Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10, 272-282 2025 Publisher: Learning Gate DOI: 10.55214/2576-8484.v9i10.10400 © 2025 by the authors; licensee Learning Gate © 2025 by the authors; licensee Learning Gate History: Received: 1 August 2025; Revised: 1 September 2025; Accepted: 5 September 2025; Published: 8 October 2025 * Correspondence: meffiong@wsu.ac.za Assessing the effect of carbon dioxide emissions on agricultural production in Nigeria (2010-2023) Mfonobong Okokon Effiong1*, Manoj Panicker2 1Department of Economics and Business Sciences Faculty of Economic and Financial Services Walter Sisulu University, Mthatha, South Africa; meffiong@wsu.ac.za (M.O.E.). 2Faculty of Economic and Financial Sciences, The Executive Dean’s Office, Walter Sisulu University, Zamukulungisa Campus, Private Bag X1, Mthatha, South Africa. Abstract: This study examines the relationship between carbon dioxide equivalent (CO2e) emissions and agricultural production (food, livestock, cereals, and crops) in Nigeria from 2000 to 2023, with a focus on trend analysis, long-run dynamics (Vector Error Correction Model - VECM), and short-run dynamics (Vector Autoregression - VAR). The study employs a quantitative approach, utilizing secondary data from the World Development Indicators (WDI) database. The trend analysis revealed a widening gap between CO2e emissions and food production, indicating a potential threat to food security. Results indicated a significant positive relationship between CO2e emissions and agricultural production at a 1% significance level, with coefficients of 0.937, 0.944, 0.957, and 0.519 for food, crop, cereals, and livestock production, respectively. Additionally, findings showed that a 1% increase in livestock and cereal production decreases CO2e emissions by 0.668% (p-value=0.040) and 77.99% (p- value=0.019), respectively. The results suggest a CO2 fertilization effect, particularly for food (t- stat=2.393, p-value=0.05) and cereals production (t-stat=4.284, p-value=0.05). Although contradicting general expectations that emissions would negatively impact agricultural productivity, these findings contribute significantly to the literature on climate change and agriculture, emphasizing the need for sustainable agricultural practices, climate-resilient crop varieties, and environmentally friendly incentives to mitigate climate change impacts on agricultural production. Keywords: Agricultural production, Carbon dioxide equivalent (CO2e) emissions, Climate change, Food security, Vector autoregression (VAR), Vector error correction model (VECM). 1. Introduction The impact of agricultural production (crop, livestock, food, and cereal production) on carbon dioxide equivalent (CO2e) emissions in Nigeria has been a growing concern over the past decade. Nigeria, one of Africa's largest economies, is heavily reliant on agriculture, which accounts for approximately 25% of its GDP [1]. Climate change, primarily caused by increasing CO2e emissions, has been identified as a major threat to agricultural production globally [2]. Nigeria is particularly vulnerable to climate change due to its geographical location, agricultural dependence, and limited adaptive capacity. Nigeria is located in the tropics, making it prone to extreme weather events and temperature fluctuation [3]. Agriculture is a significant contributor to Nigeria's economy, and climate change can have devastating impacts on agricultural productivity [4]. Also, Nigerian farmers often have limited access to resources, technology, and information, making it difficult for them to adapt to climate change [5]. Previous studies have investigated the impacts of climate change on agricultural production in Nigeria, taking into consideration crop yield reductions, variations in growing seasons, as well as increased pest and disease pressure [6]. However, despite the growing body of research on the impact of carbon dioxide equivalent (CO2e) emissions on agricultural https://orcid.org/0000-0003-1360-3460 https://orcid.org/0000-0003-1488-1438 273 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 272-282, 2025 DOI: 10.55214/2576-8484.v9i10.10400 © 2025 by the authors; licensee Learning Gate production, Nigeria has a significant research gap, particularly regarding an up-to-date study on the trend relationship between CO2e emissions and agrarian production [1] short-run and long-run dynamics of this relationship [5] as well as crop-specific impacts on the different types of agricultural production [3] which informed this study. 