EFFECT OF SELECTED INSECTICIDE ON WHITEFLY (Bemisia tabaci) INFESTING BRINJAL PLANTS 576 © 2025 AESS Publications. All Rights Reserved. Assessing climate change impacts on cereal production: The role of fertilizer use and GMM-based estimation Sayedul Anama Md. Azizur Rahmanb Md. Arif Hassanc Md. Alamgir Hossand Mst Khadijatul Kobrae a,b,c,dDepartment of Business Administration, Daffodil International University, Bangladesh. eDepartment of Tourism & Hospitality Management, Daffodil International University, Bangladesh.  anam.ba@diu.edu.bd (Corresponding author) Article History ABSTRACT Received: 17 July 2025 Revised: 16 October 2025 Accepted: 27 October 2025 Published: 19 November 2025 Keywords Carbon dioxide emission Cereal production Climate change Fertilizer usage GMM method Methane emission Nitrous oxide emission Rainfall Temperature. Environmental hazards cause a decline in food production, leading to food crises and overall instability among different nations. This scenario is more prevalent in the case of cereal production in some developing countries, particularly in South Asian regions. In line with this, research has been conducted to examine the impacts of climate change variables (rainfall, temperature, carbon dioxide emissions, methane emissions, nitrous oxide emissions) and control variables such as cereal production and fertilizer use on cereal output. The analysis considers cereal productivity while incorporating secondary data. A two-step Generalized Method of Moments (GMM) approach has been applied, considering eighteen top productive countries as dependent variables, with climate variables and control variables to analyze their connectivity. The study found that climate change variables significantly affect cereal production across the sample countries, except for methane emissions. Additionally, fertilizer use contributes to increased cereal crop yields. The findings are valuable for policymakers to identify specific negative effects of climate variables and adopt effective strategies to enhance cereal production. Ultimately, updating policies, applying modern farming techniques, and employing skilled human resources should be prioritized to address these challenges. Contribution/Originality: This study contributes by analyzing the robust relationship between climate change and cereal production using dynamic GMM estimation. We examine the effects of multiple climate change variables across leading crop-producing countries. The study highlights fertilizer usage and the area under cereal production as key adaptation strategies, incorporates production dynamics, and offers methodological improvements. DOI: 10.55493/5005.v15i4.5726 ISSN(P): 2304-1455/ ISSN(E): 2224-4433 How to cite: Anam, S., Rahman, M. A., Hassan, M. A., Hossan, M. A., & Kobra, M. K. (2025). Assessing climate change impacts on cereal production: The role of fertilizer use and GMM-based estimation. Asian Journal of Agriculture and Rural Development, 15(4), 576–585. 10.55493/5005.v15i4.5726 © 2025 Asian Economic and Social Society. All rights reserved. 1. INTRODUCTION Climate change poses a serious danger to agricultural productivity, especially in developing nations where agriculture is essential for economic growth, employment creation, and food security (Arora, 2019; Aryal et al., 2020). Asian Journal of Agriculture and Rural Development Volume 15, Issue 4 (2025): 576-585 mailto:anam.ba@diu.edu.bd https://doi.org/10.55493/5005.v15i4.5726 Asian Journal of Agriculture and Rural Development, 15(4) 2025: 576-585 577 © 2025 AESS Publications. All Rights Reserved. One-fifth of the world's population lives in South Asia, but they occupy about 5% of the world's arable land, where agriculture is still the main livelihood. Moreover, more than two-thirds of the region's population live in rural areas, indicating their dependence on agriculture as their main source of income (Almazroui, Saeed, Saeed, Islam, & Ismail, 2020; Bandara & Cai, 2014). Although we live in an era of advanced technology that ensures high crop production through the optimal use of fertilizers and seeds, climate change continues to pose serious challenges to agriculture in the region (Chandio et al., 2021). For example, high variability in temperature and rainfall patterns is having a significant impact on agricultural productivity and sustainability in South Asia (Alam, 2017; Kogo, Kumar, & Koech, 2021; Masters, Baker, & Flood, 2010). Therefore, addressing these climate-related challenges is essential to safeguard the sustainability of agriculture and livelihoods in the region. Adverse climate change is causing high crop losses, low yields, and high production costs, which are adversely affecting their income levels (Pulighe et al., 2024; Shamshad, Nawaz, Khan, & Arif, 2024). Clearly, income from agricultural livelihoods such as crop production and livestock rearing is being directly affected by climate variables, namely temperature variations, changing rainfall patterns, and elevated CO₂ levels (Verma et al., 2025). Additionally, floods, droughts, and socio-economic disasters are also common in tropical and subtropical regions due to rising temperatures and water demand (Ekele et al., 2025; Palmer et al., 2023). Other difficulties include erosion, loss of soil fertility due to the use of chemicals or pesticides, and traditional farming practices (Chatterjee et al., 2021). Therefore, a wake-up call for climate-resilient agricultural practices is needed in this region. Climate change is severely affecting low- and middle-income countries, and about fifteen percent of their GDP depends on climate-sensitive agriculture (World Bank Group, 2019). Global economic productivity is forecast to decline by 4% by 2050 in low- and lower-middle-income countries, with the decline being more severe in South Asia (Jones, 2022). Measuring the impact of climate change on cereal production in this region is a must to develop appropriate policy responses (Chuang, 2019). Therefore, exploring these climate factors and their impacts is essential to developing effective strategies to increase resilience and ensure food security in vulnerable agricultural economies. Bangladesh, one of the top crop-producing countries in South Asia, where agriculture contributes 14.2% to the national GDP, is already facing political, economic, and environmental challenges (World Bank Group, 2017). CO2 emissions per capita in Bangladesh increased from 0.11 metric tons in 1985 to 0.53 metric tons in 2016 (Ahmed, 2018). The country's cereal production, particularly rice, is highly vulnerable to weather fluctuations (Islam, Alam, Begum, Sarker, & Bhandari, 2022; Joseph et al., 2023). Rice production is particularly vulnerable to droughts and floods as a result of declining yields caused by rising temperatures and changes in rainfall patterns (Dasgupta, Hossain, Huq, & Wheeler, 2014; Hussain, 2011). It is estimated that the productivity of major crops will decline by 5-13 percent by 2030 due to climate vulnerability (Bandara & Cai, 2014). Therefore, Bangladesh needs to adopt climate-resilient agricultural strategies to ensure food security amidst climate change. Recent studies have used various methods to examine how climate change affects crop yields. Evidently, Chandio, Jiang, Rehman, and Rauf (2020) found that while higher CO2 levels may increase agricultural