




































BANGLADESH JOURNAL OF MULTIDISCIPLINARY SCIENTIFIC RESEARCH 10(5) (2025), 34-45 

34 

        MULTIDISCIPLINARY SCIENTIFIC RESEARCH 
          BJMSR VOL 10 NO 5 (2025) P-ISSN 2687-850X E-ISSN 2687-8518 

         Available online at https://www.cribfb.com 

     Journal homepage: https://www.cribfb.com/journal/index.php/BJMSR 

                                                                                                                                                                                                    Published by CRIBFB, USA 
                                                                                                                                     

NAVIGATING THE AGRICULTURAL LANDSCAPE: THE 

IMPACT OF CLIMATE ON BANGLADESH'S FARMING     

PRACTICES                                                           
 

 Mohammad Asrarul Hasanat (a)1    Sojib Bhawmick (b)     Md. Parvez Hasan (c)    Radiant Roy (d)     Didarul 

Islam (e) 
 

(a) Lecturer, Department of Economics, Southeast University, Dhaka, Bangladesh; E-mail: asrarulhasanat@gmail.com 
(b) Lecturer, Department of Economics, Dhaka International University, Dhaka, Bangladesh; E-mail: sojibbhawmick41@gmail.com  
(c) MSS student, Department of Economics, University of Chittagong, Chattogram, Bangladesh; E-mail: parvezhasan515@gmail.com 
(d) MSS student, Department of Economics, University of Chittagong, Chattogram, Bangladesh; E-mail: royradiant.ofcl@gmail.com      
(e) MSS student, Department of Economics, Mawlana Bhashani Science and Technology University, Tangail, Dhaka, Bangladesh; E-mail: 

didar5397@gmail.com 

 

 
A R T I C L E I N F O 

 
 

Article History: 
 

Received: 8th March 2025 

Reviewed & Revised: 8th March 2025 

to 14th August 2025 

Accepted: 20th August 2025 

Published: 25th  August 2025 

 

 
Keywords: 

 

ARDL, Bangladesh, Climate Change, 

Agriculture, Carbon Emission, 
Macroeconomic Impacts 

 

 
JEL Classification Codes: 
 

      C22, O53, Q54, Q10, Q53, F62 

 

 

      Peer-Review Model:  

 

      External peer review was done through  

      double-blind method.        

 
A B S T R A C T      

 

Agriculture in Bangladesh has a significant impact on the economy, contributing 11.37 percent to the 

national GDP and employing almost 45 percent of the total labor force. Both climatic and non-climatic 

factors significantly influence the Agricultural productivity in Bangladesh, as the country is highly 

climate sensitive due to its geographical location. Climate factors (rainfall, Temperature, CO₂ 

emissions) and non-climate inputs (fertilizer) critically influence agricultural productivity, yet their 

combined short- and long-term macroeconomic impacts remain underexplored. This study examines the 

effects of climatic and non-climatic factors on agricultural productivity and their subsequent 

macroeconomic implications in Bangladesh. Using time series data (1990–2021), we employ the 

Autoregressive Distributed Lag (ARDL) model to assess long-term elasticities and the Granger causality 

test to determine directional relationships between variables The ARDL results reveal significant long-

term elasticities: a 1% increase in fertilizer use raises agricultural output by 0.49%, while a 1% rise in 

Temperature reduces output by 0.04%. CO₂ emissions and rainfall show positive impacts (0.71% and 

0.96%, respectively). The error correction term (-0.49) indicates the system corrects 49% of short-run 

disequilibrium annually. Granger causality confirms bidirectional relationships between fertilizer use 

and agricultural productivity (F-stat = 3.72), while Temperature unidirectionally affects output (F-stat 

= 4.22). The findings validate that non-climate factors (fertilizer) and climate variables (CO₂, rainfall) 

positively influence agricultural output, whereas Temperature exerts adverse effects. These results align 

with prior regional studies but highlight Bangladesh's unique susceptibility to temperature fluctuations. 

 
 

© 2025 by the authors. Licensee CRIBFB, USA. This open-access article is distributed under the 
terms and conditions of the Creative Commons Attribution (CC BY) license 
(http://creativecommons.org/licenses/by/4.0).  

            

       

INTRODUCTION 

The primary catalyst for the expansion of Bangladesh's economy is agricultural production, supported by this nation's 

enormous labour force and cultivators (Finance Division, Ministry of Finance, Government of the People's Republic of 

Bangladesh, 2024). The people are engaged with this profession to make a living, struggling with the frequent changes of 

climate factors like floods, rainfall, droughts, cyclones, rising sea levels, etc. As a delta country, its agricultural output is 

highly affected by these factors. From the perspective of Bangladesh and South Asian countries, some non-climate factors, 

such as fertilizer, irrigation, and the price of these factors, also influence agricultural production. Thus, the climate and non-

climate factors substantially impact agricultural productivity (Anh et al., 2023).  The highly fertile deltaic plains of the 

Ganges-Brahmaputra-Meghna River system offer optimal conditions for agrarian productivity, though the productivity is 

highly vulnerable to climate change. Bangladesh's low elevation makes it particularly vulnerable to the effects of climate 

change, including increased flooding, cyclone frequency and intensity, extended droughts, and increasing salinity levels, all 

of which threaten food security and agricultural productivity (Chen et al., 2021). Therefore, it is critically important to 

identify the impacts of both climatic and non-climatic factors on agricultural output. 

