Simulation and Prediction of Greenhouse Gas Emissions from Beef Cattle

Abstract

Greenhouse gas emission is a key issue in the sustainable development of agriculture. To effectively predict the greenhouse gas emissions of beef cattle, a model is proposed based on system dynamics and greenhouse gas emission calculation methods, and a scenario is set as a ‘Straw to Beef’ project in Jilin Province. The model was built on a baseline emission scenario (feed precision: 60%, breeding environment: dry fattening farm, corn straw utilization: burning straw), with single- and comprehensive emission reduction scenarios considered, predicting trends and reduction potentials in greenhouse gas emissions from cattle breeding and straw burning in Jilin Province from 2013 to 2028, measured in CO₂-eq (CO₂ equivalent). The model also explored the impact of 11 controllable variables on greenhouse gas emissions. Results showed that (1) From 2013 to 2022, greenhouse gas emissions from straw burning and cattle breeding in Jilin Province increased significantly and had an annual growth rate of 6.51% in 2020. (2) Single emission reduction scenarios showed an increasing trend in greenhouse gas emissions, while comprehensive emission reduction scenarios showed a decreasing trend. Among them, the S2.2.1 scenario (feed precision: 80%, breeding environment: livestock barn manure pit, corn straw utilization: burning straw) had the strongest emission reduction ability in the single reduction scenario, the S3.2.2 scenario (feed precision: 80%, breeding environment: livestock barn manure pit, corn straw utilization: Feed-processing straw) had the strongest emission reduction ability in the comprehensive reduction scenario, reducing emissions by 5.10% and 69.24%, respectively, compared to the baseline scenario. This suggests that the comprehensive emission reduction scenarios which utilized straw resources reasonably can greatly reduce agricultural greenhouse gas emissions. (3) The optimal emission reduction scenario indicated that the higher the proportion of digestible energy in beef cattle’s total energy intake, the more perfect the fecal treatment process, and the higher the utilization rate of straw feed, the lower the greenhouse gas emissions. Therefore, to effectively reduce greenhouse gas emissions from cattle breeding and straw burning in Jilin Province, it is important to implement comprehensive emission reduction scenarios prioritizing the efficient utilization of straw resources and improving beef cattle management practices.

1. Introduction

Global greenhouse gas emissions have led to climate change and other issues. Countries and regions have adopted corresponding emission reduction plans and formulated main emission reduction goals to reduce greenhouse gas emissions and avoid extreme disasters [1,2,3]. Currently, carbon peaking and carbon neutrality have become hot research topics among scholars worldwide [4,5]. Agriculture is the second largest source of greenhouse gas emissions [6], with livestock being the main source of agricultural greenhouse gas emissions, mainly of CH₄ and N₂O [7]. After China’s rapid social and economic development following its reform and opening up, the demand for livestock and its by-products has rapidly increased, making it the world’s largest meat consumption market [8]. The greenhouse gas emissions generated from the fermentation of livestock intestines and feces management processes account for 42.8% of China’s total agricultural emissions [9], with ruminant animals being the main source of emissions [10,11]. Agricultural low-carbon emission reduction has become an important part of China’s ecological civilization construction [12,13,14]. In 2021, the Jilin Provincial Government issued the “Opinions of the Jilin Provincial People’s Government Office on the Implementation of the ‘Straw to Beef’ project and the Construction of a 10 million Head Beef Cattle Project”, which requires Jilin Province to reach 10 million head of beef cattle and utilize 24 million tons of straw as feed by 2025. As of the end of 2021, the number of beef cattle in Jilin Province was 3.383 million, and the annual production of straw exceeded 40 million tons. Effective prediction of greenhouse gas emissions from beef cattle in 2025 and beyond is significant for achieving the carbon peak and carbon neutrality in Jilin Province’s agriculture [15]. The map of Jilin Province in China and the explanation of the ‘Straw to Beef’ project are shown in Figure 1.

