Carbon Footprint of Main Grain Crop Production in Hubei and Jiangsu Provinces, 2005–2019

Abstract

Hubei and Jiangsu Provinces, significant in grain production, play a crucial role in national food security. We studied the carbon footprint of main grain crops (rice, maize, and wheat) from 2005 to 2019 in these provinces to identify trends, contributing factors, and emission efficiencies. This study seeks to inform sustainable agricultural practices and policies in the context of climate change mitigation. Jiangsu Province’s rice and wheat output surpasses Hubei’s due to higher yields per unit area. Rice consistently shows the highest carbon footprint per unit area, followed by wheat, with maize exhibiting the lowest. Carbon footprint per unit yield varies significantly: for rice, it ranges from 0.15 to 0.29 kg CO₂-eq per kg; for wheat, from 0.19 to 0.22 kg CO₂-eq per kg; and for maize, from 0.13 to 0.15 kg CO₂-eq per kg. The distribution of crop production affects these footprints; central regions generally exhibit lower values compared to southwest and southeast areas. Fertilizer and electricity together contribute significantly to carbon emissions, especially in rice production (over 75%), and to a lesser extent in maize and wheat production (approximately 69% and 85%, respectively). Improving fertilizer efficiency, irrigation, and mechanization is crucial for developing low-carbon agriculture in these pivotal grain-producing regions.

1. Introduction

The Intergovernmental Panel on Climate Change (IPCC) Fifth Assessment Report states that from 1880 to 2012, the global average temperature has increased by 0.85 °C, and this trend is still rising [1]. Human-induced greenhouse gas (GHG) emissions are the primary source of the global increase in greenhouse gases, with CO₂ emission growth mainly attributed to the combustion of fossil fuels and CO₂ produced from industrial processes, while emissions of CH₄ and N₂O primarily originate from agriculture [2]. The Second Biennial Update Report of the Ministry of Ecology and Environment of the People’s Republic of China (2019) shows that agricultural GHG emissions in China amount to approximately 830 million metric tons of CO₂ equivalent, making it the third-largest source of GHG emissions after industrial and energy activities. If no decisive actions are taken, the continued emission of anthropogenic greenhouse gases is expected to lead to global warming exceeding 4 °C [3,4]. At the 2014 United Nations Climate Summit and the 2015 Paris Climate Summit, the majority of the world’s countries reached a consensus on emission reduction, committing to reduce carbon emissions to the levels of 1990 by the year 2020 to limit the rise in global temperatures to 2 °C by 2100. Additionally, the global demand for food is expected to increase by 60% compared to the levels of 2005–2007 by the year 2050 [5]. Therefore, enhancing crop production carbon efficiency through technological and policy transformation, and promoting the development of a low-carbon economy is of utmost importance.

The term “Carbon footprint”, first introduced by William E. Rees, refers to the direct and indirect greenhouse gas emissions released by an individual, organization, or product during activities such as production, consumption, and transportation [6]. In the agricultural sector, carbon footprints are typically calculated using the life cycle assessment method to account for the total greenhouse gas emissions (WRI, 2010). Over the past few decades, the carbon footprint concept has been widely applied globally for quantifying carbon inputs related to crop production [7,8]. In China, research on carbon footprints and carbon emissions in agricultural production has been increasing. In 2007 alone, greenhouse gas emissions from Chinese agriculture reached 686 × 10⁶ t CE (CE stands for carbon dioxide equivalent), accounting for 9.2% of the country’s total emissions [9]. Beyond direct emissions from fields, the entire process of agricultural production, including pre-production, production, and post-production, indirectly results in significant greenhouse gas emissions. Inputs such as fertilizers, pesticides, and seeds as well as agricultural machinery operations during tillage, plant protection, irrigation, and harvesting all contribute to greenhouse gas emissions to varying degrees [10]. Cheng et al. (2011) used statistical data to estimate that the per-unit area and per-unit yield carbon footprints of agricultural production in China are 0.78 t CE ha⁻¹ a⁻¹ and 0.11 t CE ha⁻¹ kg⁻¹, respectively [10]. Chen et al. (2014), utilizing data from the National Bureau of Statistics, analyzed and evaluated the carbon footprints of major grain crops in China (rice, wheat, maize, and soybeans) and their compositions [9]. They found that the carbon footprint of rice based on per-unit area and per-unit yield was 9.06 Mg CO₂-eq ha⁻¹ and 1.36 kg CO₂-eq kg⁻¹, respectively—significantly higher than the other three crops.