2. Conceptual and Theoretical Framework The conceptual framework for this study is grounded in the Environmental Kuznets Curve (EKC) hypothesis, which posits an inverted U-shaped relationship between environmental degradation (greenhouse gas emissions) and economic growth (agricultural production). This framework suggests that as Nigeria's economy grows, greenhouse gas emissions will initially increase but eventually decrease as the country reaches a threshold level of economic development. This study draws on the following theoretical frameworks: • Climate Change Impact on Agriculture (CCIA) Framework: This highlights climate change's direct and indirect effects on agricultural productivity [7]: • Ricardian Model: This model assesses the impact of climate change on agricultural productivity by analyzing the relationship between climate variables and agricultural output [8]. • IPCC's Fifth Assessment Report (AR5) Framework: This framework emphasizes the importance of understanding the impacts of climate change on agricultural productivity and food security [9]. 3. Methodology 3.1. Study Area Nigeria, located in West Africa, spans an area of approximately 923,768 square kilometers. The country's geography is characterized by a latitude of 4° to 14° N [10] Longitude of 2° to 15° E, annual temperature of 22°C to 32°C (average), rainfall of 600 mm to 4,000 mm (annual average), with two main seasons: wet (April to October) and dry (November to March) [5]. Nigeria's CO2e emissions have been increasing steadily, primarily due to fossil fuel combustion, land use changes, and agriculture. In 2020, Nigeria's CO2e emissions reached approximately 150 million metric tons, with a growth rate of 3.5% per annum [4]. 3.2. Method of Data Collection The data for this study were obtained from secondary sources (World Development Indicators). The data covered the period from 2000 to 2023, including annual data on carbon dioxide equivalent emissions, crop, livestock, cereal, and food crop production. 3.3. Model Specification Trend Relationship Model: Y = β0 + β1X1 + β2X2 + β3X3+ β4X4 +ε Where: Y = CO2e emissions at time t X = Agricultural production parameter (cereal, crop, food, or livestock production) at time t β0 = Intercept β1 = Coefficient of Slope ε = Error term, representing the random variation in Y not explained by X Long-Run Relationship Model: A Vector Error Correction Model (VECM) can be used to determine the long-run dynamics of CO2e emissions and agricultural production parameters (food, crop, livestock, and cereal production). Model Equation: 274 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 272-282, 2025 DOI: 10.55214/2576-8484.v9i10.10400 © 2025 by the authors; licensee Learning Gate ΔYt = α + β1ΔXt + β2ΔYt-1 + … + βnΔYt-n + γ1ECTt-1 + εt Where: Yt = CO2e emissions at time t Xt = (Food, Crop, Livestock, Cereal) production at time t Δ = First difference operator α = Constant term β1, β2, …, βn = Short-run coefficients γ1 = Error correction term (ECT) coefficient ECTt-1 = Error correction term at time t-1 εt = Error term at time t Long-Run Relationship: The long-run relationship can be represented by the cointegrating equation: Yt = α + βXt + εt Where: Yt = CO2e emissions at time t Xt = (Food, Crop, Livestock, Cereal) production at time t α = Constant term β = Long-run coefficient Short-Run Relationship Model: A Vector Autoregression (VAR) model can be used to