Currently, research on agricultural greenhouse gas emission reduction mainly focuses on single livestock or crop emissions or macro-level agricultural greenhouse gas emissions [2,13,16]. There is limited research on the simulation and prediction of greenhouse gas emission trends for integrated crop-livestock systems [8,15,17]. Existing research mostly focuses on small-scale and specific regional emission reduction methods [1,18], with a need for more research at the provincial level. Several methods are available for predicting agricultural greenhouse gas emissions, including the IPCC method, life cycle assessment, input-output analysis, and direct measurement method [7,19,20]. However, these methods are mostly used for static simulations and do not possess predictive capabilities. System dynamic models can establish correlations between influencing factors and responsive results [21] and achieve visualized analysis and prediction of complex system operations by setting controllable factors in scenarios [22,23].

This article comprehensively considers the current agricultural situation in Jilin Province. It uses system dynamics Vensim 9.2.3 combined with the IPCC livestock greenhouse gas emission calculation to construct a model for beef cattle greenhouse gas emissions. The model investigates four influencing factors, namely beef cattle farming scale, feed accuracy, breeding environment, and corn stover utilization, forming 11 controllable variables, including beef cattle breeding quantity, digestible energy as a proportion of total energy, manure treatment methods, and stover resource utilization. The study simulates 12 greenhouse gas reduction scenarios with the goal of reducing emissions and explores the impact of controllable variables on the greenhouse gas emission system to research the changing trend of greenhouse gas emissions and reduction schemes. This provides a data reference for Jilin Province to mitigate climate change in agricultural greenhouse gas emissions, achieve emission requirements for China’s “14th Five-Year Plan”, and promote regional greenhouse gas reduction methods.

2. Materials and Methods

2.1. Data Sources

The data on the annual number of beef cattle, corn production, and straw production in Jilin Province in this article mainly come from statistical data and the National Bureau of Statistics of China (https://data.stats.gov.cn/ accessed on 13 June 2022) [9,19,24,25]. The emission factors for beef cattle and greenhouse gas emission coefficients for straw are derived from relevant studies such as the Intergovernmental Panel on Climate Change (IPCC) [3,20,21,26]. The remaining data are calculated within this article.

2.2. Model Construction

The system dynamics method was created by Forrester in the 1960s and is mainly used to simulate the interactions between internal feedback and cyclic effects within a system over a certain time. Starting from the influence of emission factors, this method forms a dynamic visualization and complex systems, which can be applied to agricultural research [26]. In this article, combined with relevant studies on beef cattle and corn stover emission simulation [27,28], a greenhouse gas emission estimation model for beef cattle was constructed in Vensim software. The model runs from 2014 to 2028 with a simulation step length of 1 year. Figure 2 depicts a greenhouse gas emission model for beef cattle and straw incineration. This model includes seven hierarchical variables: agricultural environment, beef cattle farming environment, scale of beef cattle production, corn straw, fattening cattle, pregnant cow, and dairy cow. These variables are influenced by 51 auxiliary variables such as “Corn stalk feed” and “Beef cattle feed”. The connecting lines represent the relationships and interactions between different emission variables, and the direction of the lines indicates the causal relationships between emission variables.

The corn stover is mainly simulated in this study, and the beef cattle are mainly Chinese Simmental cattle. The average weight of fattening cattle is simulated as 165 kg, with a daily weight gain of 0.75 kg. The average weight of a lactating cow is simulated as 427 kg, with a daily milk yield of 10 kg and a milk fat content of 4%. The average weight of a pregnant cow is simulated as 380 kg, and each pregnancy produces one calf. It is assumed that all stages of Chinese Simmental cattle do not participate in work activities throughout the year. The simulation assumes that carbon dioxide emission is offset by the absorption of photosynthesis by plants, and methane gas returns some of the carbon elements in gaseous form. In the greenhouse gas estimation process, CH₄ and N₂O are expressed in CO₂ equivalents (CO₂-eq) according to the global net warming potential of 28 times and 265 times that of CO₂, respectively [22].