In addition to national-scale carbon footprint studies, regional-scale research is beneficial for targeted carbon footprint reduction measures in different areas. Cao Liming and others [11] conducted a carbon footprint assessment of rice production in Shanghai using the life cycle assessment method, finding that methane contributes up to 96.6% of greenhouse gas emissions from rice fields. Ref. [12] calculated agricultural carbon emissions in Hubei Province from 1995 to 2011 based on 17 types of carbon sources in three areas: farmland use, paddy fields, and livestock breeding. The results show that efficiency factors, labor factors, and industrial structure have a strong inhibitory effect on agricultural carbon emissions in Hubei Province. Xie et al., 2021 studied the spatiotemporal changes of the carbon footprint of agricultural ecosystems in Hubei Province using statistical yearbook data, discovering that fertilizer application is the main factor causing changes in the carbon footprint, and the total carbon storage of agricultural ecosystems is generally on an upward trend [13]. Chen et al., 2019 quantitatively studied the size and composition of the carbon footprint of the rice–wheat rotation system in the lower reaches of the Yangtze River based on data from farmer surveys, using the life cycle assessment method [14]. They elucidated that vigorously developing rice–wheat rotation systems; adopting technologies for saving fertilizers, water, and no-tillage; and constructing large-scale low-carbon planting models can reduce carbon emissions.

Current carbon footprint research predominantly considers agricultural production as a whole or all crops collectively to assess the changing characteristics and driving factors of national or regional (provincial) carbon footprints. However, studies that separately calculate and evaluate the carbon footprint of different crop productions at the county level are scarce. This limitation hinders the provision of supportive carbon sequestration and emission reduction technologies, as well as innovative policy directions for specific crops or crucial agricultural areas. The Middle and Lower Yangtze River regions, encompassing Hubei and Jiangsu Provinces, stand as one of the most important primary grain-producing areas in China. These regions are characterized by high per-unit yield of grain, substantial potential for increased production, and a prominent contribution to national grain yield increase. Nevertheless, these areas also face challenges such as excessive input in grain production processes and increased carbon emissions from agricultural production. Therefore, this paper selects Hubei and Jiangsu Provinces in the Middle and Lower Yangtze River regions as the subjects of study. By collecting and organizing statistical data from 2005 to 2019 for Hubei and Jiangsu Provinces, the research separately estimates and evaluates the historical changes in the production carbon footprint and carbon input structure of the main grain crops (rice, maize, and wheat) in these regions. It compares the carbon footprint of crop production between the two provinces to identify the primary sources of carbon emissions from crop production in both provinces. The primary aim of this study is to analyze the carbon footprint associated with the production of main grain crops in Hubei and Jiangsu Provinces over a 14-year period. By examining the factors contributing to carbon emissions and assessing carbon efficiency, we aim to provide insights into the sustainability of agricultural practices in these regions. The objectives include (1) quantifying the carbon footprint of rice, maize, and wheat production; (2) identifying the main sources of carbon emissions; (3) evaluating changes in carbon efficiency over time; and (4) offering recommendations for reducing the carbon footprint of grain production.

2. Materials and Methods

2.1. Study Area

Hubei and Jiangsu Provinces, located in the middle and lower reaches of the Yangtze River in China, are important production areas for indica and japonica rice, predominantly featuring a rice–wheat rotation system. Hubei and Jiangsu are administratively divided into 15 and 13 cities/districts, respectively, with Wuhan and Nanjing serving as their respective provincial capitals. The research focused on agricultural areas within Hubei and Jiangsu Provinces as shown in Figure 1. Hubei is situated between latitudes 29°05′ N and 33°20′ N and longitudes 108°21′ E and 116°07′ E, covering roughly 185,900 square kilometers. Approximately 28.41% of this area is dedicated to farming. The farmland here varies, including plains, hills, and mountains. The region’s soil is diverse, featuring types like paddy, red, alluvial, yellow-brown, and yellow soils. Hubei is known for its subtropical monsoon climate, with average temperatures between 15–17 °C and sunshine ranging from 1100–2150 h annually. Rainfall peaks at 1400–1600 mm in the southwest and drops to 800–1000 mm in the northwest, showing a south-to-north decrease influenced by local topography. Jiangsu, on the other hand, lies between 116°18′ E and 121°57′ E longitude and 30°45′ N and 35°20′ N latitude. The province experiences average annual temperatures of 13–17 °C and enjoys a frost-free period lasting 200–240 days. Yearly rainfall averages between 800–1300 mm, diminishing from the south to the north. This province is a crucial area for agriculture in China, with a total crop area of 77,400 square kilometers. Jiangsu mainly grows two rounds of crops annually, with rice and wheat as the primary grains, followed by corn and sorghum. The province extends across three climatic zones: warm temperate, northern subtropical, and central subtropical and features varied soil types like brown loam, yellow-brown loam, brown-red loam, and swampy and paddy soils. Geographically, Jiangsu is divided into seven regions from north to south, each showing distinct differences in soil heavy metal background levels.

