Climate change and crop production nexus: assessing the role of technological development for sustainable agriculture in Vietnam

Abbas Ali Chandio (College of Economics, Sichuan Agricultural University, Chengdu, China)
Huaquan Zhang (College of Economics, Sichuan Agricultural University, Chengdu, China)
Waqar Akram (Sukkur IBA University, Karachi, Pakistan)
Narayan Sethi (National Institute of Technology Rourkela, Rourkela, India)
Fayyaz Ahmad (School of Economics, Lanzhou University, Lanzhou, China)

International Journal of Climate Change Strategies and Management

ISSN: 1756-8692

Article publication date: 12 April 2024




This study aims to examine the effects of climate change and agricultural technologies on crop production in Vietnam for the period 1990–2018.


Several econometric techniques – such as the augmented Dickey–Fuller, Phillips–Perron, the autoregressive distributed lag (ARDL) bounds test, variance decomposition method (VDM) and impulse response function (IRF) are used for the empirical analysis.


The results of the ARDL bounds test confirm the significant dynamic relationship among the variables under consideration, with a significance level of 1%. The primary findings indicate that the average annual temperature exerts a negative influence on crop yield, both in the short term and in the long term. The utilization of fertilizer has been found to augment crop productivity, whereas the application of pesticides has demonstrated the potential to raise crop production in the short term. Moreover, both the expansion of cultivated land and the utilization of energy resources have played significant roles in enhancing agricultural output across both in the short term and in the long term. Furthermore, the robustness outcomes also validate the statistical importance of the factors examined in the context of Vietnam.

Research limitations/implications

This study provides persuasive evidence for policymakers to emphasize advancements in intensive agriculture as a means to mitigate the impacts of climate change. In the research, the authors use average annual temperature as a surrogate measure for climate change, while using fertilizer and pesticide usage as surrogate indicators for agricultural technologies. Future research can concentrate on the impact of ICT, climate change (specifically pertaining to maximum temperature, minimum temperature and precipitation), and agricultural technological improvements that have an impact on cereal production.


To the best of the authors’ knowledge, this study is the first to examine how climate change and technology effect crop output in Vietnam from 1990 to 2018. Various econometrics tools, such as ARDL modeling, VDM and IRF, are used for estimation.



Chandio, A.A., Zhang, H., Akram, W., Sethi, N. and Ahmad, F. (2024), "Climate change and crop production nexus: assessing the role of technological development for sustainable agriculture in Vietnam", International Journal of Climate Change Strategies and Management, Vol. ahead-of-print No. ahead-of-print.



Emerald Publishing Limited

Copyright © 2024, Abbas Ali Chandio, Huaquan Zhang, Waqar Akram, Narayan Sethi and Fayyaz Ahmad.


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1. Introduction

The world has undergone continuous change, which has had progressive effects on the sustainability of resources, but it has also caused significant environmental problems, such as climate change. The climate is the long-term pattern of meteorological conditions, whereas any change in climate after a lengthy period due to human or nonhuman activities is considered climate change (Ahsan et al., 2020; Menegaki et al., 2022). For instance, the increasing concentration of emissions and greenhouse gases (GHG) resulting from human activities predicts an increase in temperature and a shift in rainfall patterns, thereby causing climate change (Gul et al., 2022a). National Aeronautics and Space Administration (2020) estimated that the average global temperature has augmented by 1.02°C since 1880. It caused an increase in temperature, CO2 emissions, flooding and droughts, which diminished agricultural output (Jan et al., 2021). Whereas climate change posed a threat to food security by reducing the yield of primary cereals like maize, wheat and rice (He et al., 2022).

In pursuit of this objective, the existing body of scholarly works provides substantiation about climatic variables, including temperature and precipitation, and their impacts on agricultural and cereal productivity (Gul et al., 2022b; Sivakumar, 2011), for example, stated that crop production is heavily dependent on climatic conditions and thus the principal victim of climatic vulnerabilities. According to Urban et al. (2012), by 2030–2050, aggregate crop yield could decrease by 18% on average, while temperature could rise by 1.8°C–2.2°C. As a result of their prominence and impact on global food security, the United Nations (2015) designated zero hunger and climate action as sustainable development goals (SDGs 2 and 13) for 2030.

The utilization of agricultural technologies, including fertilizers and pesticides, amplifies the climate-induced effects on agricultural systems. The extended duration of farming seasons and elevated temperatures foster the expansion of insects and weeds, hence leading to heightened utilization of fertilizers and insecticides. Nonetheless, these technologies are the principal means of preserving soil fertility and crop yields. Fertilizers (both organic and inorganic) serve as plant feeding (Guo et al., 2021). These are either produced via natural processes (e.g. animal waste, plant-based materials and biosolids) or created artificially (e.g. ammonium nitrate, di-ammonium phosphate and potassium chloride) (Finch et al., 2014). Pesticides, on the other hand, are inorganic or organic chemicals used to manage diseases, pests and weeds. Insecticides, herbicides, rodenticides, fungicides and nematicides are forms of pesticides (Sharma et al., 2019).

Fertilizers and pesticides are now an essential aspect of crop improvement and plant protection, making farming reliant on the extensive use of these technologies. Ali et al. (2020) also confirmed that the surge in crop yields in the postgreen revolution era primarily attributes to the enormous use of agricultural technologies, especially chemical fertilizers and pesticides. Besides, farmers now use these formulations due to inadequate resources, degraded arable land and an increasing population. However, the usage of fertilizers varies in countries. Isherwood (1996) reckons that fertilizer consumption was once highest in developed countries (88%); however, the developing countries (55%) are now using these more to meet their food demand. A report by FAO (2017a, 2017b, 2017c, 2017d) provides that the global consumption of three primary fertilizers, i.e. nitrogenous (N), phosphorous (P) and potassium (K), can reach 186.67 million tons (Mt). In contrast, the annual demand can grow by 1.5%, 2.2% and 2.4%, for N, P and K, respectively, during 2015–2020. In particular to nitrogenous fertilizers, which are used in more quantity, their annual consumption can reach 110 Mt, with an annual increase of 2% (FAO, 2017a, 2017b, 2017c, 2017d). A detailed map of fertilizers (N, P and K) consumption can be found in Online Supplemental Figure S1 (FAO, 2017a, 2017b, 2017c, 2017d).

