TOWARD SUSTAINABLE MAIZE PRODUCTION FOR SMALLHOLDERS THROUGH OPTIMIZED STRATEGIES IN NORTH CHINA

Highlights

● County-level sustainability assessment of maize production is presented.

● County-level improvement potential exhibits a large spatial heterogeneity.

● Promoting technical innovations can facilitate China’s agricultural transition.

Abstract

Agricultural production by smallholders is crucial for ensuring food provision in China. However, smallholders face a series of challenges on their farms including high-to-excess resource inputs, low use efficiency, as well as negative environmental impacts, which may be unfavorable for sustainable agriculture production. This study developed a county-level sustainability assessment of maize production in Hebei, China, by applying multiple data sources in combination with emergy, carbon footprint, nitrogen footprint and cost-benefit analyses. Scenario analysis was applied to explore the localized implementation strategies to achieve the sustainable farming system. The results show that the average emergy sustainability index (ESI) of maize at 2.31 is relatively low. The average greenhouse gas (GHG) emissions and reactive nitrogen (Nr) losses are 0.15 g·kcal⁻¹ CO₂-eq and 3.75 mg·kcal⁻¹ N, respectively. The average cost and net income are 12,700 and 4340 CNY·ha⁻¹, respectively. These results indicate a great potential to improve the environmental-economic sustainability of the maize production system of smallholders. In addition, the environmental and economic indicators calculated from the maize production show a substantial spatial heterogeneity among counties, indicating a requirement for different optimization strategies to improve the environment-economy sustainability at a finer scale. Based on the multiple scenario analysis, optimal strategies targeting each county are proposed. By adopting the optimal strategies, the average ESI and net income could increase by 32% and 83%, respectively, and the average GHG emissions and Nr losses reduce by 33% and 35%, respectively. These findings provide an important reference for adopting different strategies to achieve environment-economy sustainability for smallholders production systems with diverse landscapes in North China and propose a transition pathway toward achieving agriculture sustainability for smallholders worldwide.

Graphical abstract

Keywords

localized optimization strategies / small- holders production systems / sustainability assessment / technical innovations

1 INTRODUCTION

In China, crop production is conducted on 203 million smallholdings (~98% of all kinds of agricultural production enterprises)^[1] with an average farm size of ~0.52 ha, so this system is important role in ensuring food security. However, the high resource inputs and low resource use efficiency of smallholders production systems, and the overuse of nitrogen fertilizer, cause significant negative impacts on the environment, including air pollution, soil degradation, eutrophication and resource scarcity^[2–5]. Meanwhile, the negative environmental impacts jeopardize the long-term sustainability of China’s food production^[6,7]. There is an urgent need to transform smallholders production systems in China.

The transformation toward sustainable smallholders production systems must address many challenges. First, fragmented farmland and decentralized management severely hinder the development of agricultural mechanization and digital/precision agriculture^[8]. Gu et al. found the low ratio of mechanization is one of the key constraints leading to over-fertilization in smallholders production systems^[9]. Second, with increasing urbanization, the workforce transferring to secondary and tertiary industries, and population aging in the rural areas, the available of agricultural workforce is decreasing and the cost of labor is increasing^[10–12]. Young people attracted by cities are more likely to embrace new technologies while most of the aging population remaining in rural areas are not well-educated. Third, due to the large variation in resource endowment, climatic and socioeconomic conditions of smallholder production systems, selecting suitable technologies requires localized strategies^[13,14], while most of the previous assessments are at national or provincial levels^[15–18]. To address these challenges, localized strategies are needed but remain undefined.

In this study, we used maize production in North China as an example to explore the transformation pathway. North China Plain, one of China’s main grain production regions, contributes to nearly a quarter of the national grain output^[19]. To be specific, we comprehensively evaluated the status quo of county-level sustainability assessment using emergy, carbon footprint, nitrogen footprint, and cost-benefit analyses. Then, we explored the improvement potential by closing yield or efficiency gaps, and based on this analysis discuss possible solutions for sustainable maize production for smallholders.