2.2. Research Boundary

The carbon footprint has been widely applied in the assessment of agricultural production carbon emissions or greenhouse gas emissions at different scales. The boundaries of the study subjects are mostly determined based on the availability of data. Some studies calculate the agricultural carbon footprint using greenhouse gas emissions from experiments, and these emissions are divided into two parts: energy and non-energy utilization [15,16]. Moreover, due to spatial and temporal heterogeneity, there is significant uncertainty in emissions generated by crop production in field experiments [17]. Therefore, many studies calculate carbon footprints based on statistical data of crop production inputs, especially at the regional scale. Consequently, the method of evaluating carbon footprints using statistical data facilitates the innovation of technologies related to carbon emission reduction and the formulation of policies at regional or even national scales. In this study, the carbon footprint analysis and evaluation focus on the complete lifecycle of rice, maize, and wheat, from sowing to harvest, as the research boundary. The calculation of carbon footprint includes both direct and indirect carbon emissions. Direct emissions encompass greenhouse gas emissions from the field, while indirect emissions include all processes and activities that can indirectly cause carbon emissions throughout the entire lifecycle of crop production. These include inputs such as fertilizers, pesticides, seeds, and agricultural machinery operations during tillage, plant protection, irrigation, and harvesting. In the calculation of the carbon footprint for this study, only indirect emissions are considered, i.e., inputs in agricultural production (such as fertilizers, pesticides, electricity for irrigation, etc.). Due to limitations in monitoring precision and reliable data, direct greenhouse gas emissions from the field are not included in the calculation.

2.3. Data Source

The data required for this study includes three parts: (1) planting area and production data of the three main grain crops (rice, maize, and wheat) in Hubei and Jiangsu Provinces from 2005 to 2019, obtained from national data (http://data.stats.gov.cn/, accessed on 25 August 2023) and provincial statistical yearbooks; (2) data on agricultural inputs for the production of the three main grain crops in Hubei and Jiangsu Provinces from 2005 to 2019, collected from the Compilation of National Agricultural Product Cost and Benefit Data [18]; (3) prices of each type of agricultural input to convert the input data from monetary (RMB) to physical quantity, obtained from the Price Yearbook of China [19]. The period from 2005 to 2019 offers a comprehensive and reliable dataset. The data can provide a valuable baseline against which future studies can measure changes in carbon efficiency and the effectiveness of policies and practices aimed at reducing agricultural carbon footprints. The study involves six types of agricultural inputs: fertilizer, pesticide, electricity for irrigation, diesel, agricultural film, and seeds. Fertilizer inputs are further divided into nitrogen, phosphorus, potassium, and compound fertilizers. The compilation does not include data on organic fertilizer inputs. Pesticides are subdivided into insecticides, fungicides, and herbicides. Electricity input refers to electricity used for irrigation in farmlands, and diesel refers to fuel power input for agricultural machinery operations throughout the crop production process.

2.4. Calculation

The carbon footprint calculation involves considering each agricultural input for each crop as indirect carbon emissions and converting them into carbon dioxide equivalent (CE). The formula for calculating the carbon footprint based on planting area (i.e., carbon footprint per unit area, CF_A, in kg CE ha⁻¹) is as follows:

$${CF}_A=\sum _i^n{Cost}_i\times {EF}_i$$

In the formula, i represents a specific agricultural input, which is one of the inputs used in the production process of a crop, such as fertilizer, pesticides, electricity, diesel, plastic film, or seeds. Cost_i represents the quantity of a specific agricultural input used (measured in kg ha⁻¹ or kWh ha⁻¹). EF_i represents the emission factor for a specific input (measured in kg CE kg⁻¹ or kg CE·kWh⁻¹). The emission factor is used to convert agricultural input quantities into carbon dioxide equivalent. These emission factors are obtained from the Chinese Localized Life Cycle Database (CLCD), as shown in

Table 1. Emission factors of agriculture inputs used in the present estimation of carbon footprint.

Agriculture Input	Symbol	Emission Factor
Fertilizer	EFF	1.53 kg CE kg N fertilizer
		1.63 kg CE kg P fertilizer
		0.66 kg CE kg K fertilizer
		1.77 kg CE kg compound fertilizer
Pesticide	EFP	16.60 kg CE kg insecticide
		10.60 kg CE kg bactericide
		10.20 kg CE kg herbicide
Electricity for irrigation	EFE	1.23 kg CE kWh
Diesel	EFD	0.89 kg CE kg
Plastic film	EFPF	22.70 kg CE kg
Seed	EFS	0.58 kg CE kg

. The CLCD is the only publicly available database in China that provides localized life cycle assessment data. It represents the average production technology and market conditions in China [20,21,22,23].

