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Article

Maximizing Grains While Minimizing Yield-Scaled Greenhouse Gas Emissions for Wheat Production in China

by
Qi Miao
1,
Yixiang Sun
2,
Wenqi Ma
3,
Guiliang Wang
4,
Liang Wu
5,
Xinping Chen
6,
Xingshuai Tian
1,
Yulong Yin
1,
Qingsong Zhang
1,* and
Zhenling Cui
1
1
State Key Laboratory of Nutrient Use and Management, College of Resources and Environmental Sciences, China Agricultural University, Beijing 100193, China
2
Institute of Soil and Fertilizer, Anhui Academy of Agricultural Sciences, Hefei 230031, China
3
College of Resources and Environmental Science, Hebei Agricultural University, Baoding 071001, China
4
College of Environmental Science and Engineering, Yangzhou University, Yangzhou 225009, China
5
Key Laboratory of Crop Specific Fertilizer, Ministry of Agriculture and Rual Affairs, Xinyangfeng Agricultural Technology Co., Ltd., Jingmen 448001, China
6
College of Resources and Environment, and Academy of Agricultural Science, Southwest University, Chongqing 400716, China
*
Author to whom correspondence should be addressed.
Agronomy 2023, 13(11), 2676; https://doi.org/10.3390/agronomy13112676
Submission received: 27 September 2023 / Revised: 16 October 2023 / Accepted: 23 October 2023 / Published: 25 October 2023
(This article belongs to the Section Farming Sustainability)
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Abstract

:
Researchers have previously described the response of crop productivity and greenhouse gas (GHG) emissions to fertilizer nitrogen (N) additions, but they have not determined how to maximize yields while minimizing GHG emissions. We conducted an experiment at 2293 sites with four N levels to simulate both grain yield and yield-scaled GHG emissions in response to the N addition. The yield-scaled GHG emissions decreased by 16% as the N rate increased from treatments without the N addition to the minimum yield-scaled GHG emissions, which was comparable to the values associated with the maximum grain yields. The sites with both high soil productivity and high crop productivity had the highest yield and lowest yield-scaled GHG emissions, with 43% higher yield and 38% lower yield-scaled GHG emissions than sites with low soil and low crop productivity. These findings are expected to enhance evaluations of wheat production and GHG emissions in China, and thereby contribute to addressing disparities in the global food and GHG budget.

1. Introduction

A continuous increase in greenhouse gases (GHGs) in the atmosphere is expected to contribute to global warming [1,2]. The agricultural sector, including fertilizer production, is a direct contributor to 10–12% of global GHG emissions, and this percentage increases to 30% or higher when land conversion and emissions beyond the farm gate are included [3,4]. Although irrigation and fertilizer nitrogen (N) inputs often increase crop yields, the excessive utilization of these inputs has resulted in the widespread degradation of natural resources and an increase in global GHG emissions [5,6,7,8]. The imperative to balance the maximization of yields with the minimization of GHG emissions has garnered significant attention on a global scale [9,10].
Despite the potential effects of fertilizer N on environmental degradation and GHG emissions, fertilizer N remains essential for global crop production [8,11]. Highly productive agricultural systems are often associated with relatively large N losses and GHG emissions to the environment. The inherent difficulty in achieving high crop yields while simultaneously maintaining low GHG emissions stem from the fact that the crop productivity and N input are nonlinear and follow a well-established diminishing-return function. This means that the task of achieving both objectives simultaneously is inherently complex and demanding [12].
Researchers have proposed that some metrics, such as ‘GHG intensity’ or ‘yield-scaled GHG’, be used to evaluate the status of different crop production systems [13,14]. A meta-analysis for major cereal crops by Linquist et al., for example, indicated that the minimum yield-scaled global warming potential (GWP) was attained at 92% of the maximum yield for cereals [12]. A meta-analysis of rice production systems by Pittelkow et al., in contrast, indicated that the yield-scaled GWP from N2O and CH4 was minimized at optimal N rates and was 21% lower with the optimal rates than when N was not added [15]. It is apparent that a complete comprehension of the correlation between GHG emissions and crop productivity is yet to be achieved, as evidenced by the variations in these meta-analyses.
Crop productivity has often been found to be correlated with soil productivity, and soil productivity in cereal production systems is typically measured by quantifying grain yield in nonfertilized plots [16,17]. When soil productivity is defined as grain yield in the absence of a N addition, high soil productivity indicates a high soil N supply and a relatively low requirement for fertilizer N [18]. Agricultural GHG emissions from reactive N losses in the field are usually assumed to be a linear or an exponential response to N fertilizer input [6,19]. If this relationship is exponential, agricultural GHGs will be reduced with increasing soil productivity because of the relatively low requirements for fertilizer N. The extent to which the utilization of highly productive soils can reconcile agronomic and ethical considerations, specifically in terms of achieving environmentally sustainable agricultural production with lower reactive N and GHG emissions per unit crop yield, remains uncertain.
We hypothesize that the attainment of maximum wheat grain yield and the reduction of yield-scaled GHG emissions can be realized by employing soils of high productivity and management practices that promote elevated crop productivity. To test this hypothesis, we conducted large-scale field experiments that included plots that differed in soil productivity (yield without the N addition) and crop productivity (yield with the recommended level of the N addition). In China, wheat yields in farmer fields have stagnated at 5–6 Mg ha−1, even with the overuse of fertilizer and water (such as the application of >300 kg N ha−1 in intensive regions) [18]. Wheat yields obtained by agronomists, in contrast, are much higher [20]. Depending on the location, wheat in China is grown with irrigation (referred to as irrigated production), with rainfall and without significant water stress in most years (referred to as rainfall–wet production), and with rainfall and with significant water stress in most years (referred to as rainfall–dry production) (Figure 1). In this paper, these differences in water supply are considered to reflect the differences in wheat production systems.