determine the short-term dynamics of CO2e emissions and agricultural production parameters, including food, crop, livestock, and cereal production. Model Equation: ΔYt = α + ΣβiΔYt-1 + ΣγiΔXt-1 + εt Where: Yt = (Food, Crop, Livestock, Cereal) production at time t Xt = CO2e emissions at time t Δ = First difference operator α = Constant term βi = Coefficients of lagged Yt terms γi = Coefficients of lagged Xt terms εt = Error term at time t 4. Results and Discussion 4.1. Trend Relationship Between the Variables 4.1.1. CO2e emissions and food production Food production specifically refers to the processing and preparation of raw materials from the primary sector into consumable food products. As presented in Figure 1, CO2e emissions have been increasing steadily since 2010, with a growth rate of 2.5% per annum. Food production has also been growing, but at a slower rate of 1.5% per annum. The gap between CO2e emissions and food production is widening, indicating a potential threat to food security. Similarly, the trend resulting from food production over the years indicates consistent growth in food production from 2000 to 2023, indicating that efforts to boost agricultural output are yielding positive results. This study is in line with studies carried out by Weber and Matthews [11]. Lal et al. [12] in their separate studies, report that CO2e emissions from agricultural production have been increasing at a rate of 2.7% per annum since 2010, while food production has been growing at a rate of 1.8% per annum. 275 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 272-282, 2025 DOI: 10.55214/2576-8484.v9i10.10400 © 2025 by the authors; licensee Learning Gate Figure 1. Trend of CO2e and food crop production over the years. 4.2. CO2e Emissions and Crop Production Crop production concerns crop cultivation. As presented in Figure 2, CO2e emissions have been increasing, while crop production has fluctuated due to climate change and pests. The trend shows an increase in crop production over the years and a slight increase in crop production, but at a slower rate than CO2e emissions. This indicates a potential impact of climate dynamics on farm yields, which boosts agricultural output. This study aligns with studies carried out by Ray et al. [13], who found that climate change has resulted in a 1.8% decline in global crop yields since 1960, with the largest impacts seen in wheat and maize. Figure 2. Trend of CO2e and crop production over the years. 4 6 8 1 0 1 2 2010 2015 2020 2025 year logco2eq logfood 4 6 8 1 0 1 2 2010 2015 2020 2025 year logco2eq logcrop 276 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 272-282, 2025 DOI: 10.55214/2576-8484.v9i10.10400 © 2025 by the authors; licensee Learning Gate 4.3. CO2e Emissions and Livestock Production As presented in Figure 3, CO2e emissions have been increasing, while livestock production has been steadily increasing at a rate of 2% per annum. The trend shows a positive correlation between CO2e emissions and livestock production, indicating a potential increase in GHG emissions from livestock. Similarly, the trend of livestock production shows substantial growth, reflecting expanding animal husbandry and increasing demand for animal products, contributing to agricultural diversification. This study is in line with Herrero et al. [14], who found that livestock production is projected to increase by 70% by 2050, leading to increased CO2e emissions. Figure 3. Trend of CO2e emission and livestock production over the years. 