The formula for calculating the carbon footprint based on yield level (unit yield carbon footprint, ${CF}_Y$, in kg CE kg⁻¹) is as follows:

$${CF}_Y={CF}_A/Y$$

In the formula: CF_Y represents the carbon footprint per unit of crop yield (in kg CE kg⁻¹). CF_A represents the carbon footprint per unit area (in kg CE ha⁻¹). $Y$ represents the yield of a specific crop per unit area (in kg ha⁻¹).

The carbon efficiency refers to the crop yield per unit of carbon input, and the formula is as follows:

$$CEff=Y/{CF}_A$$

In the formula: CEff represents the crops yield per carbon footprint; $Y$ represents the crop’s unit area yield (kg ha⁻¹); CF_A represents the carbon footprint per unit area of the corresponding crop (kg CE ha⁻¹).

3. Results

3.1. Crop Yield and Planting Area

It can be observed that from 2005 to 2019, the unit yield of the main grain crops in Jiangsu Province has significantly increased, while in Hubei Province, the unit yield of rice and wheat has not shown significant changes and the unit yield of maize has even decreased (Figure 2). Among the unit yields of major grain crops, rice has the highest level of unit yield, with a range of 7.6 to 9.2 t ha⁻¹, and rice unit yield in Jiangsu Province is higher than in Hubei Province. Next is maize, with a range of 4.4 to 6.8 t·ha⁻¹, and maize unit yield in Jiangsu Province is also significantly higher than in Hubei Province. Wheat unit yield is significantly lower than that of rice and maize, with a unit yield of 5–5.8 t·ha⁻¹ in Jiangsu Province. In terms of planting area, the rice planting area in Hubei Province remained relatively stable until before 2016, and then increased significantly and has remained high to this day. The maize planting area increased from 435 × 10³ ha in 2005 to 738 × 10³ ha. The wheat planting area in Jiangsu Province remained relatively stable. In 2019, the total rice production amounts in Hubei and Jiangsu Provinces were 17.4 and 17.3 × 10⁶ t, respectively, with no significant difference between them. The slight difference is related to the historical period when rice production in Jiangsu Province was higher than in Hubei Province. From 2005 to 2019, the unit yield, planting area, and total production of maize in Jiangsu Province increased significantly, but due to its lower planting area compared to Hubei Province during the same period, the total production level is not significantly different from Hubei Province. Similar to maize production, the unit yield of wheat in Jiangsu Province has significantly increased and is higher than that of Hubei Province during the same period. Furthermore, the wheat planting area in Jiangsu Province increased significantly from 1731 × 10³ ha in 2005 to 2319 × 10³ ha in 2019.

The per-hectare yield of rice in Hubei Province varies greatly between years (Figure 3). Among them, Jingzhou, Jingmen, Xiangyang, and Xiantao have higher yields per hectare than other cities, and these areas are also the main regions for rice cultivation. The rice yield per hectare varies significantly between different cities, ranging from 4.7 to 10.5 t ha⁻¹. The increase in total wheat production is mainly attributed to the Xiangyang area, which leads the province in both wheat cultivation area and yield per hectare. Wheat yield per hectare also shows significant variation between cities, ranging from 1.6 to 6.0 t ha⁻¹. The trend of increasing total maize production is consistent with the expansion of cultivated area, mainly in the Xiangyang, Ezhou, Tujia, and Miao Autonomous Prefectures and the Xiaogan area. Maize yield per hectare also varies considerably between cities, ranging from 2.6 to 4.7 t ha⁻¹. The per-hectare yield of rice in Jiangsu Province shows a steady increasing trend (Figure 4). Among the cities, Zhenjiang and Nantong have higher yields than others, with Yancheng being the main area for rice cultivation. Rice yield per hectare varies significantly between different cities, ranging from 6.7 to 10.4 t ha⁻¹. The increase in total wheat production is mainly due to Yancheng, Suqian, and Lianyungang areas, with wheat yield per hectare showing significant fluctuations among cities and between years. The increase in total Maize production is attributed to Xuzhou and Yancheng, while the highest per-hectare yields are in Taizhou and Changshu. Maize yield per hectare varies considerably between cities, ranging from 2.6 to 7.9 t ha⁻¹.