2 MATERIALS AND METHODS

2.1 Data sources

In this study, the sources of data include statistics yearbooks, government reports, previous publications, and a nationwide survey conducted from 2005 to 2014^[13,14]. Details of data sources are shown in Tab.1.

Tab.1 Data sources

Source	References
County-level crop yield, chemical N, P and K application rates, and manure type and its application rate	[13,14]
Meteorological data	[20–22]
Irrigating water	[23]
Pesticide	[16]
Labor, seed and plastic mulch	[24]
Electricity, diesel, gasoline and coal	[24–26]
Mechanical equipment	[27,28]
Top soil losses	[29]

2.2 Emergy analysis

Emergy is the total amount of available energy directly or indirectly required for the production of products or services^[30]. All components are converted into solar emjoules (sej) by unit emergy values with the baseline at 12.0 × 10²⁴ sej·yr^−1[31]. All inputs of the maize production system in our study were divided into four categories: free renewable resources (including solar energy, wind energy, rainfall and geothermal heat), free nonrenewable resources (mainly referring to the net loss of topsoil), purchased renewable resources (including irrigating water, manure, human labor and seeds) and purchased nonrenewable resources (including mineral fertilizer, electricity, fuels, mechanical equipment, pesticides and plastic mulch). The output of the system considers grains, excluding byproducts such as straw. The major emergy flows are shown in Fig.2 and indicators used in Tab.2.

Fig.1 System diagram for the emergy analysis (a), carbon footprint (b), nitrogen footprint (c), and cost-benefit (d) analysis.

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Tab.2 Emergy indicators

Emergy indicators	Formula	Meaning
Emergy yield ratio (EYR)	Y/(PR + PN)	Efficiency of purchased resource investments
Environmental loading ratio (ELR)	(FN + PN)/(FR + PR)	Environmental pressure on regional eco-economic system
Emergy sustainability index (ESI)	EYR/ELR	System sustainability

Note: Y, total system output; PR, purchased renewable resources; PN, purchased nonrenewable resources; FN, free nonrenewable resources; and FR, free renewable resources.

2.3 Carbon footprint

The carbon footprint analysis of maize from cradle to farmgate includes direct and indirect greenhouse gas (GHG) emissions, such as the on-site soil N₂O emissions from nitrogen fertilizer and other GHG emissions for fuels used, and upstream emissions from the supply chain of fertilizers, pesticides, plastic mulch, seeds and electricity. The system boundary is shown in Fig.2. The carbon footprint is calculated as:

(1)$CF={CF}_{\mathrm{i}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t}}+{CF}_{{\mathrm{N}}_2\mathrm{O}}$

(2)$CF=\sum _i{Q}_i\times {EF}_{\mathrm{C}}+F_{\mathrm{N}}\times {EF}_{{\mathrm{N}}_2\mathrm{O}}\times 44/28\times 265$

where, $CF$ is the total GHG emissions for each county expressed as CO₂ emissions equivalent (kg CO₂-eq), ${CF}_{\mathrm{i}\mathrm{n}\mathrm{p}\mathrm{u}\mathrm{t}}$ is the GHG emissions caused by the agricultural inputs, ${CF}_{{\mathrm{N}}_2\mathrm{O}}$ is the on-site soil N₂O emissions, $i$ are agricultural inputs, $Q_i$ is the application rate of agricultural inputs (kg or kWh) and ${EF}_{\mathrm{C}}$ are their corresponding emission factors, $F_{\mathrm{N}}$ is the nitrogen fertilizer application rate (kg), ${EF}_{{\mathrm{N}}_2\mathrm{O}}$ is the emission factor of soil N₂O emissions (kg·kg⁻¹ CO₂-eq), and $44/28$ is the molecular ratio of N₂O to nitrogen. N₂O is converted to CO₂ equivalent (CO₂-eq) according to IPCC AR5 100 year Global Warming Potential (265 times).