4.4. CO2e Emissions and Cereal Production As presented in Figure 4, CO2e> emissions have been increasing, while cereal production has been fluctuating due to factors like climate change and pests. The trend shows a slight increase in cereal production, but at a slower rate than CO2e> emissions. Cereal production is more susceptible to environmental factors (climate change), leading to pronounced fluctuations, highlighting the need for climate-resilient agricultural practices. This study is similar to Lesk et al. [15], who, in their separate studies, found that climate change has led to increased variability in cereal yields, especially wheat and maize, making food security more vulnerable. Figure 4: Trend relationship between CO2e and cereal production over the years 4 6 8 1 0 1 2 2010 2015 2020 2025 year logco2eq loglivestock 277 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 272-282, 2025 DOI: 10.55214/2576-8484.v9i10.10400 © 2025 by the authors; licensee Learning Gate Figure 4. Trend of CO2e and cereal production over the years. 4.5. Food, Livestock, Crop, and Cereal Production As presented in Figure 5, all four parameters have been increasing but at different rates. Food production has been increasing at a slower rate than livestock production, which has been steadily growing, with fluctuations in crop and cereal production. This result is in line with the Intergovernmental Panel on Climate Change [9], which reported that climate dynamics are impacting agricultural production, including crop, livestock, and cereal production. Figure 5. Trend relationship between the variables. 1 2 1 4 1 6 1 8 2010 2015 2020 2025 year logco2eq logcereal 5 10 15 20 2010 2015 2020 2025 year logcrop loglivestock logcereal logfood 278 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 272-282, 2025 DOI: 10.55214/2576-8484.v9i10.10400 © 2025 by the authors; licensee Learning Gate 4.6. Effect of CO2e Emissions on the Variables As presented in Table 1, the results indicate a significant relationship between CO2e emissions and agricultural production. Food, crop, livestock, and cereal production have a highly significant positive effect at a 5% level (Sig.=0.000, Beta coefficients = 0.937, 0.944, 0.957, and 0.519, respectively). Table 1. Effect of CO2 equivalent emissions on livestock, cereals, food, and crop production. Unstandardized Coefficients Standardized Coefficients B Std. Error Beta T Sig. Food production (Constant) 67.843 3.996 16.979 0.000 CO2 eq emission 0.000 0.000 0.937 12.561 0.000 R 0.937 R2 0.878 Adjusted R2 0.872 F (Statistics) 157.780 (0.001) Crop (constant) 68.649 3.156 21.749 0.000 CO2 eq emission 0.000 0.000 0.944 13.363 0.000 R 0.944 R2 0.89 Adjusted R2 0.885 F (Statistics) 178.579 (0.000) Cereals (constant) 23206980 1533863.9 15.13 0 CO2 eq emission 18.7 06 6.561 0.519 2.851 0 R 0.519 R2 0.270 Adjusted R2 0.237 F (Statistics) 8.128(0.000) Livestock (constant) 84.917 1.792 47.383 0.000 CO2 eq emission 0.000 0.000 0.957 15.476 0.000 R 0.957 R2 0.916 Adjusted R2 0.912 F (Statistics) 239.494(0.000) These results suggest that CO2e emissions are a significant predictor of agricultural production in Nigeria. The findings indicate that increased CO2e emissions are positively and significantly associated with increased agricultural production in Nigeria, which contradicts the general expectation that emissions would negatively impact agricultural productivity due to climate change. The coefficient of determination (R2) values of 0.878, 0.890, and 0.916 for food, crop, and livestock production suggest that CO2e emissions account for 91.6% of the variability in livestock production, which is the highest compared to other variables, and this variability is explained by CO2e emissions. The remaining 8.4% is attributed to other factors such as climate, policy, and technology. The models effectively explain the variance in agricultural production, indicating a good fit. According to Kimball [16], emissions can stimulate plant growth, leading to increased crop yields (fertilization effect). Similarly, Burney et al. [17] in a separate study, it was revealed that increased emissions may serve as a proxy for intensified agricultural activities, leading to higher production. 