3.2. Area-Scaled Carbon Footprint

It can be observed that from 2005 to 2019, the unit area carbon footprint of rice has consistently been the highest in both Hubei and Jiangsu Provinces, followed by wheat, and maize has the lowest carbon footprint per unit area (Figure 5). However, the trends in the unit area carbon footprint for these three cereal crops vary between the provinces. In Hubei Province, the unit area carbon footprint of rice initially increased, then decreased, and increased again over the study period, with an average of 1167 ± 2 kg CE ha⁻¹. In contrast, in Jiangsu Province, the unit area carbon footprint of rice showed a steady upward trend, with an average value of 2599 ± 317 kg CE ha⁻¹. For maize production from 2005 to 2019, the unit area carbon footprint increased gradually in Jiangsu Province but decreased gradually in Hubei Province. The average unit area carbon footprint for maize was 787 ± 128 kg CE ha⁻¹ in Jiangsu Province and 768 ± 6 kg CE ha⁻¹ in Hubei Province. Throughout the study period, the unit area carbon footprint for wheat production in both Hubei and Jiangsu Provinces showed a continuous upward trend, but the growth rate was higher than that of rice and maize. The average unit area carbon footprint for wheat production was higher in Jiangsu Province compared to Hubei Province.

Additionally, we observe that in Hubei Province, rice, wheat, and maize have the highest unit area carbon footprints for fertilizers. For rice, the unit area carbon footprints of electricity and pesticides are second only to the fertilizer unit area carbon footprint. In Hubei Province, for wheat, both seeds and pesticides have relatively high unit area carbon footprints. In the case of maize, the unit area carbon footprint of plastic film is higher than that of other production inputs. In Jiangsu Province, the carbon footprints of production inputs differ from Hubei Province. In rice production, the unit area carbon footprint of electricity is higher than that of fertilizers, followed by pesticides. For wheat and maize, the overall pattern is consistent, with the highest unit area carbon footprint being that of fertilizers.

3.3. Yield-Scaled Carbon Footprint

We can see that from 2005 to 2019, there were significant differences in the unit yield carbon footprints of the three major grain crops, rice, maize, and wheat between Hubei and Jiangsu Provinces (Figure 6). Overall, maize consistently had the lowest unit yield carbon footprint. In Hubei Province, the unit yield carbon footprint of rice production had a lower trend before 2010 but increased thereafter (averaging 0.15 ± 0.03 kg kg⁻¹), which is related to changes in the unit yield carbon footprint of electricity. The unit yield carbon footprint of wheat in Hubei increased steadily (averaging 0.22 ± 0.06 kg kg⁻¹), primarily driven by the unit yield carbon footprint of fertilizers. The unit yield carbon footprint of maize in Hubei first decreased and then increased, averaging 0.15 ± 0.04 kg kg⁻¹. In Jiangsu Province, both rice and wheat showed a continuous increase in the unit yield carbon footprint (averaging 0.29 ± 0.03 kg kg⁻¹ and 0.19 ± 0.03 kg kg⁻¹, respectively), with rice having a higher unit yield carbon footprint than Hubei Province, while the unit yield carbon footprint of wheat and maize was generally lower than in Hubei Province. From a spatial perspective (Figure 7), the central regions of Hubei Province had lower unit yield carbon footprints for rice, wheat, and maize, while the southwest and southeast regions had the highest unit yield carbon footprints. In Jiangsu Province, the unit yield carbon footprint of rice showed a higher trend in northern Jiangsu compared to southern Jiangsu, with the coastal southeast region having lower unit yield carbon footprints. The spatial distribution of the unit yield carbon footprint for wheat exhibited the opposite trend to that of rice. In the central region of Jiangsu, the unit yield carbon footprint of maize was lower than in other regions. The unit yield carbon footprint of production inputs was consistent with the overall trend in unit yield carbon footprints.

3.4. Carbon Input Components

In Hubei Province from 2005 to 2019, the carbon inputs in rice production were primarily dominated by fertilizers, accounting for the largest proportion ranging from 37% to 54% (Figure 8). Irrigation electricity came next, with an average proportion of approximately 28%. Carbon emissions from only fertilizers and electricity accounted for around 75% of the total carbon emissions. Pesticides had a proportion slightly below fertilizers and electricity, with a relatively stable contribution, while seeds and fuel had smaller proportions. Regarding wheat production in Hubei, fertilizers still constituted the largest proportion of carbon input, although there was a decreasing trend from 72% in 2005 to 68% in 2019. Pesticides showed an increasing trend, averaging 5%, while seeds had a stable contribution. In the case of maize production in Hubei, the top three contributors to the carbon footprint were fertilizers, pesticides, and plastic film, together accounting for over 95% of the carbon footprint. Fertilizer input increased from 67% in 2005 to 78% in 2019, while pesticides and plastic film inputs decreased from 14% to 12%. For Jiangsu Province, in rice production, irrigation electricity had the highest proportion, averaging around 44.3%, followed by fertilizers and pesticides, averaging 52.4%. Seeds and plastic film had the smallest contributions. In wheat production in Jiangsu, the composition of carbon input was similar to Hubei, with fertilizers constituting the largest proportion and showing a decreasing trend. Pesticides had an increasing proportion. In the case of maize production in Jiangsu, the top three contributors were fertilizers, pesticides, and irrigation electricity, with average proportions of 76.5%, 16.2%, and 7.3%, respectively. However, the trends in carbon input composition were opposite to those in Hubei Province.