2. Materials and Methods

2.1. Regions of Experimental Sites

A total 2293 experimental sites were designated in farmer wheat fields from 2007 to 2010. Among these sites, 924 sites were in irrigated production, 776 sites were in rainfall–wet production, and 593 sites were in rainfall–dry production (Figure 1). The area where wheat is grown with irrigation on the North China plain has a warm, temperate, subhumid, continental monsoon climate with cold winters and hot summers. The annual cumulative mean temperature for days with mean temperatures above 10 °C is 4000–5000 °C, and the annual frost-free period is 175 to 220 days. Annual precipitation is 500 to 700 mm, with approximately 30–40% of the rainfall occurring during the winter wheat-growing season (from the beginning of October to the middle of June).
In the Yangtze River plain, wheat is grown with rainfall–wet production, which has a humid subtropical and tropical climate with high temperatures and adequate rainfall throughout the year. The annual cumulative mean temperature for days with mean temperatures above 10 °C is 5000–5500 °C, and the annual frost-free period is 270 to 280 days. Annual precipitation is 1000 to 1200 mm, with approximately 40–50% of the rainfall occurring during the winter wheat-growing season (from the beginning of October to middle of June).
In the northwest of China, wheat is grown with rainfall–dry production. This region has a warm temperate monsoon or continental semiarid climate with cold winters and hot summers. The annual cumulative mean temperature for days with mean temperatures above 10 °C is 3500–4000 °C, and the annual frost-free period is 200 to 220 days. Annual precipitation is 300 to 500 mm, with approximately 30–40% of the rainfall occurring during the winter wheat-growing season (from the beginning of October to the middle of June).

2.2. On-Farm Field Experiments: Design, Crop Management, and Sampling Procedures

Every site was subjected to four N treatments: 0, 50%, 100%, and 150% of the recommended N rate (RNR). The RNR was recommended by local agricultural extension employees based on experience and the yield target. N was applied as granular urea [CO(NH2)2]. In irrigated and rainfall–wet production, about one-third of the urea was applied as a broadcast at sowing, and the remainder was applied at the stem-elongation stage before irrigation (for irrigated production) or before rainfall (for rainfall–wet production). In rainfall–dry production, all urea was applied as a broadcast at sowing. In irrigated production, and depending on the weather, winter wheat typically receives three irrigations (about 90 mm per irrigation): one before winter, one at the stem-elongation stage, and one around the anthesis stage. No irrigation water was applied for rainfall–wet and rainfall–dry production systems. All plots received approximately 90 kg P2O5 ha−1 as triple superphosphate [Ca(H2PO4)2∙H2O] and about 60 kg K2O ha−1 as potassium chloride (K2SO4) before planting.
Each site was divided into four plots to which the N treatments were randomly assigned, i.e., treatments were not replicated within a site. Plot size ranged from 41 to 120 m2. A randomized complete block design was used, with each site within each of the three production systems serving as a block.
The plots at each site were managed using each individual farmer’s current crop-management practices, except for the N-fertilization rate. Local agronomists recommended new varieties with resistance to disease, environmental stress, and lodging, and with the potential for high yields. These new varieties varied among sites. In addition, the planting date and plant density were based on local weather (e.g., mean temperatures) and were used to optimize the crop canopy and to make maximal use of light and temperature. Weeds were well-controlled with herbicide sprays or manual pulling. Pests and diseases were controlled with insecticides and fungicides that were applied before the stem-elongation stage and after anthesis. No obvious weed, pest, or disease problems were observed during the wheat-growing season for any of the plots. A network of extension collaborators provided guidance on-site during key operations, including sowing, fertilization, irrigation, and harvest.
At maturity, three separate areas (each 2–3 m2) were harvested manually in each plot. All plant samples were oven-dried at 70 °C in a forced-draft oven to a constant weight, and yields were adjusted to 125 g kg−1 moisture content.