The meat cattle emission subsystem is based on the livestock greenhouse gas emission estimation formula recommended by the IPCC method, combined with the meat cattle growth shape data, estimating the CH₄ and N₂O produced through intestinal fermentation and fecal management processes during three different growth stages in the feeding process [29,30], as shown in Formulas (1) and (2), where Formula (1) is the estimation method of CH₄ emissions from intestinal fermentation, and Formula (2) is the estimation method of N₂O emissions from fecal treatment processes. The straw burning subsystem is based on the pollutant greenhouse gas emission estimation formula, combined with corn straw data from Jilin Province, to estimate the greenhouse gas produced by corn straw burning [4], as shown in Formulas (3) and (4). Some emission factor values are shown in

Table 1. Emission factor.

Gas Type	Selected Parameter	Recommended Value	Unit	Source
CH4	GET	GET = 105.7 (T = fattening cattle)	MJ·day−1	[25]
		GET = 289.97 (T = lactating cow)
		GET = 258.38 (T = pregnant cow)
	YM	YM = 6.5	%	[31]
	Y	365	day	-
	B	B = 55.65	MJ·kgCH4−1	[31]
N2O	Nex(T)	Nex(T) = 20.47 (T = fattening cattle)	kgN·head−1·year−1	[25]
		Nex(T) = 63.89 (T = lactating cow)
		Nex(T) = 51.52 (T = pregnant cow)
	MS (T, S)	MS(T,S) = 46	%	[31]
	EF3(s)	EF3(s) = 0.02	kgN2O·kgN−1	[31]
	EF4(s)	EF4(s) = 0.01	kgN2O·kgN−1	[31]
	FracGasMS	FracGasMS = 30	%	[31]
	N	N = 44/28	N2O (mm)·(N2O-N) (mm)−1	[31]
	E(x)	E(x) = 0.43 (x = N2O)	g·kg−1	[15]
CO2	E(x)	E(x) = 1261.5 (x = CO2)	g·kg−1	[15]

.

The methane emissions E-CH₄ (kg∙CH₄∙head⁻¹∙y⁻¹) can be expressed as follows:

$$\mathrm{E}-{\mathrm{CH}}_4={\displaystyle \sum _{\mathrm{T}}\left({\mathrm{GE}}_{\mathrm{T}}\times \mathrm{YM}\times \mathrm{Y}/\mathrm{B})\right.}$$

(1)

T represents three different growth states of beef cattle: fattening cattle, lactating cows, and pregnant cows. GE_T is the total energy type of the T species. YM is the methane conversion factor. Y represents the number of days in the year, and B represents the methane energy content. The nitrous oxide emission E-N₂O (kg∙NO₂∙head⁻¹∙y⁻¹) is expressed as follows:

$$\mathrm{E}-{\mathrm{N}}_2\mathrm{O}={\displaystyle \sum _{\mathrm{S}}\left[{\displaystyle \sum _{\mathrm{T}}\left[\left({\mathrm{N}}_{\left(\mathrm{T}\right)}\times {\mathrm{Nex}}_{\left(\mathrm{T}\right)}\times {\mathrm{MS}}_{(\mathrm{T},\mathrm{S})}\right)\times \left[{\mathrm{EF}}_{3\left(\mathrm{S}\right)}+(\frac{{\mathrm{Frac}}_{\mathrm{GasMS}}}{100}{)}_{(\mathrm{T},\mathrm{S})}\times {\mathrm{EF}}_{4\left(\mathrm{S}\right)}\right]+\left({\mathrm{N}}_{\left(\mathrm{T}\right)}\times {\mathrm{Nex}}_{\left(\mathrm{T}\right)}\times {\mathrm{MS}}_{(\mathrm{T},\mathrm{S})}\right)\right]}\right]}\times \mathrm{N}$$

(2)

T represents three different growth states of beef cattle: fattening cattle, lactating cows, and pregnant cows, and S represents three different fecal management methods: dry fattening pens, solid storage, and livestock barn manure pits. N_(T) is the number of T species of beef cattle, Nex_(T) is the annual nitrogen excretion rate of T species of beef cattle, MS_(T,S) is the proportion of total annual nitrogen excretion of T species of beef cattle in fecal management system S, EF_3(S) is the emission factor for direct N₂O emissions in management system S in kg N₂O·kgN⁻¹, Frac_GasMS is the proportion of nitrogen managed by T species of beef cattle through NH₃ and NO_x volatilization in fecal management system S as a percentage, EF₄ is the emission factor for N₂O emissions from nitrogen deposition in soil and water surface atmosphere in kg N₂O·kgN⁻¹, and N is the conversion factor (N₂O-N) (mm) for converting nitrogen oxide emissions to N₂O (mm) emissions.