productivity in the short run, temperature fluctuations and rainfall have a long-term adverse impact. However, Pickson, He, Ntiamoah, and Li (2020) revealed that high temperatures and CO2 levels negatively affect cereal production in China. Other studies also confirm that CO2 has both long-term and short-term effects on cereal yields (Ahsan, Chandio, & Fang, 2020). These findings emphasize the necessity for adaptable farming methods to mitigate climate change's effects on crop production. Most studies examine climate change's impact on crop production using time-series or panel data (Rahim & Puay, 2017). However, this study applied the Generalized Method of Moments (GMM) to a panel dataset of the top 18 productive countries from 2019 to 2020. This method allows for a dynamic model that addresses endogeneity and minimizes cross-country scale differences. We explain the impact of climate change on crop production in these selected countries, controlling for fertilizer use and arable land to reduce selection bias. 2. REVIEW OF LITERATURE 2.1. Theoretical Framework This research is grounded in multiple theoretical frameworks that explain the complex interactions between climate change and agricultural productivity, especially crops. Climate change impact theory shows how climatic factors such as temperature and precipitation have a direct impact on crop production. These environmental factors cause changes in seasons, soil fertility, and plant anatomy, which impact crop production (Lobell, Schlenker, & Costa- Roberts, 2011). In addition, the Environmental Kuznets Curve (EKC) hypothesis is used to explain how greenhouse gas emissions affect agriculture in two ways. This means that there is an inverted U-shaped relationship between economic growth and environmental damage. Also, pollution levels usually increase when the economy moves towards growth, but they decrease as technology improves and people become more aware of the environment (Dinda, 2004). Other variables like methane (CH₄), carbon dioxide (CO₂), and nitrous oxide (N₂O) emissions are examined. To explore the economic aspect, previous studies are based on the theory of the agricultural production function, which holds that food grain output is a product of input combinations (including fertilizer usage and cultivated area). These covariates have a direct effect on production and reduce the bias of the estimates due to unobserved environmental or socioeconomic variables (Coelli, Rao, O'Donnell, & Battese, 2005; Dai, 2025). The theory of technological change in agriculture predicts that the negative impacts of climate change on crop productivity can be mitigated by adopting adaptation measures, such as improving fertilizer use efficiency (Schultz, 1964). As the Sustainable Livelihood Framework (SLF) states, the availability of institutions, technologies, and resources can improve the sustainability and resilience of rural livelihoods (Chambers & Conway, 1992). Thus, the theoretical basis Asian Journal of Agriculture and Rural Development, 15(4) 2025: 576-585 578 © 2025 AESS Publications. All Rights Reserved. indicates the institutional support, technological innovation, and input optimization required for climate-resilient crop production systems and rural livelihoods. Indeed, within the framework of agroecosystem resilience theory, it has been postulated that an agricultural system should become resilient (i.e., able to generate yield even under harsh or changing environmental conditions) (Folke, 2006). Fertilizer buffer found in this study is an example of resilience tactics. Finally, although econometric theory (omitted variable bias and confounding) provides evidence that omitting relevant control variables (land area or fertilizer use) can lead to biases in effect estimates for climate-related variables on agricultural output (Wooldridge, 2020). Thus, including these variables enhances the empirical validity of hypotheses. These perceptions of the above theories provide a comprehensive framework for analyzing climate change related to environmental, economic, and methodological aspects of cereal production. Therefore, a cross-functional approach combining agricultural economics, environmental science, and climate modeling is necessary to measure the effects of climate change on grain production. Fundamental elements of this framework include relationships between climate variables (e.g., precipitation, temperature, CO₂ concentration), agricultural inputs (e.g., fertilizer, land use), and crop production (Berhane, 2018; Saud et al., 2022). Thus, changes in climate, such as rainfall and temperature, and indirect consequences, such as soil erosion, insect outbreaks, and water availability, must be captured (Alotaibi, Alhajeri, Al- Fadhli, Al Jabri, & Gabr, 2023). Besides, the framework aims to consider human activities, such as the use of fertilizer, which have the capacity to decrease or increase the impact of climate change on crop productivity (Crane-Droesch, 2018). Therefore, to calculate past data, predict future trends, and develop climate-resilient agricultural policies, sophisticated statistical methods are necessary. Use of advanced econometric methods to overcome omitted variable bias, endogeneity, and spatial autocorrelation to ensure analytical rigor is required under this framework. Incorporating crop simulation, cross-sectional analysis, partial, and general equilibrium models can offer a holistic view of the agricultural system under the climate crisis (Delince, Ciaian, & Witzke, 2015). In addition, it needs to remember that fertilizer use is intrinsic and influenced by weather and crop performance. Effective assessment of fertilizer performance requires instrumental variables and simultaneous equations. The methodology should also consider regional crop types and climate vulnerability and assess the cost-effectiveness of adaptation options (Georgopoulou et al., 2017). Identifying the role of fertilizer use during climate change is central to sustaining agricultural productivity, given the upward global food demand (Farah, Mohamed, Musse, & Nor, 2025; Islam, 2025). Eventually, a strong interdisciplinary framework helps us better understand how climate change impacts cereal production and supports the development of practical, empirically supported adaptation and mitigation strategies to guarantee global food security. 