2.3. Calculation of GHG Emissions

At each site, total GHG emissions (including CO2, CH4, and N2O) during the whole cycle of wheat production were divided into three components: (1) those emitted during the N fertilizer application, including direct and indirect N2O emissions, which can be calculated based on an empirical N-loss model (see below); (2) those emitted during fertilizer production and transportation; (3) those emitted during the production and transportation of pesticides to the farm gate and during the use of diesel fuel in farming operations, such as sowing, tilling, irrigating, and harvesting (Table A1). The effect of the GHG emissions was calculated as CO2 equivalents (CO2 eq). The 100 yr GWP of CH4 and N2O are 25 and 298 times greater, respectively, than that of CO2 on a mass basis. The soil CO2 flux as a contributor to GWP was not included in our analysis, because net flux has been estimated to represent <1% of the GHG emissions from agriculture on a global scale [2]. The change in soil organic carbon content was also not included in our analysis because it was difficult to detect such a small magnitude of change over a short time [21].
We used values in the published literature to simulate the relationship between the N-loss and N-application rate and to estimate GHG emissions from N fertilization. The N losses were calculated based on an empirical model that employed the following equations [9]:
Direct N2O emissions (kg N ha−1) = 0.54 × exp (0.00653 × Nsurplus)
NH3 volatilization (kg N ha−1) = 0.17 × N rate − 4.95
N leaching (kg N ha−1) = 13.59 × exp (0.009 × Nsurplus)
N surplus was defined as the N application minus the above-ground N uptake. The above-ground N uptake was estimated from the relationship between the crop N uptake and grain yield [22]:
N uptake = −14 + 41 × Y 0.77
where the N uptake is the uptake of N by the wheat and Y is the grain yield of the wheat.
The indirect N2O emissions can be estimated by following the IPCC methodology, where 1% and 0.75% of the volatilized N–NH3 and leached N–NO3 is lost as N2O–N, respectively. Using the N loss–N input response curve, we calculated the direct, indirect, and total N2O emissions per unit area, expressed as kg N ha−1 [19].
The system boundaries were set as the periods of the lifecycle from production inputs (such as fertilizers and pesticides), delivery of inputs to the farm gate, farming operations, and wheat harvesting. Using the emission factors for all agricultural inputs given in Table A1, we calculated total GHG emissions per unit area, expressed as kg CO2 eq ha−1, and the GHG intensity, expressed as kg CO2 eq Mg−1 grain.

2.4. Categories for Yield and Soil Productivity

For each production system, sites were divided into four categories (LL, LH, HL, and HH) according to the wheat grain yield in 0% RNR (i.e. 0N control) and 100% RNR treatments: LL (low yield with both 0% and 100% RNR), LH (low yield with 0% RNR and high yield with 100% RNR), HL (high yield with 0% RNR and low yield with 100% RNR), and HH (high yield with both 0% RNR and 100% RNR) (Figure 2). Soil productivity was characterized by grain yield levels without the nitrogen fertilizer application at the experimental sites, where sites at LL and LH were considered low soil productivity, and sites at HL and HH were considered high soil productivity. For all experimental sites with each kind of wheat production system, low yield and high yield refer to yields that were below and above, respectively, the 95% CI for the mean yield for the production system.

2.5. Data Analysis

SAS software 9.4 (SAS Institute, Cary, NC, USA, 1998) was used for the statistical analysis [23]. A mixed ANOVA model was used to assess the effect of the production system (irrigated, rainfall–wet, and rainfall–dry) and N treatments (0, 50, 100, and 150% of RNR) on the grain yield across all sites.
Curves describing wheat yield and yield-scaled GHG emissions as a function of the N rate for different wheat production systems were generated using SAS [24]. The relationship between the grain yield and N rate was evaluated with quadratic models, quadratic models with plateaus, and linear models with plateaus [24,25]. In most cases, the relationship between the N rate and yield was best described by a linear model with a plateau, and the relationship between the N rate and yield-scaled GHG emissions was best described by a quadratic model. Table A2 presents the data from all sites, which was analyzed using both of these two kinds of models.

3. Results

3.1. Yield and GHG Emissions of Different Nitrogen Levels

Wheat yield was the highest with irrigated production, followed by rainfall–wet production, and rainfall–dry production (Table 1). The highest grain yields for the four N levels were achieved with 100% RNR, and these yields were 7.2, 6.6, and 4.9 Mg ha−1 for irrigated, rainfall–wet, and rainfall–dry production, respectively. On average, the 100% RNR treatment provided 198, 193, and 143 kg N ha−1, and these values were similar to the estimated crop N uptake, which were 174, 161, and 126 kg N ha−1 for irrigated, rainfall–wet, and rainfall–dry production, respectively. With the 0% RNR, grain yield with irrigation production averaged 5.3 Mg ha−1, which was significantly higher than the 3.7 and 3.4 Mg ha−1 obtained with rainfall–wet and rainfall–dry production. The grain yield response to the N-application rate (defined as the difference between the yields with the 100% RNR and 0% RNR) averaged 1.9, 2.9, and 1.5 Mg ha−1 for irrigated, rainfall–wet, and rainfall–dry production, respectively.
Using established empirical models and lifecycle-assessment methods, we evaluated total GHG emissions and yield-scaled GHG emissions (expressed as kg of CO2 eq per ha and per million g) [9]. Total GHG emissions significantly increased with the increased N application, but yield-scaled GHG emissions were significantly lower with the 100% RNR than with the 0 or 150% RNR, in part because the grain yield was high with the 100% RNR, and because excessive N resulted in high area-scaled GHG emissions (Table 1).

