Integrated Models of Cleaner Production Technologies for Maize Cultivation in China’s Black Soil Regions

Abstract

Incorporating carbon footprints into the production efficiency framework to construct a standardized technology model for cleaner production in black soil regions is of great significance for improving the soil environment and the sustainable development of agriculture. This study used an orthogonal experimental design and the DEA–Malmquist method to calculate the carbon footprint and total factor productivity of orthogonal experimental groups of cleaner production technologies for maize cultivation in China’s black soil region and then identified integrated models of cleaner production technology in the black soil region. The results showed that the carbon footprint of maize cultivation and total factor productivity were generally higher in the experimental group using cleaner production techniques than in the control group. Still, none of them reached the optimum. In the future, the synergistic effect of technological progress and technological efficiency enhancement should be brought into play, and the integrated models of “Soil testing and formulation + Full mobile sprinkler irrigation + Straw tilling and field return” and “No tillage in spring + Soil testing and formulation + Straw tilling and field return” should be promoted in semi-arid and semi-humid black soil regions, which can improve the low carbon productivity of maize by 20.3% and 15.4%, respectively.

1. Introduction

Black soil is an important land resource in the world. The Northeast China Plain, the Ukraine Plain in Eastern Europe, and the Mississippi Plain in the United States are known as the world’s three largest black soil regions, possessing the world’s most fertile soil, and are regarded as the most suitable land for farming [1]. As an important advantageous area of grain production and the largest commercial grain production base, the black soil region of northeastern China bears a heavy burden of responsibility for national food security. However, in recent years, due to the irregular operation of agricultural production and low utilization of resources and other factors [2], the black soil region has been facing serious threats such as soil erosion, land consolidation, reduction of soil organic matter content, and pollution of water bodies [3]. At the same time, CO₂ emissions from agricultural production activities account for about 10% of the country’s total CO₂ emissions from all types of industries [4], contrary to the sustainability requirements of agrarian development. To effectively curb this phenomenon, the Chinese government, after promulgating the Law of the People’s Republic of China on the Promotion of Clean Production in 2002, has successively issued the revised version of the Law of the People’s Republic of China on the Promotion of Clean Production, the National Cleaner Production Promotion Program for the 14th Five-Year Plan, and the Implementation Program for Reducing Carbon Emissions and Sequestration in Agriculture in 2012, 2021, and 2022. The introduction of this series of laws and regulations in favor of the continued promotion of eco-agriculture and green agriculture is a tangible reflection of China’s ambition to address the irregularities in agricultural production practices (as well as an indication of the urgency and necessity of implementing appropriate cleaner production technologies in agriculture at this stage) and of the transition of the sector to a more economic and productive one.

Cleaner agricultural production technologies fall into six categories: fertilizer reduction and substitution [5], pesticide reduction and efficiency [6], straw comprehensive utilization [7], water-saving irrigation [8], agricultural film recycling and treatment [9], and conservation tillage [10] technologies. At present, research perspectives on cleaner production technologies in agriculture mostly focus on the overall analysis and extension of the concept of cleaner production technologies in agriculture, such as the principle of the technology [11], the impact of technology application on soil activity [12], the analysis of the behavior [13] and willingness [14] of farmers to adopt the technology, and economic compensation [15], etc. There is still a lack of research on how to maximize the role of cleaner production technologies in agricultural production practice and how to effectively integrate a variety of cleaner production technologies to improve the green production capacity of agriculture. Therefore, how to maximize technical and economic benefits by constructing a relatively optimal technical model is a problem that needs to be explored and solved urgently.

Agricultural cleaner production technology is a fundamental initiative to save the agricultural means of production and to protect the farmland environment, but at present, the practice of agricultural cleaner production in China is still in the early exploratory stage. The standardization technology system of cleaner production technology is still at the stage of establishing frameworks [16], such as the framework of an environmental standard system for agricultural production [17] and the framework of a multi-level linkage system for cleaner production [18]. Cleaner production technologies have been applied and researched more in industry but relatively little in agriculture, and studies have generally focused on crops such as rice [11,13] and vegetables in facilities [12,19], with less attention paid to maize. Maize is one of the most widely grown food crops in the world, and China is the world’s second-largest producer of maize, with its 2022 maize production accounting for 24.1%1 of global maize production. As one of the prime advantageous production areas for maize production in China, the black soil region plays a crucial role in stabilizing the global maize supply. To alleviate the negative impact of CO₂ emissions on the environment during maize cultivation, to promote the application of cleaner production technologies, to improve the productivity of maize and the ecological development of maize production, and to realize the goal of low-carbon cultivation of maize, there is an urgent need to construct and promote a standardized technology system for the cleaner production of maize cultivation in the black soil region. At the same time, safeguarding the ecological conditions of the main maize-producing areas is a critical prerequisite for ensuring maize production capacity, and the clean and sustainable production of maize cultivation plays an essential role in ensuring national and even global food security as well as environmental protection of black soil. As a result, it is imperative to explore an integrated model of cleaner production technology for maize cultivation in black soil regions.