2.2. Previous Empirical Literature Evidence Empirical research on climate change and crop production demonstrates that climatic factors and agricultural productivity have an intricate relationship. Temperature and precipitation are the most frequently studied aspects of the climate in Asia, Europe, Africa, and Latin America. They frequently lower crop yields, particularly in harsh environments. For instance, Singh, Arora, and Chandra Babu (2024) used ARDL, finding that rising temperatures significantly reduce cereal output, while rainfall has a positive but diminishing impact. Remarkably, Ma, Karimi, Mohammed, Shahzadi, and Dai (2024) also emphasized the role of fertilizer use in increasing crop yield, which is closely consistent with the aim of this study. Similarly, inconclusive empirical evidence from Africa also underscores a positive rainfall effect but emphasizes the high vulnerability of the cereal production process to temperature variability and CO₂ emissions (Pickson, Boateng, Gui, & Chen, 2024; Sissoko et al., 2023). These studies highlight the need for focused adaptation measures, such as farmer support policies and climate-resilient technologies, particularly in rainfed areas. Meanwhile, Kelkouli, Bouderbala, and Haddad (2024) decoded a significant drop in crop production in Algeria due to the shortfall of rainfall, reinforcing the importance of water availability and irrigation infrastructure. Research in developed regions such as the EU adds a different dimension: despite technological advancements, yield stagnation linked to heat and drought stress has begun to erode the climate mitigation benefits of cereal farming (Łącka, Suproń, & Szczepaniak, 2024; Riedesel et al., 2024). These findings suggest that adaptation may not be keeping pace with climatic shifts, and that location-specific soil quality and energy consumption patterns play crucial roles. Methane and nitrous oxide, though less frequently assessed, are gaining traction. For example, Magazzino, Gattone, Usman, and Valente (2024) and Zhang, Waldhoff, Wise, Edmonds, and Patel (2023) incorporate agricultural emissions into broader sustainability discussions, showing that excessive emissions contribute to declining yields and compromise food security, even when technological innovation is high. While many studies rely on time-series econometrics (ARDL, FMOLS, cointegration), a growing number integrate panel data methods that account for cross-sectional dependence and heterogeneity (e.g., (Łącka et al., 2024; Pickson et al., 2024)), offering more robust insights for multi-country analyses. However, gaps remain in fully modeling fertilizer dynamics, especially their interaction with emissions. Most analyses treat fertilizer as a control rather than a focal variable, leaving room for this study to make a significant contribution by exploring its mitigating potential amid climate-induced stressors. In summary, prior studies confirm the detrimental effects of climate change on cereal production, while pointing to technology and input use, especially fertilizers as promising avenues for resilience. Yet, few works explicitly model the interaction between fertilizer application and environmental emissions in a cross-country framework (Gyamerah, Asare, Mintah, Appiah, & Kayode, 2023; Pickson et al., 2024; Rötter, Hoffmann, Koch, & Müller, 2018; Singh et al., Asian Journal of Agriculture and Rural Development, 15(4) 2025: 576-585 579 © 2025 AESS Publications. All Rights Reserved. 2024; Stadnik, Tobiasz-Salach, & Migut, 2024). This highlights a critical research gap that this study is well-positioned to address. Despite the extensive body of empirical work, few studies systematically incorporate a multi-country, multi-year panel framework that captures both climatic and agronomic factors in tandem, particularly those related to fertilizer dynamics (Baris-Tuzemen & Lyhagen, 2024; Łącka et al., 2024; Magazzino et al., 2024). The prevailing focus on single- country analyses or simplified aggregate measures often limits their generalizability and policy relevance (Ali, Dahir, & Yusuf, 2023; Massagony, Tam Ho, & Shimada, 2022). Thus, empirical findings remain inconclusive. Consequently, this creates a significant gap in understanding how crop production systems can protect themselves from the impacts of climate change in different agroecological regions. Filling this gap is essential, as it informs both local and global programs designed to ensure food security and promote climate-resilient agriculture. This study fills a gap by providing a comprehensive and policy-relevant analysis that links climate risks to production-based solutions, providing practical insights for sustainable agricultural planning. 3. ECONOMETRIC MODEL For dynamic panel data, the system generalized method of moments (GMM) is more fitted to estimate the relationship (Arellano & Bover, 1995). Consider the following regression equation. 𝐶𝑃𝑖𝑡 − 𝐶𝑃𝑖𝑡−1 = (𝛼 − 1)𝐶𝑃𝑖𝑡−1 + 𝛽0𝐶𝑉𝑖𝑡 + 𝜇𝑖 + 𝜀𝑖𝑡 (1) Where CPit is the cereal production, CPit – CPit-1 is the rate of cereal production growth, CPit-1 is the initial level of cereal production, CVit represents a vector of explanatory variables, μi is an unobserved country-specific effect, εi is the error term, and the subscripts i and t represent country and time period, respectively. Rewriting (1), we obtain. 𝐶𝑃𝑖𝑡 = 𝛼 𝐶𝑃𝑖𝑡−1 + 𝛽0 𝐶𝑉𝑖𝑡 + 𝜇𝑖 + 𝜀𝑖𝑡 (2) To eliminate country-specific effects, we take first differences of (2). 𝐶𝑃𝑖𝑡 − 𝐶𝑃𝑖𝑡−1 = 𝑎(𝐶𝑃𝑖𝑡−1 − 𝐶𝑃𝑖𝑡−2) + 𝛽0(𝐶𝑉𝑖𝑡 − 𝐶𝑉𝑖𝑡−1) + 𝜀𝑖𝑡 − 𝜀𝑖𝑡−1 (3) Levine, Loayza, and Beck (2000) suggest the use of instruments for two reasons: to address the likely endogeneity of cereal production and climate variables adoption, and because, by construction, the new error term (εit – εit-1) in the above equation is correlated with the lagged dependent variable (CPit-1 – CPit-2). 3.1. Variables and Data For the effect of climate change, unlike others, we used multiple variables to measure the impact on cereal. Some previous studies used only rainfall as a climate variable to measure the climate effect on cereal production (Wakjira et al., 2021). CO₂ is also a commonly used climate proxy in many studies (Onour, 2019). Furthermore, GHGs, e.g., methane and nitrous oxide emissions, are also common proxies to establish the relationship between climate change effects and cereal production (Simionescu, Bilan, Gędek, & Streimikiene, 2019). In some recent studies, researchers commonly used rainfall and drought (Ayanlade, Radeny, Morton, & Muchaba, 2018); rainfall and temperature (Amin, Zhang, & Yang, 2015; Eder, Salhofer, & Quddoos, 2024; Quiroga & Iglesias, 2009); rainfall, temperature, and CO₂ (Köprücü & Acaroğlu, 2023; Xiang & Solaymani, 2022); rainfall, temperature, radiation, and CO₂ (Xiong et al., 2010) as climate change to estimate cereal production. Some studies used control variables besides the climate variables to capture the result (Chandio et al., 2021). Most of the existing studies are country-specific and use time series data. There are very few studies that use panel data to establish the relationship between cereal production and climate change, where one study considers lower-middle- income countries (Kumar, Sahu, Kumar, & Ansari, 2021), one study considers East Africa (Abdi, Warsame, & Sheik- Ali, 2023), and another study considers Asian countries (Ozdemir, 2022). The majority of the studies are based on FMOLS, FGLS, and ARDL models for analyzing panel data. The dynamic panel system-Generalized Methods of Moment (GMM) estimator developed by Arellano and Bover (1995) and Blundell and Bond (1998) which is more suitable for analyzing dynamic panels because it can address concerns about identification, account for lagged responses to any exogenous shock, reverse causality, and help to obtain unbiased parameters (Bond, Bowsher, & Windmeijer, 2001; Hauk Jr & Wacziarg, 2009). The use of the two-step system GMM is missing in the existing literature to measure the effect of climate change on cereal production. Previous research shows empirical insights into the impact of various climate variables on cereal production, often based on data from diverse country groups. However, these studies typically overlook cereal (land) productivity. Moreover, none have concurrently examined the effects of rainfall, temperature, carbon dioxide