3.2. The Response of Yield and Yield-Scaled GHG Emissions to the N Rate in Different Production Systems

Using yields with the 0% RNR and 100% RNR treatments, and as noted earlier, we grouped the experimental sites into four categories (Figure 2): LL (low yield with both the 0% and 100% RNR), LH (low yield with the 0% RNR and high yield with the 100% RNR), HL (high yield with the 0% RNR and low yield with the 100% RNR), and HH (high yield with both the 0% RNR and 100% RNR). Grain yield was significantly greater in soil with high productivity (the HH and HL categories) than in soils with low productivity (LH and LL). The response of grain yield to the N-application rate (reflecting crop productivity, i.e., crop management) was highest in the LH category (from 2.35 to 3.78 Mg ha−1), lowest in the HL category (from 0.87 to 1.62 Mg ha−1), and intermediate in the other categories (Figure 3).
Wheat yield significantly increased as the N rate increased to an optimum N, and then plateaued (Figure 4, Table 2). The minimum N rates needed to achieve maximum grain yield were 147, 141, and 119 kg N ha−1 for irrigated, rainfall–wet, and rainfall–dry production systems, respectively, and 139 and 133 kg N ha−1 for soils with low productivity (the LL and LH categories) and high productivity (the HH and HL categories). The calculated maximum grain yield was 6.9, 6.3, and 5.0 Mg ha−1 for irrigated, rainfall–wet, and rainfall–dry production systems, respectively, and 5.0, 6.5, 5.7, and 7.2 Mg ha−1 for the LL, LH, HL, and HH categories, respectively. The corresponding yield-scaled GHG emissions with maximum grain yield averaged 254, 155, and 183 kg CO2 eq Mg−1 grain for irrigated, rainfall–wet, and rainfall–dry production systems, respectively, and 236, 181, 190, and 180 kg CO2 eq Mg−1 grain for the LL, LH, HL, and HH categories, respectively. Across all three production systems, grain yield was 42% higher and yield-scaled GHG emissions was 36% lower for the HH category than for the LL category.
The yield-scaled GHG emissions tended to drop slightly, but then increase as the N rate increased, and these curves were evaluated with quadratic regression models (Figure 4). Across the four categories, the calculated minimum yield-scaled GHG emissions averaged 184 kg CO2 eq Mg−1, and ranged from 106 (HH for rainfall–wet production) to 267 (LL for irrigated production) kg CO2 eq Mg−1 grain. Across the three production systems, the calculated minimum yield-scaled GHG emissions from sites with low soil productivity (the LH and LL categories) averaged 205 kg CO2 eq Mg−1 grain, which is lower by 21%, compared to 259 kg CO2 eq Mg−1 grain with the 0% RNR treatment. In contrast, the minimum yield-scaled GHG emissions for sites with high soil productivity (the HL and HH categories) averaged 163 kg CO2 eq Mg−1 grain, which is lower by 6%, compared to 172 kg CO2 eq Mg−1 grain with the 0% RNR treatment.
For soils with low productivity (LL and LH), the values for yield-scaled GHG emissions obtained with maximum grain yields were equivalent to the minimum values for yield-scaled GHG emissions (Table 2). For soils with high productivity (HL and HH), the values for yield-scaled GHG emissions obtained with maximum grain yields were slightly higher (7–57 kg CO2 eq Mg−1 grain) than the minimum values for yield-scaled GHG emissions (Table 2). For soils with high soil productivity, the minimum yield-scaled GHG emissions was achieved at 80–93% of the maximum grain yield (Table 2). The N rate required to achieve the minimum yield-scaled GHG emissions ranged from 1.5 to 147 kg N ha−1 and averaged 85 kg N ha−1, which is significantly lower than the average N rate of 136 kg N ha−1 required to achieve the maximum grain yield (Table 2).