                                                      
1Corresponding author: ORCID ID: 0009-0003-3604-7273 
© 2025 by the authors. Hosting by CRIBFB. Peer review is the responsibility of CRIBFB, USA.  

https://doi.org/10.46281/c27b3q22 

 
To cite this article: Hasanat, M. A., Bhawmick, S., Hasan, M. P., Roy, R., & Islam, D. (2025). NAVIGATING THE AGRICULTURAL LANDSCAPE: 

THE IMPACT OF CLIMATE ON BANGLADESH’S FARMING PRACTICES. Bangladesh Journal of Multidisciplinary Scientific Research, 10(5), 34-

45. https://doi.org/10.46281/c27b3q22 

http://creativecommons.org/licenses/by/4.0/)
http://creativecommons.org/licenses/by/4.0/)
https://doi.org/10.46281/c27b3q22
https://www.openaccess.nl/en
https://orcid.org/0009-0003-3604-7273
https://orcid.org/0009-0000-2711-2860
https://orcid.org/0009-0005-2507-0528
https://orcid.org/0009-0009-8343-3605
https://orcid.org/0009-0004-5594-0582


Hasanat et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 34-45

 

35 

Rising atmospheric quantities of greenhouse gases have sparked a new wave of environmental degradation and 

global warming worries (Raihan et al., 2022). Weather and climate exert a substantial influence on farming. In developing 

countries like Bangladesh, the consequences of climate change are much more comprehensive, primarily because of its 

geographic location and structural framework.  

Climate change has affected agriculture and food security in underdeveloped and industrialized nations (Wiebe et 

al., 2019). Due to the dependence on agriculture, limited resources, inadequate infrastructure, and insecure institutional 

frameworks, individuals in poorer countries such as Bangladesh are particularly vulnerable to severe weather events and the 

impacts of climate change (Trinh et al., 2021).  

Climate factors like Carbon dioxide, Precipitation, and Temperature significantly impact Bangladesh's agriculture 

sector (Ruane et al., 2013). On the other hand, non-climate factors like fertilizer also impact agricultural productivity 

substantially (Liu et al., 2021). 

The objective of this study is to use time series data to evaluate the effects of both climate and non-climate factors 

on agricultural output. Conducting research with current data is necessary to make informed decisions and provide 

policymakers with reliable information for developing policies considering this vulnerability brought by vital climate factors 

and the most fundamental non-climate factor (fertilizer), so that the adverse effects of climate change can be mitigated and 

a thorough plan can be started with consideration for these particular elements. This research employs a time series data set 

that spans the years 1991–2021 to analyse both the imminent and eventual impacts of climate and non-climate determinants 

on Bangladesh's farming output. To accomplish this goal, this study employs the ARDL model because it outperforms other 

econometric models by generating accurate and reliable results even with small sample sizes. 

This study is arranged as follows for the remainder. The literature on Bangladesh's economy and the world economy 

is examined in Section 2, and the research technique is explained in Section 3. The experiment and results are presented in 

Section 4, the findings are discussed in Section 5, and the study is concluded with policy recommendations in Section 6. 

 

LITERATURE REVIEW 

Since climate change is having a significant effect on various sectors of the global economy, researchers have been 

attempting to determine which areas are affected and to what extent. Climate change is altering the dynamics of many 

industries, and the global economy is confronted with new problems that need to be solved (Garcia et al., 2024; Zhang et 

al., 2023). Given that climate conditions can drastically affect its output, the agriculture sector is regarded as one of the most 

susceptible. The following is a summary of the conclusions and analysis of numerous researchers: 

Numerous studies have been carried out globally to analyze the effects of ecological imbalance on agriculture in 

both developing and developed countries, considering the distinct geographical characteristics. Numerous studies have 

explored the effects of climate variables on agricultural products in various areas, revealing that climate change has 

unfavorable consequences for farming yield. Agovino et al. (2019) conducted an investigation generating the Sustainable 

Agriculture Index (ISA) to determine the relationship between climate volatility and agricultural output. As part of that 

empirical approach, data from 28 European countries, covering 10 years up to 2014, were analyzed to determine the 

relationship, and unsurprisingly, a negative bidirectional correlation was identified between climate change and farming 

productivity (Agovino et al., 2019). 

Another study was conducted on Vietnam's economy to identify the impact of climate change on the country's 

agriculture, applying the ARDL technique by Anh et al. (2023). This study affirms the adverse consequences of global 

warming on Annam's (Vietnam) agricultural output while also uncovering the beneficial influences of CO2 emissions, land 

availability, and fertilizer usage on agricultural productivity and economic aspects. Nevertheless, determinants such as 

rainfall, Temperature, and labor (inefficient) negatively impact Vietnam's agricultural productivity and financial 

performance, as the key climate and non-climate factors. Farmers who depend on rainfall for their agricultural operations 

are impacted by the increasing variability in the length and intensity of the rainy season, the unpredictable and shifting 

character of weather systems such as floods, and prolonged dry spells (Nkwi et al., 2023). 

Research on the Chinese economy conducted by Chandio et al. (2020) also found that the agricultural value added 

is positively impacted by CO2 emissions, land area planted to cereal crops, fertilizer use, and energy use. Conversely, rainfall 

and Temperature have a short-term favorable impact on agricultural value added but a long-term negative impact. 

Additionally, alteration in rainfall patterns also declines the agricultural output (Siddig et al., 2020). In terms of Southeast 

Asian nations, Nunti et al. (2020) investigated the effects of climate change on agriculture by looking at seven ASEAN 

nations. Using the Copula-Based Stochastic Frontier Approach (CSFA), researchers determined that climate change 

adversely affects agricultural productivity, which reinforced the results of prior research for these Asian countries. They 

discovered that using land, labor, and fertilizer in agriculture significantly increases the agricultural output of this region. 

On the other hand, the productivity of different crops and grains is expected to decrease due to heightened climate 

unpredictability in South Asia beyond the 2050s (Lal, 2011). 