emissions, methane emissions, and nitrous oxide emissions as climate variables while also including the area under cereal cultivation and fertilizer use as control variables. This study aims to fill that gap and add to the existing body of empirical evidence. This study utilizes a strongly balanced panel dataset covering 18 countries with high cereal (land) productivity, each surpassing a productivity threshold of 0.2 (refer to Appendix, Table 1a) over the period 2016 to 2020. Data spanning 18 countries across 5 years were analyzed. The System GMM estimation method was employed for the analysis. Secondary data were sourced from the World Development Indicators (WDI) and the Climate Change Knowledge Portal (CCKP). The study examines the impact of climate-related variables, including rainfall (RF), temperature (TEM), carbon dioxide emissions (CO₂), methane emissions (ME), and nitrous oxide emissions (N2O), on cereal production (CP) in countries with high agricultural land productivity. Additionally, the area under cereal cultivation (CL) and fertilizer use are included as control variables. A detailed description of the variables and data sources is presented in Table 1. Asian Journal of Agriculture and Rural Development, 15(4) 2025: 576-585 580 © 2025 AESS Publications. All Rights Reserved. Table 1. List of study variables for the analysis. V-abbreviations Full-form Units D-source CP Cereal production Metric Tons (In Lac) WDI RF Rain Fall Millimeter CCKP TEM Temperature Degree Celsius CCKP CO₂ Carbon dioxide Kilotons (In Lac) WDI ME Methane emissions Thousand metric tons of CO₂ equivalent (In lac) WDI N2O Nitrous oxide emissions Thousand metric tons of CO₂ equivalent (In Lac) WDI CL Area under cereal production Hectares WDI FER Fertilizer Kilograms per Hectare of Arable Land WDI Note: WDI-World development indicators, CCKP-Climate change knowledge portal 4. RESULTS 4.1. Descriptive Statistics and Correlation Analysis Table 2 illustrates the descriptive statistics of the variables in the model to be measured. The table shows the mean, median, maximum value, minimum value, variance, standard deviation, skewness, and kurtosis of the variables. In this descriptive analysis, the standard deviation of each variable is high, indicating that the data under the variables are more scattered, which arises mainly due to considering different sizes (in terms of agricultural land area) of countries in the world. Additionally, all the variables are positively skewed, but in the case of kurtosis, carbon dioxide, methane emission, nitrous oxide emission, and the area under cereal production are highly peaked. The descriptive analysis (Table 2) and correlation analysis (Table 3) of these variables are given below. Table 2. Descriptive statistics. CP RF TEM CO₂ ME N2O CL FER Mean 1080 1065.558 17.300 11.670 0.935 0.602 205 206.735 Median 381 776.510 14.540 2.670 0.495 0.229 81.313 166.818 Maximum 6170 3176.990 27.490 109 5.022 3.565 1020 574.182 Minimum 140 10.850 7.770 0.210 0.025 0.057 23.377 18.743 Variance 2.79e+11 505466.900 50.132 6.41e+07 1.61e+05 8.04e+04 9.07e+09 18093.270 Std. deviation 1670 710.962 7.080 25.310 1.260 0.897 301 134.511 Skewness 0.918 0.918 0.153 2.920 2.148 2.109 2.007 1.153 Kurtosis 3.097 3.097 1.273 10.623 6.698 6.250 5.410 3.470 Table 3. Correlation analysis. Variables CP RF TEM CO₂ ME N2O CL FER CP 1 RF -0.150 1 TEM -0.281 0.574 1 CO₂ 0.951 -0.183 -0.3697 1 ME 0.800 -0.0399 0.0757 0.6596 1 N2O 0.971 -0.172 -0.2203 0.9237 0.8895 1 CL 0.920 -0.090 -0.084 0.8264 0.9605 0.970 1 FER 0.226 -0.064 0.164 0.325 0.099 0.241 0.186 1 4.2. Analysis of GMM for Dynamic Panel Data Table 4 shows the different types of GMM model results to identify climate change's impact on cereal production in various countries. We use the Hansen and Sargan tests for the validity of instruments, and the residuals or error terms are not correlated with the instrument variables, respectively. Again, AR(1) and AR(2) tests justify the error term's first-order and second-order autocorrelation/serial correlation. The findings from the two-step system GMM model validate that the model is well-specified, as shown by the F-test (Prob > F = 0.000), indicating a strong overall fit. The Hansen test (Prob > chi² = 0.465) endorses the appropriateness of the instrumental variables used. Similarly, the Sargan test (Prob > chi² = 0.002) supports the validity of the over-identifying restrictions across all model specifications. Additionally, the AR(2) test result (Pr > z = 0.16) indicates that there is no second-order autocorrelation evidence found for further validating the model’s reliability. The result of the two-step system GMM shows a consistent but insignificant negative relationship between rainfall (RF) and cereal production. The result indicates that, on average, a percentage increase in rainfall leads to a 71.96662- unit decrease in cereal production, with other factors remaining constant. Previous research has demonstrated that rainfall can have both positive and negative effects on cereal production. Kumar et al. (2021) found that rainfall has a positive effect on cereal production; on the other hand (Eder et al., 2024) found out the negative influence on cereal production. Again, another climate variable, a negative and statistically significant impact of temperature (TEM) on cereal production is reported, which indicates that temperature has marginally led to a decrease in cereal production in the studied countries. Asian Journal of Agriculture and Rural Development, 15(4) 2025: 576-585 581 © 2025 AESS Publications. All Rights Reserved. The results disclose that, on average, a 1% increase in temperature results in a decrease of 288,304.2 units in cereal production, assuming other factors remain unchanged. This finding aligns with the study by Xiang and Solaymani (2022), which reported that a 1% rise in temperature corresponds to a 2.87% reduction in general cereal production and a 3.52% decline in projected estimates. Additionally, the analysis illustrates a negative and statistically significant effect of carbon dioxide (CO₂) on cereal output. Specifically, a 1% increase in CO₂ emissions leads to an average decrease of 4.493874 units in cereal production, holding other variables constant. This result is consistent with existing empirical studies, such as Chandio et al. (2021), who found that CO₂ has an adverse impact on cereal production in both the short and long term. Again, the system GMM results for Greenhouse Gas (GHG) emissions, specifically methane (CH4) and nitrous oxide (N2O), indicated a statistically significant positive and negative impact on cereal production, respectively. The results showed that, on average, a percentage increase in methane led to a 90.54769-unit increase in cereal production, while a percentage increase in nitrous oxide resulted in a 254.8543-unit decrease, with all other factors held constant. Simionescu et al. (2019) showed that GHGs have a positive influence on cereal production. On the other hand, Thapa, Chatterjee, Awale, McGranahan, and Daigh (2016) proved that nitrous oxide has negatively impacted cereal production. The model illustrated that the control variable, a positive and statistically significant impact of fertilizer (FER) on cereal production, is reported, which indicates that fertilizer has marginally led to an increase in cereal production in the studied countries. The results indicated that, on average, a percentage increase in fertilizer leads to a 12,111.79- unit increase in cereal production, with other factors remaining constant. This result is proved by Yousaf et al. (2017), who showed that the use of mineral fertilizer can increase rice production from 19% to 41% and rapeseed production from 61% to 76%. Another control variable, a negative and statistically insignificant impact of the area under cereal production (CP) on cereal production, is reported, which indicates that the area under cereal production has marginally led to a decrease in cereal production. This result is proved by Harini, Ariani, Supriyati, Satriagasa, Susilo, and Giyarsih (2018), who examined that in North Kalimantan Province, where the agricultural land area increased while the agricultural production decreased. Table 4. Stamatis and dynamic panel estimation, over the period 2016 to 2020. Note: *** p < 0.01, ** p < 0.05, * p < 0.1 show statistical significance at 1%, 5%, and 10% level, respectively. 