4. Discussion

One of the most significant challenges facing humanity today is the optimization of agricultural systems with respect to both food production and environmental protection [8,26]. Although some general approaches have been proposed to achieve these goals, reducing GHG emissions in agriculture remains a complex task due to the lack of specific technologies and management practices, as well as environmentally favorable policies and incentives [15]. Our results demonstrate that soils that produce high yields without a N addition (soils with high productivity), and that produce even higher yields with the recommended rate of N addition (the HH category), can achieve the highest grain yield while minimizing yield-scaled GHG emissions.
Although previous experimental studies and meta-analyses have documented a linkage between GHG emissions and N inputs or N surplus in wheat and other cereals [14,27], there has been a notable absence of research into the strategies that farmers can utilize to achieve both maximum yield and minimize GHG emissions. For instance, some studies have documented a high yield with low GWP [9,15,28]. On the other hand, Linquist et al. reported that the lowest yield-scaled GWP values were achieved with 92% of the maximal yield for cereals [12].
It is proposed that the impact of the N addition on crop yield and GHGs can be segregated into two successive stages. As the N rate initially increases from a N deficit without the N addition, the N supplied is utilized by both plants and microorganisms. This results in a linear increase in the grain yield with the availability of N. The emissions of GHGs from N losses are controlled by the uptake of N by plants and microbes. While yield-scaled GHG emissions decline as the yield increases, GHG emissions remain stable during this initial phase. However, a quadratic increase in GHG emissions may occur as N rates exceed crop demand and as yields plateau [14,27,29], i.e., losses of reactive forms of N increase rapidly with an excess N supply.
In this study, the yield-scaled GHG emission values associated with the maximum grain yields were equivalent to the estimated minimum yield-scaled GHG emission values for sites with low soil productivity (LH and LL), and were slightly higher for sites with high soil productivity (HL and HH). For sites with high soil productivity, the yield-scaled GHG emission values without the N addition were low because of the high grain yield, and therefore the N rate resulting in the minimum yield-scaled GHG emissions is lower than the N rate resulting in the maximum grain yield. It was observed that sites with high crop productivity exhibited a yield-scaled GHG emission value of 160 CO2 eq Mg−1 (LH and HH), whereas those with low crop productivity had a value of 209 CO2 eq Mg−1 (LL and HL). Although previous studies have reported high yields with low yield-scaled GHG emission values [9,25,28], they have not indicated that soil with high productivity has substantially lower yield-scaled GHG emission values compared to soil with low productivity. This highlights the importance of considering soil productivity when evaluating the environmental impact of agricultural practices.
Globally, because of the lack of comprehensive data linking soil resources to environments, the projections of future crop production and GHG emissions fail to consider the changes in the soil resource base or the building of soil capital and other land improvements as critical components of agricultural investment [8,16,30]. Worldwide, irrigated land accounts for approximately 20% of the arable land, yet it produces 40% of the total crop yield [31]. In this study, wheat production under irrigation resulted in the highest grain yield, i.e., with the 100% RNR, the grain yield was 10% and 46% more than the wheat yield under rainfall–wet and rainfall–dry conditions, respectively (Table 1). The implementation of irrigation involves the building of dams, as well as the production, transportation, and application of pumps and pipelines, along with the associated fuels [31]. These activities generally consume high amounts of energy and emit significant amounts of GHGs [32]. In irrigated wheat production in China, the GHG emissions resulting from the use of electricity for irrigation (which involves the application of >300 mm of water three times during the wheat growing season) averaged 1539, 410, and 103 kg CO2 eq ha−1 when water was obtained from deep wells (>200 m deep), shallow wells (<100 m deep), and rivers, respectively [33,34,35]. About one-third of the irrigation water is sourced from each of these three types of water sources. In the context of irrigated China’s wheat production, the proportion of total GHG emissions that can be attributed to irrigation activities is 41%.
About 40% of the land used for growing wheat in China is irrigated, and wheat production in northern China relies heavily on groundwater for irrigation. Consequently, groundwater levels in this region dropped by as much as 1 m annually between 1974 and 2000, forcing the populace to excavate hundreds of meters into the ground to access fresh water [36,37]. This behavior has resulted in declining groundwater tables, environmental degradation, and a surge in energy consumption [7,38]. The declining groundwater levels will increase energy use, as deeper wells require more carbon-intensive, electricity-driven pumps. When obtaining groundwater from a depth of 100 m, the energy required is 32 times greater than when obtaining it from the surface [39].
To compensate for the lack of water resources in northern China, extensive water-transfer schemes from the southern regions are being established, and these involve high energy costs for their construction, maintenance, and pumping operations [40]. As the intensity of GHG emissions from irrigation is primarily influenced by water-use efficiency, improvement in water-use efficiency (both technical and managerial) can be an effective way to mitigating emissions. The adoption of an improved strategy for managing water utilization, ensuring a balanced exploitation of water resources to avoid excessive groundwater consumption, and actively promoting water-use efficiency can lead to the mitigation of GHG emissions, the conservation of water resources, and the promotion of sustainable agricultural production [41,42].
As this analysis is founded on an N-loss model and field experiments, it is subject to a few limitations. First, we defined soil productivity by using yield at sites treated with the 0% RNR (no added N). Previous studies have indicated that soils with high inherent fertility (i.e., those capable of producing acceptable crop yields without the application of fertilization) exhibit consistently higher cereal yields than those with low inherent fertility [16,17,43]. This is attributed to the fact that these inherently fertile soils have distinctive properties related to other aspects of soil quality that support higher crop productivity. Crop-based soil-productivity measurements (grain yield without the N addition) refer to yields produced in the field, but these yields often vary year-to-year with variations in the soil N supply, climate, crop and nutrient management, and soil processes [16]. As such, further research is needed to obtain a comprehensively understanding the relationship between specific or multiple-soil productivity and inherent crop productivity, and to accurately quantify the effects of different variables that contribute to differences in crop and regional management practices.
Second, despite the fact that our study encompassed a variety of soil-productivity levels, crop-establishment practices, and irrigation regimes (Table 1), it is important to note that the database used for the N-loss model did not account for all regions or climates in which wheat is cultivated. Notably, in addition to the N-application rate, the amount of N loss was also influenced by specific local conditions and management practices. These factors encompass topography, soil type, climate, the method of N application, and the type of N fertilizer used [44]. Therefore, it is recommended that these environmental and crop-related factors should be considered when estimating reactive nitrogen losses from a specific site or treatment.