2.4 Nitrogen footprint

The nitrogen inputs in this study include chemical nitrogen fertilizer, manure, straw, biological nitrogen fixation, atmospheric nitrogen deposition, and seeds. The reactive nitrogen (Nr) losses include both direct and indirect losses. The direct losses in farmland mainly include NH₃ volatilization, N₂O and NO_x emissions, nitrogen leaching and runoff during the application of chemical nitrogen fertilizer and manure, NH₃ volatilization, NO_x emissions caused by straw combustion, and N₂O emissions from straw returning to the field. The indirect losses are mainly from the upstream production and transportation of agricultural inputs, such as mineral fertilizers, manure, electricity, fuels, pesticides and plastic mulch. The nitrogen flow for this analysis is shown in Fig.2. The nitrogen footprint is calculated as:

(3)$\begin{array}{rl}\mathrm{N}\mathrm{F}=& \sum _i{Q}_i\times {EF}_{\mathrm{N}}+{NF}_{\mathrm{N}{\mathrm{H}}_3}+{NF}_{{\mathrm{N}}_2\mathrm{O}}+{NF}_{\mathrm{N}{\mathrm{O}}_x}+{NF}_{\mathrm{l}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{g}}\\ & +{NF}_{\mathrm{r}\mathrm{u}\mathrm{n}\mathrm{o}\mathrm{f}\mathrm{f}}+{NF}_{\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{w}\mathrm{b}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}}+{NF}_{\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{w}\mathrm{r}\mathrm{e}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}}\end{array}$

where, $i$ are agricultural inputs, $Q_i$ is the application rate of agricultural inputs (kg), ${EF}_{\mathrm{N}}$ are their corresponding emission factors, ${NF}_{\mathrm{N}{\mathrm{H}}_3}$, ${NF}_{{\mathrm{N}}_2\mathrm{O}}$, ${NF}_{\mathrm{N}{\mathrm{O}}_x}$, ${NF}_{\mathrm{l}\mathrm{e}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{i}\mathrm{n}\mathrm{g}}$, ${NF}_{\mathrm{r}\mathrm{u}\mathrm{n}\mathrm{o}\mathrm{f}\mathrm{f}}$ are the Nr losses in the form of NH₃ volatilization, N₂O and NO_x emissions, nitrogen leaching and runoff during the application of nitrogen fertilizer and manure, ${NF}_{\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{w}\mathrm{b}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}}$ is to the NH₃ volatilization and NO_x emissions of straw combustion, and ${NF}_{\mathrm{s}\mathrm{t}\mathrm{r}\mathrm{a}\mathrm{w}\mathrm{r}\mathrm{e}\mathrm{t}\mathrm{u}\mathrm{r}\mathrm{n}\mathrm{i}\mathrm{n}\mathrm{g}}$ is the N₂O emissions from straw returning.

2.5 Cost-benefit analysis

Combined with the cost-benefit data^[24] of maize production in Hebei Province in 2012, the cost and the net income per unit area of maize production were calculated. The total cost includes fertilizers, fuels, pesticides, plastic films, electricity, seeds, machinery, human labor and land cost (Fig.2).

2.6 Scenario analysis

An earlier study found that yield, nitrogen fertilizer and phosphorus fertilizer were the main drivers of the sustainability of staple food crops in China^[13], and closing yield and nutrient use efficiency gaps could improve the sustainability and reduce negative environmental impacts^[13,14]. Therefore, four scenarios were applied to explore the improving potential for ESI, GHG emissions, Nr losses, and net income for maize. Baseline (S0) was the status quo; the first scenario (S1) was for all the farmland producing the highest 10% yield in each county; the second scenario (S2) was for highest 10% in crop N efficiency at each county; the third scenario (S3) was for simultaneously highest 10% in both crop yield and N efficiency through multiplying top 32% yield by highest 32% N fertilizer efficiency at each county.