5. CONCLUSION AND POLICY IMPLICATIONS This study analyzed the impact of climate change on crop production in 18 top crop-producing countries known for their high land productivity in crop cultivation from 2016 to 2020. The analysis utilizes a two-step system GMM estimator to address various econometric issues, including serial correlation, panel group-wise heteroscedasticity, cross-sectional dependence, and heterogeneity. Climate change is influenced by average annual rainfall, temperature, and emissions of CO₂, CH₄, and N₂O. The results confirmed that climate change significantly affects crop production in the sampled countries. In particular, temperature, CO₂, and N2O emissions have statistically significant negative effects on crop production, while a negligible negative relationship is seen with precipitation. Conversely, CH₄ emissions are positively associated with cereal production. Among the control variables, fertilizer use demonstrates a strong positive contribution to cereal yields, whereas an increase in cereal cultivation correlates with reduced productivity, possibly indicating diminishing marginal returns to land expansion. The findings carry important policy implications. Given the adverse effects of rising temperatures, the development and dissemination of heat-resistant cereal crop varieties should be prioritized to bolster climate resilience and food security. Although CO₂ emissions appear to be positively associated with cereal output, this relationship may Variables POOLED OLS DIFF-1 GMM DIFF-2 GMM SYS-1 GMM SYS-2 GMM CP 0.811*** -0.067 -0.098 1.055*** 1.149*** RF 1542.490 790.534 568.952 206.661 -71.967 TEM 165211 -1748902 -2107256 -186537.100 -288304.200** CO2 -2.993 11.002 11.851** -3.157 -4.494*** ME -182.838** -601.731 -527.731*** 18.260 90.548* N2O 423.413** 1110.147*** 1325.551*** -141.368 -254.854** CL 0.726** 1.930** 1.845*** 0.288 -0.073 FER -8522.470 -95568.060* -47022.890 14854.620 12111.790* Constant -2006650 1.01e+08* 6.31e+07* -1141438 842353.500 Observations 72 72 12 72 72 Countries 18 18 18 18 18 Instrument 11 11 12 12 F-state(p-value) 0 0 0 0 0 Sargan 0.002 0.002 0.002 0.002 Hansen 0.281 0.465 AR(1) 0.079 0.62 0.004 0.06 AR(2) 0.078 0.201 0.004 0.16 Asian Journal of Agriculture and Rural Development, 15(4) 2025: 576-585 582 © 2025 AESS Publications. All Rights Reserved. be misleading, as carbon-intensive agricultural practices can have adverse health and environmental consequences. Hence, future research and policy should emphasize sustainable, low-carbon cereal production systems. Further, temperature-induced stresses such as increased evapotranspiration, altered cropping seasons, and irrigation demands suggest a need for adaptive agronomic strategies. These may include modified sowing calendars, short-duration crop varieties, and improved water-use efficiency (Ali & Erenstein, 2017). Historical evidence from African countries illustrates successful adaptation through flexible planting periods, diversification of income sources, soil conservation, and varietal adjustments in response to climate variability (Bryan, Deressa, Gbetibouo, & Ringler, 2009; Maddison, 2007). In addition, the study identifies a negative association between rural population and cereal productivity, implying low labor efficiency in the agricultural sector. Enhancing farm labor productivity through mechanization, modern agronomic training, and entrepreneurship development could significantly strengthen the sector. Tailored climate policies, integrated with local adaptation capacities, are essential for enabling lower-middle-income countries to mitigate the adverse effects of climate change and sustainably improve cereal production. 6. LIMITATIONS AND FUTURE RESEARCH This research selected 18 countries based on land productivity, with a threshold of 0.2 land productivity; other parameters for country selection were excluded. As climate variables, this paper used rainfall, temperature, CO₂, CH₄, and N₂O variables, while irrigation, technological adoption, and natural digester factors such as droughts, floods, and cyclones were ignored. For data analysis, this research employed a two-step system GMM model, which may suffer from small sample bias or weak instruments, potentially affecting the robustness of the results. Future research could integrate additional climate and technological variables. It may also focus on other agricultural crops and expand the analysis to include more countries. Funding: This study received no specific financial support. Institutional Review Board Statement: Not applicable. Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects of the investigation have been omitted, and that any differences from the study as planned have been clarified. This study followed all writing ethics. Competing Interests: The authors declare that they have no competing interests. Authors’ Contributions: All authors contributed equally to the conception and design of the study. All authors have read and agreed to the published version of the manuscript. REFERENCES Abdi, A. H., Warsame, A. A., & Sheik-Ali, I. A. (2023). Modelling the impacts of climate change on cereal crop production in East Africa: Evidence from heterogeneous panel cointegration analysis. Environmental Science and Pollution Research, 30(12), 35246-35257. https://doi.org/10.1007/s11356-022-24773-0 Ahmed, B. (2018). Who takes responsibility for the climate refugees? International Journal of Climate Change Strategies and Management, 10(1), 5-26. https://doi.org/10.1108/IJCCSM-10-2016-0149 Ahsan, F., Chandio, A. A., & Fang, W. (2020). Climate change impacts on cereal crops production in Pakistan: Evidence from cointegration analysis. International Journal of Climate Change Strategies and Management, 12(2), 257-269. https://doi.org/10.1108/IJCCSM-04-2019-0020 Alam, G. M. M. (2017). Livelihood cycle and vulnerability of rural households to climate change and hazards in Bangladesh. Environmental Management, 59(5), 777-791. https://doi.org/10.1007/s00267-017-0826-3 Ali, A., & Erenstein, O. (2017). Assessing farmer use of climate change adaptation practices and impacts on food security and poverty in Pakistan. Climate Risk Management, 16, 183-194. https://doi.org/10.1016/j.crm.2016.12.001 Ali, D. A., Dahir, A. M., & Yusuf, S. M. (2023). Autoregressive