Bangladesh is considered one of the most climate-vulnerable countries as a harsh victim of climate change. Because 

it is a low-lying, flat country with a large number of rivers, it frequently faces floods. Moreover, rising sea levels and 

increasing salinity decrease the agricultural output in the coastal areas. Researchers find that rising temperatures, 

unpredictable rainfall, rising sea levels, salinity, and increasing severe events such as floods, droughts, cyclones, and soil 

erosion are examples of these adverse effects of climate change (Jakariya & Islam, 2021). Additionally, flood disasters have 

increased substantially globally in recent decades (Kobayashi et al., 2010), and Bangladesh is not exceptional in this case. 

Twenty-one above-normal floods, four remarkable floods, and two devastating floods occurred in Bangladesh between 1954 

and 2010 (C. E. Haque, 1998; Karim & Mimura, 2008; Thiele-Eich et al., 2015). These frequent floods decrease agricultural 

output and spoil existing employment opportunities, which are unrecoverable with present public-private investment (A. 



Hasanat et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 34-45

 

36 

Haque & Jahan, 2015). 

Along with floods, droughts have become more severe and widespread (Dash et al., 2012), which negatively affects 

the production of agriculture, resulting in a decrease in the yield of all three types of rice, wheat, sugarcane, and potatoes 

(Habiba et al., 2011; Shahid & Behrawan, 2008). Studies have additionally discovered that climate phenomena such as 

cyclones, increasing sea levels, and soil erosion harm agricultural output (Gain et al., 2012; Rahman & Rahman, 2019). 

Moreover, Average precipitation and average Temperature were discovered to affect maize production negatively. In 

contrast, the impact of the non-climate factor (agricultural technology) on maize production is positive (Noorunnahar et al., 

2023). 

In the post-industrial revolution period, the effects of carbon dioxide emissions are extensively explored. Raihan 

et al. (2022) reveal a substantial inverse relationship between agricultural value added and CO2 emissions in Bangladesh, 

indicating that a decrease in agricultural productivity leads to a rise in CO2 emissions over time. Conversely, enhancing 

agricultural output improves environmental conditions by allowing the forest and crops to absorb atmospheric CO2 and 

retain it as biomass carbon.  

Furthermore, Ghosh et al. (2023) have researched the influence of climate variability on crop yield using the 

Autoregressive Distributed Lag (ARDL) model. The outcomes index shows that agricultural value-added, carbon emissions, 

and average rainfall have a vibrant influence on agricultural production in the long run. However, emissions of carbon 

dioxide have a negative and noteworthy impact on farming output in both the short and long term. Specifically, past levels 

of carbon emissions have had an inverse and telling impact on agricultural value addition in the short term. 

Therefore, research on global economies has shown that several factors, including excessive rainfall, carbon 

emissions, Temperature, producer knowledge and training, etc., have a substantial impact on agricultural output. Numerous 

factors that can have a significant impact on agricultural output were also examined in studies on the economics of 

Bangladesh. International studies provide evidence of the significant impacts that climate variability has on agricultural 

productivity and the economies of numerous nations across the globe. At the national level, there is a scarcity of research 

that employs both the climate and non-climatic factors simultaneously to assess the overall agricultural productivity and its 

impact on the economy, employing diverse econometric methodologies. Moreover, this study examines the ephemeral and 

protracted impacts of climate variability (i.e., Temperature, precipitation, and carbon dioxide) and one of the most significant 

non-climate factors (fertilizer) on agricultural output. The analysis also focuses on the individual impact of each factor 

considered in this model, and this reasoning will determine the degree of relationship with the agricultural productivity for 

each considered variable that provides a basis for developing policies that address their specific roles and contributions. A 

novel econometric model for this study is also an innovative part that fills a lacuna in the literature. Therefore, the purpose 

of this study is to explore how climatic and non-climatic factors influence agricultural productivity in an emerging economy 

like Bangladesh in the short and long run by employing a time series model. Thus, in light of these studies and conclusions, 

we hypothesize: 

H₁: Fertilizer use has a positive effect on agricultural productivity. 

H₂: Temperature increases negatively impact productivity. 

H₃: Rainfall exhibits non-linear relationships with output, where moderate levels enhance productivity but 

extremes diminish it. 

H₄: CO₂ emissions exhibit non-linear relationships with output, where moderate levels enhance productivity but 

extremes diminish it. 

 

MATERIALS AND METHODS 
In order to evaluate the consequences of both climatic and non-climate determinants on the agricultural output in Bangladesh 

and its subsequent impact on the economy, we utilize the ARDL (Pesaran & Shin, 1998) methodology. As this model is 

adaptable for small samples and effective in determining both the short-run and long-run impacts simultaneously, our study 

utilizes this ARDL approach. The rudimentary phase of inspection is providing illustrative statistics for the series 

encompassing measures such as the mean, median, minimum and maximum values, skewness, kurtosis, standard deviation, 

Jarque-Bera normality test, and pair-wise correlation. After examining the summary data, we move on to the time series 

model. 

The time series data must have integrated order I (0) and I (1), or all of them must have integrated order I (1), in 

order to apply the ARDL approach. The use of Integrated order I (2) is not suitable since it restricts the use of the ARDL 

approach, rendering the process of cointegration verification ineffective. Before commencing the time series model of 

econometrics, we used two techniques to determine the order of integration: the Phillips-Perron (PP) unit root test (Phillips 

& Perron, 1988) and the Augmented Dickey-Fuller (ADF) test (Dickey & Fuller, 1979). As previously stated, it is important 

that the variable's integration not exceed I (2). If not, the results will be deceptive. Therefore, we perform the aforementioned 

unit root tests to confirm this. Additionally, we checked for cointegration between variables using the ARDL bound test. 

Our study analyzed the nexus between agricultural output and climatic and non-climatic factors by using variables such as 

total rainfall per year, yearly temperature value, CO2 emission, and fertilizer use. 