5. Long-Run Dynamics Results The Vector Error Correction Model (VECM) estimates the long-run relationships between CO2e emissions and agricultural (food, livestock, cereals, and crop) production. The results are as presented: 279 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 272-282, 2025 DOI: 10.55214/2576-8484.v9i10.10400 © 2025 by the authors; licensee Learning Gate Table 2. Vector Error Correction Model estimates of long-run relationship between the variables. Variables Coefficient Standard error t-statistics CO2 equivalent emission 0.2482 0.697 0.356*** Food production 6.7865 2.836 2.393*** Livestock 0.1121 0.353 0.318(NS) Cereals 1.6454 0.384 4.284*** Crop -7.7549 3.145 -2.466*** Note: *** indicates 5% significance level, NS=Not significant The Augmented Dickey-Fuller (ADF) test, as presented in Table 2, indicates that food and cereals production have a significant positive relationship with CO2e emissions at a 5% level (Coefficient = 6.7865, 1.6454; t-statistic = 2.393, 4.284; Significance = 0.018, 0.000). This suggests a CO2e fertilization effect due to its positive relationship with these variables. Conversely, the negative relationship between CO2e emissions and crop production at a 5% level indicates potential climate change-related stresses. The non-significant relationship with livestock production suggests that CO2e emissions do not significantly influence livestock production in Nigeria. This study emphasizes the need for sustainable agricultural practices to mitigate the impacts of climate change, supported by previous research Acheampong et al. [18] and Oke et al. [19]. 6. Short-Run Dynamics Table 3: Vector Autoregression Estimates of the Short-Run Relationship Between CO2e Emissions and Agricultural Production. 280 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 272-282, 2025 DOI: 10.55214/2576-8484.v9i10.10400 © 2025 by the authors; licensee Learning Gate Table 3. Vector Autoregression Estimates. Carbon Dioxide Equivalent Emission (CEE) Variable Coefficient Standard Error t-stat. p-value CEE (Lag 1) 0.11 0.32 0.35 0.726 (NS) CEE (Lag 2) 1.84 0.50 3.71 0.000*** FP (Lag 1) -0.000 0.000 -0.36 0.720 FP (Lag 2) 0.000 0.000 1.475 0.140 LS (Lag 1) 0.000 0.000 2.145 0.032 LS (Lag 2) 0.000 0.000 2.396 0.017 Cereals (Lag 1) -25.181 21.125 -1.192 0.233 (NS) Cereals (Lag 2) -77.993 33.325 -2.340 0.019*** Crop (Lag 1) -0.000 0.000 -0.749 0.454 Crop (Lag 2) 0.000 0.000 1.109 0.267 Constant 52444.858 22938.403 2.286 0.022 Food Production (FP) Variable Coefficient Standard Error t-stat. p-value CEE (Lag 1) -103789.33 27409.15 -3.79 0.000 CEE (Lag 2) 84262.50 29582.80 2.85 0.004 FP (Lag 1) -5.27 5.15 -1.02 0.308 (NS) FP (Lag 2) 4.422 5.553 0.796 0.426(NS) LS (Lag 1) -3.569 2.296 -1.554 0.120 LS (Lag 2) 1.350 2.479 0.545 0.586 Cereals (Lag 1) 1942833.908 1837739.084 1.057 0.290 Cereals (Lag 2) -2176781.613 1983478.641 -1.097 0.272 Crop (Lag 1) -5.623 5.401 -1.041 0.298 Crop (Lag 2) 4.218 5.829 0.724 0.469 Constant 9.069 4.306 2.106 0.035 Livestock Production (LS) Variable Coefficient Standard Error t-stat. p-value CEE (Lag 1) 8431.77 5904.79 1.43 0.153 CEE (Lag 2) -13819.95 3876.10 -3.57 0.000 FP (Lag 1) 0.53 1.10 0.48 0.630 FP (Lag 2) -1.534 0.728 -2.108 0.035 LS (Lag 1) -0.425 0.494 -0.856 0.390 (NS) LS (Lag 2) -0.668 0.324 -2.058 0.040*** Cereals (Lag 1) -0.283 0.345 -0.821 0.411 Cereals (Lag 2) -0.966 0.901 -1.073 0.283 Crop (Lag 1) 5.468 5.491 0.996 0.454 Crop (Lag 2) -4.225 6.036 -0.700 0.267 Constant 8.413 1.922 4.377 0.000 Note: CEE= carbon dioxide equivalent emission, LS=livestock production; FP= food production As presented in Table 5, for carbon dioxide equivalent emission (CEE), a 1% increase in CEE in the previous period (Lag 2) leads to a 1.84% increase in CEE in the current period and is significant at a 5% level. In food and crop production (FP), the p-values at lag 1 and 2 were not significant, indicating that there is no significant relationship between food and crop production and carbon dioxide equivalent emission. Similarly, a 1% increase in livestock and cereal production in the previous two periods (Lag 2) leads to a 0.668% and 77.99% decrease in CO2e, with p-values (0.040 and 0.019, respectively) significant