 May 2024 Revised 1 September 2024 Accepted 22 October 2024 KEYWORDS Climate change; Central Vietnam; FGLS model; paddy productivity; seasonal effects SUBJECTS Sustainable Development; Economics and Development; Environmental Economics; Rural Development 1. Introduction Climate change has exerted a significant impact on agriculture worldwide and poses a serious threat to global food security (Malhi et al., 2021; Tilahun, 2021; Emeru, 2022; Mubenga-Tshitaka et al., 2023). The United Nations Framework Convention on Climate Change (UNFCCC) defines climate change as a phenomenon resulting from human activities that alter the composition of the global atmosphere, leading to shifts beyond the natural climate variability observed over comparable periods (WHO, 2016). These changes are monitored through key indicators identified by the Global Climate Observing System (GCOS), including surface temperature, ocean heat content, atmospheric CO2 levels, and sea levels, Arctic & Antarctic sea ice extent, ocean acidification, and glacier (WMO, 2023). In light of the difficulties in collecting comprehensive climate data, surface temperature is often used to assess the impact of climate change on agriculture. However, climate change is now reconsidered to refer to any alterations in climatic indicators over time, such as temperature or precipitation (Li, 2023). According to NASA (2023), the ten most recent years rank as the warmest on record, and Earth’s average temperature in 2023 was approximately 1.36 C higher than the late 19th century (1850–1900) preindustrial average. This indicates that climate change will continue to be a worrying concern for global agriculture. CONTACT Phuong Thi Minh Nguyen [email protected] University of Economics, Hue University, Hue City, Vietnam ß2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent. COGENT ECONOMICS & FINANCE 2024, VOL. 12, NO. 1, 2421894 https://doi.org/10.1080/23322039.2024.2421894 Besides, among various crops, rice is the third-largest cultivated cereal crop worldwide (Bazargan et al., 2014), and it serves as the fundamental food staple for more than half of the world’s population, with an annual production of approximately 480 million metric tons of milled rice (Muthayya et al., 2014; Irshad et al., 2018). During the early 1980s, Vietnam transitioned from its status as a foodimporting nation to emerging as one of the foremost rice exporters globally within a period of less than two decades (Vu & Nguyen, 2021). By 2023, Vietnam surpassed Thailand to become the second-largest rice exporter globally, with a total volume of 5.9 million tons (USDA, 2023). Rice cultivation in Vietnam plays a pivotal role in enhancing global food security (Shrestha et al., 2016). However, Vietnam is among the countries that are most vulnerable to the impacts of climate change (Dasgupta et al., 2009; World Bank Group and Asian Development Bank, 2020; Nguyen and Scrimgeour, 2022; Anh et al., 2023). Nguyen et al. (2008) and Anh et al. (2023) indicated that Vietnam’s extensive coastline, flanked by high mountains and flat floodplains, exposed over 70% of its population to various natural hazards. Among these regions, the Mekong River Delta, the north-central region, and the central coastal region face heightened vulnerability to the impacts of global warming. Given the challenges posed by climate change, understanding its effects on rice production is vital for devising solutions to enhance current food security and bolster the resilience of agricultural systems. Moreover, because Vietnam has complex seasonal cropping systems, with crop calendars and patterns varying across agroecological zones, it is crucial to assess these impacts within the context of specific seasonal variations to effectively investigate the impacts of climate change on rice production (Trinh, 2018). Nevertheless, research on the impact of climate variability on rice production in Vietnam, particularly in the central regions, remains scarce (Chung et al., 2015). Recent studies have explored the impact of climate change on agricultural production in Vietnam, particularly emphasizing the cultivation of rice, given its significance as one of the world’s major riceproducing regions. Despite many studies on the economic impact of climate change in many fields, most of them used cross-sectional data and time series data (Chung et al., 2015; Huynh et al., 2020), and a few used panel data (Trinh, 2018; Nguyen & Scrimgeour, 2022). In addition, most of these studies have utilized Ordinary Least Square, fixed and random effect methods. To the best of our knowledge, there are a limited number of studies in the existing literature that utilize feasible