3 RESULTS

3.1 The status quo of sustainability of maize production

3.1.1 Emergy analysis

The higher the crop ESI values, the stronger the sustainability of the production system (in a range from 1 to 10) would be. The ESI values ranged from 1.2 to 5.7, with an average of 2.3, indicating that there was a great potential to improve the sustainability of the maize production system in Hebei Province. The average of the upper quartile 25% (5.7–2.6), interquartile range (< 2.6–1.9) and lower quartile (< 1.9–1.2) of ESI were 3.1, 2.3 and 1.7, respectively. The upper quartile of the counties, which is attributed to the high yield and low environmental pressure (Fig.2), were mainly located in the south (Fig.2). The lower quartile of the counties had a scattered distribution (e.g., Qian’an City, Quyang County, Wangdu County, Nangong City and Guangping County), mainly due to the relatively high inputs of both mineral fertilizer and fuel resulting in high environmental loading (Fig.2). The interquartile range counties were concentrated in the north of Hebei Province.

Fig.2 Distribution of emergy yield ratio (EYR) (a), environmental loading ratio (ELR) (b), emergy sustainability index (ESI) (c), GHG emissions (d–f) and Nr losses (g–i) for maize in Hebei Province. GHG emissions are expressed in ton CO2-eq per hectare harvested area (d), gram CO2-eq per kilocalorie (e), total GHG emissions per county (f), Nr losses are expressed in kilogram N per hectare harvested area (g), milligram N per kilocalorie (h), and total Nr losses per county (i).

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3.1.2 Carbon footprint

The distribution of the carbon footprint of maize production is shown in Fig.2. The GHG emissions per unit area of maize production ranged from 0.72 to 6.66 t·ha⁻¹ CO₂-eq, with an average value of 3.40 t·ha⁻¹ CO₂-eq. The emission hotspots were concentrated in the east (Fig.2). Mineral fertilizer input was the largest contributor in 111 counties, accounting for more than 50% of the total GHG intensity per unit area. Taking Guangping County as an example, the high application rates of nitrogen fertilizer were more than twice of average provincial level (468 vs 202 kg·ha⁻¹).

The GHG emissions per unit calorie of maize production ranged from 0.03 to 0.29 g·kcal⁻¹, with an average of 0.15 g·kcal⁻¹ CO₂-eq. The GHG emission hotspots were concentrated in the east and the south-west (e.g., Kuancheng, Manchu Nationality Autonomous County, Qian’an City and Wu’an city) due to the relatively high application rates of nitrogen fertilizer. The northern and central regions in Hebei Province emitted less GHGs (Fig.2).

The total GHG emissions from maize production in each county varied from 660 t to 0.23 Mt, with an average of 0.08 Mt. Cang County emitted the most GHGs, followed by Yutian County, Linzhang County, Fengrun District and Shenzhou City, which is mainly attributed to the relatively high maize production of these cities (accounting for 7% of the total maize production in Hebei). In the west of Hebei, the total GHG emissions were relatively low (Fig.2).

3.1.3 Nitrogen footprint

The distribution of Nr losses for maize is shown in Fig.2. The Nr losses per unit area of maize production in 126 counties ranged from 21.1 to 246 kg·ha⁻¹ N, with an average of 83.4 kg·ha⁻¹ N (Fig.2). The Nr losses per kcal maize ranged from 0.92 to 10.6 mg·kcal⁻¹ N, with an average of 3.75 mg·kcal⁻¹ N (Fig.2). The hotspots of Nr losses both per unit area and per calorie were mainly concentrated in Guangping, Wangdu and Qian’an city, which is attributed to the high nitrogen fertilizer inputs.

The total Nr losses from maize production in each county had obvious spatial heterogeneity, ranging from 18.5 to 5720 t N, with an average of 1890 t N. The counties with high Nr losses were concentrated in Cang, Linzhang, Qian’an, Fengrun and Shenzhou, due to the high application rates of nitrogen fertilizer. Also, the counties with low Nr losses were concentrated in the west of Hebei Province (Fig.2).