The straw burning E-M_ix is expressed as follows:

$${\mathrm{E}-\mathrm{M}}_{\mathrm{ix}}={\displaystyle \sum _{\mathrm{i}}^{\mathrm{n}}{\mathrm{S}}_{\left(\mathrm{i}\right)}}\times {\mathrm{E}}_{\left(\mathrm{x}\right)}\times {10}^{-3}$$

(3)

S_(i) represents the amount of straw burning for the S_(i) type of crop, and E_(x) represents the emission factor for the straw burning of x-type gas.

2.3. Scenario Setting

Based on the existing characteristics of greenhouse gas emissions from beef cattle, combined with the agricultural situation in Jilin Province and the policy of converting straw into meat, using the initial emission scenario as a baseline, scenarios are designed for beef cattle greenhouse gas emissions considering four aspects: cattle scale, feed accuracy, breeding environment, and corn straw utilization. Through dynamic model adjustments, data for the four scenarios are modified to construct single and comprehensive reduction scenarios. The specific scenario descriptions and parameter settings are shown in

Table 2. Different simulation scenario settings.

		Factor	Feed Precision			Breeding Environment			Corn Straw Utilization
	Controlled Variable		60	70	80	Dry Fattening Farm	Livestock Barn Manure Pit	Solid Broadcast	Burning Straw	Feed-Processing Straw
Scenario			1	2	3	1	2	3	1	2
Baseline Scenario		BAU	√			√			√
Single Emission Reduction Scenario		S2.1.1		√		√			√
		S2.2.1		√			√		√
		S2.3.1		√				√	√
		S2.1.1			√	√			√
		S3.2.1			√		√		√
		S3.3.1			√			√	√
Comprehensive Emission Reduction Scenario		S2.1.2		√		√				√
		S2.2.2		√			√			√
		S2.3.2		√				√		√
		S3.1.2			√	√				√
		S3.2.2			√		√			√
		S3.3.2			√			√		√

. In the model, the rows represent the factors, while the columns represent the simulated emission scenarios. The controlled variables are the variables within each factor that can be controlled and adjusted. For example, in S2.1.1, the factor “Feed precision” has a controlled variable of 70 (2), the factor “Breeding environment” has a controlled variable of “Dry fattening farm” (1), and the factor “Corn straw utilization” has a controlled variable of “Burning straw” (1).

3. Results and Analysis

In this study, a regression analysis was used to estimate the greenhouse gas emissions from beef cattle and straw in Jilin Province from 2013 to 2028 as baseline data to verify the accuracy of the simulated greenhouse gas emission model data. Six single emission reduction scenarios (S2.1.1, S2.2.1, S2.3.1, S3.1.1, S3.2.1, and S3.3.1) and six comprehensive emission reduction scenarios (S2.1.2, S2.2.2, S2.3.2, S3.1.2, S3.2.2, and S3.3.2) were simulated, and the best emission reduction scenario was determined based on the emission reduction potential of each scenario. The influencing factors of greenhouse gas emissions were explored, and the optimal emission reduction plan was established for these factors.

3.1. Model Verification

To validate our model, we designed a comparative experiment. Based on the number of beef cattle in Jilin Province greenhouse gas emission data obtained by our method and the regression method, the experimental results were compared and analyzed, as shown in

Table 3. Comparison of model validation results.