The trend of pesticide utilization is also not different, as China is the leading country with 1.77 Mt, followed by the USA (0.41 Mt), Brazil (0.38 Mt) and Argentina (0.20 Mt). In contrast, it is the lowest in the developing nations in Asia and Africa (FAO, 2017a, 2017b, 2017c, 2017d). A detailed map of pesticides consumption can be found in Online Supplemental Figure S2. Besides this, the annual consumption of pesticides reached 3.5 Mt in 2020, with a greater share of herbicides (≈47.5%) and insecticides (≈29.5%) (Sharma et al., 2019). Therefore, due to the dominance and impact on global food security, the area is crucial for researchers and practitioners in determining the global and country-specific impact of agricultural technologies and climatic changes on agricultural productivity.

Specific to the Socialist Republic of Vietnam, agriculture is considered one of the primary contributing sectors to the national gross domestic product (GDP), adding nearly 20% (Trinh, 2018). It also employs half of its labor force (GFDRR, 2011) and offers a livelihood for one-third of its people (Shrestha, 2014). However, the United Nations Development Organization has ranked Vietnam vulnerable to climatic changes owing to regular floods, droughts, intrusion of saltwater and increased temperature. Figure 1 provides the annual average increase of 1.02°C in Vietnam’s temperature since 1961 (Bank, 2020).

At present, Vietnam’s agriculture industry is dealing with a decrease in arable area, increased attacks of pests and droughts and negative effects on farming. These are mostly the result of climate change (Yen, 2021), as rising temperatures reduce water resources and affect crop and animal outputs. It also turns cultivable areas into barren lands due to water shortage. Furthermore, climate change reduces biodiversity, induces untimely rainfall, supports the lives of harmful pests and encourages agricultural diseases (Huynh et al., 2020). These concerns call into question farmers’ livelihoods and threaten Vietnam’s national food security (Huynh et al., 2020). Therefore, the government made many initiatives to modernize crop production through automation and technology use, as well as through government-backed financing schemes (Linh et al., 2019). These technologies range from farm inputs and machines to the most recent precision agriculture technology. Their use increased the efficiency of farm methods, resulting in higher crop yields in terms of both quality and quantity. Figure 2 and Online Supplemental Figure S3 provide the trend of agricultural technology application (i.e. nitrogenous fertilizers and pesticides) and explain the adoption by farmers. However, the Vietnamese government banned many hazardous pesticides due to their adverse, residual impact on human health and the environment (Hoi et al., 2016). Therefore, the pesticide application trend remains flat from 2001, as shown in Online Supplemental Figure S3. Likewise, several developed countries, i.e. the USA, China, the EU countries and Brazil, are also phasing out the use of hazardous pesticides (Donley, 2019).

Due to escalating environmental issues in Vietnam and around the globe, sustainable technological methods are urgently required in the current context (Baker et al., 2020; Migheli, 2020; Baker et al., 2020) suggested the use of integrated nutrient management – balancing organic and chemical fertilizers as per the crop nutrients requirement and integrated pest management – balancing chemical, physical, biological controls over pests’ management. In addition, the current literature requires more country-specific evidence to mitigate climatic effects and formulate future policies. This study examines the impact of agricultural technologies and climate changes on crop productivity in Vietnam using time series data from 1990 to 2018 and the autoregressive distributed lag (ARDL) modeling. The study concentrates on these elements’ immediate and long-term effects.

Additionally, a number of studies have looked at how climate change is affecting a number of variables, such as Vietnamese households’ livelihoods and adaptation strategies (Duffy et al., 2021; Gilfillan et al., 2017; Lindegaard, 2020; Phuong et al., 2018; Thuc et al., 2016; Thuy, 2019; Van Huynh et al., 2020); the relationship between pesticide use and vegetable production (Hoi et al., 2016); and the impact of weather variations and natural disasters on the agriculture sector (Huong et al., 2019; Trinh et al., 2021; Trinh, 2018; Van Phu, 2021). However, this study’s goal is to investigate how, between 1990 and 2018, agricultural technologies and climate change affected Vietnam’s crop productivity. The empirical analysis in this paper used a diverse range of econometric methodologies. The results of this study will provide valuable insights for scholars and policymakers in shaping their research inquiries and formulating policies specific to Vietnam.

The following paper is organized as follows: Section 2 elaborates on the existing body of literature on the topic under consideration and presents hypotheses, followed by data and methodology in Section 3. Section 4 contains the results and discussion, and Section 5 final portion concludes the complete research.

2. Literature review and research hypothesis

The agricultural sector in Vietnam has been a major source of employment in other economic sectors, employing nearly 18.8 million people as of 2019. During the 1990s, agricultural and aquacultural exports grew exponentially. However, the expansion of the tourism industry and urbanization have reduced the agricultural sector’s output. In addition, climate change, coastal erosion and salinity intrusion have led to a decline in the fertility of the nation’s agricultural lands. Therefore, the difficulty resides in institutionalizing the use of modern agricultural technologies to increase crop yield and productivity in this economic sector. Hence, the impact of climatic variations on agriculture and the use of agricultural technologies for crop production across countries have been extensively discussed in this study.

2.1 The nexus of crop production and climate change

The intergovernmental panel on climate change (IPCC, 1990) recognized the effects of climatic changes on agricultural production. However, the economic impact of climatic changes on agricultural yields still needs to be explored (Adams et al., 1990; Mendelsohn et al., 1994). Further, the literature records a shift of studies/researchers focusing on the USA to developing countries in this aspect. The assessment of the economic effect of climatic changes on crops/cereals production includes two primary approaches. The former is the computed general equilibrium model, which considers complex interactions of different segments of the economy (Winters et al., 1996). Whereas the latter is the partial equilibrium model, which can further be classified into the Ricardian approach, agroecological zoning approach and production function approach (Fonta et al., 2018).

Deressa and Hassan (2009) conducted a study based on farm households’ responses toward climate change and agricultural production in different agroecological zones in Ethiopia. Their results predicted a gradual decrease in net revenue from each hectare by 2050, indicating the detrimental impacts of climatic changes. Sridharan et al. (2019) studied the impact of climatic changes on the production of rain-fed crops in Uganda, the irrigation needs of such crops in different climatic regions and the energy consumption required for the same. The results predict an increase of 8% and a reduction of 11% in rainfed crops’ production in wet and arid climates, respectively.