3.1.4 Cost-benefit analysis

The cost of maize production ranged from 11,200 to 14,200 CNY·ha⁻¹, with an average of 12,700 CNY·ha⁻¹, of which the human labor cost contributed the most. In the east of Hebei, the cost per unit area was higher than the rest of the area (Fig.3). The net income of maize production ranged from ~5870 to 9400 CNY·ha⁻¹, with an average of 4340 CNY·ha⁻¹ (Fig.3). The areas with higher net income per unit area were scattered, for example Kangbao County, Huai’an County, Funing County, and Fengnan District. In some counties, the cost per unit area was not proportional to the net income, for example Kangbao County where the cost per unit area was lower but the net income was relatively high with a high yield. Due to the low nitrogen and phosphate fertilizer input costs, and yield, both the cost and net income per unit area in Guyuan County were the lowest.

Fig.3 The state of production cost (a) and net income (b) for maize in Hebei Province.

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3.2 Improvement potential

Compared to the baseline S0 (business as usual case), S1, S2 and S3 increased the average ESI by 16%, 28% and 26% (Fig.4), reduced the mean GHG emissions by 13%, 30%, and 28% (Fig.4), and mean Nr losses by 11%, 32%, and 30% (Fig.4), respectively; the average net income increased by 83%, 32%, and 66%, respectively (Fig.4); Overall, S2 gave the greatest improvement potential for ESI, GHG emissions and Nr losses, followed by S3 and S1. However, for net income, S1 gave the greatest improvement potential, followed by S3 and S2.

Fig.4 The emergy sustainability index (a), GHG emissions (b), Nr losses (c) and net income (d) of maize production with different scenarios.

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For each county, the improvement in ESI, carbon footprint, nitrogen footprint and net income varied with optimization strategies. Overall, S2 was the best strategy with the largest improvement potential for 62% of counties in ESI, for 73% of counties in reducing GHG emissions and for 75% of counties in reducing Nr losses. However, S1 gave the highest improvement potential for 89% of counties in net income. In addition, S3 was more effective for 27% of counties in ESI and 5% of counties in net income, and 24% of the counties in reducing GHG emissions and Nr losses. To enhance ESI of maize production, about 10% of the counties were more suitable for S1, while less than 3% of the counties reduced GHG emissions and Nr losses under S1. Therefore, with optimized strategies adopted, the average ESI and net income could increase by 32% and 83%, respectively, the average GHG emissions and Nr losses would decrease by 33% and 35%, respectively (Fig.5). These optimization potentials were higher than adopting same strategies.

Fig.5 Optimal strategies for improving the emergy sustainability index (a), GHG emissions (b), Nr losses (c) and net income (d) of maize production in Hebei Province.

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3.3 Uncertainty analysis

Uncertainty of carbon footprint and nitrogen footprint analysis comes mainly from the downscaled data associated with pesticides, plastic mulch, electricity, fuels and seeds, and emission factors. We assumed these activity levels and emission factors obey the normal distribution^[32–34] and the coefficient of variation is 10%^[35]. Monte Carlo simulation is selected to estimate uncertainties with 10,000 random sample generation, and results show the uncertainties of GHG emissions and Nr losses estimates of maize in 126 counties of Hebei at the 95% confidence interval are (~66%–85%) and (~61%–69%), respectively.

4 DISCUSSION

The average ESI of the maize production system in Hebei Province for the period studied was relatively low (2.3), indicating that there was a great potential to improve the sustainability of the system. The overuse of nitrogen fertilizer generated a large amount of GHG emissions and Nr losses, and decreased the sustainability of production systems in some counties, for example Guangping County and Qian’an City. In contrast, some of the counties (e.g., Kangbo and Gaoyi County) had large ESI but low GHG emissions and Nr losses, due to the relatively high nitrogen use efficiency. Some counties, for example Gaoyang, Julu, and Xinhe, emitted more GHGs and Nrs while being more sustainable in maize production. This is mainly attributed to the high input of irrigation water and manure as a renewable purchased resource. The average cost of maize production in Hebei Province was 12,700 CNY·ha⁻¹ and human labor cost contributed most, indicating that agricultural production in Hebei was heavily dependent on the workforce. With optimal strategies adopted, the average ESI and net income could increase by 32% and 83%, respectively, and the average GHG emissions and Nr losses would reduce by 33% and 35%, respectively, suggesting the need for localized strategies. The economic benefits under S1 would be superior, but S1 would be not the best option considering its environmental performance.