distributed lag modeling of climate and non-climatic determinants affecting cereal production: Empirical evidence from Somalia. International Journal of Energy Economics and Policy, 13(5), 577-584. https://doi.org/10.32479/ijeep.14553 Almazroui, M., Saeed, S., Saeed, F., Islam, M. N., & Ismail, M. (2020). Projections of precipitation and temperature over the South Asian countries in CMIP6. Earth Systems and Environment, 4(2), 297-320. https://doi.org/10.1007/s41748-020-00157-7 Alotaibi, M., Alhajeri, N. S., Al-Fadhli, F. M., Al Jabri, S., & Gabr, M. (2023). Impact of climate change on crop irrigation requirements in arid regions. Water Resources Management, 37(5), 1965-1984. https://doi.org/10.1007/s11269-023-03465- 5 Amin, M. R., Zhang, J., & Yang, M. (2015). Effects of climate change on the yield and cropping area of major food crops: A case of Bangladesh. Sustainability, 7(1), 898-915. https://doi.org/10.3390/su7010898 Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29-51. https://doi.org/10.1016/0304-4076(94)01642-D Arora, N. K. (2019). Impact of climate change on agriculture production and its sustainable solutions. Environmental Sustainability, 2(2), 95-96. https://doi.org/10.1007/s42398-019-00078-w Aryal, J. P., Sapkota, T. B., Khurana, R., Khatri-Chhetri, A., Rahut, D. B., & Jat, M. L. (2020). Climate change and agriculture in South Asia: Adaptation options in smallholder production systems. Environment, Development and Sustainability, 22(6), 5045- 5075. https://doi.org/10.1007/s10668-019-00414-4 Ayanlade, A., Radeny, M., Morton, J. F., & Muchaba, T. (2018). Rainfall variability and drought characteristics in two agro-climatic zones: An assessment of climate change challenges in Africa. Science of the Total Environment, 630, 728-737. https://doi.org/10.1016/j.scitotenv.2018.02.196 Bandara, J. S., & Cai, Y. (2014). The impact of climate change on food crop productivity, food prices and food security in South Asia. Economic Analysis and Policy, 44(4), 451-465. https://doi.org/10.1016/j.eap.2014.09.005 https://doi.org/10.1007/s11356-022-24773-0 https://doi.org/10.1108/IJCCSM-10-2016-0149 https://doi.org/10.1108/IJCCSM-04-2019-0020 https://doi.org/10.1007/s00267-017-0826-3 https://doi.org/10.1016/j.crm.2016.12.001 https://doi.org/10.32479/ijeep.14553 https://doi.org/10.1007/s41748-020-00157-7 https://doi.org/10.1007/s11269-023-03465-5 https://doi.org/10.1007/s11269-023-03465-5 https://doi.org/10.3390/su7010898 https://doi.org/10.1016/0304-4076(94)01642-D https://doi.org/10.1007/s42398-019-00078-w https://doi.org/10.1007/s10668-019-00414-4 https://doi.org/10.1016/j.scitotenv.2018.02.196 https://doi.org/10.1016/j.eap.2014.09.005 Asian Journal of Agriculture and Rural Development, 15(4) 2025: 576-585 583 © 2025 AESS Publications. All Rights Reserved. Baris-Tuzemen, O., & Lyhagen, J. (2024). Revisiting the role of climate change on crop production: Evidence from Mediterranean countries. Environment, Development and Sustainability. https://doi.org/10.1007/s10668-024-04991-x Berhane, A. (2018). Climate change and variability impacts on agricultural productivity and food security. Journal of Climatology & Weather Forecasting, 6, 240. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115-143. https://doi.org/10.1016/S0304-4076(98)00009-8 Bond, S., Bowsher, C., & Windmeijer, F. (2001). Criterion-based inference for GMM in autoregressive panel data models. Economics Letters, 73(3), 379-388. https://doi.org/10.1016/S0165-1765(01)00507-9 Bryan, E., Deressa, T. T., Gbetibouo, G. A., & Ringler, C. (2009). Adaptation to climate change in Ethiopia and South Africa: Options and constraints. Environmental Science & Policy, 12(4), 413-426. https://doi.org/10.1016/j.envsci.2008.11.002 Chambers, R., Conway, G. (1992). Sustainable rural livelihoods: Practical concepts for the 21st century. United Kingdom: Institute of Development Studies. Chandio, A. A., Jiang, Y., Akram, W., Adeel, S., Irfan, M., & Jan, I. (2021). Addressing the effect of climate change in the framework of financial and technological development on cereal production in Pakistan. Journal of Cleaner Production, 288, 125637. https://doi.org/10.1016/j.jclepro.2020.125637 Chandio, A. A., Jiang, Y., Rehman, A., & Rauf, A. (2020). Short and long-run impacts of climate change on agriculture: An empirical evidence from China. International Journal of Climate Change Strategies and Management, 12(2), 201-221. https://doi.org/10.1108/IJCCSM-05-2019-0026 Chatterjee, D., Kuotsu, R., Ray, S. K., Patra, M. K., Thirugnanavel, A., Kumar, R., . . . Deka, B. C. (2021). Preventing soil degradation in shifting cultivation using integrated farming system models. Archives of Agronomy and Soil Science, 68(13), 1841-1857. https://doi.org/10.1080/03650340.2021.1937139 Chuang, Y. (2019). Climate variability, rainfall shocks, and farmers’ income diversification in India. Economics Letters, 174, 55-61. https://doi.org/10.1016/j.econlet.2018.10.015 Coelli, T. J., Rao, D. S. P., O'Donnell, C. J., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis. New York: Springer US. Crane-Droesch, A. (2018). Machine learning methods for crop yield prediction and climate change impact assessment in agriculture. Environmental Research Letters, 13(11), 114003. Dai, J. (2025). Impact of climate policy uncertainty on agriculture development: Multidimensional analysis from land use, food structure, and carbon emissions. Land Degradation & Development, 36(13), 4545-4561. https://doi.org/10.1002/ldr.5652 Dasgupta, S., Hossain, M. M., Huq, M., & Wheeler, D. (2014). Climate change, soil salinity, and the economics of high-yield rice production in coastal Bangladesh. World Bank Policy Research Working Paper, No. 7140. Delince, J., Ciaian, P., & Witzke, H.-P. (2015). Economic impacts of climate change on agriculture: The AgMIP approach. Journal of Applied Remote Sensing, 9(1), 097099. https://doi.org/10.1117/1.jrs.9.097099 Dinda, S. (2004). Environmental Kuznets curve hypothesis: A survey. Ecological Economics, 49(4), 431-455. https://doi.org/10.1016/j.ecolecon.2004.02.011 Eder, A., Salhofer, K., & Quddoos, A. (2024). The impact of cereal crop diversification on farm labor productivity under changing climatic conditions. Ecological Economics, 223, 108241. https://doi.org/10.1016/j.ecolecon.2024.108241 Ekele, J. U., Webster, R., Perez de Heredia, F., Lane, K. E., Fadel, A., & Symonds, R. C. (2025). Current impacts of elevated CO2 on crop nutritional quality: A review using wheat as a case study. Stress Biology, 5(1), 34. https://doi.org/10.1007/s44154- 025-00217-w Farah, A. A., Mohamed, M. A., Musse, O. S. H., & Nor, B. A. (2025). The multifaceted impact of climate change on agricultural productivity: A systematic literature review of SCOPUS-indexed studies (2015–2024). Discover Sustainability, 6(1), 397. https://doi.org/10.1007/s43621-025-01229-2 Folke, C. (2006). Resilience: The emergence of a perspective for social–ecological systems analyses. Global Environmental Change, 16(3), 253-267. https://doi.org/10.1016/j.gloenvcha.2006.04.002 Georgopoulou, E., Mirasgedis, S., Sarafidis, Y., Vitaliotou, M., Lalas, D. P., Theloudis, I., . . . Zavras, V. (2017). Climate change impacts and adaptation options for the Greek agriculture in 2021–2050: A monetary assessment. Climate Risk Management, 16, 164-182. https://doi.org/10.1016/j.crm.2017.02.002 