Taking the actual application of cleaner production technology for maize cultivation in China’s black soil regions as the starting point, guided by whole life cycle assessment (LCA), this study creatively combines the orthogonal experimental design method, the method of measuring the carbon footprint of maize farmland ecosystems, and the DEA (data envelopment analysis)–Malmquist index evaluation method to provide a low-carbon, high-yield, sustainable and easily replicable integrated model of cleaner production technology for maize cultivation, thereby bridging the gap in standardized technology systems for cleaner production in the field of maize cultivation. At the same time, it also broadens research ideas in the field of verification of agricultural technology application and model promotion. The main research contents are as follows: (1) Determination of the orthogonal test group of clean production technology for maize cultivation in black soil regions based on the orthogonal experimental design method. (2) Measurement of the carbon footprint of each orthogonal experimental group of maize planting using cleaner production technologies in black soil regions by applying the carbon footprint measurement method of maize farmland ecosystems to provide a data reference for the integration of cleaner maize planting production technologies in black soil regions. (3) Construction of a production efficiency evaluation model for orthogonal test groups of cleaner maize planting production technology in black soil regions, measurement of the total factor production efficiency of orthogonal test groups, and finally combination of the results of the DEA–Malmquist evaluation to effectively identify an integrated model of cleaner maize planting production technology in black soil regions.

2. Materials and Methods

2.1. Orthogonal Experimental Design Method

The orthogonal experimental design method is a scientific method for studying and handling multifactorial experiments. It was first proposed by Genichi Taguchi [20], a Japanese quality management expert, to scientifically select test conditions and rationally arrange test programs using orthogonal tables. The main cleaner production technologies applied to maize cultivation in China’s black soil regions include fertilizer reduction and substitution (F), water-saving irrigation (I), straw comprehensive utilization (S), and conservation tillage (T), each of which can be subdivided into several sub-technologies. For example, fertilizer reduction and substitution technology (F) is subdivided into soil testing and formulation (F₁) and increased application of organic fertilizers2 (F₂). Water-saving irrigation (I) is subdivided into integration of water and fertilizer (I₁) and spray irrigation (I₂). Straw comprehensive utilization (S) is subdivided into straw mulching and field return (S₁) and straw tilling and field return (S₂). Conservation tillage (T) is subdivided into no tillage (T₁) and reduced tillage (T₂). In this study, the black soil regions of China were divided into semi-arid (A) and semi-humid (H) black soil regions according to annual rainfall, and three orthogonal test factors and two orthogonal test levels were determined in each of the two regions (

Table 1. Factors and levels of orthogonal test of cleaner production technology for maize cultivation in black soil regions.

Test Level	Semi-Arid Black Soil Regions			Semi-Humid Black Soil Regions
	F	I	S	T	F	S
1	F1	I1	S1	T1	F1	S1
2	F2	I2	S2	T2	F2	S2

), which provided a theoretical basis for the scientific design of the integrated program of cleaner production technologies for maize cultivation in the black soil regions and facilitated the clarification of the economic and ecological values of maize production in the black soil regions brought about by the mixed application of multiple cleaner production technologies.

2.2. Carbon Footprint Measurement Method of Maize Farmland Ecosystems

In Equation (1), the carbon footprint of maize agro-ecosystems ($N_t$) is the difference between the amount of carbon absorbed ($C_t$) during the whole life cycle of maize and the carbon emissions ($GHGemissions)$from farm inputs. When $N_t>0$, it indicates that the agro-ecosystem has carbon sink status; when $N_t<0$, it indicates that the agro-ecosystem has carbon source status.

$$N_t=C_t-GHGemissions$$

(1)

$$C_t=C_f{D}_w=C_f{Y}_w(1-W)/H$$

(2)

$C_f$ in Equation (2) represents the amount of carbon that needs to be absorbed by maize to synthesize a unit mass of dry matter. The biological yield of maize is the weight of all dry matter harvested per unit area of land, including the dry weight of straw and seeds, generally excluding underground roots, and is expressed as $D_w$. The economic yield of maize is the yield of seeds harvested per unit area of land and is expressed as $Y_w$. The economic coefficient of maize is the proportion of economic yield to biological yield and is expressed as $H$. $W$ indicates the water content of maize. The calculated coefficients [21] are shown in

Table 2. Economic coefficients, carbon sequestration, and water content of maize.

	Economic Coefficients	Carbon Sequestration	Water Content
Maize	0.40	0.47	0.13

.

Carbon emissions from agricultural inputs refer to the sum of CO₂ emissions from seeds, composite fertilizers, organic fertilizers, pesticides, irrigation electricity, and the diesel fuel usage process as well as direct N₂O emissions from farmland from maize from sowing to harvest. The formulae are as follows:

$$GHGemissions=\sum _{i=1}^{n}A{I}_i\times E{F}_i+GH{G}_{N_{2}O}$$

(3)

$$GH{G}_{N_{2}O}=F_N\times {\delta }_N\times \frac{44}{28}\times 265$$

(4)

In Equation (3), $GHGemissions$ are the total amount of carbon emissions (kg·CO₂-eq) during maize production, $n$ indicates the number of inputs of agricultural materials during maize production, $A{I}_i$ indicates the inputs of type $i$, and $E{F}_i$ indicates the emission parameter of the inputs of type $i$. (

Table 3. CO₂ emission parameters and sources.