Wielogorska et al. (2019) assessed samples of crops like maize, sorghum and wheat in Somalia, which revealed the presence of mycotoxins produced by a particular type of fungus, which usually grows under unfavorable and harsh environmental conditions, which subsequently tend to affect the pre- and post-harvesting yield. Sperry et al. (2019) hold global warming as a repercussion of excessive concentration of GHG to be a significant cause of reduction in agricultural yields, which would, in turn, threaten global food security.

FAO (2017a, 2017b, 2017c, 2017d) argue that extreme climatic conditions reduce the crop yield in Asia and Africa, which would, in turn, hamper the economic growth of the economy. Cline (2007), Bruinsma (2017) and UNCTAD (2015) explored the influences of climatic changes on agricultural productivity in developing and least developing countries located in South-eastern Asia, sub-Saharan Africa and Western Asia. Similarly, Attiaoui and Boufateh (2019), Fonta et al. (2018) and Sadiq et al. (2019) assert that the short-run influences of precipitation on crop yields are positive. At the same time, the long-run impacts of climatic changes on crop production are negative. However, Abbas and Mayo (2021) carried out research to look at how temperature and precipitation affected rice productivity in Pakistan’s Punjab province between 1981 and 2018. The study found a statistically significant positive relationship during the tillering season between rainfall and rice yield. However, detrimental impacts were observed in the flowering and fruiting phases. Similarly, the yield of rice is negatively impacted by rising temperatures.

Researchers Kumar et al. (2021) examine how variations in the climate between 1971 and 2016 affected the output of grain crops in low- and middle-income nations. Along with control variables, including CO2 emissions, the population of rural areas and the amount of land already farmed for cereal production, the main variables considered for estimation were yearly rainfall and temperature. The FGLS model’s output determined how rainfall and temperature rise affected cereal production in the relevant nations. The outcomes were further validated by the Driscoll–Kraay standard regression robustness tests. On the other hand, Warsame et al.’s study from 2021 presents conflicting results regarding how precipitation affects Somalia’s agricultural production. Higher rainfall and agricultural production appear to have a positive long-term association but a negative short-term relationship, according to the results of the Granger causality analysis and ARDL testing.

Moreover, Ali et al. (2021) investigated the combined effects of modern agricultural techniques and climatic factors on sugarcane in Pakistan by using the data from 1989 to 2015. Results obtained from bounds F-test for cointegration confirm a positive and insignificant relationship between temperature and sugarcane yield and a significant negative impact of the use of agricultural machinery on the same. Thus, the researchers hypothesize the following:


Crop productivity is negatively impacted by climate change.

2.2 The nexus of crop production and agricultural technologies

There has been growing literature on the connection between technological advancements and crop production globally. Green technologies encompass various aspects of sustainable development, including energy production, waste management and opening up opportunities for a clean environment (Ismael et al., 2018). The most important factors influencing the yield of crops in different climatic zones are the availability and accessibility of water, consumption and expenditure on fertilizers and availability and distribution of credit at cheaper rates of interest. Winpenny et al. (2010) reveal that the agriculture sector uses 70% of the extracted global freshwater. Owing to the increasing demand for freshwater by households and industries in urban areas, there has been an increase in the use of wastewater for irrigation purposes (Scott et al., 2004).

Wanyama et al. (2009) studied the positive and significant effects of fertilizers, seeds, pesticides and modern technology consumption in improving the total yield of crops in sub-Saharan Africa. Chandio et al. (2021b) indicated a rise in agricultural income and productivity as a result of fertilizer usage in Pakistan. Financial development is also a critical factor in enhancing agricultural production. Financial development is broadly perceived to let farmers invest and adopt new technologies, which can increase the income from agriculture. An accessible financial system with lower interest rates would encourage poor farmers to purchase inputs like fertilizers, seeds, pesticides and other agrochemicals, that boost yield.

Zakaria et al. (2019) investigated how financial development impact agricultural productivity during 1973–2015. Their results report the existence of an inverted U-shaped relationship between the variables. Ismael et al. (2018) confirmed the positive effects of modern agricultural techniques on agricultural productivity and yield. However, research evidence also supports the argument that modern agricultural techniques like tractors have been a significant source of carbon dioxide emissions (Arapatsakos and Gemtos, 2008).

Zou et al. (2015) found that 60% of emissions in the agriculture sector in China are due to energy activities related to irrigation facilities. Conversely, Directorate-General for Internal Policies (2014) indicated a decline in the emission of GHG with the reduction in the use of fertilizers and fossil fuel energy as agricultural inputs. Chandio et al. (2021a) investigated the impact of technological improvements and climatic changes on rice production in Nepal. The authors used proxy variables such as carbon emissions, average rainfall, temperature, usage of fertilizer and improved seeds. The ARDL model results indicate that a 1% surge in carbon emissions decreases rice production by 0.13%. In contrast, a 1% increase in fertilizer use and easy agricultural credit leads to 0.05% and 0.02% increase in rice production. The results were verified with appropriate robustness tests of impulse response and variance decomposition models.

Rehman et al. (2019) explored the impact of the adoption of modern agricultural techniques like fertilizer use, water and credit availability on Pakistan’s agricultural value addition to national GDP for the period 1978–2015. They found a long-term, significant positive association between the variables except for water availability, which had negative yet insignificant effects. Thus, the researchers hypothesize the following:


Technological advancement is expected to play a dynamic role and improve crop production.

Irrespective of the numerous investigations on the effects of climate change and technological development on crop production, a rigorous study is missing for Vietnam to the best of our knowledge. Therefore, the researchers organized this scholarship to fill this gap. The dynamic nexus between climate change, agricultural technologies and crop production are shown in Figure 3.

3. Data and methodology

3.1 Data

This research investigates the impacts of agricultural technologies and climate change on crop production in Vietnam, using annual data spanning from 1990 to 2018. The average yearly temperature data was sourced from the website of the World Bank Group Climate Change Portal, whereas the data on crop production index (2014–2016 = 100) and cultivated area (hectares) were gathered from the website of the World Bank. Similarly, data on the total fertilizer consumption by nutrient (tons) and the total pesticide use (tons) were acquired from the FAOSTAT database. Finally, data on energy consumption (million tons of oil equivalent) was taken from the Statistical Review of World Energy (SRWE). Table 1 provides the description, measurement and data sources of the undertaken antecedents. Figure 4 displays the trends of the variables.