generalized least squares (FGLS) models to examine the impacts of climate change. FGLS models are advantageous in dealing with issues such as group-wise heteroscedasticity and autocorrelation within panels of the stochastic disturbance term. These issues could deviate from the strict assumptions of traditional panel models, potentially leading to biased estimations (Wu et al., 2021). Therefore, applying the model with the strengths outlined above offers a promising approach to resolving existing challenges or issues left unresolved in previous studies in Vietnam. This application is expected to yield reliable results in assessing the impact of climate on rice production in the study area, thereby enhancing the comprehensive understanding and effective management of climate-related risks in the region’s agricultural sector. The objective of this study is to employ a feasible generalized least squares model to examine how climate change, with specific seasonal characteristics, affects rice productivity in the Central Coast of Vietnam from 1996 to 2021. This study makes two main contributions to the existing literature: (i) this study is the first to apply the FGLS model for more in-depth seasonal research of climate change impacts over an extremely extended period of time in the Central Vietnam, while addressing both group-wise heteroscedasticity and autocorrelation to obtain robust estimates for the panel analysis; and (ii) the findings of this study provide scientific evidence for Vietnamese policymakers to propose appropriate farming measures to seasonal climate factors, aimed at adapting to and reducing the adverse impact of climate change on the rice production sector. This scientific evidence can also be referenced for regions with similar climate characteristics in other rice-cultivating countries in the world. The remainder of this article is organized as follows. Section 2 reviews the literature. Section 3 describes the data sources and the methodology used. Section 4 represents and discusses the results. Finally, Section 5 concludes the study with a summary of the findings and policy implications. 2 P.T.M. NGUYEN, P.T. HO, AND H.X. PHAM 2. Literature review Researchers are increasingly directing their attention towards the economic consequences of climate change. Previous research has yielded varying results regarding the anticipated response of rice to forthcoming climate change. Rayamajhee et al. (2021) highlighted the vulnerability of rural agricultural households in Nepal to climate change. Employing the stochastic frontier function approach, their findings underscored the significant negative effects on rice production resulting from alterations in both average and extreme precipitation and temperatures. Emphasis was placed on the threats posed by irregular extreme rainfall patterns and a sustained rise in average temperatures. Massagony et al. (2023) applied the feasible generalised least squares (FGLS) model to examine the effects of climate change on rice production in Indonesia using historical data from 1986 to 2016. Their results revealed that increases in temperature and precipitation have adverse effects on rice production, whereas higher relative humidity has a positive impact. Recognizing the adverse impact of temperature changes on rice production, Mukhopadhyay and Das (2023) proposed a proactive solution and found that the adoption of new rice seed varieties with heightened temperature tolerance is more effective in mitigating the challenges posed by climate change. Numerous studies have examined the impacts of climate change on rice production and yield, spanning diverse scopes (Kontgis et al., 2019), including national (Felkner et al., 2009; Nahar et al., 2018, Firdaus et al., 2020), regional (Matthews et al., 1997; Li et al., 2017; van Oort & Zwart, 2018) and global levels (Olszyk et al., 1999; Lobell & Gourdji, 2012). The primary tools for assessing the effects of climate change on crop yield are crop simulation and statistical models (Shi et al., 2013). These models integrate scientific principles from agronomy, agrometeorology, physiology, and soil science to simulate the complex interactions between rice crops and their environments (Chavas et al., 2009; Shabbir et al., 2020; Solaymani, 2023). On the other hand, statistical models for estimating the impact of climate change on rice yields which involve using quantitative methods to analyze historical data (Trinh, 2018; Rayamajhee et al., 2021; Tan et al., 2021). These models employ techniques such as regression analysis or other machine learning algorithms to identify patterns and correlations between climate variables and crop production (Elbasi et