Agricultural Materials	Emission Parameters	Parameter Sources
Maize seeds	1.93 kgCO2eq/kg	Ecoinvent 2.2
Composite fertilizers	1.77 kgCO2eq/kg	CLCD 0.7
Herbicides	10.15 kgCO2eq/kg	Ecoinvent 2.2
Insecticides	16.61 kgCO2eq/kg	Ecoinvent 2.2
Diesel fuel usage process	4.10 kgCO2eq/kg	Ecoinvent 2.2
Organic fertilizers (N)	9.18 kgCO2eq/kg	SimaPro 9.5.0
Organic fertilizers (P)	1.18 kgCO2eq/kg	SimaPro 9.5.0
Organic fertilizers (K)	0.67 kgCO2eq/kg	SimaPro 9.5.0
Irrigation electricity	1.23 kgCO2eq/kWh	CLCD 0.7
Direct on-farm N2O emissions	0.01 kgN/kg	IPCC

). In Equation (4), $GH{G}_{N_{2}O}$ is the direct N₂O emission from farmland (kg·N₂O-N), $F_N$ is the pure nitrogen input, ${\delta }_N$ is the direct N₂O emission parameter from farmland, 44/28 is the proportion of N₂O to N₂ molecular weight, and 265 is the conversion of N₂O to a relative global warming trend on a 100a scale. Carbon emissions were characterized uniformly in terms of CO₂ equivalent (CO₂eq)3.

2.3. Malmquist Index

The Malmquist model was introduced in 1953 and was initially used to study changes in consumption over time. In 1994, Fare [22] combined the Malmquist model with the DEA method to construct the Malmquist index $M\left(x^t,y^t,x^{t+1},y^{t+1}\right)$ from period $t$ to $t+1$ to identify the internal causes affecting the change in total factor productivity ($Tfp$) from the perspectives of technical efficiency change ($Effch$) and technical change ($Techch$). When $Tfp>1$, it indicates that overall factor productivity outperformed the previous year, and vice versa.

$$M\left(x^t,y^t,x^{t+1},y^{t+1}\right)=\sqrt{\frac{D^t\left(x^{t+1},y^{t+1}\right)}{D^t\left(x^t,y^t\right)}\times \frac{D^{t+1}\left(x^{t+1},y^{t+1}\right)}{D^{t+1}\left(x^t,y^t\right)}}$$

(5)

In Equation (5), $D^t$ and $D^{t+1}$ indicate the distance functions for the periods $t$ and $t+1$, $x^t$ and $x^{t+1}$ the inputs for the periods $t$ and $t+1$, and $y^t$ and $y^{t+1}$ the corresponding outputs. Equation (5) is further broken down into Equation (6).

$$M\left(x^t,y^t,x^{t+1},y^{t+1}\right)=\frac{D^{t+1}\left(x^{t+1},y^{t+1}\right)}{D^t\left(x^t,y^t\right)}\times \sqrt{\frac{D^t\left(x^t,y^t\right)}{D^{t+1}\left(x^t,y^t\right)}\times \frac{D^t\left(x^{t+1},y^{t+1}\right)}{D^{t+1}\left(x^{t+1},y^{t+1}\right)}}$$

(6)

In Equation (6), $\frac{D^{t+1}\left(x^{t+1},y^{t+1}\right)}{D^t\left(x^t,y^t\right)}=Effch$ represents the change in technical efficiency between two periods, indicating the change in the distance between DMUs, the level of technology used, and management at the production frontier in different periods, while $Effch>1$ represents technical efficiency improvement and vice versa. In the variable returns to scale model ($VRS$), technical efficiency change ($Effch$) may be decomposed into scale efficiency change ($Sech$) and pure technical efficiency change ($Pech$). $\sqrt{\frac{D^t\left(x^t,y^t\right)}{D^{t+1}\left(x^t,y^t\right)}\times \frac{D^t\left(x^{t+1},y^{t+1}\right)}{D^{t+1}\left(x^{t+1},y^{t+1}\right)}}=Techch$ represents the level of technical change and innovation in the manufacturing of each DMU, whereas $Techch>1$ represents a shift in front of the frontier and technological development, and vice versa.

2.4. Statistical Analysis

In this study, the application of cleaner production technologies as well as the input and output of maize farmers’ respective agricultural resources were investigated in the field using a questionnaire, random sampling, and interview methods in the area covered by black soil in Jilin Province, China; a total of 589 questionnaires and 568 valid questionnaires were collected, showing a validity rate of 96.4%, with 450 adopting cleaner production technologies and 118 not. The orthogonal experimental design table was created using SPSSAU (Version 21.0). Microsoft Excel 2016 was used to collect data, create datasets, and calculate the carbon footprint of maize agroecosystems. DEAP (Version 2.1) was used to assess Malmquist total factor productivity and deconstruct each production factor using variable returns to scale. There are four input indicators for measuring maize cultivation’s cleaner production efficiency in semi-humid black soil regions: the number of seeds, fertilizers, pesticides, and machinery used per hectare. In semi-arid black soil regions, irrigation inputs (mostly the cost of electricity for irrigation) were added to the four input indicators, while accounting for precipitation. The output indicators are the total ecosystem carbon footprints per hectare of maize field.