3.2 Model construction

Keeping in view the studies of Ahsan et al. (2020) and Kumar et al. (2021), the present paper undertakes average annual temperature as a proxy to measure climatic changes. Furthermore, based on the latest studies of Ali et al. (2020), Ali et al. (2021) and Chandio et al. (2021a), this study uses fertilizer use and pesticide usage as indicators of agricultural technologies. Furthermore, the article incorporates farmed area and energy consumption as control variables. Equation (1) establishes the relationship between agricultural technologies, climate change and their respective effects on crop production:

(1) CPt=f(AATt, FCt, PUt, CAt, ECt)
To get consistent outcomes, the researchers transformed all the study variables into a log form; therefore, equation (2) is as follows:
(2) LnCPt=β0+β1LnAATt+β2LnFCt+β3LnPUt+β4LnCAt+β5LnECt+εt
where LnCP donates the natural log of crop production, LnAAT denotes the natural log of average annual temperature, LnFC shows the natural log of fertilizer consumption, LnPU symbolizes the natural log of pesticide use, LnCA means the natural log of cultivated area and LnEC defines the natural log of energy consumption. β0 represents the constant term, β1, β2, β4, β4 and β5 indicate the coefficients, and εt is the error term. Figure 5 shows the research framework of the study.

3.3 Autoregressive distributed lag method

The present study used an appropriate and unique ARDL bounds cointegration or error correction modeling approach of Pesaran et al. (2001), which has statistical superiority over other cointegrating procedures (Menegaki, 2019; Pesaran et al., 2001; Shahbaz et al., 2013), as it gives both long-term and short-term estimations in a single step. In addition, this technique can be used whether the variable is stationary at a level I(0) or first difference I(1) and mixed of integration other than I(2) series. Pesaran et al. (2001) also suggested the appropriateness of the ARDL approach for a small sample as it testifies robust and reliable long-term and short-term estimates in a small data set. So, we modify equation (3) in error correction format as given below:

(3) ΔLnCPt=Υ0+i=1pΥ1ΔLnCPti+j=0qΥ2ΔLnAATtj+k=1rΥ3ΔLnFCtk+l=1sΥ4ΔLnPUtl+m=1tΥ5ΔLnCAtm+n=1uΥ6ΔLnECtn+δ1LnCPt1+δ2LnAATt1+δ3LnFCt1+δ4LnPUt1+δ5LnCAt1+δ6LnECt1+εt
The first set of parameters in equation (3) (γ1, γ2, γ3, γ4, γ5, γ6) shows the short-run dynamics, while the second set of parameters (δ1, δ2, δ3, δ4, δ5, δ6) signifies the long run. This study tests the null hypothesis that a long-term cointegration relationship exists between the study variables against the alternative hypothesis as follows:
The reliability of the long-run cointegration association among the undertaken variables is verified through the F-test of Pesaran et al. (2001). It implies that when the estimated values of the F-test fall below the lower bound I(0), we reject the null hypothesis and suggest the absence of long-run cointegration. Whereas if the values fall above the upper bound I(1), the conclusion is the presence of long-term cointegration. The outcomes are inconclusive when the F-test values fall between the I(0) and I(1) bounds limit. In addition, the short-run dynamics association between the variables can be observed from the following equation as:
(4) ΔLnCPt=Υ0+i=1pΥ1ΔLnCPti+j=0qΥ2ΔLnAATtj+k=1rΥ3ΔLnFCtk+l=1sΥ4ΔLnPUtl+m=1tΥ5ΔLnCAtm+n=1uΥ6ΔLnECtn+θ1ECTt1+εt

4. Results and discussion

4.1 Descriptive and correlation analysis

Table 2 reports the summarized descriptive statistics. The mean crop production, average annual temperature, fertilizer consumption, pesticide use, farmed area and energy use are 4.18, 3.20, 13.87 nutrients tons, 9.91 tons, 15.91 hectares and 3.21 million tons of oil equivalent, respectively. In addition, the correlation matrix shown in the lower panel of Table 2 reveals that average annual temperature, fertilizer usage, cultivated area and energy use are positively and significantly associated with crop production. Whereas pesticide use is negatively correlated with crop production.

4.2 Unit root test results

The present study used the Phillips–Perron (PP) (Phillips and Perron, 1988) and augmented Dickey–Fuller (ADF) (Dickey and Fuller, 1979) unit root tests to check the order of integration of the selected variables. The PP and ADF estimations include trend as well as intercept. Table 3 provides the estimated results of the PP and ADF tests on the integration properties of crop production (LnCP), average annual temperature (LnAAT), fertilizer consumption (LnFC), pesticide use (LnPU), cultivated area (LnCA) and energy consumption (LnEC). The results reveal that both estimations generate mixed outcomes for the undertaken variables, i.e. integrated at the level I(0) and the first difference I(1). Furthermore, the unit root techniques estimates show that among all variables, only LnAAT, LnFC and LnPU are stationary at the level. Thus, the unit root tests are applied to the study variables, taking the first difference. The results demonstrate that LnCP, LnCA and LnEC are stationary at first difference.

4.3 Determination of the cointegration relationship

Once the validation of the study variables’ combined degree of integration, specifically I(1) and I(0), is completed, the ARDL bounds approach is used to ascertain the presence of the long-term cointegration connection. Table 4 displays the empirical findings obtained from the application of the ARDL bounds technique to cointegration. At a significance level of 1%, the estimated value of the F-statistic (5,1161) exhibits statistical significance. The findings suggest the presence of long-term cointegration among crop production (LnCP), annual average temperature (LnAAT), fertilizer consumption (LnFC), pesticide use (LnPU), cultivated area (LnCA) and energy consumption (LnEC).

To assess the reliability of the ARDL bounds test, this study also uses the Johansen cointegration technique, using the trace statistic and Max–Eigen statistic tests. The outcomes of the previously described cointegration analysis exhibit similarities with the ARDL bounds test, indicating the presence of a long-term cointegration association among LnCP, LnAAT, LnFC, LnPU, LnCA and LnEC variables (see Table 5). Based on the empirical findings, it can be concluded that the variables exhibit integration at both I(0) and I(1) levels, indicating the presence of long-term cointegration. Therefore, we proceed to examine the effects of fertilizer use, pesticide use, cultivated area, average yearly temperature and energy consumption on crop output in both the short- and long-term.