al., 2023). Unlike mechanistic crop simulation models, statistical models do not simulate the underlying physiological processes but focus on empirical relationships within the data. They provide valuable insights into how climatic factors influence crop yields, aiding in the assessment of potential impacts under changing climatic conditions (Lobell & Burke, 2010). According to Wu et al. (2021), previous studies indicated that statistical models exhibit strong explanatory capabilities and surpass simulation models in accurately assessing data at specific spatial scales. Additionally, the results generated by crop simulation models are sensitive to factors such as soil conditions, weather, and management indices (Shi et al., 2013), whereas studies lack reliable data on soil and management, offering ‘best-guess’estimates with limited information on uncertainties from model choices (Schlenker & Lobell, 2010). In addition, climate change has gradually unfolded, and its impact is not immediately apparent. A model must be applied over an extended period of time to capture and understand these effects. Hence, panel data analysis enhances the efficiency and consistency in estimating parameters, particularly when dealing with smaller sample sizes, leading to more robust and reliable results (Baltagi, 2008; Hsiao, 2014). Nevertheless, conventional panel models, including pooled Ordinary Least Squares (OLS), fixed effects, and random effects models are influenced by strict assumptions, such as the absence of group-wise heteroscedasticity, and autocorrelation within panels of the stochastic disturbance term (Wu et al., 2021). Violating these assumptions can result in biased and inefficient parameter estimates (Wooldridge, 2010). Thus, it is important to apply a model that overcomes these limitations described above (Wu et al., 2021). Internationally, several studies have used the FGLS model to investigate the impacts of climate change (Ali et al., 2017; Kassaye et al., 2021; Wu et al., 2021; Massagony et al., 2023). 3. Materials and methods 3.1. Study area To analyze the impact of climate change on rice yield in Central Coast of Vietnam, we utilized a panel dataset of the 9 district-level units in Thua Thien Hue Province throughout 1996 to 2021. We chose to COGENT ECONOMICS & FINANCE 3 study in Thua Thien Hue Province because it is one of Vietnam’s most climate-vulnerable areas and is often affected by natural disasters such as drought, typhoons, and flooding (Phuong et al., 2018; UNDP, 2018). As shown in Figure 1, Thua Thien Hue Province is located in the Central Coast region, covering about 5025 km2:It borders Quang Tri Province to the north and Da Nang City to the south, shares a 81 km boundary with Laos to the west, and has a 120 km coastline to the east. The province’s territory extends in a northwest-southeast direction, with the longest section reaching 120 km along the coast and the shortest, 44 km, in the west. Horizontally, it spans a northeast-southwest direction, with the widest part measuring 65 km and the narrowest, in the southernmost area, approximately 2 to 3 km (TTHPPC, 2024). 3.2. Data collection This study used the entire secondary dataset collected from various issues of the Thua Thien Hue Statistical Yearbook, which is published annually from 1996 to 2021 by Thua Thien Hue Statistical Office to reflect the local social, economic, and climatic situations. The data on rice yield were collected for both the winter–spring and summer–autumn seasons from the nine district-level units of the province represented three types of areas: coastal plains, midlands, and highlands. The monthly climatic data were provided by the three-land based meteorological stations from Center for Hydrometeorological Forecasting, located in three areas in Thua Thien Hue Province. The monthly data on meteorological variables, such as temperature and relative humidity, were the averages of daily temperatures and daily relative humidity within each month. The monthly data on precipitation were the total precipitation of all days in the month. The daily data, collected using measurement methods, techniques, and precise equipment, are aggregated into representative average monthly data by meteorological experts and officially published by the Thua Thien Hue Statistics Office, ensuring a certain level of reliability in the study. Furthermore, the long-term research period is designed to offer a comprehensive view of climate change impacts, so minor errors, if present, are considered acceptable. Besides, minimum and maximum climatic variables used in this study are crucial for understanding the impact of climate change on rice production. Extremes in weather conditions often contribute to variations in rice production and may exert a greater influence on yields (Saud et al., 2022). Additionally, different crops have distinct optimal minimum and maximum climatic conditions, such as temperature and rainfall (Kumar et al., 2021). Therefore, understanding these impacts is necessary for establishing an early warning and forecasting system for extreme weather events (Saud et al., 2022). Figure 1. Map of the study area. Source: Wolf et al. (2021). 