Gyamerah, S. A., Asare, C., Mintah, D., Appiah, B., & Kayode, F. A. (2023). Exploring the optimal climate conditions for a maximum maize production in Ghana: Implications for food security. Smart Agricultural Technology, 6, 100370. https://doi.org/10.1016/j.atech.2023.100370 Harini, R., Ariani, R. D., Supriyati, Satriagasa, M. C., Susilo, B., & Giyarsih, S. R. (2018). The effect of land conversion on agricultural production in North Kalimantan Province during 2012–2016 period. IOP Conference Series: Earth and Environmental Science, 145, 012093. https://doi.org/10.1088/1755-1315/145/1/012093 Hauk Jr, W. R., & Wacziarg, R. (2009). A Monte Carlo study of growth regressions. Journal of Economic Growth, 14(2), 103-147. https://doi.org/10.1007/s10887-009-9040-3 Hussain, S. G. (2011). Assessing impacts of climate change on cereal production and food security in Bangladesh. In R. Lal, M. V. K. Sivakumar, S. M. A. Faiz, A. H. M. M. Rahman, & K. R. Islam (Eds.), Climate change and food security in South Asia. In (pp. 459-476). Dordrecht, Netherlands: Springer Islam, F., Alam, G. M. M., Begum, R., Sarker, M. N. I., & Bhandari, H. (2022). Farm level adaptation to climate change: Insight from rice farmers in the coastal region of Bangladesh. Local Environment, 27(6), 671-681. https://doi.org/10.1080/13549839.2022.2068139 Islam, S. (2025). Agriculture, food security, and sustainability: A review. Exploration of Foods and Foodomics, 3, 101082. https://doi.org/10.37349/eff.2025.101082 Jones, M. (2022). How hard could climate change hit the global economy, and where would suffer most? New York: World Economic Forum and Reuters. Joseph, M., Moonsammy, S., Davis, H., Warner, D., Adams, A., & Timothy Oyedotun, T. D. (2023). Modelling climate variabilities and global rice production: A panel regression and time series analysis. Heliyon, 9(4), e15480. https://doi.org/10.1016/j.heliyon.2023.e15480 Kelkouli, M., Bouderbala, A., & Haddad, B. (2024). Climate impact on cereal yields in the Upper Cheliff plain, Northern Algeria. Pakistan Journal of Agricultural Sciences, 61(1), 299-306. https://doi.org/10.1007/s10668-024-04991-x https://doi.org/10.1016/S0304-4076(98)00009-8 https://doi.org/10.1016/S0165-1765(01)00507-9 https://doi.org/10.1016/j.envsci.2008.11.002 https://doi.org/10.1016/j.jclepro.2020.125637 https://doi.org/10.1108/IJCCSM-05-2019-0026 https://doi.org/10.1080/03650340.2021.1937139 https://doi.org/10.1016/j.econlet.2018.10.015 https://doi.org/10.1002/ldr.5652 https://doi.org/10.1117/1.jrs.9.097099 https://doi.org/10.1016/j.ecolecon.2004.02.011 https://doi.org/10.1016/j.ecolecon.2024.108241 https://doi.org/10.1007/s44154-025-00217-w https://doi.org/10.1007/s44154-025-00217-w https://doi.org/10.1007/s43621-025-01229-2 https://doi.org/10.1016/j.gloenvcha.2006.04.002 https://doi.org/10.1016/j.crm.2017.02.002 https://doi.org/10.1016/j.atech.2023.100370 https://doi.org/10.1088/1755-1315/145/1/012093 https://doi.org/10.1007/s10887-009-9040-3 https://doi.org/10.1080/13549839.2022.2068139 https://doi.org/10.37349/eff.2025.101082 https://doi.org/10.1016/j.heliyon.2023.e15480 https://doi.org/10.21162/PAKJAS/24.230 Asian Journal of Agriculture and Rural Development, 15(4) 2025: 576-585 584 © 2025 AESS Publications. All Rights Reserved. Kogo, B. K., Kumar, L., & Koech, R. (2021). Climate change and variability in Kenya: A review of impacts on agriculture and food security. Environment, Development and Sustainability, 23(1), 23-43. https://doi.org/10.1007/s10668-020-00589-1 Köprücü, Y., & Acaroğlu, H. (2023). How cereal yield is influenced by eco-environmental factors? ARDL and spectral causality analysis for Turkey. Cleaner Environmental Systems, 10, 100128. https://doi.org/10.1016/j.cesys.2023.100128 Kumar, P., Sahu, N. C., Kumar, S., & Ansari, M. A. (2021). Impact of climate change on cereal production: Evidence from lower- middle-income countries. Environmental Science and Pollution Research, 28(37), 51597–51611. https://doi.org/10.1007/s11356-021-14373-9 Łącka, I., Suproń, B., & Szczepaniak, I. (2024). Does climate change and energy consumption affect the food security of European Union countries? Empirical evidence from a panel study. Energies, 17(13), 3237. https://doi.org/10.3390/en17133237 Levine, R., Loayza, N., & Beck, T. (2000). Financial intermediation and growth: Causality and causes. Journal of Monetary Economics, 46(1), 31-77. https://doi.org/10.1016/S0304-3932(00)00017-9 Lobell, D. B., Schlenker, W., & Costa-Roberts, J. (2011). Climate trends and global crop production since 1980. Science, 333(6042), 616-620. https://doi.org/10.1126/science.1204531 Ma, B., Karimi, M. S., Mohammed, K. S., Shahzadi, I., & Dai, J. (2024). Nexus between climate change, agricultural output, fertilizer use, agriculture soil emissions: Novel implications in the context of environmental management. Journal of Cleaner Production, 450, 141801. https://doi.org/10.1016/j.jclepro.2024.141801 Maddison, D. (2007). The perception of and adaptation to climate change in Africa. United States: World Bank Publications. Magazzino, C., Gattone, T., Usman, M., & Valente, D. (2024). Unleashing the power of innovation and sustainability: Transforming cereal production in the BRICS countries. Ecological Indicators, 167, 112618. https://doi.org/10.1016/j.ecolind.2024.112618 Massagony, A., Tam Ho, T., & Shimada, K. (2022). Climate change impact and adaptation policy effectiveness on rice production in Indonesia. International Journal of Environmental Studies, 80(5), 1373-1390. https://doi.org/10.1080/00207233.2022.2099110 Masters, G., Baker, P., & Flood, J. (2010). Climate change and agricultural commodities. CABI Working Paper No. 2. Wallingford, United Kingdom: CABI Publishing. Onour, I. A. (2019). Effect of carbon dioxide concentration on cereal yield in Sudan. Management and Economics Research Journal, 5(S3), 740622. Ozdemir, D. (2022). The impact of climate change on agricultural productivity in Asian countries: A heterogeneous panel data approach. Environmental Science and Pollution Research, 29, 8205–8217. https://doi.org/10.1007/s11356-021-16291-2 Palmer, P. I., Wainwright, C. M., Dong, B., Maidment, R. I., Wheeler, K. G., Gedney, N., . . . Turner, A. G. (2023). Drivers and impacts of Eastern African rainfall variability. Nature Reviews Earth & Environment, 4, 254-270. https://doi.org/10.1038/s43017-023-00397-x Pickson, R. B., Boateng, E., Gui, P., & Chen, A. (2024). The impacts of climatic conditions on cereal production: Implications for food security in Africa. Environment, Development and Sustainability, 26(7), 18333-18360. https://doi.org/10.1007/s10668- 023-03391-x Pickson, R. B., He, G., Ntiamoah, E. B., & Li, C. (2020). Cereal production in the presence of climate change in China. Environmental Science and Pollution Research, 27, 45802-45813. https://doi.org/10.1007/s11356-020-10430-x Pulighe, G., Di Fonzo, A., Gaito, M., Giuca, S., Lupia, F., Bonati, G., & De Leo, S. (2024). Climate change impact on yield and income of Italian agriculture system: A scoping review. Agricultural and Food Economics, 12(1), 23. https://doi.org/10.1186/s40100-024-00317-7 Quiroga, S., & Iglesias, A. (2009). A comparison of the climate risks of cereal, citrus, grapevine and olive production in Spain. Agricultural Systems, 101(1-2), 91-100. https://doi.org/10.1016/j.agsy.2009.03.006 Rahim, S., & Puay, T. G. (2017). The impact of climate on economic growth in Malaysia. Journal of Advanced Research in Business and Management Studies, 6(2), 108-119. Riedesel, L., Ma, D., Piepho, H.