4.4 Long- and short-run outcomes from the autoregressive distributed lag

Table 6 indicates the baseline ARDL estimations for a linear specification of the impacts of our explanatory variables (agricultural technologies and climate change) on crop production in Vietnam.

Temperature: The data, both short- and long-term, show that temperature has a detrimental effect on crop productivity. It suggests that there will be a short-term reduction in production of 0.67% and a long-term reduction of 2.74% with every 1% increase in temperature. There are multiple arguments to support the results of this investigation. First, research has shown that growing global warming affects the output of cereals (Hansen et al., 2010). Rice production decreased as a result of the planet’s recent 0.5°C–0.6°C warming (Zhao and Fitzgerald, 2013). According to Nelson et al. (2009), there could be a 10%–15% decrease in cereal production as a result of climate change, which could drive up costs. Additionally, it was proven by Chandio et al. (2021a, 2021b) that the temperature decreased rice yield by 4% in a number of Asian countries, including Bangladesh, India, Indonesia, Pakistan, Sri Lanka, Thailand and Vietnam. Furthermore, Kumar et al. (2021), Ozdemir (2021) and Attiaoui and Boufateh (2019) have all found similar detrimental effects of temperature on agricultural productivity.

Fertilizer: Fertilizer consumption, as a technological component, has a significant beneficial long- and short-term impact on crop productivity. More specifically, crop productivity will increase by 0.02% and 0.04% for every 1% increase in fertilizer consumption. Improved seed quality, fertilizer application and pesticide use are examples of advanced agricultural technology that have a major impact on agricultural output. Nitrogen fertilizers are being used not just to increase crop yields but also to reduce pollution to the environment and transition to long-term sustainable agricultural production. For example, Chandio et al. (2021a) found that the use of fertilizers and higher-quality seeds increased rice production in Nepal. Similarly, more recently, Ozdemir (2021) looked at how fertilizer use and climate change affected agricultural productivity in a few Asian nations. The results showed that while fertilizer consumption greatly increased agricultural productivity, climate change drastically decreased it. The present study’s findings are consistent with previous research in the literature (Ali et al., 2020; Chandio et al., 2021a; Chandio et al., 2021b; Rayamajhee et al., 2021; Rehman et al., 2019), who examined the impact of fertilizer usage on cereal output.

Pesticide: When considered as a technological element, the predicted results show how pesticide affect agricultural productivity both short- and long-term. The implication is that extended pesticide use has a negative effect on agricultural productivity. Specifically, a 1% increase in pesticide would result in a 0.09% decrease in crop productivity. To increase agricultural yields of both food and nonfood crops and generate large profits, pesticides have become a crucial input ingredient for plant protection (De Bon et al., 2014). Agriculture in emerging nations, particularly in Southeast Asia, is using more pesticides than ever before (Schreinemachers and Tipraqsa, 2012). More than 20% of pesticides are used in emerging countries, according to the WHO report, and this percentage is rising. According to Schreinemachers et al. (2020), Southeast Asia imports 61% of all pesticides, 5% from Laos and 10% from Vietnam.

Cultivated area and energy consumption: Cultivated area and energy consumption have beneficial long- and short-term effects on crop productivity as control variables. The findings support the earlier research of (Qureshi et al., 2016; Warsame et al., 2021). They suggest that a 1% increase in cultivated area and electricity consumption can increase crop yield by 0.54%, 0.92%, 0.09% and 0.31%.

4.5 Diagnostic and stability tests

The results of the ARDL estimations in Table 6 provide that the model fits well (R2 = 0.9997). The Durbin–Watson stat value, 2.42, negates the presence of spurious regression in the ARDL estimation. The present scholarship also uses other diagnostic tests (i.e. serial correlation, ARCH, normality and Ramsey RESET) to check the consistency of the ARDL estimations. Table 7 shows the results of the diagnostic tests, confirming that the ARDL is free from all problems and stable. In addition, the present study also applied the CUSUM and CUSUM square tests to verify the stability and reliability of the ARDL estimations. The test results prove the stability of the ARDL model (see Online Supplemental Figures S4 and S5).

4.6 Robustness check

Table 8 exhibits the robustness check of findings with the robust least-squares technique. The outcome reveals that temperature as a proxy for climate change negatively affects crop production. It is evident that temperature, the primary variable of interest, reduces crop production. Besides, fertilizers usage as a technological input variable affects crop production positively. The robustness check results provide that extensive usage of pesticides negatively affects crop production. Additionally, cultivated area and energy consumption significantly increase crop production in line with robust least-squares method results.

4.7 Results of impulse response function and variance decomposition method

This paper used the impulse response function (IRF) and variance decomposition method (VDM) to investigate the association among temperature, cultivated area, pesticide use, fertilizer, energy use and crop production for additional periods in Vietnam. The outcomes of IRF suggested that average temperature has negatively affected rice production, and variations are not apparent during these periods. On the other hand, energy use, fertilizer and cultivation area have a significant positive impact on crop production. Though minor variations occurred, the positive response is relatively stable at the end for these variables. The response of pesticide use confirmed gradual improvement. Initially, it did not work, and the impact remained negative for some time. The use of pesticide positively impacts crop production after the fifth period and remains steady till the last (see Figure 6).

Similarly, Table 9 displays the VDM test results. Crop production breaks down to reveal that 97.57% of its value can be explained by novel shocks. Both energy usage and fertilizer contribution are increasing over time, although fertilizer’s share is larger. Furthermore, while at a slower rate, pesticide and the farmed area also showed an increase. When compared to all other factors exhibiting a negative correlation, the average contribution of temperature to crop output is minimal. The VDM further verifies that while the intensity of these effects varies, their overall impact increases progressively. As a result, both the short- and long-term effects of climate change on crop productivity in Vietnam are consistent. Ozdemir (2021) verified that although fertilizer consumption greatly increased agricultural productivity, climatic change dramatically decreased agricultural output. Likewise, Warsame et al. (2021) discovered that fertilizers and energy use had a favorable effect on agricultural yield. As a result, the current study’s findings are solid and in line with previous long- and short-term research.