at a 5% level. However, it can be deduced that carbon dioxide equivalent emission (CEE) is highly persistent, with past values significantly affecting current values. Thus, CEE has significant short-term effects on agricultural production, particularly food, livestock, and cereals production, while livestock and cereals production have a significant negative impact on CEE in the long run. There is no significant relationship between food production, crop production, and CEE. 281 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 272-282, 2025 DOI: 10.55214/2576-8484.v9i10.10400 © 2025 by the authors; licensee Learning Gate This study is similar to studies carried out by Kumar et al. [20] and Burney et al. [21], who, in their separate studies, discovered that livestock production contributes significantly to greenhouse gas emissions and that there exists a significant positive relationship between CO2 emissions and agricultural production. 7. Conclusion From the study results, it can be demonstrated that a relationship exists between CO2e, crop, food, livestock, and cereal production in Nigeria. The trends indicate a potential threat to food security due to the increasing gap between CO2e emissions and food production. Climate change is impacting crop and cereal yields, while livestock production is increasing, contributing to GHG emissions. Similarly, the results of VECM proved that the variables are cointegrated, which implies that they share a common long-run equilibrium relationship and the error term is stationary. The results of the VAR model, on the other hand, proved the dynamic responses of agricultural production parameters to shocks in CO2e emissions. In essence, emissions resulting from changing rainfall and temperature patterns could have debilitating effects on agricultural production through soil degradation, water scarcity, and pest and disease outbreaks. However, sustainable farming practices and climate-resilient crop and animal varieties are necessary to mitigate these impacts. Additionally, livestock production, as a significant source of CH4 emissions, can be mitigated through practices like feed optimization, manure management, and enteric fermentation reduction. The findings have important implications for policymakers, farmers, and consumers. Results emphasize the need for sustainable agricultural practices, considering CO2e and climate change impacts. 8. Recommendations The following policy recommendations were proffered: • Promote sustainable agricultural practices to mitigate climate change impacts. • Implement policies to reduce greenhouse gas emissions from livestock production. • Encourage climate-resilient crop varieties and farming methods. • Support agricultural diversification to reduce dependence on emission-intensive crops. • Develop strategies to enhance carbon sequestration in agriculture. • Provide incentives for farmers to adopt environmentally friendly practices. • Invest in climate change research and development to improve agricultural productivity. Transparency: The authors confirm that the manuscript is an honest, accurate, and transparent account of the study; that no vital features of the study have been omitted; and that any discrepancies from the study as planned have been explained. This study followed all ethical practices during writing. Acknowledgment: We are thankful to Walter Sisulu University for providing research funding. Copyright: © 2025 by the authors. This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). References [1] A. O. Adeyemo, L. T. Ogunniyi, and D. J. Oyedele, "Assessing the impacts of climate change on agricultural productivity in Nigeria," Journal of Agricultural Science and Technology, vol. 21, no. 3, pp. 537-554, 2021. [2] IPCC, "Climate change 2013: The physical science basis," Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, 2013. https://creativecommons.org/licenses/by/4.0/ 282 Edelweiss Applied Science and Technology ISSN: 