3. Results

3.1. Orthogonal Test Matrix of Cleaner Production Technologies for Maize Cultivation in Black Soil Regions

In this study, we assessed the application of cleaner production technologies as well as the input and output of maize farmers’ respective agricultural practices according to the orthogonal test factors and levels of cleaner production technology for maize cultivation in black soil regions (Table 1). We identified four treatments with a total of eight orthogonal test groupings for cleaner production technology for maize cultivation in semi-arid black soil regions and semi-humid black soil regions, respectively. (

Table 4. Orthogonal experimental design [L₄(2³)].

Semi-Arid Black Soil Regions					Semi-Humid Black Soil Regions
No.	F	I	S	Treatment	No.	T	F	S	Treatment
A1	F1	I1	S1	F1I1S1	H1	T1	F1	S1	T1F1S1
A2	F1	I2	S2	F1I2S2	H2	T1	F2	S2	T1F2S2
A3	F2	I1	S2	F2I1S2	H3	T2	F1	S2	T2F1S2
A4	F2	I2	S1	F2I2S1	H4	T2	F2	S1	T2F2S1
A-CK	F3	I3	S3	F3I3S3	H-CK	T3	F3	S3	T3F3S3

Note: A-CK, Semi-arid control check; H-CK, Semi-humid control check.

). The semi-arid black soil regions were controlled by traditional fertilization (F₃), traditional irrigation (I₃), and straw not returned to the field (S₃), and the semi-humid black soil regions were controlled by traditional tillage (T₃), traditional fertilization (F₃), and straw not returned to the field (S₃).

3.2. Carbon Footprint Performance of Subgroups of Orthogonal Tests of Cleaner Production Technologies for Maize Cultivation in Black Soil Regions

Based on the results of the field questionnaire survey and using Equations (1)–(4) in Section 2.2, the carbon footprints of the orthogonal test groupings of maize cultivation with cleaner production technologies in China’s black soil regions were measured from 2019 to 2023, as shown in

Table 5. $GHGemissions$, $C_t$, and $N_t$ of the orthogonal test group of cleaner production technology for maize cultivation in black soil regions of China. Units: kg CO₂eq/ha.

	No.	Treatment	2019	2020	2021	2022	2023	Mean
$GHGemissions$	A1	F1I1S1	3323.86	4345.70	4340.05	4231.31	4385.11	4125.21
	A2	F1I2S2	4166.29	4542.26	4360.34	4351.88	4388.32	4361.82
	A3	F2I1S2	4126.94	5156.16	5059.06	6337.14	5866.61	5309.18
	A4	F2I2S1	4320.85	5236.17	5092.04	5992.89	5648.97	5258.18
	A-CK	F3I3S3	3560.40	4515.52	4521.57	4689.63	4744.14	4406.25
	H1	T1F1S1	4145.71	4028.63	4126.46	3731.00	3691.58	3944.68
	H2	T1F2S2	3684.85	4043.61	3858.24	4059.43	4683.76	4065.98
	H3	T2F1S2	4178.86	4181.98	4288.40	3782.34	3842.85	4054.89
	H4	T2F2S1	4163.37	5349.61	5129.80	5143.94	5219.16	5001.18
	H-CK	T3F3S3	5260.01	4558.65	4549.10	4573.35	4524.44	4693.11
$C_t$	A1	F1I1S1	6817.37	8598.69	8244.94	6527.88	8754.19	7788.61
	A2	F1I2S2	7163.47	8005.95	7620.02	4875.83	7784.24	7089.90
	A3	F2I1S2	7270.01	7837.77	8207.28	6995.73	8524.20	7767.10
	A4	F2I2S1	7438.14	8232.58	7570.32	6882.30	8319.04	7688.48
	A-CK	F3I3S3	5924.15	7771.67	7908.17	5589.47	6295.16	6697.72
	H1	T1F1S1	7788.03	8868.12	9067.09	5411.83	7884.67	7803.95
	H2	T1F2S2	8177.15	9299.21	9395.90	9131.71	9405.14	9081.82
	H3	T2F1S2	7396.33	8454.23	8728.17	7349.48	8017.50	7989.14
	H4	T2F2S1	8423.55	9525.58	9740.03	9834.02	8683.05	9241.25
	H-CK	T3F3S3	8475.99	8475.99	8391.36	6535.13	7212.31	7818.16
$N_t$	A1	F1I1S1	3493.51	4252.99	3904.89	2296.57	4369.08	3663.41
	A2	F1I2S2	2997.18	3463.69	3259.68	523.95	3395.92	2728.08
	A3	F2I1S2	3143.08	2681.61	3148.22	658.58	2657.59	2457.82
	A4	F2I2S1	3117.29	2996.41	2478.28	889.41	2670.07	2430.29
	A-CK	F3I3S3	2363.75	3256.15	3386.59	899.84	1551.03	2291.47
	H1	T1F1S1	3642.32	4839.49	4940.63	1680.83	4193.09	3859.27
	H2	T1F2S2	4492.30	5255.60	5537.66	5072.27	4721.38	5015.84
	H3	T2F1S2	3217.47	4272.25	4439.77	3567.15	4174.66	3934.26
	H4	T2F2S1	4260.19	4175.98	4610.23	4690.09	3463.89	4240.08
	H-CK	T3F3S3	3215.98	3917.34	3842.26	1961.78	2687.88	3125.05