Year	Number of Beef Cattle (Ten Thousand Head)	Greenhouse Gas Emissions (Metric Tons)		Relative Error%	Quantity of Straw (Metric Tons)	Greenhouse Gas Emissions (Metric Tons)		Relative Error%
		Our Method	Training Data for Regression			Our Method	Training Data for Regression
2013	437.62	736.95	736.95	-	2653.03	3647.78	3647.78	-
2014	430.91	725.66	725.66	-	2673.71	3676.22	3676.22	-
2015	450.72	759.02	759.02	-	2793.51	3840.93	3840.93	-
2016	427.28	719.54	719.54	-	2924.79	4021.44	4021.44	-
2017	337.56	568.45	568.45	-	2893.19	3978	3978	-
2018	325.29	547.79	547.79	-	2491.89	3426.23	3426.23	-
2019	331.48	558.21	558.21	-	2710.32	3726.55	3726.55	-
2020	285.48	480.75	480.75	-	2646.36	3638.61	3638.61	-
2021	338.3	569.67	569.67	-	2846.61	3913.95	3913.95	-
2022	390.3	657.27	657.27	-	2918.92	4013.36	4013.36	-
Year	Predicted data	Our method	Regression method	Relative error%	Predicted data	Our method	Regression method	Relative error%
2023	448.85	755.86	786.14	4.01	2948.48	4054.01	4002.86	1.26
2024	516.17	869.23	881.85	1.45	2978.35	4095.08	4033.35	1.51
2025	593.6	999.63	1044.08	4.45	3008.52	4136.56	4139.7	0.08
2026	613.19	1032.61	1003.38	2.83	3039	4178.47	4198.41	0.48
2027	633.43	1066.69	1088.33	2.03	3069.78	4220.8	4200.6	0.48
2028	654.33	1101.89	1111.79	0.9	3100.88	4263.55	4277.23	0.32

. In our experiment, based on the data of beef cattle breeding and corn planting from 2013 to 2022, we predict the number of beef cattle and quantity of straw from 2023 to 2028. According to the proposed method, the greenhouse gas emissions of cattle breeding and straw burning from 2023 to 2028 were calculated. In addition, by the regression method, the result of greenhouse gas emissions from beef cattle and straw from 2023 to 2028 was predicted. As a result, the trends in greenhouse gas emissions from beef cattle obtained from our model and the regression analysis were consistent, with a relative error within 5%. Similarly, the trends in greenhouse gas emissions from straw burning obtained from the dynamic system model and the regression analysis were consistent, with a relative error within 2%. These results demonstrate that the simulated data values from our model are reliable and have the ability to reflect the emission relationships between variables [25]. Additionally, the simulation scenarios were also validated using Vensim software [32].

3.2. Trends in Greenhouse Gas Emissions under Different Scenarios

This study simulated the changes in greenhouse gas emissions from straw burning and cattle breeding in Jilin Province from 2013 to 2028 and estimated the greenhouse gas emissions under the baseline scenario, as shown in Figure 3. Figure 3a represents the changes in greenhouse gas emissions from straw burning and cattle breeding in Jilin Province from 2013 to 2022. Figure 3b represents the number of beef cattle and the amount of straw in Jilin Province. From 2013 to 2022, the overall greenhouse gas emissions from straw burning (mainly corn straw) in Jilin Province showed an increasing trend with an annual growth rate of 1.28%. There were two growth phases for straw emissions. From 2013 to 2016, the total greenhouse gas emissions from straw burning increased from 36.48 million tons to 40.22 million tons, with an annual growth rate of 3.41%. In the second phase, the emissions increased from 36.39 million tons in 2020 to 40.14 million tons in 2022, with an annual growth rate of 5.12%. From 2013 to 2022, the overall greenhouse gas emissions from beef cattle in Jilin Province showed a decreasing trend, with an annual decrease rate of 5.3%. The beef cattle population increased from 4.81 million tons in 2020 to 6.58 million tons in 2023, with an annual growth rate of 12.2%. The increase in beef cattle and straw quantity after 2020 may be due to the implementation of various policy plans in Jilin Province, which encourage beef cattle farming, provide appropriate subsidies to farmers, and implement important food and other agricultural product guarantee strategies, thereby increasing the number of beef cattle and corn.