5. Conclusions

Understanding how to maximize yield while minimizing GHG emissions has become an important issue. In this study, we found that grain yield increased linearly to a plateau with the increasing N addition. The yield-scaled GHG emissions, which was described by quadratic equations, decreased by 16% as the N rate increased from treatments without the N addition to the minimum yield-scaled GHG emissions, but then increased at the higher rate of the N addition. The corresponding yield-scaled GHG emission values associated with the maximum grain yields were equivalent to the calculated minimum yield-scaled GHG emission values for sites with low soil productivity, and were slightly higher (6–18 kg CO2 eq Mg−1 grain) for sites with high soil productivity. The sites with both high soil productivity and high crop productivity had the highest yield and lowest yield-scaled GHG emissions, with 43% higher yield and 38% lower yield-scaled GHG emissions than sites with low soil and low crop productivity. Yield-scaled GHG emissions were higher for irrigated than for nonirrigated wheat because of the high cost of irrigation, even though yields were higher with irrigation. Our research results indicate that the highest wheat grain yield and the minimization of yield-scaled GHG emissions can be achieved through the utilization of highly productive soils and the implementation of management strategies that enhance crop productivity. The findings of this research provided an effective method for producing more grains with reduced environmental impacts, which can help to achieve food and environmental security in China and other regions worldwide. To ensure greater productivity and environmental performance, farmers should be equipped with improved management technologies in the future.

Author Contributions

Q.M.: data curation and writing—original draft; Y.S.: writing—review and editing; W.M.: project administration; G.W.: methodology and software; L.W.: methodology, resources and data curation; X.C.: conceptualization and project administration; X.T.: software; Y.Y.: methodology; Q.Z.: supervision, validation, and writing—review and editing; Z.C.: conceptualization, methodology, and supervision. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the National Key Research and Development Program of China (2021YFD1900901) and the Hainan Provincial Joint Project of Sanya Yazhou Bay Science and Technology City (2021JJLH0015).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. GHG emissions resulting from the following factors: fertilizer, pesticide production and transportation, and energy use for irrigation and soil tillage.
Table A1. GHG emissions resulting from the following factors: fertilizer, pesticide production and transportation, and energy use for irrigation and soil tillage.
FactorUnitGHG Emissions
(kg CO2 eq per Unit Input)		Reference
CO2CH4N2OTotal
P fertilizer productionkg P2O50.710.02-0.73[45]
K fertilizer productionkg K2O0.480.02-0.50[45]
P fertilizer transportationkg P2O50.05--0.06[46,47,48,49,50]
K fertilizer transportationkg K2O0.04--0.05[46,47,48,49,50]
Pesticide production and transportationkg18.280.800.0519.12[50,51]
Diesel fuelkg3.380.010.363.75[47,48,49]
Electricity for irrigationkWh---1.14[49]
Table A2. Parameter estimates and R2 values for linear-plateau models describing the relationship between grain yield and the N rate (y = a + bx, if x < x0; y = y0, if x ≥ x0), and for quadratic regression models describing the relationship between yield-scaled GHG emissions and the N rate (y = a + bx + cx2), for four site categories (LL, LH, HL, and HH) and three wheat production systems (irrigated, rainfall–wet, and rainfall–dry). The y-intercept coefficient (a) represents emissions without the N addition, the linear coefficient (b) represents the linear effect of the N rate on GHG emissions, and the quadratic coefficient (c) represents the curvilinear effect of the N rate on GHG emissions. Estimates with *** are significant at the 0.001 probability level, with ** are significant at the 0.01 probability level, and with * are significant at the 0.05 probability level. All response variables were modeled as a function of N surplus (x), defined as the N-application rate minus the crop N uptake within each site. Categories are explained in Figure 2.
Table A2. Parameter estimates and R2 values for linear-plateau models describing the relationship between grain yield and the N rate (y = a + bx, if x < x0; y = y0, if x ≥ x0), and for quadratic regression models describing the relationship between yield-scaled GHG emissions and the N rate (y = a + bx + cx2), for four site categories (LL, LH, HL, and HH) and three wheat production systems (irrigated, rainfall–wet, and rainfall–dry). The y-intercept coefficient (a) represents emissions without the N addition, the linear coefficient (b) represents the linear effect of the N rate on GHG emissions, and the quadratic coefficient (c) represents the curvilinear effect of the N rate on GHG emissions. Estimates with *** are significant at the 0.001 probability level, with ** are significant at the 0.01 probability level, and with * are significant at the 0.05 probability level. All response variables were modeled as a function of N surplus (x), defined as the N-application rate minus the crop N uptake within each site. Categories are explained in Figure 2.
Production
System		CategoryYield and N RateYield-Scaled GHG Emissions and N Rate
abR2abcR2
IrrigatedLL428213.900.56 **3280.38900.00410.85 **
LH467717.440.76 **2960.10530.00360.87 ***
HL56785.750.59 **2420.83610.00250.96 ***
HH64428.470.43 **2420.64290.00440.96 ***
Rainfall–wetLL275016.060.63 **2700.28950.00550.67 *
LH312323.160.88 **2190.23900.00330.91 ***
HL418815.250.69 **1411.20420.00220.95 ***
HH514515.580.58 **1240.74770.00220.93 ***
Rainfall–dryLL255112.180.42 **2611.65310.00490.80 **
LH296415.630.71 **2300.74080.00390.82 **
HL37719.470.53 **1562.00370.00040.95 ***
HH465011.900.49 **1311.29480.00130.92 ***