4 P.T.M. NGUYEN, P.T. HO, AND H.X. PHAM Table 1. Descriptive statistics of variables used in the winter–spring season model and summer–autumn season model. Variable Definition Mean Std.Dev Minimum Maximum Winter–spring RY Rice yield (ton/ha) 5.22 0.92 2.87 7.79 Tmin Minimum temperature (C) 19.27 1.46 15.1 21.8 Tmax Maximum temperature (C) 27.67 1.44 23.1 29.6 Prmin Minimum precipitation (mm) 30.62 29.11 1.6 161.1 Prmax Maximum precipitation (mm) 428.56 248.67 24.4 1218.8 Hmin Minimum relative humidity (%) 82.37 3.20 75 89 Hmax Maximum relative humidity (%) 93.51 1.99 87 98 Summer–autumn RY Rice yield (ton/ha) 4.81 0.98 2.03 6.49 Tmin Minimum temperature (C) 26.90 1.58 20.2 29.4 Tmax Maximum temperature (C) 28.84 1.28 24.7 31.1 Prmin Minimum precipitation (mm) 54.18 48.50 1.7 217.8 Prmax Maximum precipitation (mm) 257.37 136.14 52.6 650 Hmin Minimum relative humidity (%) 77.05 3.04 71 84 Hmax Maximum relative humidity (%) 83.93 2.81 76 91 Source: Authors’estimation results. Figure 2. The variations in climatic factors affecting rice yield from 1996 to 2021 in Thua Thien Hue Province. Source: Authors’calculation results. COGENT ECONOMICS & FINANCE 5 The dataset was collected annually in accordance with the summer–autumn and winter–spring seasons and was compiled over the complete a 26-year period. The data for the winter–spring model were obtained over a 6-month period, spanning from December to May of the subsequent year. The data for the summer–autumn model were acquired over a 4-month period, ranging from May to August. The total dataset was complete, and no missing data were imputed in this study. Finally, a panel dataset of 234 observations for each season model was used in this study, as shown in Table 1. As demonstrated in Table 1, the minimum and maximum relative humidity indices in the winter– spring season are 82.37 and 93.51, respectively, surpassing those in the summer–autumn season, which stand at 77.05 and 83.93, respectively. While the minimum precipitation in the summer–autumn season (54.18 mm) exceeded that in the winter–spring season (30.62 mm), the maximum precipitation in the winter–spring season (428.56 mm) surpassed the minimum precipitation in the summer–autumn season (257.37 mm). Furthermore, the minimum and maximum temperature indices in the winter–spring season are 19.27 C and 27.67 C, respectively, both lower than those in the summer–autumn season, 26.9 C and 28.84 C, respectively. Figure 2 illustrates the trends of climatic factors from 1996 to 2021 in the Central Coast of Vietnam, with the dotted lines indicating the trends of these factors. As shown in Figure 1, there were upward trends in the maximum temperature in both seasons. Over the 26-year period, the maximum temperature increased by 1.8 C in the summer–autumn season and 1.9 C in the winter–spring season. During the summer–autumn season, declines were observed in both maximum precipitation and maximum relative humidity, whereas in the winter–spring season, both climate indicators showed increasing trends. The minimum temperature, precipitation, and relative humidity exhibited fluctuations across most seasons, except for the upward trend of minimum temperature in the summer–autumn season by 3.4 C. Regarding precipitation, the maximum precipitation index with the greatest fluctuations was observed in 2005–2010 in the summer–autumn season, and in 2015–2020 in the winter–spring season. In fact, during the aforementioned periods, the province experienced extreme weather events, especially in 2009/2010 and 2016/2017, with unusually high rainfall of up to 1176.3 mm (2017), leading to historic floods that greatly affected rice production and the livelihoods of farming households. 