3.5. Crop Carbon Efficiency

We can see that although the carbon efficiency of each crop production in the two provinces varied differently from 2005 to 2019 (Figure 9). In Hubei Province, the carbon efficiency of rice production showed a trend of gradual decrease, followed by an increase and then a decrease, while in Jiangsu Province, it showed a trend of gradual decrease followed by stabilization. The average carbon efficiencies of rice production in the two provinces are 6.8 and 3.4 kg·kg⁻¹ CEff, respectively. For maize production, in Hubei Province, carbon efficiency initially increased, then decreased to the 2005 level, while in Jiangsu province, it decreased significantly after 2012. The average carbon efficiencies for maize production are 6.3 and 7.3 kg·kg⁻¹ CEff in the two provinces, respectively. Both Hubei and Jiangsu Provinces showed a decreasing trend in carbon efficiency for wheat production, with average values of 4.5 and 5.0 kg·kg⁻¹ CEff, respectively. Overall, in Hubei Province, the carbon efficiency of rice and maize production is higher than that of wheat, while in Jiangsu Province, the carbon efficiency of maize and wheat production is higher than that of rice.

4. Discussion

In order to find effective methods to reduce carbon input in agriculture, scholars worldwide have conducted extensive research on the carbon footprint assessment of crop production [24]. As China is the world’s largest producer and consumer of grains, domestic scholars have also shown increasing interest in the issue of carbon emissions in crop production [10]. In the assessment of agricultural carbon emissions, the carbon footprint is widely used. Some studies calculate agricultural carbon footprints based on greenhouse gas emissions [15,16]. However, due to the existence of temporal and spatial heterogeneity, it is challenging to measure actual carbon emissions in field production processes. Therefore, researchers often determine the boundaries of their study based on data availability [17]. At the regional level, many studies calculate carbon footprints based on statistical data related to crop production inputs. In this study, we utilized relevant national and provincial statistical data available from 2005 to 2019 to estimate and analyze the temporal and spatial carbon footprints and carbon efficiency of rice, maize, and wheat, the three major grain crops, in the Yangtze River middle and lower reaches region, including Hubei and Jiangsu Provinces. The goal is to propose targeted technical approaches to guide low-carbon agricultural production.

4.1. Grain Crop Production, Sowing Area, and Agricultural Input

For rice, from 2005 to 2019, both Jiangsu and Hubei Provinces have seen an increase in production, with Jiangsu Province showing the most significant increase. Jiangsu Province achieved this through measures such as variety improvement and optimizing water and fertilizer management to enhance yield per unit area. Hubei Province, on the other hand, benefited mainly from an expansion in planting area. Regarding wheat, in Jiangsu Province, there have been substantial increases in yield, planting area, and total production. In Hubei Province, the wheat planting area and yield per unit area have remained relatively stable. However, due to the relatively small original planting area of wheat in Jiangsu Province, the actual increase in production is not substantial. Jiangsu Province has a higher wheat planting area and yield per unit area than Hubei Province, accounting for 5.4% of the national total, making it one of the major wheat-producing regions in China, contributing to the country’s food security. For maize, both Jiangsu and Hubei Provinces have seen similar increases in total production. In Jiangsu Province, the increase is mainly attributed to higher yield per unit area, while in Hubei Province, it is due to an expansion in planting area. Analyzing agricultural production data at the city level, the increase in total rice production in Hubei Province can be attributed to cities like Jingzhou, Xiangyang, and Huanggang. Total wheat production is mainly influenced by Xiangyang City, and maize production benefits from the expansion of planting areas in cities like Xiangyang, Ezhou, and Xiaogan. The relationship between crop planting area, yield per unit area, and changes in total production is weaker in Hubei Province. In contrast, in Jiangsu Province, there is a close relationship between total production of the three major grain crops and the planting area and yield per unit area in each city. This is due to efficient agricultural input practices in the region. In summary, rice, wheat, and maize production in Jiangsu and Hubei Provinces play a significant role in China’s grain production. However, in recent years, there have been noticeable differences in changes in planting area and yield per unit area for these three crops.