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Agronomy 13 02676 g001
Figure 1. The distribution of regions in China with three wheat production systems based on water supply: irrigated production (n = 924), rainfall–wet production (n = 776), and rainfall–dry production (n = 593). We first made the standard module with ArcGIS 10.2 for the province level and then copied it to PowerPoint 2019. Then, the maps were generated in PowerPoint 2019.
Figure 1. The distribution of regions in China with three wheat production systems based on water supply: irrigated production (n = 924), rainfall–wet production (n = 776), and rainfall–dry production (n = 593). We first made the standard module with ArcGIS 10.2 for the province level and then copied it to PowerPoint 2019. Then, the maps were generated in PowerPoint 2019.
Agronomy 13 02676 g001
Agronomy 13 02676 g002
Figure 2. Categorization of sites based on wheat grain yield with 0N and the recommended N rate (RNR) treatments for irrigated production ((A), n = 924), rainfall–wet production ((B), n = 776), and rainfall–dry production ((C), n = 593) in China. LL indicates low yield with both 0N (0% RNR) and RNR, LH indicates low yield with 0N and high yield with RNR, HL indicates high yield with 0N and low yield with RNR, and HH indicates high yield with both 0N and RNR. The vertical dashed line in each panel divides wheat grain yields into yields that were below the 95% CI or above the 95% CI of the mean for 0% RNR plots. The horizontal dashed line does the same for yields in the 100% RNR plots.
Figure 2. Categorization of sites based on wheat grain yield with 0N and the recommended N rate (RNR) treatments for irrigated production ((A), n = 924), rainfall–wet production ((B), n = 776), and rainfall–dry production ((C), n = 593) in China. LL indicates low yield with both 0N (0% RNR) and RNR, LH indicates low yield with 0N and high yield with RNR, HL indicates high yield with 0N and low yield with RNR, and HH indicates high yield with both 0N and RNR. The vertical dashed line in each panel divides wheat grain yields into yields that were below the 95% CI or above the 95% CI of the mean for 0% RNR plots. The horizontal dashed line does the same for yields in the 100% RNR plots.
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Agronomy 13 02676 g003
Figure 3. Wheat grain yield at experimental sites with irrigated production (A), rainfall–wet production (B), and rainfall–dry production (C). The numbers in the brackets indicate the number of sites. See Figure 2 for explanation of categories LL, HL, LH, and HH.
Figure 3. Wheat grain yield at experimental sites with irrigated production (A), rainfall–wet production (B), and rainfall–dry production (C). The numbers in the brackets indicate the number of sites. See Figure 2 for explanation of categories LL, HL, LH, and HH.
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Agronomy 13 02676 g004
Figure 4. The grain yield and yield-scaled GHG emission response to the N rate for three wheat production systems (irrigated, rainfall–wet, and rainfall–dry) and four categories (LL, LH, HL and HH). The latter categories are based on yield response to 0N and the recommended N rate (RNR), and are explained in Figure 2. The orange hollow circles and solid line indicate grain yield, and the blue hollow triangles and dotted line indicate yield-scaled GHG emissions.
Figure 4. The grain yield and yield-scaled GHG emission response to the N rate for three wheat production systems (irrigated, rainfall–wet, and rainfall–dry) and four categories (LL, LH, HL and HH). The latter categories are based on yield response to 0N and the recommended N rate (RNR), and are explained in Figure 2. The orange hollow circles and solid line indicate grain yield, and the blue hollow triangles and dotted line indicate yield-scaled GHG emissions.
Agronomy 13 02676 g004
Table 1. Mean N-application rates, grain yield, GHG emissions, and yield-scaled GHG emissions (Mg ha−1) for the four N treatments with irrigation (I, n = 924), rainfall–wet (R-W, n = 776), and rainfall–dry (R-D, n = 593) wheat production. Values are means ± SE. Means in a column followed by different letters are significantly different (p < 0.05).