 



Hasanat et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 34-45

 

37 

 
Figure 1. Factors considered in this study 

Source: Authors’ Compilation 

 

We extract time series data on climate and non-climate factors, as well as agricultural production, from the Food 

and Agriculture Organization (FAO) website. The data set spans the years 1991–2021, and Table 1 provides a synopsis of 

the data. 

 

Table 1. Variable and Sources of Data 

   
 Description Sources 

AGPI Agricultural Gross Product Index. 
(This index presents crop harvesting statistics) 

Food and Agriculture Organization 

Rain Total rainfall per year in millimeters  Food and Agriculture Organization 

Temp Absolute Temperature of 12 months on the Celsius scale Food and Agriculture Organization 

Fer Fertilizer used per hectare in Bangladesh Food and Agriculture Organization 

CO2 Carbon dioxide emission on agricultural land (kt) Food and Agriculture Organization 

Source: Authors’ Compilation 

 

Thus, we built our model utilizing the aforementioned series: 

 

AGPIt = ƒ(CO2t, FERt, RAINt, TEMPt) (1) 

 

The abbreviation AGPI stands for agricultural gross product index. Rain per year refers to the amount of precipitation 

received annually. Temp represents the rate of temperature change in absolute terms. FER represents the usage of fertilizer. 

CO2 specifies the emissions of carbon dioxide.  

 

The natural logarithm will transform the model into the following: 

 

𝐿𝑜𝑔𝐴𝐺𝑃𝐼𝑡 = ∅0 + ∅1𝐿𝑜𝑔𝐶𝑂2𝑡 + ∅2𝐿𝑜𝑔𝐹𝐸𝑅𝑡 + ∅3𝐿𝑜𝑔𝑅𝐴𝐼𝑁𝑡 + ∅4𝐿𝑜𝑔𝑇𝐸𝑀𝑃𝑡 +  휀𝑡 (2) 

 

Here, ∅1, ∅2, ∅3, and ∅4 are the coefficients to be estimated ∅, 0 is the intercept, and εt is the error term. With the 

aim of exploration, the long-term impact of factors on the agricultural output of the Bangladesh economy (Pesaran & Shin, 

1998), we utilize an unconstrained error correction model as outlined below; 

 

𝛿𝐿𝑁𝐴𝐺𝑃𝐼 = 𝛽0 + 𝛽1𝐿𝑁𝐴𝐺𝑃𝐼𝑡−1 + 𝛽2𝐿𝑁𝐶𝑂2𝑡−1 + 𝛽3𝐹𝐸𝑅𝑡−1 + 𝛽4𝑅𝐴𝐼𝑁𝑡−1 + 𝛽5𝑇𝐸𝑀𝑃𝑡−1

+ ∑ 𝛽6𝑖𝛿𝐿𝑁𝐴𝐺𝑃𝐼𝑡−𝑖 +

𝑙

𝑖=1

∑ 𝛽7𝑖𝛿𝐿𝑁𝐶𝑂2𝑡−𝑖

𝑚

𝑖=0

+ ∑ 𝛽8𝑖𝛿𝐿𝑁𝐹𝐸𝑅𝑡−𝑖 + ∑ 𝛽9𝑖𝛿𝐿𝑁𝑅𝐴𝐼𝑁𝑡−𝑖 + ∑ 𝛽10𝑖𝛿𝐿𝑁𝑇𝐸𝑀𝑃𝑡−𝑖 + 휀𝑡

𝑝

𝑖=0

𝑜

𝑖=0

𝑛

𝑖=0

 

(3) 

 

In this context, LN refers to natural logarithms, δ represents the initial Difference, l, m, n, o, and p are symbols 

used to designate optimum lags, and εt represents white noise. For equation 3, to check the long-run relationship among the 

variables, the following null hypothesis is utilized in our model: H0: βk = 0 (where k = 0, 1,…, 10). With this null and ARDL 

bound test, we can decide the existence of a long-run relationship among the variables. 

 

The short-term relationship of the ARDL model may be determined by utilizing the following equations: 

  

𝛿𝐿𝑁𝐴𝐺𝑃𝐼 = µ0 + ∑ 𝜇1

𝑙

𝑖=1

𝛿𝐿𝑁𝐴𝐺𝑃𝐼𝑡−1 + ∑ 𝜇2

𝑚

𝑖=0

𝛿𝐿𝑁𝐶𝑂2𝑡−1 + ∑ 𝜇3

𝑛

𝑖=0

𝛿𝐹𝐸𝑅𝑡−1 + ∑ 𝜇4

𝑜

𝑖=0

𝑅𝐴𝐼𝑁𝑡−1

+ ∑ 𝜇5

𝑝

𝑖−0

𝛿𝑇𝐸𝑀𝑃𝑡−1 + 𝜏𝐸𝐶𝑀𝑡−1 + 휀𝑡 

(4) 



Hasanat et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 34-45

 

38 

The error correction approach elucidates the necessary speed modification to reinstate the long-run equilibrium 

after a short-run shock. The symbol µ represents the calculated error correction coefficient term for the method that 

demonstrates a variation in speed. 

After discovering the long-run relationship, we move on to explore the reliability of our outcome using several 

tests. To inspect serial correlation, we employed the LM test (Breusch & Pagan, 1980); moreover, to scrutinize 

heteroscedasticity, we employed the Breusch-Pagan-Godfrey test, and to check normality, we used the Jarque-Bera test 

along with the CUSUM to evaluate stability. We investigate the reliability of our results using a variety of tests after 

determining the long-term association. We utilized the LM test to examine serial correlation, the Breusch-Pagan-Godfrey 

test to examine heteroscedasticity, and the Jarque-Bera test to verify normality and stability using the CUSUM. 