5. Conclusion and policy recommendations

This study uses cultivated area and energy consumption as control variables to experimentally investigate the short- and long-term effects of pesticide, fertilizer and climate change on crop output in Vietnam from 1990 to 2018. Our research primarily aims to address agricultural sustainability in the examined area by examining the relationship between crop output and climate change in the context of agricultural technologies. The ARDL approach was used in our analysis to estimate the equilibrium relationship over the long run between the independent and dependent variables under investigation. To confirm the resilience of the ARDL limits test, we additionally use the Johansen cointegration technique in conjunction with the trace and Max–Eigen statistical tests. To verify the trend and intercept, as well as the order of integration of the chosen variables, we use the PP and ADF unit root tests. Additionally, the Durbin–Watson stat and diagnostic tests suggest that the ARDL is stable and devoid of errors, and they also rule out the possibility of false regression in the ARDL estimation. Furthermore, the results of the CUSUM and CUSUM square tests validate the stability of the ARDL model.

According to the estimates, crop productivity is negatively impacted by climate change in the short- and long-term. Our research supports H1, which states that agricultural productivity is negatively impacted by climate change. On the other hand, the results of agricultural technology exhibit variability due to the influential role of fertilizer application on the production of crops. Moreover, it is worth noting that the utilization of pesticides has the potential to enhance agricultural yield in the short term; nevertheless, it is important to acknowledge that their long-term impact is predominantly detrimental. Therefore, in the long run, agricultural technology, such as the use of fertilizers, supports H2, but the use of pesticides contradicts it. The anticipated impact of technological progress on crop output is projected to be significant and transformative. Besides, crop output is positively affected by cultivated area and energy usage. The study also uses several robustness methods, i.e. ROBUST OLS, IRF and VDM. These tests also confirm the significant impact of technological and climatic factors on crop production; however, intensity varies among these factors. Henceforth, the impact of climatic changes on crop production is consistent in Vietnam in the long- and short-run.

The researchers propose policies for practitioners and farmers based on their findings. First, as pesticides have a negative long-term impact on crop production, policies should encourage the use of biopesticides or natural methods (organic pest control) and enhance plant- and society-friendly environmental conditions. Thus, policymakers and the Government of Vietnam must frame policies that encourage the use of biopesticides as a better substitute for the use of pesticides in the agriculture sector. Findings of our study help policymakers to frame policies that encourage subsidies for the use of biopesticides and impositions of tax on the use of pesticides which ultimately helps to attend agricultural sustainability by enhancing crop productions. Second, the extension services must adequately advise farmers on the most effective and optimal use of pesticides and fertilizers with an aim to enhance agricultural productions in Vietnam. From a policy implications point of view, our study suggested to adopt agricultural extension services that provide technical aid to farmers, essential inputs and services which helps to increase agricultural production in Vietnam. The inclusion of agricultural extension services should be prioritized in many programs, schemes and activities aimed at providing farmers with access to scientific research and novel knowledge in agricultural practices, hence augmenting agricultural productivity.

Dissemination of new ideas, techniques and information regarding effective use of fertilizers and pesticides, risk and farm management in agriculture sector to increase agricultural productions via different sources of communications to farmers must give priorities in Vietnam. Agricultural productions can be enhanced by using cost-effective agricultural technologies, i.e. biopesticides or natural methods, new and effective agricultural tools, techniques and methods. Third, when developing strategies to combat climate change, policymakers should take a comprehensive approach. To this end, the banking sector (public and private institutions) that provides growers with access to input credit at a lower markup may assist in the adoption of climate change strategies and the improvement of crop production. The researchers conclude that agricultural research centers should implement crop production technologies to meet future needs. Thus, our study add to existing literature as well as it helps to formulate policies that help to enhance agricultural production in Vietnam.

Basic limitations of our study are discussed as follows: first, our study considers only single country, Vietnam. Second, our study only considers climate change, fertilizer consumption, pesticide use, cultivated area and energy consumption variables and ignores the impacts of other variables on agricultural production. The variables i.e. agricultural credit, ICT, carbon emissions (CO2), methane (CH4), carbon footprint and timely irrigations that play a significant role in determining agricultural productions are not considered in our study. Third, due to the lack of data on the aforementioned variables, our study limits its test to a certain extent. Fourth, we used annual temperature as a proxy for climate change, and for agricultural technologies, we used fertilizer consumption and pesticide use as a proxy. However, we could use precipitations, deforestations, humidity, populations and human capital as a proxy for climate change that affects agricultural productions and other agricultural technologies can be used as a proxy in further research. While more precisely considering opportunities of future research, our study could be expanded to several geographical regions as well as different income groups of countries.


Mean annual temperature in Vietnam (1990–2018)

Figure 1.

Mean annual temperature in Vietnam (1990–2018)

Usage of nitrogen fertilizer in Vietnam (1990–2018)

Figure 2.

Usage of nitrogen fertilizer in Vietnam (1990–2018)

Dynamic connection between agricultural technologies, climate change and crop production

Figure 3.

Dynamic connection between agricultural technologies, climate change and crop production

Time series plots of the study variables

Figure 4.

Time series plots of the study variables

Research framework of the study

Figure 5.

Research framework of the study

Impulse response function

Figure 6.

Impulse response function

Unit of measurement and data sources of the variables

VariablesMeasurement unitData sources
CP Crop production index (2014–2016 = 100) World Bank
AAT Average annual temperature (°C) World Bank
FC Fertilizer consumption (tons) FAOSTAT
PU Pesticide use (tons) FAOSTAT
CA Cultivated area (hectares) World Bank
EC Energy consumption (million tons of oil equivalent) SRWE

Source: Authors’ own creation

Descriptive statistics and correlations

Mean 4.1834 3.2035 13.8752 9.9147 15.9132 3.2158
Median 4.2708 3.2051 13.9532 9.8602 15.9397 3.3436
Maximum 4.6785 3.2304 14.3436 10.4048 16.0209 4.4518
Minimum 3.5067 3.1784 12.9607 9.71123 15.6834 1.8646
SD 0.3630 0.0117 0.3540 0.15277 0.0944 0.8050
Kurtosis 1.9163 3.3240 3.1884 7.9204 2.8694 1.8440
Skewness −0.3909 −0.0136 −0.9054 2.2453 −0.9993 −0.1756
Jarque–Bera 2.1576 0.1277 4.0051 53.6232 4.8477 1.7638
Pro. 0.3399 0.9381 0.1349 0.0000 0.0885 0.4139
Observations 29 29 29 29 29 29
LnAAT 0.1728
t-stat 0.9117
p-value 0.3700
LnFC 0.8151 0.3286
t-stat 7.3114 1.8081
p-value 0.0000 0.0817
LnPU −0.4318 −0.1480 −0.3190
t-stat −2.4881 −0.7776 −1.7494
p-value 0.0193 0.4436 0.0916
LnCA 0.9482 0.1067 0.8503 −0.4323
t-stat 15.5228 0.5577 8.3951 −2.4917
p-value 0.0000 0.5816 0.0000 0.0192
LnEC 0.9940 0.1974 0.8013 −0.4019 0.9235
t-stat 47.4172 1.0468 6.9619 −2.2810 12.51691
p-value 0.0000 0.3044 0.0000 0.0307 0.0000