Table 1. Mean N-application rates, grain yield, GHG emissions, and yield-scaled GHG emissions (Mg ha−1) for the four N treatments with irrigation (I, n = 924), rainfall–wet (R-W, n = 776), and rainfall–dry (R-D, n = 593) wheat production. Values are means ± SE. Means in a column followed by different letters are significantly different (p < 0.05).
% of RNR aN-Application Rate
(kg N ha−1)		Grain Yield
(Mg ha−1)		GHG Emissions
(kg CO2 eq ha−1) b		Yield-Scaled GHG Emissions (kg CO2 eq Mg−1)
IR-WR-DIR-WR-DIR-WR-DIR-WR-D
00005.3 ± 1.1 d3.7 ± 1.2 d3.4 ± 1.0 d1178.1 ± 42.9 d622.4 ± 37.8 d611.3 ± 38.5 d234.6 ± 60.6 b190.1 ± 78.1 b196.8 ± 70.9 b
5099.3 ± 13.596.5 ± 15.471.7 ± 21.06.4 ± 1.0 c5.5 ± 1.2 c4.2 ± 1.1 c1338.0 ± 53.7 c771.5 ± 55.5 c704.7 ± 81.7 c214.5 ± 39.6 d149.2 ± 44.3 d189.8 ± 69.3 b
100198.2 ± 26.7192.7 ± 30.7143.4 ± 41.87.2 ± 0.9 a6.6 ± 1.2 a4.9 ± 1.1 a1572.9 ± 98.5 b1000.6 ± 128.5 b916.4 ± 167.4 b221.7 ± 35.1 c158.7 ± 49.8 c197.6 ± 69.1 b
150297.6 ± 40.2289.1 ± 46.0214.9 ± 62.96.8 ± 0.9 b6.1 ± 1.2 b4.8 ± 1.2 b2060 ± 249.5 a1514.2 ± 388.7 a1247.2 ± 388.5 a331.0 ± 67.6 a265.7 ± 145.0 a285.2 ± 140.6 a
Source of variation c
N (3 df)*********
Production (2 df)*********
N × production*********
a RNR = recommended N rate. b CO2 eq = CO2 equivalents. c For analysis of variation, *** indicates a significant main effect (p < 0.001) of production system.
Table 2. Estimated minimum grain yield, maximum grain yield, and optimal N rate to achieve maximum grain yield based on for linear-plateau models between the yield and N rate. Calculated Δyield (Max. yield–Min. yield), yield-scaled GHG emission, agronomy N-use efficiency, and partial factor productivity with the optimal N rate to achieve maximum grain yield for N fertilizer. Estimated minimum values for yield-scaled GHG emissions and the corresponding N rate and grain yields as indicated by a quadratic model describing the relationship between yield-scaled GHG emissions and the N rate for three wheat production systems and four categories of sites.
Table 2. Estimated minimum grain yield, maximum grain yield, and optimal N rate to achieve maximum grain yield based on for linear-plateau models between the yield and N rate. Calculated Δyield (Max. yield–Min. yield), yield-scaled GHG emission, agronomy N-use efficiency, and partial factor productivity with the optimal N rate to achieve maximum grain yield for N fertilizer. Estimated minimum values for yield-scaled GHG emissions and the corresponding N rate and grain yields as indicated by a quadratic model describing the relationship between yield-scaled GHG emissions and the N rate for three wheat production systems and four categories of sites.
Production
System		Category aMin. YieldMax. YieldΔ YieldN RateYield-Scaled GHG EmissionsMin. GHG EmissionsN RateYield
Mg ha−1kg N ha−1kg CO2 eq Mg−1kg CO2 eq Mg−1kg N ha−1Mg ha−1
IrrigatedLL4.286.101.821302672671225.98
LH4.687.222.541462212201437.17
HL5.686.550.87151239226756.11
HH6.447.811.37162287230476.84
Rainfall–wetLL2.755.142.391492032011184.65
LH3.126.903.781631421361476.90
HL4.195.811.62106158133615.12
HH5.147.432.29146116106836.44
Rainfall–dryLL2.553.741.1997239233653.34
LH2.965.312.351501791721104.68
HL3.774.710.94991741571.53.79
HH4.656.211.56131138126485.22
a The categories are based on yield response to 0% and 100% recommended N rate (RNR) and are explained in Figure 2.
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Miao, Q.; Sun, Y.; Ma, W.; Wang, G.; Wu, L.; Chen, X.; Tian, X.; Yin, Y.; Zhang, Q.; Cui, Z. Maximizing Grains While Minimizing Yield-Scaled Greenhouse Gas Emissions for Wheat Production in China. Agronomy 2023, 13, 2676. https://doi.org/10.3390/agronomy13112676

AMA Style

Miao Q, Sun Y, Ma W, Wang G, Wu L, Chen X, Tian X, Yin Y, Zhang Q, Cui Z. Maximizing Grains While Minimizing Yield-Scaled Greenhouse Gas Emissions for Wheat Production in China. Agronomy. 2023; 13(11):2676. https://doi.org/10.3390/agronomy13112676

Chicago/Turabian Style

Miao, Qi, Yixiang Sun, Wenqi Ma, Guiliang Wang, Liang Wu, Xinping Chen, Xingshuai Tian, Yulong Yin, Qingsong Zhang, and Zhenling Cui. 2023. "Maximizing Grains While Minimizing Yield-Scaled Greenhouse Gas Emissions for Wheat Production in China" Agronomy 13, no. 11: 2676. https://doi.org/10.3390/agronomy13112676

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