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, CO
2 emissions from agricultural production activities account for about 10% of the country’s total CO
2 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 CO
2 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 (F
1) and increased application of organic fertilizers
2 (F
2). Water-saving irrigation (I) is subdivided into integration of water and fertilizer (I
1) and spray irrigation (I
2). Straw comprehensive utilization (S) is subdivided into straw mulching and field return (S
1) and straw tilling and field return (S
2). Conservation tillage (T) is subdivided into no tillage (T
1) and reduced tillage (T
2). 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), 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 (
) is the difference between the amount of carbon absorbed (
) during the whole life cycle of maize and the carbon emissions (
from farm inputs. When
, it indicates that the agro-ecosystem has carbon sink status; when
, it indicates that the agro-ecosystem has carbon source status.
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
. The economic yield of maize is the yield of seeds harvested per unit area of land and is expressed as
. The economic coefficient of maize is the proportion of economic yield to biological yield and is expressed as
.
indicates the water content of maize. The calculated coefficients [
21] are shown in
Table 2.
Carbon emissions from agricultural inputs refer to the sum of CO
2 emissions from seeds, composite fertilizers, organic fertilizers, pesticides, irrigation electricity, and the diesel fuel usage process as well as direct N
2O emissions from farmland from maize from sowing to harvest. The formulae are as follows:
In Equation (3),
are the total amount of carbon emissions (kg·CO
2-eq) during maize production,
indicates the number of inputs of agricultural materials during maize production,
indicates the inputs of type
, and
indicates the emission parameter of the inputs of type
. (
Table 3). In Equation (4),
is the direct N
2O emission from farmland (kg·N
2O-N),
is the pure nitrogen input,
is the direct N
2O emission parameter from farmland, 44/28 is the proportion of N
2O to N
2 molecular weight, and 265 is the conversion of N
2O to a relative global warming trend on a 100a scale. Carbon emissions were characterized uniformly in terms of CO
2 equivalent (CO
2eq)
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
from period
to
to identify the internal causes affecting the change in total factor productivity (
) from the perspectives of technical efficiency change (
) and technical change (
). When
, it indicates that overall factor productivity outperformed the previous year, and vice versa.
In Equation (5),
and
indicate the distance functions for the periods
and
,
and
the inputs for the periods
and
, and
and
the corresponding outputs. Equation (5) is further broken down into Equation (6).
In Equation (6), 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 represents technical efficiency improvement and vice versa. In the variable returns to scale model (), technical efficiency change () may be decomposed into scale efficiency change () and pure technical efficiency change (). represents the level of technical change and innovation in the manufacturing of each DMU, whereas 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.