Ln = natural logarithm; CP = crop production; AAT = average annual temperature; FC = fertilizers consumption; PU = pesticides use; CA = cultivated area and EC = energy consumption

Source: Authors’ own creation

Unit root test results

Variables Level First difference Level First difference Integration order
LnCP −0.8101 (0.9526) −6.0829 (0.0002) −0.3424 (0.9849) −8.1839 (0.0000) I(1)
LnAAT −5.1615 (0.0015) −5.1419 (0.0018) −5.1620 (0.0014) −15.8709 (0.0000) I(0)
LnFC −4.0507 (0.0184) −5.8372 (0.0003) −4.0318 (0.0192) −12.9554 (0.0000) I(0)
LnPU −4.7810 (0.0036) −6.2190 (0.0003) −4.9153 (0.0025) −11.8785 (0.0000) I(0)
LnCA −1.4336 (0.8280) −5.9325 (0.0002) −1.2956 (0.8682) −5.9808 (0.0002) I(1)
LnEC −1.5858 (0.7708) −6.7209 (0.0000) −2.2567 (0.4422) −10.4871 (0.0000) I(1)

PP = Phillips–Perron; ADF = Augmented Dickey–Fuller; Ln = log from; CP = Crop production; AAT = Average annual temperature; FC = Fertilizer consumption; PU = Pesticide use; CA = Cultivated area and EC = Energy consumption

Source: Authors’ own creation

Results of bounds testing

Test stat. Value k
F-stat. 5.1161 5
Levels of significance I0 Bound I1 Bound
10% 2.26 3.35
5% 2.62 3.79
1% 3.41 4.68
F-stat. 8.2675
Pro. (F-stat.) 0.0075
R2 0.9632
Adjusted R2 0.8467

Source: Authors’ own creation

Results of Johnson’s cointegration test

Hypothesized TS 0.05
No. of CE(s) EV** CV*** Prob.
None* 0.9008 150.6501 107.3466 0.0000
At most_1* 0.7997 88.2629 79.3414 0.0090
At most_2 0.6230 44.8472 55.2457 0.2951
At most_3 0.3126 18.5050 35.0109 0.7962
At most_4 0.2396 8.3816 18.3977 0.6437
At most_5 0.0357 0.9830 3.8414 0.3214
TS = trace test specifies two cointegrating eqn(s) at 0.05 level
*Shows that the hypothesis is rejected at 0.05 level
Hypothesized M-E 0.05
No. of CE(s) EV** Stat. CV*** Prob.
None* 0.9008 62.3872 43.4197 0.0002
At most_1* 0.7997 43.4156 37.1635 0.0085
At most_2 0.6230 26.3422 30.8150 0.1600
At most_3 0.3126 10.1233 24.2520 0.8972
At most_4 0.2396 7.3985 17.1476 0.6693
At most_5 0.0357 0.9830 3.8414 0.3214

Max-eigen stat test indicates two cointegrating eqn(s) at 0.05 level. *Indicates that the hypothesis is rejected at 0.05 level; **hows eigenvalues; ***Shows critical values

Source: Authors’ own creation

ARDL estimation results

Variables Coeff. Std. er. t-stat. Pro.
Long-run effect
LnAAT −2.745446 1.596272 −1.719911 0.1362
LnFC 0.041211 0.091647 0.449667 0.6687
LnPU −0.091006 0.093599 −0.972299 0.3685
LnCA 0.927176 0.443691 2.089689 0.0816
LnEC 0.314171 0.024062 13.056935 0.0000
Short-run effect
D(LnAAT) −0.670538 0.344152 −1.948375 0.0993
D[LnAAT(−1)] −0.395244 0.371838 −1.062945 0.3287
D[LnAAT(−2)] 0.169470 0.217643 0.778658 0.4658
D(LnFC) 0.020983 0.023008 0.912006 0.3969
D[LnFC(−1)] −0.031949 0.025096 −1.273063 0.2501
D(LnPU) 0.035414 0.043801 0.808524 0.4497
D[LnPU(−1)] −0.182566 0.059713 −3.057402 0.0223
D[LnPU(−2)] 0.132170 0.036800 3.591570 0.0115
D(LnCA) 0.540827 0.176103 3.071082 0.0219
D[LnCA(−1) −0.429411 0.328884 −1.305660 0.2395
D(LnEC) 0.099550 0.068511 1.453063 0.1964
D[LnEC(−1)] −0.316860 0.077573 −4.084683 0.0065
D[LnEC(−2)] 0.060496 0.074968 0.806953 0.4505
CointEq(−1) −0.645231 0.153563 −4.201725 0.0057
Constant −1.535148 7.123762 −0.215497 0.8365
R-squared 0.999793
Durbin–Watson stat 2.417901
SE of regression 0.009029
F-statistic 126.951
Pro. (F-statistic) 0.000000

Source: Authors’ own creation

Diagnostic tests results

Tests F-stat. Pro.
Serial correlation 1.778381 0.2802
ARCH 2.260308 0.1463
Normality 0.96104 0.61846
Ramsey RESET 1.610357 0.1682

Source: Authors’ own creation

Robust least squares method results

Variables Coeff. Std. er. z-stat. Pro.
LnAAT −0.553539 0.374669 −1.477408 0.1396
LnFC 0.008800 0.022646 0.388605 0.6976
LnPU −0.050152 0.027442 −1.827524 0.0676
LnCA 0.635646 0.131884 4.819745 0.0000
LnEC 0.374650 0.012419 30.16658 0.0000
Constant −4.993111 2.701616 −1.848194 0.0646
R-squared 0.734964