The simulated emission amounts and proportions under different scenarios from 2023 to 2028 are shown in Figure 4 and Figure 5. The figures indicate that the emissions for the single emission reduction scenario show an upward trend, with greenhouse gas emissions for scenarios S2.1.1, S2.2.1, S2.3.1, S3.1.1, S3.2.1, and S3.3.1 increasing from 46.55 million tons, 46.06 million tons, 46.14 million tons, 45.63 million tons, 45.13 million tons, and 45.75 million tons, respectively, to 51.40 million tons, 50.68 million tons, 50.81 million tons, 50.06 million tons, 49.34 million tons, and 50.23 million tons, with annual growth rates of 2.08%, 2%, 2.02%, 1.94%, 1.87%, and 1.96%, respectively. However, the comprehensive emission reduction scenarios all show a downward trend. The annual reduction rates for scenarios S2.1.2, S2.2.2, S2.3.2, S3.1.2, S3.2.2, and S3.3.2 in the integrated reduction scenarios are 5.9%, 6.24%, 6.18%, 6.58%, 6.94%, and 6.47%, respectively. Although the emission amounts for the single emission reduction scenario still account for over 90% of the baseline scenario amount, the emission amounts for the comprehensive emission reduction scenario only account for less than 35% of the baseline scenario amount. This indicates that the capacity for the comprehensive emission reduction scenario is greater than that for the single emission reduction scenario. While single-reduction technologies can reduce greenhouse gas emissions to some extent, the effect is not significant. In comparison, comprehensive reduction technologies have better results in reducing emissions.

3.3. Potential of Greenhouse Gas Emission Reduction in Different Scenarios

Emission potential expresses the difference between the emission levels under different scenarios and the baseline scenario over a certain period, providing a reference for emission reduction methods. The cumulative emissions and emission reduction ratios of greenhouse gases from different scenarios of the ‘Straw to Beef’ project in 2023–2028 are shown in Figure 6 and Figure 7. The figures indicate that the cumulative emission reductions in scenarios S2.1.1 and S3.1.1 in 2028 are 11.88 million tons and 19.00 million tons, respectively, which represent a reduction of 3.86% and 6.17% compared to the baseline scenario, respectively. This suggests that the higher the proportion of digestible energy in total energy, the lower the CH₄ emissions from cattle enteric fermentation [5]. Similarly, the emission reductions in scenarios S2.1.1, S2.2.1, and S2.3.1 are 11.88 million tons, 15.68 million tons, and 15.04 million tons, respectively, representing reductions of 3.86%, 5.10%, and 4.89%, respectively, compared to the BAU scenario. This indicates that the more improved the options for cattle manure management, the less greenhouse gas emissions from cattle [18]. Scenario S3.2.2 has the highest emission reduction potential, with a cumulative emission reduction of 213.07 million tons, indicating that the greenhouse gas emissions from straw burning account for a large proportion. The strict control of straw burning will effectively reduce greenhouse gas emissions from agriculture. Therefore, it is not enough to only change greenhouse gas emissions from cattle or greenhouse gas emissions from straw burning. Effective emission reduction measures must be taken throughout the entire process of the ‘Straw to Beef’ project, fully leveraging the advantages of Jilin Province’s agriculture and adopting a combination of planting and breeding to reduce emissions. Improvements in straw feed technology and a reduction in straw burning are of great significance for reducing greenhouse gas emissions from the ‘Straw to Beef’ project in Jilin Province [12].

3.4. Analysis of Factors Affecting Greenhouse Gas Emissions

Agricultural development in Jilin Province has been rapid, with the beef and corn industries entering a period of rapid growth. This has led to a sharp increase in greenhouse gas emissions from agriculture. In order to better understand the proportion of greenhouse gas emissions from beef and straw and their future emissions, a detailed analysis of the emission types was conducted for scenario S3.2.2, which has the greatest potential for emission reduction in 2028. The results show the greenhouse gas emissions from straw burning and cattle breeding production in Jilin Province from 2023 to 2028 under scenario S3.2.2, as well as a detailed explanation of the greenhouse gas emission data at different stages of beef growth. The greenhouse gas emissions from straw burning and beef production from 2023 to 2028 are shown in Figure 8. The total emissions from cattle breeding are increasing, while the emissions from straw burning are decreasing. This result indicates that the reasonable utilization of straw resources will effectively control the emissions of greenhouse gases from straw burning as the number of beef cattle increases. The greenhouse gas emissions from beef at different growth stages from 2023 to 2028 are shown in Figure 9. The figure shows that the CH₄ emissions from the three different growth stages of beef are much higher than the NO₂ emissions. It also shows that during the process of beef greenhouse gas emissions, the CH₄ emissions from intestinal fermentation in beef are much higher than the NO₂ emissions from feces. Therefore, it is concluded that reducing emissions through feed improvement has a greater effect than reducing emissions through improvements in the beef farming environment.