. From the comprehensive assessment of regional carbon footprints, in semi-arid and semi-humid black soil regions of China, the carbon footprints of the orthogonal experimental groups using cleaner production technologies were higher than those of the traditional control group as a whole, which to a certain extent proved the carbon reduction effect of the application of cleaner production technologies on maize farmland. In terms of the performance of the carbon footprint of each group, the carbon footprint performance of the F₁I₁S₁ test group in semi-arid black soil regions was the best, and the carbon footprint performance of the T₁F₂S₂ test group in semi-humid black soil regions was also the best. During the five years, carbon emissions from maize cultivation in semi-arid black soil regions all demonstrated an upward trend, and fluctuating downward trends were observed in semi-humid black soil regions, except for T₁F₂S₂ and T₂F₂S₁, which were on the rise. The carbon absorption of maize cultivation in semi-arid and semi-humid black soil regions fluctuated upward, except for T₃F₃S₃, which decreased. The carbon footprint of maize cultivation in semi-arid black soil regions fluctuated downward, except for an increase in F₁I₁S₁ and F₁I₂S₂, while in semi-humid black soil regions, it fluctuated upward, except for a decrease in T₂F₂S₁ and T₃F₃S₃.

3.3. Evaluation of Production Efficiency of Orthogonal Test Subgroups of Cleaner Production Technology for Maize Cultivation in Black Soil Regions

3.3.1. Comprehensive Technical Efficiency Analysis

The comprehensive technical efficiency ($Ce$) reflects the combined level of efficiency of the measured experimental subgroup; if the comprehensive technical efficiency = 1, it indicates that the subgroup is DEA-efficient and on the production frontier; if the comprehensive technical efficiency < 1, it indicates that the subgroup is relatively inefficient in its production, probably due to low pure technical and scale efficiency. From the overall performance of comprehensive technical efficiency (

Table 6. Comprehensive technical efficiency ($Ce$) of the orthogonal test group of cleaner production technology for maize cultivation in black soil regions of China.

No.	Treatment	2019	2020	2021	2022	2023	Mean
A1	F1I1S1	0.924	0.958	0.958	0.953	0.959	0.950
A2	F1I2S2	0.965	0.982	0.976	0.991	0.984	0.980
A3	F2I1S2	0.942	0.962	0.959	0.941	0.971	0.955
A4	F2I2S1	0.935	0.946	0.948	0.952	0.970	0.950
A-CK	F3I3S3	0.880	0.894	0.898	0.907	0.963	0.908
H1	T1F1S1	0.845	0.895	0.897	0.909	0.938	0.896
H2	T1F2S2	0.821	0.974	0.970	0.972	0.966	0.939
H3	T2F1S2	0.877	0.944	0.947	0.965	0.969	0.940
H4	T2F2S1	0.864	0.945	0.930	0.944	0.951	0.926
H-CK	T3F3S3	0.840	0.844	0.850	0.860	0.898	0.858

), the production efficiency of the experimental subgroups of cleaner production technologies for maize cultivation in China’s black soil regions showed an overall increasing trend and was higher than that of the control group, among which the production efficiency of the experimental subgroup F₁I₂S₂ in the semi-arid black soil regions was relatively optimal, and the eco-efficiency of the experimental subgroup T₂F₁S₂ in the semi-humid black soil regions was relatively optimal. However, none of the experimental groups of cleaner production technologies reached the effective frontier of DEA during the examination period. To ensure the sustainability of the experimental groups of cleaner production technologies for maize cultivation in the black soil regions to increase production and reduce carbon emissions, a reasonable allocation of factors is the key to stabilizing production and improving production efficiency [23], and there is an urgent need to optimize the structure of inputs of factors of production to further enhance the production efficiency of the experimental groups of cleaner production technologies.

3.3.2. Tfp Index and Decomposition Analysis

From the Malmquist index and its decomposition value (

Table 7. Malmquist index summary of annual means of the orthogonal test group of cleaner production technology for maize cultivation in black soil regions of China.