Source: Authors’ own creation

Variance decomposition results

Variance decomposition of LnCP
1 0.02148 100.0000 0.00000 0.00000 0.00000 0.00000 0.00000
2 0.02934 97.57708 0.27610 1.45327 0.02485 0.14100 0.52767
3 0.03492 95.37443 0.46566 2.71803 0.02271 0.28496 1.13419
4 0.03933 93.73720 0.61681 3.51836 0.02109 0.41933 1.68720
5 0.04300 92.51364 0.74272 4.01051 0.04578 0.54219 2.14513
6 0.04615 91.56617 0.85274 4.31175 0.10273 0.65699 2.50960
7 0.04890 90.80977 0.94983 4.49416 0.18659 0.76661 2.79302
8 0.05135 90.19265 1.03519 4.59979 0.28897 0.87332 3.01006
9 0.05355 89.68115 1.10962 4.65389 0.40239 0.97878 3.17414
10 0.05553 89.25153 1.17400 4.67226 0.52149 1.08422 3.29647
Variance decomposition of LnAAT
1 0.01163 21.51075 78.48925 0.00000 0.00000 0.00000 0.00000
2 0.01347 16.79659 58.60057 9.61147 5.78659 5.64542 3.55933
3 0.01438 14.83699 51.38019 10.59587 11.70464 8.08247 3.39982
4 0.01522 13.27105 46.01096 11.14709 16.35293 10.15936 3.05862
5 0.01592 12.21129 42.12572 11.44953 19.53514 11.85981 2.81850
6 0.01654 11.45066 39.11110 11.74073 21.62544 13.35672 2.71535
7 0.01709 10.86192 36.66402 12.05323 22.99334 14.70673 2.72076
8 0.01760 10.36891 34.59995 12.39273 23.89898 15.94107 2.79834
9 0.01807 9.93090 32.80851 12.75306 24.51072 17.07567 2.92113
10 0.01853 9.52704 31.22040 13.12693 24.93400 18.12064 3.07099
Variance decomposition of LnFC
1 0.19012 1.61001 1.50322 96.88676 0.00000 0.00000 0.00000
2 0.19942 1.63537 4.64911 89.90101 0.63713 1.40053 1.77683
3 0.20120 1.74581 4.68730 88.71674 1.20541 1.71875 1.92597
4 0.20255 1.99389 4.72668 87.53314 1.76128 2.00974 1.97525
5 0.20371 2.28418 4.71829 86.55221 2.22235 2.25249 1.97046
6 0.20477 2.58922 4.70005 85.67718 2.59621 2.48369 1.95363
7 0.20575 2.88553 4.67615 84.89277 2.90103 2.70925 1.93524
8 0.20666 3.16217 4.64960 84.17990 3.15667 2.93294 1.91870
9 0.20751 3.41408 4.62172 83.52449 3.37872 3.15605 1.90493
10 0.20833 3.63982 4.59329 82.91538 3.57832 3.37916 1.89401
Variance decomposition of LnPU
1 0.12044 0.00219 5.27706 2.74667 91.97407 0.00000 0.00000
2 0.14619 0.03660 8.05172 2.70672 88.84407 0.30061 0.06025
3 0.15544 0.03870 8.53590 2.57337 88.28542 0.51274 0.05385
4 0.15949 0.03814 8.66297 2.58587 87.90228 0.74550 0.06520
5 0.16150 0.03722 8.64122 2.66202 87.58132 0.97952 0.09867
6 0.16270 0.03803 8.57789 2.77692 87.24452 1.21453 0.14809
7 0.16356 0.04142 8.50641 2.91491 86.88071 1.44839 0.20815
8 0.16428 0.04761 8.43693 3.06729 86.49313 1.68030 0.27470
9 0.16493 0.05654 8.37133 3.22862 86.08854 1.90981 0.34513
10 0.16554 0.06803 8.30912 3.39550 85.67282 2.13665 0.41787
Variance decomposition of LnCA
1 0.01837 23.40690 1.96484 7.041204 1.23091 66.35614 0.00000
2 0.02409 22.52216 2.67983 7.444116 1.82803 63.38855 2.13729
3 0.02958 21.03258 1.88819 10.06463 5.16321 59.25775 2.59364
4 0.03456 19.46329 1.40575 11.56233 8.83684 55.80974 2.92205
5 0.03918 18.00576 1.09430 12.54898 12.04941 53.15894 3.14262
6 0.04345 16.69619 0.89127 13.24911 14.65580 51.17500 3.33262
7 0.04742 15.52069 0.75168 13.81076 16.72403 49.68144 3.51139
8 0.05114 14.45865 0.65009 14.30235 18.36225 48.53912 3.68753
9 0.05465 13.49198 0.57244 14.75676 19.66813 47.64698 3.86370
10 0.05798 12.60669 0.51076 15.18936 20.71884 46.93386 4.04047
Variance decomposition of LnEC
1 0.05651 4.392982 4.04496 1.52714 7.50447 1.41046 81.11997
2 0.07186 10.75166 3.25962 3.18050 12.34539 0.87256 69.59026
3 0.08482 18.33381 4.20772 4.18013 13.97466 0.63050 58.67317
4 0.09525 24.96921 4.69786 5.23680 13.79587 0.50001 50.80024
5 0.10394 30.54313 4.91544 6.05852 12.92056 0.41990 45.14244
6 0.11134 35.13595 4.94544 6.69595 11.89473 0.36603 40.96188
7 0.11777 38.90560 4.88059 7.18281 10.93229 0.32718 37.77151
8 0.12345 42.00846 4.77391 7.55500 10.09715 0.29781 35.26765
9 0.12851 44.58067 4.65435 7.84042 9.39292 0.27493 33.25669
10 0.13306 46.73282 4.53626 8.06024 8.80336 0.25682 31.61048

Source: Authors’ own creation

Supplementary material

The supplementary material for this article can be found online.


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Further reading

NASA (2020), “Global land-ocean temperature index”, available at:


Funding: This research was funded by the National Social Science Fund of China (Grant number: 19CSH029) and the Foreign Youth Talents Program of the Ministry of Science and Technology: Research on the Impact of ICT, Technology Development and Climate Change on Grain Production in Asian Countries (No.: QN2022036001).

Conflicts of interest: The authors declare no conflict of interest.

Corresponding author

Huaquan Zhang can be contacted at:

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