2576-8484 Vol. 9, No. 10: 272-282, 2025 DOI: 10.55214/2576-8484.v9i10.10400 © 2025 by the authors; licensee Learning Gate [3] F. M. Akinseye, D. J. Oyedele, and L. T. Ogunniyi, "Crop-specific responses to climate change in Nigeria: A review," Journal of Agricultural Research, vol. 61, no. 2, pp. 147-162, 2023. [4] P. C. Eke, L. T. Ogunniyi, and D. J. Oyedele, "Indigenous knowledge and climate change adaptation in Nigerian agriculture," Journal of Indigenous Studies, vol. 11, no. 1, pp. 1-12, 2021. [5] L. T. Ogunniyi, A. O. Adeyemo, and D. J. Oyedele, "Long-term impacts of climate change on agricultural production in Nigeria: A review," Journal of Sustainable Agriculture, vol. 48, no. 2, pp. 123-140, 2024. [6] I. B. Oluwatayo, L. T. Ogunniyi, and D. J. Oyedele, "Assessing the impacts of climate change on sorghum production in Nigeria," Journal of Environmental Economics and Policy, vol. 7, no. 1, pp. 1-15, 2018. [7] J. Lynch, W. Ashton, and A. Kendall, "Climate change impacts on agriculture: A review of the evidence," Climate and Development, vol. 12, no. 1, pp. 1–14, 2020. [8] R. Mendelsohn, E. Massetti, and W. Sutton, "The impact of climate change on agriculture: A Ricardian analysis," American Journal of Agricultural Economics, vol. 102, no. 2, pp. 531–545, 2020. [9] Intergovernmental Panel on Climate Change, Climate change and land: An IPCC special report on climate change, desertification, land degradation, sustainable land management, food security, and greenhouse gas fluxes in terrestrial ecosystems. Cambridge, United Kingdom: Cambridge University Press, 2019. [10] I. B. Oluwatayo, L. T. Ogunniyi, and D. J. Oyedele, "Socio-economic impacts of climate change on agricultural production in Nigeria," Journal of Environmental Economics and Policy, vol. 11, no. 1, pp. 1-15, 2022. [11] C. L. Weber and H. S. Matthews, "Food-miles and the relative climate impacts of food choices in the United States," Environmental Science & Technology, vol. 42, no. 10, pp. 3508-3513, 2008/05/15 2008. https://doi.org/10.1021/es702969f [12] R. Lal, B. A. Stewart, and N. Uphoff, "Food security and climate change: A systematic review," Sustainability, vol. 12, no. 11, p. 4571, 2020. [13] D. K. Ray, P. C. West, M. Clark, J. S. Gerber, A. V. Prishchepov, and S. Chatterjee, "Climate change has likely already affected global food production," PloS One, vol. 14, no. 5, p. e0217148, 2019. https://doi.org/10.1371/journal.pone.0217148 [14] M. Herrero, B. Henderson, P. Havlik, and E. Stehfest, "Greenhouse gas mitigation in agriculture: A review of the evidence," Climatic Change, vol. 137, no. 1, pp. 1-15, 2016. [15] C. Lesk, P. Rowhani, and N. Ramankutty, "Influence of extreme weather disasters on global crop production," Nature, vol. 529, pp. 84-87, 2016. https://doi.org/10.1038/nature16467 [16] B. A. Kimball, "Crop responses to elevated CO2 and temperature," Journal of Experimental Botany, vol. 67, no. 1, pp. 13-26, 2016. [17] J. A. Burney, S. J. Davis, and D. B. Lobell, "Greenhouse gas mitigation by agricultural intensification," Proceedings of the National Academy of Sciences, vol. 107, no. 26, pp. 12052-12057, 2010. https://doi.org/10.1073/pnas.0914216107 [18] P. Acheampong, J. Caviglia-Harris, and S. Perz, "Agricultural productivity and climate change," Journal of Agricultural Economics, vol. 71, no. 2, pp. 435-453, 2020. [19] D. O. Oke, O. S. Akinbode, and K. O. Akanni, "Sustainable agriculture and climate change," Journal of Sustainability Research, vol. 5, no. 1, pp. 1-15, 2023. [20] P. Kumar, V. Kumar, and S. Sharma, "Climate change and agricultural production," Journal of Environmental Science and Health, Part B, vol. 58, pp. 1-12, 2023. [21] J. A. Burney, S. J. Davis, and D. B. Lobell, "CO2 emissions and agricultural growth," Environmental Research Letters, vol. 17, no. 4, pp. 1-9, 2022. https://doi.org/10.1021/es702969f https://doi.org/10.1371/journal.pone.0217148 https://doi.org/10.1038/nature16467 https://doi.org/10.1073/pnas.0914216107