4. Discussion

Based on the ‘Straw to Beef’ project in Jilin Province, a model for greenhouse gas emission prediction was constructed, which provides a data reference for agricultural carbon emission estimation in Jilin Province. However, our research has limitations due to incomplete data. In the emission estimation process, there was a lack of relevant data on feces management processes in Jilin Province, such as the parameters MS_(T,S), EF_3(S), and EF₄, which resulted in errors in N emission data [31]. But, this result is consistent with the data in the relevant references [33,34,35]. The accuracy of N emission data can be improved by collecting more accurate data on feces management processes in future research. Furthermore, livestock greenhouse gas emissions are influenced by factors such as emission factors, activity levels, and environmental conditions. In this research, uncertain variables were supplemented with default values provided by the IPCC, resulting in lower results compared to other studies using the same IPCC methodology [33]. This difference can be attributed to significant differences in physical characteristics between Simmental cattle and other beef cattle breeds [34]. Uncertainty sources in comparison to studies conducted in other countries on beef cattle greenhouse gas emissions primarily stem from the lack of activity level data and variability in livestock feed absorption. In the simulated scenario of this study, the fluctuation ranges of CH₄ and N emissions correspond to previous research [35]. The CH₄ fluctuation range was between 86% and 98%, and the N emission fluctuation range was between 57% and 82% [36]. In future work, through investigation and research, we will establish the standards for calculating greenhouse gas emissions of beef cattle. Also, our research will provide technical support for emission reduction and sustainable development technologies throughout the entire agricultural development process.

5. Conclusions

Taking the ‘Straw to Beef’ project in Jilin Province as an example, the article utilized a system dynamics approach to simulate greenhouse gas emissions, combined with the calculation standard of IPCC and existing agricultural production data. We estimated the emissions under the baseline scenario and the 12 simulated scenarios; an analysis of greenhouse gas emissions from the 12 simulated scenarios was conducted to identify the optimal simulation scenario. Effective methods to reduce emissions were proposed based on the most optimal simulation scenario. Firstly, greenhouse gas emissions from straw burning and cattle breeding have an increasing trend from 2013 to 2022. In 2020, in Jilin Province, the total emissions reached 41.2 million tons (measured in CO₂-eq), which subsequently increased to 46.72 million tons in 2022, representing an annual growth rate of 6.7%. The growth rate during this period was significantly higher than that observed between 2013 and 2016. Furthermore, from 2023 to 2028, scenarios S2.1.1 and S3.1.1 achieved cumulative emission reductions of 11.88 million tons and 19 million tons, respectively, corresponding to reductions of 3.86% and 6.17% compared to the baseline scenario. Emission reductions of 11.88 million tons, 15.68 million tons, and 15.04 million tons, equivalent to reductions of 3.86%, 5.10%, and 4.89%, respectively, compared to the BAU scenario, were observed for scenarios S2.1.1, S2.2.1, and S2.3.1. Scenario S3.2.2 demonstrated the highest emission reduction capacity, achieving a cumulative reduction of 213.07 million tons, highlighting the substantial contribution of greenhouse gas emissions from straw burning. Thirdly, targeted emission reduction measures should be implemented to address the agricultural situation in Jilin Province. Implementation of these methods will effectively reduce greenhouse gas emissions by integrating corn planting [37] and cattle breeding [38]. These methods will effectively reduce greenhouse gas emissions from beef cattle and straw burning [39]. Moving forward, Jilin Province should carry an ecological recycling agricultural production and significantly enhance the production capacity of the beef cattle industry through environmentally friendly and efficient practices.