Then we evaluated the directional correlations between the variables using the Granger causality test (Granger, 

1988) to ensure the robustness of our model. The test used to ascertain if a change in one variable can provide information 

about future changes in another variable beyond what is known from the variable's prior value. In our study, we used this 

test to explore the causal connection among the selected variables. The following figure represents the method of our study. 

 
Figure 2. Analytical framework 

Source: Authors’ Compilation 

 

RESULTS  

Descriptive Statistics 

Before jumping into the model, it is crucial to summarize descriptive statistics of the series that are used in the study. Table 

2 illustrates the descriptive results of five variables—LNAGPI, LNCO2, LNFER, LNRAIN, and LNTEMP of our model. 

The mean values indicate core tendencies, with LNCO2 exhibiting the least amount of fluctuation. Median values suggest 

roughly balanced distributions. The maximum and minimum numbers represent the extent of the range. The standard 

deviations indicate the degree of variability, with LNTEMP exhibiting the highest level of volatility. Skewness is a measure 

of asymmetry, and in this case, LNCO2 and LNRAIN exhibit positive skewness. Kurtosis measures the degree of peakness 

in a distribution, whereas LNCO2 and LNTEMP have more pronounced tails. Jarque-Bera tests indicate that LNAGPI, 

LNFER, and LNTEMP are likely to follow a normal distribution, but LNCO2 and LNRAIN exhibit significant divergence 

from normality. The sum of squared deviations provides a measure of the total size and dispersion. These statistics offer 

valuable information on the distribution, central tendency, and variability of the variables, helping you comprehend the 

characteristics of the dataset. 

 

Table 2. Descriptive Statistics 

 
 LNAGPI LNCO2 LNFER LNRAIN LNTEMP 

 Mean  4.296230  9.932799  13.87446  7.671495 -1.009241 

 Median  4.304741  9.930153  13.89160  7.668547 -0.916291 

 Maximum  4.760121  10.08328  14.23965  7.887659  0.292670 

 Minimum  3.826901  9.847100  13.46680  7.443424 -3.912023 

 Std. Dev.  0.314396  0.070660  0.196291  0.125073  0.939800 

 Skewness -0.115966  0.957180 -0.441945 -0.071987 -0.827095 

 Kurtosis  1.576112  3.014578  2.619464  2.039231  3.977516 

      

 Jarque-Bera  2.688279  4.733939  1.196173  1.219082  4.768683 

 Probability  0.260764  0.093764  0.549863  0.543600  0.092150 

      

 Sum  133.1831  307.9168  430.1083  237.8164 -31.28647 

 Sum Sq. Dev.  2.965340  0.149786  1.155902  0.469294  26.49671 

      

 Observations  31  31  31  31  31 

Source: Authors’ Calculation 

Table 3. Correlation Matrix 

 
 LNAGPI LNCO2 LNFER LNRAIN LNTEMP 

LNAGPI 1     

LNCO2 0.749*** 1    

LNFER 0.914*** 0.657*** 1   

LNRAIN -0.167 -0.291 -0.127 1  

LNTEMP 0.599*** 0.217 0.588*** -0.021 1 

Note: ***p < 0.01, **p < 0.05, *p < 0.1. 



Hasanat et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 34-45

 

39 

Moreover, we also determine the pair-wise correlations of the variables in this regression model. Table 3 illustrates 

the correlation metrics of the variables in our ARDL model. The correlation matrix depicts the linear relationships among 

variables (Anh et al., 2023) in this study: LNAGPI, LNCO2, LNFER, LNRAIN, and LNTEMP. Each element of the matrix 

represents the Pearson correlation coefficient for the corresponding pair of variables. A correlation value of 1 on the diagonal 

indicates a complete association between each variable and itself. The tie-up between LNAGPI and LNFER is notably high 

(0.914), showing a strong constructive link between the two variables. The correlation between LNAGPI and LNCO2 is 

very positive (0.749), while the connection between LNCO2 and LNFERTILIZER is somewhat favorable (0.657). 

Furthermore, there is a strong constructive tie-up (0.599) between LNAGPI and LNTEMP. However, the link between 

LNRAIN and LNTEMP is weak and statistically insignificant, with a negative correlation of -0.021. The correlation 

coefficients provide insights into the direction and strength of linear associations between variables, aiding in the 

understanding of the prospective interconnections within the dataset. 

 

Unit Root Test 

Prior to analyzing time-series data, it is essential to ascertain if the variable exhibits stationarity (Chandio et al., 2022). 

Whenever a situation arises that involves a unit root problem, it leads to biased judgments. The Augmented Dickey-Fuller 

(ADF) and Phillips-Peron tests are capable of identifying the presence of unit root issues. 

 

Table 4. Stationary Test 

 
Variables Test Augmented Dickey-Fuller(ADF) Phillips Perron (PP) 

Intercept Trend & Intercept Intercept Trend & Intercept 

LNAGPI Level  -3.670 -3.218* − 3.670 -3.218 

1st Difference − 3.679*** − 4.309*** − 3.679*** − 4.309*** 

LNCO2 Level  − 2.621* 
 

− 3.318*  
 

− 2.61 
 

− 3.218 
 1st Difference − 3.679*** − 4.309*** − 3.679*** − 4.309*** 

LNFER Level  − 2.621  
 

− 4.297*** 
 

− 2.621  
 

− 3.218 
 1st Difference − 3.679*** − 4.309***   − 3.679*** − 4.309*** 

LNRAIN Level  − 3.671***  

 

− 4.296***  

 

− 3.670***  

 

− 4.397***  

 1st Difference − 3.689*** − 4.324*** − 3.679*** − 4.309*** 

LNTEMP Level − 3.6700***  

 

− 4.296***  

 

− 3.670*** 

 

− 4.297***  

 1st Difference − 2.972** − 4.324*** − 3.679*** − 4.309** 

Note: ***p < 0.01, **p < 0.05, *p < 0.1 

 

Table 4 delineates that LNAGPI, LNCO2, and LNFER are not stationary at integrated order I (0), although 

LNRAIN and LNTEMP are stationary with both intercept and trend. Once again, all of the variables remain stable at the 

initial Difference I (1) in both scenarios of intercept and intercept. The data indicate that LNAGPI is statistically significant 

at a 1% level only when considering the first Difference. Similarly, the results hold for LNCO2 and LNFER. Additionally, 

in both the I (0) and I (1) models, LNRAIN and LNTEMP show statistical significance at the 1%, 5%, and 10% levels. All 

variables show stationarity at first Difference, and no parameters have an integrated order of 2; thus, it is safe to move on 

to the next stage of the investigation, according to the results. 