No.	Treatment	Effch	Techch	Pech	Sech	Tfp
A1	F1I1S1	0.942	1.098	1.010	0.933	1.034
A2	F1I2S2	1.171	1.028	1.005	1.164	1.203
A3	F2I1S2	0.474	1.023	1.008	0.471	0.486
A4	F2I2S1	0.709	1.019	1.010	0.702	0.723
A-CK	F3I3S3	0.527	0.928	1.025	0.514	0.489
H1	T1F1S1	1.067	1.004	1.030	1.036	1.071
H2	T1F2S2	1.016	0.979	1.046	0.972	0.995
H3	T2F1S2	1.116	1.034	1.027	1.087	1.154
H4	T2F2S1	0.462	1.003	1.026	0.450	0.463
H-CK	T3F3S3	0.541	1.100	1.021	0.530	0.595

), the total factor productivity change ($Tfp$) of test groups F₁I₁S₁ and F₁I₂S₂ in the semi-arid black soil regions was greater than 1, indicating that the total factor productivity of these two orthogonal test subgroups of cleaner production technologies for maize cultivation generally performed better than that of the previous year, i.e., the maize cultivation productivity of test groups F₁I₁S₁ and F₁I₂S₂ showed an upward trend over time. The change in total factor productivity ($Tfp$) for the T₁F₁S₁ and T₂F₁S₂ experimental groups in the semi-humid black soil regions is the same. To maximize resource utilization and outputs, a differentiated strategy has been adopted to address the overall performance of the different pilot groups of cleaner production technologies. Further pursuing the internal causes of low total factor productivity ($Tfp$) in the F₂I₁S₂, F₂I₂S₁, and T₂F₂S₁ test groups, it is not difficult to find that technical efficiency changes ($Effch$) are the most important. This indicates that the improvement of production efficiency in the test group of cleaner production technology for maize cultivation in the black soil regions should be synchronized with the improvement of the practical operational efficiency of the application of cleaner production technology and innovation in management methods in order to reverse in time the decline in the total factor production efficiency. At the same time, the scale effect of the concentration of China’s black soil regions should be brought into play to adapt to the new and changing situation of agricultural development. Given the reality that $Techch<1$ in the T₁F₂S₂ experimental group, this experimental group should enhance the driving role of technological progress in total factor productivity and improve the degree of technological innovation through the transformation and upgrading of cleaner production technology to realize the goal of comprehensively and omni-directionally upgrading the production efficiency of cleaner production technology for maize cultivation in black soil regions.

Combining the trends of Malmquist index for each cleaner maize cultivation and production technology orthogonal test group in China’s black soil regions (

Table 8. Trends and decomposition of the Malmquist index in orthogonal test groups of cleaner production technologies for maize cultivation in black soil regions of China.

No.	Treatment	Year	Effch	Techch	Tfp	No.	Treatment	Year	Effch	Techch	Tfp
A1	F1I1S1	2019–2020	0.890	1.383	1.230	H1	T1F1S1	2019–2020	1.240	1.177	1.459
		2020–2021	0.105	1.008	0.105			2020–2021	0.871	1.175	1.023
		2021–2022	0.019	1.053	0.020			2021–2022	0.001	0.808	0.001
		2022–2023	445.093	0.990	440.780			2022–2023	1751.561	0.910	1593.960
		Mean	0.942	1.098	1.034			Mean	1.067	1.004	1.071
A2	F1I2S2	2019–2020	1.975	1.065	2.104	H2	T1F2S2	2019–2020	1.062	1.187	1.261
		2020–2021	0.769	0.827	0.636			2020–2021	1.162	0.899	1.045
		2021–2022	0.983	0.857	0.842			2021–2022	0.841	0.956	0.804
		2022–2023	1.261	1.479	1.865			2022–2023	1.028	0.900	0.925
		Mean	1.171	1.028	1.203			Mean	1.016	0.979	0.995
A3	F2I1S2	2019–2020	0.115	1.099	0.127	H3	T2F1S2	2019–2020	1.202	1.227	1.475
		2020–2021	0.806	0.985	0.793			2020–2021	1.009	0.980	0.989
		2021–2022	0.001	0.880	0.001			2021–2022	0.140	1.019	0.143
		2022–2023	430.357	1.153	495.992			2022–2023	9.149	0.933	8.533
		Mean	0.474	1.023	0.486			Mean	1.116	1.034	1.154
A4	F2I2S1	2019–2020	0.613	1.235	0.757	H4	T2F2S1	2019–2020	1.093	0.952	1.041
		2020–2021	0.106	0.976	0.103			2020–2021	1.158	0.994	1.151
		2021–2022	0.002	0.918	0.002			2021–2022	0.909	1.152	1.047
		2022–2023	1915.691	0.972	1862.819			2022–2023	0.040	0.928	0.037
		Mean	0.709	1.019	0.723			Mean	0.462	1.003	0.463
A-CK	F3I3S3	2019–2020	0.981	2.219	2.176	H-CK	T3F3S3	2019–2020	3.904	1.328	5.184
		2020–2021	1.399	1.013	1.417			2020–2021	1.005	0.969	0.974
		2021–2022	0.000	0.498	0.000			2021–2022	0.003	0.826	0.003
		2022–2023	149.639	0.661	98.949			2022–2023	6.785	1.376	9.337
		Mean	0.527	0.928	0.489			Mean	0.541	1.100	0.595