In terms of agricultural input, for rice production in Hubei Province, the proportion of fertilizer in carbon input is the largest, with irrigation electricity ranking second. The carbon emissions from fertilizer and electricity alone account for approximately 75% of the total carbon emissions. The proportion of pesticides is lower but relatively stable. For wheat production, the proportion of fertilizer in carbon footprint composition has decreased from 72% in 2005 to 68% in 2019. However, there is a trend of increasing pesticide proportion, averaging around 5%. In the case of maize production in Hubei Province, the top three contributors to carbon footprint are fertilizer, pesticides, and plastic film, which together account for over 95% of the carbon footprint. In Jiangsu Province’s rice production, electricity for irrigation has the highest proportion in carbon input. For wheat production, the proportion of fertilizer in the carbon footprint composition is the largest, but it shows a decreasing trend, while pesticides show an increasing trend. In maize production, the top three contributors to the carbon footprint are fertilizer, pesticides, and irrigation electricity.

Fertilizer input is identified as the largest contributor to carbon emissions in agricultural production, consistent with findings from other studies [7,8].Cheng et al. [25] suggested that reducing nitrogen fertilizer usage by 30% could lead to a 6.5% reduction in the carbon footprint of rice and a 25.5% reduction in the carbon footprint of maize in their study on the carbon footprint of major crops in China in 2010. Therefore, both Hubei and Jiangsu Provinces should continue to research and promote the efficient use of fertilizers and increase the utilization of new types of fertilizers. Furthermore, in Jiangsu Province, rice cultivation involves significant electricity usage for irrigation. Adopting practices like alternate wetting and drying and drought-resistant rice varieties can effectively reduce the carbon footprint associated with electricity usage in rice production.

4.2. Carbon Footprint

During the period from 2005 to 2019, the unit area carbon footprint of rice in the study area was relatively high overall, with Jiangsu Province experiencing a higher increase compared to Hubei Province. This increase in rice’s unit area carbon footprint in Jiangsu Province is related to the increase in the unit area carbon footprint of electricity in the province. The unit area carbon footprint of both rice and wheat in Jiangsu Province and Hubei Province significantly increased, aligning with the increasing trend in the unit area carbon footprint of fertilizers. However, the unit area carbon footprint of maize in these two provinces exhibited opposite trends, with an increase in Jiangsu Province and a decrease in Hubei Province. In terms of unit grain yield carbon footprint, rice had the highest footprint in Jiangsu Province, followed by wheat, and maize had the lowest footprint. However, the changes in rice’s footprint during this period were relatively gradual. In Hubei Province, the differences in unit yield carbon footprints among the three crops were smaller, with wheat having an average unit yield carbon footprint higher than rice and maize. Looking at the spatial scale, in Hubei Province, the central regions of rice, wheat, and maize had lower unit yield carbon footprints, while the southwest and southeast regions had the highest unit yield carbon footprints. For Jiangsu Province, the unit yield carbon footprint of rice showed higher values in the northern part (SuBei), lower values in the southern part (SuNan), and even lower values in the southeastern coastal areas. The spatial distribution of unit yield carbon footprints for wheat exhibited the opposite trend to rice. In the SuZhong region of Jiangsu Province, maize had a lower unit yield carbon footprint compared to other regions. The unit input carbon footprint per unit yield was consistent with the overall unit yield carbon footprint trends.

Through the analysis of the carbon input structures in the two provinces, it can be observed that during the research period, Jiangsu Province had indirect carbon emissions primarily due to irrigation electricity, accounting for a high proportion of 44.3%, with fertilizers and pesticides ranking second, averaging 52.4%. In contrast, Hubei Province had the highest proportion of carbon emissions from fertilizers, ranging from 37% to 54%, while electricity for irrigation ranked second, averaging 28%. Notably, the carbon emissions from only fertilizers and electricity accounted for approximately 75% of the total carbon emissions. Reducing the impact of these two factors is crucial for the carbon footprint of crop production in these regions. In recent years, the promotion of technologies such as high-yield and water-saving irrigation for rice, as well as nitrogen reduction and density control techniques for wheat and maize, has been beneficial in improving water and fertilizer utilization efficiency. Additionally, the development and investment in agricultural machinery have contributed to increased yields and improved efficiency in both provinces’ major cereal crops. These efforts are expected to simultaneously reduce carbon emissions, although there may be variations among different crops and provinces.