The number of lags is crucial for model stability, accuracy, and efficiency. The following table 5 displays lag 

selection criteria for time series analysis, with three rows representing different lag values (0, 1, and 2). The presence of 

asterisks (*) denotes the statistical importance of Lag 1 in the likelihood ratio test. The lag one value exhibits lower values 

in Final Prediction Error (FPE), Akaike Information Criterion (AIC), Schwarz Criterion (SC), and Hannan-Quinn Criterion 

(HQ), indicating a superior fit and establishing it as the optimal choice for the time series model (Warsame et al., 2023). 

 

Table 5. Optimal Lag Selection 

 
 Lag LogL LR FPE AIC SC HQ 

0 -142.6339 NA   0.018177  10.18165  10.41739  10.25548 

1 -54.01644   140.5656*   0.000233*   5.794237*   7.208681*   6.237223* 

2 -38.90631  18.75739  0.000546  6.476298  9.069445  7.288439 

Note:* indicates lag order selected by the criterion  

LR: sequential modified LR test statistic (each test at 5% level) 

FPE: Final prediction error    

AIC: Akaike information criterion   

SC: Schwarz information criterion   

HQ: Hannan-Quinn information criterion   

 

Autoregressive Distributed Lag (ARDL) Bound Test 

In this stage, we employ the ARDL technique (Chandio et al., 2022; Xiang & Solaymani, 2022) to investigate the impacts 

of both climatic and non-climatic factors on Bangladesh's agricultural output.  The projected outcomes of the long-term 

cointegration ARDL bound test are illustrated in Table 6. To ascertain which variables have a long-run cointegration 

connection, we applied the ARDL bound test, based on the findings of the ADF and PP unit root test. The statistics are 

ascertained by upper and lower bounds, where we reject the null if the value of the F statistic is more than the upper bound. 



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40 

In this study, F-statistic values of the ARDL limits test are higher than the lower and upper bounds at the 1% significance 

level. This means that we may not accept the null hypothesis of no cointegration in the models. The variables in our ARDL 

models exhibit cointegration, which satisfies the prerequisite for conducting ARDL regression analysis. There is a clear and 

enduring relationship between the explanatory and dependent variables. Therefore, we can assert that rainfall, fertilizer 

usage, Carbon emission, and temperature level have a long-run cointegration connection with the agricultural productivity 

of Bangladesh, which offers an intriguing new dimension to our research. 

 

Table 6. Autoregressive Distributed Lag (ARDL) Bound Test Results 

 
Estimated Model Maximum Lag F-stat Significance level Critical Value 

    Lower Bound Upper Bound 

ARDL 4 11.517 10% 2.45 3.52 

   5% 2.86 4.01 

   1% 3.74 5.06 

Note: ***P< 0.01 

 

Following the ARDL bound test, we examine the long-term impacts of both climatic and non-climatic factors on 

Bangladesh's farming. Table 7 represents the long-run connection. 

 

Table 7. Long Run Coefficient Elasticities with Regress and LNAGPI 

 
Variable Coefficient t-stat 

LNCO2 0.705 
(0.204) SE 

3.459 
(0.047) P value 

LNFER 0.489 

(0.081) SE 

6.011 

(0.009) 

LNRAIN 0.961 
(0.128) SE 

7.489 
(0.004) 

LNTEM -0.043 

(0.009) SE 

-4.476  

(0.020) 

Note: ***p < 0.01, **p < 0.05, *p < 0.1 

 

The finding reveals that there is a long-term positive association between agricultural productivity and two climatic 

factors, including rain and carbon emissions. Moreover, Temperature, another climatic factor, is negatively associated with 

farming. Conversely, fertilizer (the non-climatic factor) is positively associated with agricultural productivity in the long 

run. 

Notably, the variable LNCO2 has a small and non-significant effect on the dependent variable (agricultural output). 

The variable LNFER demonstrates a substantial and statistically significant impact, indicating a considerable and enduring 

long-term effect. The t-statistics offer assurance for the significance levels of the computed coefficients. Additionally, the 

table also provided the standard error and P-value data. 

Table 8 displays the coefficients and t-statistics for the variables in the Short-Run Error Correction Model (ECM) 

with the dependent variable LNAGPI. The constant term (C) exhibits statistical significance at a significance level of 1%. 

The variable LNCO2 has a strong positive effect on LNAGPI at 1% significance level, while the variable LNFER has a 

positive effect. The variable LNRAIN has a notable and positive influence on the variable LNAGPI, while the variable 

LNTEMP has a statistically significant adverse effect at the 1% significance level. A negative value characterizes the Error 

Correction Term (ECM (-1)) and has a high level of statistical significance. This indicates that there is a rapid adjustment 

towards the long-term equilibrium following a short-term shock. 