), the changes in total factor productivity ($Tfp$) for test groups F₁I₁S₁, F₂I₁S₂, F₂I₂S₁, T₁F₁S₁, and T₂F₁S₂ during the period under investigation reached their lowest values in 2021–2022, and 2023 was the year with the fastest relative growth in production efficiency for each cleaner maize cultivation and production technology orthogonal test group. The change in total factor productivity ($Tfp$) for the F₁I₂S₂ and T₁F₂S₂ test groups reached its lowest value in 2020–2021, with 2020 being the year with the fastest relative growth in production efficiency for the two cleaner maize-growing production technology test groups. The change in total factor productivity ($Tfp$) for the T₂F₂S₁ test group reached its lowest value in 2022–2023, and 2021 was the year with the fastest rate of growth in production efficiency for the cleaner maize-growing production technology test group. Further pursuing the internal reasons for the change in total factor productivity ($Tfp$), we found that there were 18 time periods during the examination period when the change in technical efficiency ($Effch$) was smaller than the change in technology ($Techch$), which suggests that there is a mismatch between the application capacity of the relevant cleaner production technologies and the capacity for technological innovation, i.e., the actual operational efficiency of the application of the cleaner production technologies is significantly lower than the efficiency of technological innovation. In contrast, technical efficiency change ($Effch$) was bigger than technical change ($Techch$) over the 14 periods examined. Therefore, if we want to achieve improvements in total factor productivity in the orthogonal test groups of cleaner production technologies in black soil regions, it is important that we promote the coordinated and integrated development of technological innovation and technological application capacity, establish and improve the market-based service systems for the promotion and application of cleaner production technologies, realize a precise balance between technological innovation and practical application, improve the conversion rate of achievements in cleaner production technology, and promote the conversion of cleaner production technology into productivity.

4. Discussion

4.1. Analysis of Carbon Reduction Pathways of Maize Cultivation in Black Soil Regions

The carbon footprint of farmland is affected by a combination of carbon emissions and carbon sequestration [24], and the performance of the carbon footprint of farmland responds positively only if carbon emissions move in a single direction which is opposite that of carbon sequestration (carbon emissions decrease and carbon sequestration increases). Carbon emissions are an important indicator of the “cleanliness” of cleaner agricultural production technologies; therefore, controlling carbon emissions from cleaner agricultural production technologies is essential. The carbon emission composition of maize cultivation in China’s black soil regions was determined by calculating the average value of carbon emissions per hectare of farm inputs such as seeds, fertilizers, pesticides, diesel fuel, and irrigation in maize cultivation in China’s black soil regions from 2019 to 2023 (Figure 1).

Improving the “cleanliness” of maize cultivation in China’s black soil regions must revolve around three major sources of carbon emissions: fertilizers, diesel fuel, and direct on-farm N₂O emissions. (1) The proportion of carbon emissions from chemical fertilizers is much higher than that from organic fertilizers. The future scenario should be adjusted according to the soil nutrient measurements of the active ingredients in fertilizer and increase the proportion of organic fertilizer to replace chemical fertilizer; however, we must be wary of an “into” organic fertilizer and a “not back” fertilizer situation occurring. At the same time, effective control of nitrogen sources can reduce soil N₂O emissions [25]. (2) Agricultural mechanization is an indispensable and important method of reducing labor intensity, improving agricultural productivity, and increasing agricultural economic output. However, the massive use of agricultural machinery has caused a continuous rise in energy consumption, and agricultural machinery has thus become an important source of farmland environmental pollution that cannot be ignored; it is an important factor affecting CO₂ emissions from agriculture in China [26]. In the future, we should focus on the development, application, and promotion of green and clean agricultural machinery; accelerate research and development into agricultural machinery powered by electricity, solar energy, and other clean energy; reduce emissions; improve the efficiency of the use of energy power; and promote the transformation of agricultural energy consumption to cleaner energy consumption. (3) Although the proportion of carbon emissions from herbicides and insecticides is not high, the control of pests, diseases, and grasses during maize cultivation in China’s black soil regions is still dominated by chemical control methods. There are cases of applying insecticides with chlorantraniliprole, omethoate, cypermethrin, chlorpyrifos, etc., as the active ingredient. The threats to agroecosystems will undoubtedly be exacerbated by the fact that as pest resistance increases over the years, farmers are increasing their use of less toxic insecticides or preferring to apply more potent and more toxic ones. In the future, we should speed up the development and promotion of the biological control of pests and weeds and green control products and at the same time select insect-resistant high-yield varieties according to local conditions.

Carbon sinks are one of the ecological functions of food crops and the most desirable cropping state for maize agroecosystems. The United States and other developed countries have taken decades to explore the implementation of agro-ecological compensation measures [27], using carbon sink revenue to force food farmers to adopt conservation tillage, organic fertilizer substitution, straw return and other carbon sequestration and emission reduction modes in their fields to replace “passive adoption” with “subjective initiative” of food farmers and form a benign development cycle process. At the same time, carbon sinks are closely related to crop yields, and the cropping structure of farmland should be reasonably adjusted to build a good farmland ecological environment, fully releasing the functions of oxygen generation, purification, soil protection, water conservation, and maintenance of biodiversity [28] during the growth process of maize to increase crop yields while enhancing the carbon absorption capacity. Maize yields fluctuated during the study period due to climatic factors, which also contributed to the low trend in carbon sequestration. In the future, the emergency management system and system for stabilizing and maintaining production in response to extreme weather in agriculture should be further strengthened in order to minimize crop production losses.