Crop carbon efficiency is a good indicator of crop yield per unit of carbon input. During the period from 2005 to 2019, in terms of crop production carbon efficiency, Jiangsu Province had the highest efficiency for maize, followed by wheat, and rice had the lowest efficiency. The low carbon efficiency of rice in Jiangsu Province can be attributed to the excessive use of fertilizer in rice cultivation. In Hubei Province, there were significant variations in the carbon efficiency of rice over the years. However, during the period from 2005 to 2019, the carbon efficiency of rice, wheat, and maize all decreased. This decline was primarily due to the excessive input of fertilizers, which led to an increase in carbon input. Research by Chen et al. [26] in a long-term nitrogen fertilizer optimization experiment in Hunan, China demonstrated that a 20% reduction in artificial nitrogen application could reduce greenhouse gas emissions in the upstream production process of rice.

4.3. Emission Reduction Measures

Research has shown that reducing the carbon footprint primarily relies on improving the efficiency of agricultural production resources [27]. (1) Improving the efficiency of agricultural production resources is key to reducing the carbon footprint. Scientific planning and managing field irrigation not only increase crop yields but also help reduce carbon emissions. Additionally, optimizing fertilizer application to enhance fertilizer efficiency and establishing clear pesticide usage standards to reduce pesticide use and enhance effectiveness are important measures to reduce the carbon footprint [27]. (2) With the rapid development of agricultural modernization, the increasing use of diesel fuel due to the improvement of agricultural mechanization poses a challenge to low-carbon agriculture. Therefore, extending the service life of agricultural machinery and increasing machinery utilization to reduce fossil fuel consumption are crucial directions for promoting low-carbon agriculture. Agricultural mechanization boosts productivity and environmental efficiency in the sector [28], crucial for sustainable agriculture. Trans-regional agricultural machinery operations can cut energy use and carbon emissions by spreading grain production benefits spatially [29]. (3) Implementing practices like organic farming and other environmentally friendly soil fertility management measures can effectively sequester carbon and reduce carbon emissions from agricultural production [30,31,32]. (4) By optimizing breeding techniques and developing high-yield, low-emission crop varieties, it is possible to increase crop yield per unit area while effectively controlling carbon emissions from crop production [33,34]. (5) When formulating agricultural carbon reduction policies, regional differences should be considered. Using financial and tax incentives, carbon market trading, and other means to balance carbon reduction responsibilities with regional production levels is important [35]. As agricultural industry scales up, the pressure to reduce emissions in major grain-producing regions increases. Adhering to the principles of scientifically developing agriculture, improving resource utilization efficiency, and reducing environmental pollution is the fundamental way to achieve the development of low-carbon agriculture.

This study utilized national and provincial-level statistical data to calculate the carbon footprints of crop production. The calculation process only considered the six main agricultural inputs in crop production. It achieved a relatively comprehensive calculation, measurement, and comparison of indirect carbon emissions for each crop under the same standard. It objectively reflected the similarities and differences in the carbon footprints of the three major grain crops in Hubei and Jiangsu Provinces. However, the assessment of carbon footprints is limited by the lack of data related to direct greenhouse gas emissions and soil carbon sequestration levels in crop production. At the current stage, only data from specific experimental points obtained in previous studies can be used to calculate direct carbon emissions. Nevertheless, there are significant differences in direct carbon emissions and soil carbon sequestration levels among different crop systems [36]. Therefore, extrapolating from point to area may not be representative and may differ significantly from the actual situation. If we want to comprehensively represent the absolute carbon footprint levels of different grain crop production in the regions, it would require the establishment of a large number of representative experimental points to collect data. However, further research and discussion are needed to implement this method effectively.

5. Conclusions

In Jiangsu Province, rice had the highest carbon footprint per unit of grain yield, followed by wheat, and maize had the lowest carbon footprint. However, rice’s carbon footprint remained relatively stable during this period. In Hubei Province, the differences in carbon footprints per unit yield among the three crops were relatively small, with wheat having an average higher carbon footprint per unit yield compared to rice and maize. Chemical fertilizers and irrigation electricity were the main sources of carbon emissions in crop production in both provinces. Particularly in Jiangsu Province, irrigation electricity was a significant contributor, while Hubei Province relied more on chemical fertilizers. The assessment of carbon footprints highlighted that inefficient use of chemical fertilizers was a major driving factor for carbon emissions. Therefore, improving the efficiency of fertilizer and water resource utilization, reducing pesticide use, and promoting environmentally friendly agricultural practices such as drought-resistant rice varieties and straw returning are considered crucial for reducing the carbon footprint of crop production in these regions. Furthermore, optimizing breeding techniques, developing high-yield low-emission crop varieties, and considering regional differences to formulate precise agricultural carbon reduction policies are expected to provide more effective pathways for achieving low-carbon agricultural development.