-P., Laidig, F., Möller, M., Golla, B., . . . Feike, T. (2024). Climate change induced heat and drought stress hamper climate change mitigation in German cereal production. Field Crops Research, 317, 109551. https://doi.org/10.1016/j.fcr.2024.109551 Rötter, R. P., Hoffmann, M. P., Koch, M., & Müller, C. (2018). Progress in modelling agricultural impacts of and adaptations to climate change. Current Opinion in Plant Biology, 45, 255-261. https://doi.org/10.1016/j.pbi.2018.05.009 Saud, S., Wang, D., Fahad, S., Alharby, H. F., Bamagoos, A. A., Mjrashi, A., . . . Hassan, S. (2022). Comprehensive impacts of climate change on rice production and adaptive strategies in China. Frontiers in Microbiology, 13, 926059. https://doi.org/10.3389/fmicb.2022.926059 Schultz, T. W. (1964). Transforming traditional agriculture. United States: Yale University Press. Shamshad, J., Nawaz, A. F., Khan, M. B., & Arif, M. (2024). Climate change and food security. In S. Fahad, S. Saud, T. Nawaz, L. Gu, M. Ahmad, & R. Zhou (Eds.), environment, climate, plant and vegetation growth. In (pp. 265-284). Switzerland: Springer Nature. https://doi.org/10.1007/978-3-031-69417-2_9 Simionescu, M., Bilan, Y., Gędek, S., & Streimikiene, D. (2019). The effects of greenhouse gas emissions on cereal production in the European Union. Sustainability, 11(12), 3433. https://doi.org/10.3390/su11123433 Singh, A., Arora, K., & Chandra Babu, S. (2024). Examining the impact of climate change on cereal production in India: Empirical evidence from ARDL modelling approach. Heliyon, 10(18), e36403. https://doi.org/10.1016/j.heliyon.2024.e36403 Sissoko, P., Guindo, S. S., Togola, S., Dembélé, B. D., Grimsby, L. K., & Aune, J. B. (2023). Effect of adoption of climate-smart- agriculture technologies on cereal production, food security and food diversity in Central Mali. Agriculture, 13(6), 1196. https://doi.org/10.3390/agriculture13061196 Stadnik, B., Tobiasz-Salach, R., & Migut, D. (2024). Influence of foliar application of microelements on yield and yield components of spring malting barley. Agriculture, 14(3), 505. https://doi.org/10.3390/agriculture14030505 Thapa, R., Chatterjee, A., Awale, R., McGranahan, D. A., & Daigh, A. (2016). Effect of enhanced efficiency fertilizers on nitrous oxide emissions and crop yields: A meta‐analysis. Soil Science Society of America Journal, 80(5), 1121-1134. https://doi.org/10.2136/sssaj2016.06.0179 Verma, K. K., Song, X.-P., Kumari, A., Jagadesh, M., Singh, S. K., Bhatt, R., . . . Li, Y.-R. (2025). Climate change adaptation: Challenges for agricultural sustainability. Plant, Cell & Environment, 48(4), 2522-2533. https://doi.org/10.1111/pce.15078 https://doi.org/10.1007/s10668-020-00589-1 https://doi.org/10.1016/j.cesys.2023.100128 https://doi.org/10.1007/s11356-021-14373-9 https://doi.org/10.3390/en17133237 https://doi.org/10.1016/S0304-3932(00)00017-9 https://doi.org/10.1126/science.1204531 https://doi.org/10.1016/j.jclepro.2024.141801 https://doi.org/10.1016/j.ecolind.2024.112618 https://doi.org/10.1080/00207233.2022.2099110 https://doi.org/10.1007/s11356-021-16291-2 https://doi.org/10.1038/s43017-023-00397-x https://doi.org/10.1007/s10668-023-03391-x https://doi.org/10.1007/s10668-023-03391-x https://doi.org/10.1007/s11356-020-10430-x https://doi.org/10.1186/s40100-024-00317-7 https://doi.org/10.1016/j.agsy.2009.03.006 https://doi.org/10.1016/j.fcr.2024.109551 https://doi.org/10.1016/j.pbi.2018.05.009 https://doi.org/10.3389/fmicb.2022.926059 https://doi.org/10.1007/978-3-031-69417-2_9 https://doi.org/10.3390/su11123433 https://doi.org/10.1016/j.heliyon.2024.e36403 https://doi.org/10.3390/agriculture13061196 https://doi.org/10.3390/agriculture14030505 https://doi.org/10.2136/sssaj2016.06.0179 https://doi.org/10.1111/pce.15078 Asian Journal of Agriculture and Rural Development, 15(4) 2025: 576-585 585 © 2025 AESS Publications. All Rights Reserved. Wakjira, M. T., Peleg, N., Anghileri, D., Molnar, D., Alamirew, T., Six, J., & Molnar, P. (2021). Rainfall seasonality and timing: Implications for cereal crop production in Ethiopia. Agricultural and Forest Meteorology, 310, 108633. https://doi.org/10.1016/j.agrformet.2021.108633 Wooldridge, J. M. (2020). Introductory econometrics: A modern approach. Singapore: Cengage Learning Asia Pte Limited. World Bank Group. (2017). Agriculture, value added (% of GDP) – Bangladesh. Washington, DC: World Bank Database. World Bank Group. (2019). Bangladesh climate-smart Agriculture investment plan: Investment opportunities in the agriculture sector's transition to a climate resilient growth path. United States: World Bank Group. Xiang, X., & Solaymani, S. (2022). Change in cereal production caused by climate change in Malaysia. Ecological Informatics, 70, 101741. https://doi.org/10.1016/j.ecoinf.2022.101741 Xiong, W., Holman, I., Lin, E., Conway, D., Jiang, J., Xu, Y., & Li, Y. (2010). Climate change, water availability and future cereal production in China. Agriculture, Ecosystems & Environment, 135(1–2), 58-69. https://doi.org/10.1016/j.agee.2009.08.015 Yousaf, M., Li, J., Lu, J., Ren, T., Cong, R., Fahad, S., & Li, X. (2017). Effects of fertilization on crop production and nutrient- supplying capacity under rice-oilseed rape rotation system. Scientific Reports, 7(1), 1270. https://doi.org/10.1038/s41598- 017-01412-0 Zhang, Y., Waldhoff, S., Wise, M., Edmonds, J., & Patel, P. (2023). Agriculture, bioenergy, and water implications of constrained cereal trade and climate change impacts. PLoS One, 18(9), e0291577. https://doi.org/10.1371/journal.pone.0291577 Appendix Table 1a. Country-wise cereal productivity in terms of land. Name of the country Cereal production (in million tons) Area under cereal production (thousand KM2) Productivity Egypt 25.900 36.050 0.718 Bangladesh 59.800 97.260 0.615 Vietnam 50.300 108.000 0.466 China 642.000 1476.920 0.435 Hungary 19.300 46.960 0.411 Germany 49.100 123.870 0.396 Poland 39.900 117.260 0.340 UK 17.300 61.630 0.281 France 62.500 226.610 0.276 Myanmar 31.300 114.340 0.274 USA 456.000 1681.820 0.271 Philippines 28.300 108.000 0.262 Argentina 97.300 397.590 0.245 Pakistan 48.700 227.680 0.214 Italy 19.900 94.600 0.210 India 368.000 1765.260 0.208 Romania 20.200 97.740 0.207 Thailand 41.700 203.180 0.205 Turkey 46.800 240.550 0.195 Ethiopia 34.200 180.000 0.190 Brazil 144.000 800.480 0.180 Ukraine 61.000 347.670 0.175 Mexico 42.400 259.300 0.164 Spain 27.100 171.830 0.158 South Africa 18.100 124.330 0.146 Indonesia 67.700 478.050 0.142 Canada 72.100 519.200 0.139 Iran 24.700 197.790 0.125 Russia 143.000 1265.260 0.113 Kazakhstan 22.600 242.510 0.093 Nigeria 32.100 412.930 0.078 Australia 29.900 487.690 0.061 Views and opinions expressed in this article are the views and opinions of the author(s), Asian Journal of Agriculture and Rural Development shall not be responsible or answerable for any loss, damage or liability etc. caused in relation to/arising out of the use of the content. https://doi.org/10.1016/j.agrformet.2021.108633 https://doi.org/10.1016/j.ecoinf.2022.101741 https://doi.org/10.1016/j.agee.2009.08.015 https://doi.org/10.1038/s41598-017-01412-0 https://doi.org/10.1038/s41598-017-01412-0 https://doi.org/10.1371/journal.pone.0291577