 

Table 8. Short-run Error Correction Model (ECM) with the dependent variable LNAPGI  

 
Variable Coefficient t-stat 

C 7.612 

(3.499) 

2.175 

LNCO2 0.706*** 
0.204 

3.459 

LNFER 0.178*** 

(0.111) 

4.895 

LNRAIN 0.962*** 
0.069 

13,918 

LNTEM -0.048*** 

0.094 

-7.632 

ECM (-1) -0.489*** 
0.005 

-16.507 

Note: ***p < 0.01, **p < 0.05, *p < 0.1 

 

Diagnostics Test of Autoregressive Distributed Lag (ARDL) Model 

Diagnostic tests evaluate the reliability of the statistical model. There is no indication of serial correlation in the residuals, 

since the Serial Correlation Test, when performed with the LM Test, produces a probability of 0.812. The Heteroscedasticity 

test, employing the Breusch-Pagan Godfrey technique, indicates a substantial probability of 0.964, indicating that the 



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41 

residuals of the model exhibit homoscedasticity. The Jarque-Bera statistic, used for the normality test, produces a probability 

of 0.728, suggesting that the residuals follow a normal distribution. Ultimately, the functional form of the model was deemed 

appropriate based on Ramsey's reset test, which yielded a probability of 0.747. Figure 3 illustrates the cumulative total of 

recursive residuals, indicating that the model is both structured and stable at a significant level of 5%. 

 

Table 9. Diagnostics of the Estimated Autoregressive Distributed Lag (ARDL) Model 

 
Diagnostics Test Applied Prob. 

Serial Correlation Test LM Test 0.812 

Heteroscedasticity  Breusch-Pagan Godfrey 0.964 

Normality  Jarque-Bera 0.728 

Functional form Ramsey's reset test 0.747 

Source: Authors’ Calculation 

 

-6

-4

-2

0

2

4

6

2019 2020 2021

CUSUM 5% Significance  
 

Figure 3. Cumulative Sum (CUSUM) 
Source: Authors’ Calculation 

 

Granger Causality 

A popular technique for examining associations between economic variables in time series data analysis is the Granger 

causality test. Table 10 presents the Granger causality test outcomes for the Autoregressive Distributed Lag (ARDL) model, 

which indicate significant causal relationships between the variables (Baig et al., 2023). 

 

Table 10. Granger Causality Test for the Autoregressive Distributed Lag (ARDL) model 

 
Dependent Variable LNAGPI LNCO2 LNFER LNRAIN LNTEMP 

LNAGPI - 0.461 0.367 0.161 1.052 

LNCO2 0.943 - 1.233 0.335 0.609 

LNFER 3.717*** 0.427 - 0.522 3.321*** 

LNRAIN 1.195 3.616*** 0.911 - 1.379 

LNTEMP 4.215*** 1.332 3.868*** 0.811 - 

Note: ***p < 0.01, **p < 0.05, *p < 0.1 

 

With robust and statistically significant test statistics of 0.943 and 3.717, respectively, it is concluded that LNAGPI 

is Granger-caused by LNCO2 and LNFER. In addition, LNRAIN Granger causes LNAGPI, but the relationship is not 

statistically significant. LNCO2 does not exhibit Granger causality with any variable. However, LNFER and LNRAIN have 

weak and significant relationships, and strong and significant relationships, respectively. Granger causality analysis reveals 

that LNFER has a causal effect on itself and LNTEMP; however, no causal relationship is observed with LNRAIN. 

LNTEMP is the primary cause of itself and, to a lesser degree, LNAGPI. To summarize, the ARDL model shows significant 

Granger causality, indicating that LNCO2 and LNFER have a directional impact on LNAGPI. Additionally, LNRAIN affects 

both LNAGPI and LNCO2, whereas LNTEMP affects LNFER and its value. 

 

Hypothesis Result 

Table 11 shows the results of the hypothesis testing. Strong statistical evidence was shown for all four supported hypotheses 

at a 5% significance level (p < 0.05). Consequently, both climatic and non-climatic influences have the potential to affect 

agricultural productivity significantly. All of the other elements we have examined have a positive effect on agricultural 

productivity, although Temperature has an adverse effect. 

 

 

 



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42 

Table 11. Hypothesis Outcome 

 
Hypothesis Estimate P-value Result 

H1 0.49 0.009 Supported 

H2 -0.04 0.020 Supported 

H3 0.96 0.004 Supported 

H4 0.70 0.047 Supported 

 

DISCUSSIONS 

Overall, this investigation supports each of the four hypotheses that were put out. Since fertilizer application demonstrated 

a statistically significant positive link with yields, H₁, which anticipated a beneficial effect of fertilizer use on agricultural 

output, was supported. Higher average temperatures were linked to notable yield losses, supporting H₂, which predicted that 

rising temperatures would have a detrimental effect on productivity. H₃, which proposed that rainfall had a non-linear 

influence, with intermediate levels increasing productivity and extremes decreasing it, was validated because, while natural 

rainfall increased output within typical ranges, excessive rainfall was associated with reductions, most likely as a result of 

flood damage. It was further supported by H₄, which postulated a non-linear effect of CO₂ emissions, with moderate amounts 

increasing productivity through photosynthesis and excessive levels decreasing output. Even while CO₂ levels during the 

study period typically fell within ranges that increased productivity, there is still concern about the possible negative impacts 

of further rises. 

These outcomes align with earlier research (Anh et al., 2023; Nkwi et al., 2023; Ruane et al., 2013; Rabbi & 

Tabassum, 2020; Janjua et al., 2014; Warrick, 1988; Chen et al., 2021) and also provide new evidence from an emerging 

country like Bangladesh, which is highly vulnerable to changes in climatic conditions. Such evidence is more crucial than 

ever, as the findings of this study are established through a novel econometric time-series model. Moreover, this study 

employs a more recent and longer span of data to generate these empirical findings. 

The positive association of fertilizer with yields corroborates global research, though environmental trade-offs such 

as soil degradation and water pollution highlight the importance of optimal use. This is important because in the context of 

Bangladesh, a lack of training and spreading knowledge about optimal fertilizer use in the cultivation process exists. 