3.3. Model specification Based on the literature review, the variables were used in the models including temperature, precipitation, and humidity (Ali et al., 2017; Kumar et al., 2021; Massagony et al., 2023; Tan et al., 2021; Li, 2023). The general form reflecting the relationship between rice yield and climatic factors in this study is written as: RYit ¼f Tminit,Tmaxit,Prminit,Prmaxit,Hminit,Hmaxit ðÞ (1) where RY denotes the dependent variable, rice yield, in District iat time t;Tmin denotes minimum temperature; Tmax denotes maximum temperature; Prmin denotes minimum precipitation; Prmax denotes maximum precipitation, Hmin denotes minimum relative humidity; Hmax denotes maximum relative humidity. irepresents the district and trepresents time. Eq. (1) is transformed into an econometric model in the logarithmic form denoted by Eq. (2), as: lnRYit ¼b0þb1lnTminit þb2lnTmaxit þb3lnPrminit þb4lnPrmaxit þb5lnHminit þb6lnHmaxit þlit (2) where b0represents the constant term; while b1,b2,b3,b4,b5, and b6stand for the coefficients associated with explanatory variables; and lit signifies the error term. 3.4. Econometric methods For empirical analysis using panel data, two commonly used models are the fixed effects model (FE) and random effects model (RE). However, it is necessary to conduct a robustness check of these models to validate the results derived from the appropriate model (Wu et al., 2021). The findings show that the results derived from these two traditional models cannot address the issues of group-wise heteroscedasticity or 6 P.T.M. NGUYEN, P.T. HO, AND H.X. PHAM autocorrelation within panels. According to Wooldridge (2010), effectively addressing extensive datasets and challenges related to heteroscedasticity and autocorrelation can be achieved by employing the Feasible Generalized Least Squares (FGLS) method. Considerable focus has been directed towards FGLS in recent years, with several studies employing this method to examine the impact of climate change on agricultural output (Reed & Ye, 2011;Alietal.,2017; Kumar et al., 2021; Massagony et al., 2023). Specifically, FGLS is particularly advantageous, producing efficient and consistent estimates of standard errors provided that the panel time dimension (T) is greater than the cross-sectional dimension (N) (Beck & Katz, 1995; Naveen et al., 2021). This condition is satisfied when N<T, where Nrepresents the number of cross-sectional units (districts in our case) and Tis the time period. In our study with nine districts, the cross-sectional dimension (N) was less than the time period (T¼26), confirming the feasibility of the FGLS method. Therefore, we utilized the feasible generalized least square (FGLS) method in this study. Following Kumar et al. (2021), the general model suggested by Parks (1967) can be expressed as follows: ^ bFGLS ¼X0^ X −1X  −1X0^ X −1y(3) Var ^ bFGLS  ¼X0^ X −1X  −1(4) where ^ Xis the assumption of autocorrelation and heteroscedasticity, ^ bFGLS denotes the FGLS estimator of b,ydenotes the dependent variables, Xdenotes the vector of independent variables, and X0denotes the transpose of X. 3.5. Diagnostic tests Conducting diagnostic tests is crucial to ensure the robustness of the model. The primary tests conducted in this study were as follows: To determine whether to employ fixed or random effects models, researchers frequently utilize the Hausman test proposed by Hausman in 1978. Nonetheless, it is important to recognize that the Hausman test may not always offer a conclusive decision, as its validity is contingent upon stringent conditions (Buckley et al., 2013). Therefore, we employed a Hausman-like alternative test, known as xtoverid, which presents the Sargan–Hansen test of overidentifying restrictions for a panel data estimation statistic (Schaffer & Stillman, 2016). The null hypothesis states that there is no systematic difference in the coefficients. To ensure the robustness of the applied panel regression, the Wooldridge’s(2010) test was employed to detect autocorrelation. This is necessary because autocorrelation can result in biased standard errors and a reduction in the efficiency of the parameter estimates (Hamilton, 1994). The null hypothesis of the Wooldridge test is that there is no first-order autocorrelation. The Modified Wald test, as proposed by Baum (2000), is a statistical test used to assess the presence of panel groupwise heteroscedasticity in the fixed-effect regression model. The null hypothesis indicates panel group-wise homoscedasticity and the alternative hypothesis signifies the existence of group-wise heteroscedasticity within the model. In light of the aforementioned validation, the feasible generalized least squares (FGLS) model was employed to address the autocorrelation and groupwise heteroscedasticity issues of the models. 4. Results and discussions 4.1. Correlation analysis Tables 2 and 3show the pairwise correlation matrices between variables used in the models for the summer–autumn and winter–spring seasons, respectively. The primary method for detecting multicollinearity is through a pairwise correlation analysis using a correlation matrix. 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