4.2. Identification of Integrated Models of Cleaner Production Technologies for Maize Cultivation in Black Soil Regions

Based on the measurement results of comprehensive technical efficiency ($Ce$) and total factor productivity ($Tfp$) of eight orthogonal test groups, the efficiency evaluation matrix of the integrated model of cleaner production technology for maize cultivation in black soil regions was constructed. The eight orthogonal test groups were divided into four regions using a geometric mean of total factor productivity of 0.769 and a mean of comprehensive technical efficiency of 0.930 as boundaries, respectively, and the classification results are shown in Figure 2.

Region (a) belongs to the high-$Tfp$ low-$Ce$ region. It includes the T₁F₁S₁ test group, which, although relatively backward in terms of integrated technical efficiency, has a high A growth rate and high potential for catching up with advanced models of cleaner production technology. Region (b) belongs to the double-high region, including T₂F₁S₂, T₁F₂S₂, F₁I₂S₂, and F₁I₁S₁ test groups, which contains test groups with above-average integrated technical efficiency, $Tfp>1$ and has a fast growth rate and is the region with the best efficiency in the application of cleaner production technologies for maize cultivation in black soil regions. Region (c) belongs to the double-low region, including the T₂F₂S₁ experimental group and two control groups, and the experimental group contained in this region presents an integrated technological inefficiency and risk of retreat in $Tfp$, making it a relatively poor experimental group for the current application of cleaner production technologies for maize cultivation in black soil regions. Region (d) belongs to the high-$Ce$ low-$Tfp$ region, including F₂I₂S₁ and F₂I₁S₂, with high comprehensive technical efficiency, but when $Tfp<1$, there is a risk of retreat, and the future should focus on the innovation of cleaner production technology and the enhancement of the application capacity of current technology.

5. Conclusions and Recommendations

5.1. Conclusions

(1)

The carbon footprint of maize cultivation in the orthogonal experimental group using cleaner production technologies was generally higher than that of the traditional control group, which showed that the application of cleaner production technologies in the black soil regions is crucial for increasing production and reducing carbon emissions and that fertilizers, diesel fuel, and direct emissions of N₂O from farmland are the three main sources of carbon emissions from maize cultivation in China’s black soil regions; future focus should be placed on these three aspects to further improve maize cultivation’s carbon footprint in black soil regions.

(2)

The production efficiency of each experimental group of cleaner production technology for maize cultivation in China’s black soil regions showed an overall upward trend and was higher than that of the control group, but none of them reached DEA effectiveness. The uncoordinated development of technical efficiency and the rate of technical progress are the most important reasons for low production efficiency.

(3)

According to the efficiency evaluation matrix of orthogonal experimental groups for cleaner production of maize cultivation in black soil regions, the integrated technical efficiency and total factor production efficiency of test group F₁I₂S₂ in semi-arid black soil regions and test group T₂F₁S₂ in semi-humid black soil regions were relatively optimal.

5.2. Recommendations

(1)

Close attention should be paid to the combined impact of carbon emissions and carbon sequestration on the carbon footprint of the black soil region, and through the application of a reasonable combination of cleaner production technology modes, the excessive carbon emissions caused by the redundancy of agricultural material inputs during the reproductive period should be reduced. At the same time, a sound system of emergency management should be established for stable production and security of supply to enhance the degree of carbon sequestration.

(2)

Focusing on the innovation of cleaner production technologies, strengthening technology diffusion, and bringing into play the synergistic effect of technological progress and enhancements in technological efficiency is crucial in accelerating the construction of low-carbon, high-yield, and sustainable maize cultivation.

(3)

In semi-arid black soil regions, efforts should be made to demonstrate and promote the integrated model (F₁I₂S₂) of “Soil testing and formulation + Full mobile sprinkler irrigation + Straw tilling and field return” technology, which, together with effective yield-enhancement measures such as precision sowing, chemical control and anti-fall, and biological control of pests and diseases, can effectively enhance the low-carbon productivity of maize by 20.3%. In semi-humid black soil regions, efforts should be made to demonstrate and promote the “No tillage in spring + Soil testing and formulation + Straw tilling and field return” technology integrated model (T₂F₁S₂) supplemented by the construction of high-quality maize genetic genome groups; enhance unified prevention and control and improve the coverage of green control and prevention; and reduce the number of chemical pesticides used alongside other measures, which may effectively enhance the low-carbon productivity of maize by 15.4%.

5.3. Limitations and Future Prospects

This study analyzes models of cleaner production technology for maize cultivation in the black soil region of China, and more generalizable findings will be obtained if a larger sample size is established. This paper used the geometric mean of the Malmquist index to measure the impact of the cleaner production technology model on the low-carbon productivity of maize. To cope with maize yield losses caused by extreme weather, further research should be carried out on the relationship between the application of the models of cleaner production technology and the stability of maize production under climate change.