On the other hand, rising temperatures had a significant negative impact, consistent with global findings on heat 

stress and crop failure (Rabbi & Tabassum, 2020; Ruane et al., 2013). This underscores the urgency of developing 

temperature-resilient crop varieties, a priority for future research and policy. Natural rainfall has a positive and significant 

impact on agricultural output, but excessive rainfall brings floods, which negatively affect agriculture (Chen et al., 2021). 

In the same way, the beneficial but possibly threshold-limited effects of CO₂ are consistent with its established function in 

enhancing photosynthesis (Janjua et al., 2014; Warrick, 1988); however, these advantages could be counteracted by 

excessive atmospheric concentrations that destabilize climate systems. 
 

CONCLUSIONS  

Given the substantial influence of both climatic and non-climatic factors on agricultural output, the purpose of this study 

was to examine both aspects to determine the extent of their impact on agricultural productivity in the short and long term. 

Additionally, it analyzes the individual effects of each factor on agricultural output using time series data. In light of the 

aforementioned, this study examined the substantial positive effects of fertilizer, carbon dioxide emissions, rainfall, and the 

slight adverse effects of Temperature. By filling up the gaps in previous research that relied on static models, our application 

of the ARDL and Granger causality tests offers solid proof of cointegration and directional linkages. The study's conclusion 

firmly supports earlier studies conducted in various areas. The substantial effects that climate variability has on agricultural 

output and the economics of many countries worldwide are demonstrated by international studies. Few studies at the national 

level use a variety of econometric techniques to evaluate total agricultural productivity and its effects on the economy while 

simultaneously taking into account both climatic and non-climatic elements. Based on our research, policymakers are given 

the following suggestions to enhance the performance of Bangladesh's agricultural sector. Since Temperature has a short 

and long-term negative impact on agricultural production, the government should expand the afforestation and agroforestry 

programs. Initiating creative and easily implementable actions is essential, which will assist the government in promoting 

mechanisms to raise capacity for effective climate change planning (SDG Target 13.B). 

The finding that Temperature affects agricultural productivity negatively reflects the climate vulnerability of 

Bangladesh, and the developed countries are mostly responsible for global warming. Therefore, the comprehensive policies 

of different international organizations should be extended to pressure those responsible countries. Moreover, the climate 

resilience fund (CRF) can play a vital role in mitigating agricultural damage and, therefore, proper fund management is 

essential to ensure this role. As rainfall has a positive impact on agriculture, proper water management is necessary for the 

overall agriculture sector. So, the Bangladesh government should implement comprehensive strategies for the national 

irrigation projects, which will also help to achieve the SDG goal of sustainable water management. (SDG-6)  

The government should design a monitoring framework to regularly assess the relationship between CO₂ emissions 

and agricultural outputs, as the relationship is not specific. This framework can address the perception gap, as the general 

perception of CO2 emissions is negative. It can measure the optimal level of CO2 emissions that enhances agricultural 

production.  Policymakers should decentralize their focus to a more balanced approach beyond merely focusing on CO2 

emissions. The broader conception should be that CO2 emissions are not harmful up to a certain level, and therefore, 

unnecessary extensive focus on this emission is not wise. The authority should evaluate the optimal usage of fertilizer, as 

our research demonstrates how fertilizer improves agricultural output. As there is an environmental concern, the massive 

use of fertilizer should be detrimental. Considering this issue, the government should plan for fertilizer management around 



Hasanat et al., Bangladesh Journal of Multidisciplinary Scientific Research 10(5) (2025), 34-45

 

43 

the country. Incentives or subsidies are an effective tool to enhance agricultural output. Our research shows the positive 

impact of fertilizer on agricultural output, which urges the authorities to provide the necessary amount of fertilizer to the 

farmers.   

Our study holds significant potential for further exploration and application in broader contexts. While this 

research utilized data from Bangladesh over the past 30 years, future studies could incorporate a wider data span or include 

data from other regions to enhance the scope and applicability of the findings. However, since this study focuses on the 

economy of Bangladesh, it may not apply to other areas with distinct geographical characteristics. The effects of a few 

climatic and non-climatic factors on total agricultural output were examined in this study. Future research could examine 

more climatic and non-climatic elements and assess the effects on various agricultural products. 

 

 
Author Contributions: Conceptualization, S.B. and M.A.H; Data curation, M.A.H.; Formal analysis, S.B. and M.P.H; Funding acquisition, R.R. and D.I.; 
Investigation, S.B., M.A.H. and R.R.; Methodology, S.B., M.A.H., D.I. and M.P.H.; Project administration, S.B.; Resources, M.A.H.; Software, R.R.; 

Supervision, S.B. and M.A.H.; Validation, R.R. and D.I.; Visualization, S.B. and R.R.; Writing – Original Draft Preparation, M.A.H., M.P.H. and D.I., 

Writing – Review & Editing, M.A.H. Authors have read and agreed to this version of the manuscript.  

Institutional Review Board Statement: Ethical review and approval were waived for this study, due to that the research does not deal with vulnerable 

groups or sensitive issues. 

Funding: Authors received no funding for this research.  
Acknowledgments: We would like to express our gratitude to all those who contributed to this study. Despite in the absence of funding we are thankful 

to each other for the support and encouragement. Special thanks to Jannat Sharmin Moon, lecturer, Department of Economics, Netrokona University, 

Netrokona, Bangladesh. 
Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. 

Data Availability Statement: The data presented in this study are available on request from the corresponding author. The data are not publicly available 

due to restrictions. 
Conflicts of Interest: The authors declare no conflict of interest.                                                                                                                                                                                                                                   
.                                                                                                                                                                                                                                  

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