In the difficult quest for achieving sustainable agriculture for smallholders production systems, we recommend facilitating China’s agricultural transition from three aspects: overall layout, technical innovations and field demonstrations. First, policy frameworks should be developed to cover the concerns about modernizing the basic rural operation system, socialized services systems for agriculture, talent cultivation and technical innovations^[36]. Second, technically, a green transition for agriculture can progress through three phases: optimize, replace and redesign^[37]. The initial step is the optimization of resource use efficiency, especially nitrogen fertilizer, mainly through adopting technologies like 4R technology^[38], digital agriculture^[39], soil testing and fertilizer recommendation^[40,41], knowledge-based nitrogen management^[42], and straw return^[34,43]. The replacement phase relies on substitute technologies, including new phenotyping technology (e.g., increasing yield potential and resource use efficiency)^[44,45], upstream mitigation from the supply chain of agriculture input (e.g., electrical synthesis of ammonia^[46]) and alternative of electricity. The redesign phase entails a complete system redesign of agricultural practices, for example, integrated soil-crop system management^[47] and precision agriculture^[48], which still requires multiple cycles of development. Third, the collaborative weaving of research and practice restructured the relationship between farmers and researchers via on-farm experimentation^[48], the Science and Technology Backyard^[49] platform, and other co-production modes^[50]. Farmers have knowledge of the background and circumstance that will underpin localization production, while researchers have responsibility for technical innovations and integrating capabilities. Combined with the local resource endowments and policymaking, they can harness community of interest, ensuring the implementation and promotion of technical innovations.

5 CONCLUSIONS

China’s smallholders production systems face enormous challenges to achieve environment-economy sustainability. In this study, we took maize production in Hebei Province as an example and provided a spatially explicit assessments of the smallholders production system from multiple sources. Results indicate a great potential to improve the environmental-economic sustainability of the maize production system on smallholdings. And the environmental and economic indicators exhibit spatial heterogeneity among counties, indicating the necessity of localized optimization strategies for improving the environment-economy sustainability. The transition pathway of smallholders production systems requires comprehensive policy making, technical innovations, and associated promoting. Meanwhile, our study provides an important reference for countries facing the challenge of smallholders production system transition for the future. Although the downscaled data (e.g., pesticide, seed, plastic mulch and fuel) add some uncertainty to the process, our results should to a greater or less degree reflect county situations based on the large-scale surveys of farmers. Future studies should focus on obtaining more precise data via nationwide investigations and digital technology.

References

[1] NationalBureau of Statistics of China (NBSC). Main data bulletin of the third National Agricultural Census in China. Beijing: NBSC, 2017. Available at NBSC website on January 20, 2022

[2] GuoJ H, LiuX J, ZhangY, ShenJ L, HanW X, ZhangW F, ChristieP, GouldingK W T, VitousekP M, ZhangF S. Significant acidification in major Chinese croplands. Science, 2010, 327( 5968): 1008–1010

[3] ChenX, CuiZ, FanM, VitousekP, ZhaoM, MaW, WangZ, ZhangW, YanX, YangJ, DengX, GaoQ, ZhangQ, GuoS, RenJ, LiS, YeY, WangZ, HuangJ, TangQ, SunY, PengX, ZhangJ, HeM, ZhuY, XueJ, WangG, WuL, AnN, WuL, MaL, ZhangW, ZhangF. Producing more grain with lower environmental costs. Nature, 2014, 514( 7523): 486–489

[4] CuiZ, YueS, WangG, MengQ, WuL, YangZ, ZhangQ, LiS, ZhangF, ChenX. Closing the yield gap could reduce projected greenhouse gas emissions: a case study of maize production in China. Global Change Biology, 2013, 19( 8): 2467–2477

[5] GuB, JuX, ChangS X, GeY, ChangJ. Nitrogen use efficiencies in Chinese agricultural systems and implications for food security and environmental protection. Regional Environmental Change, 2017, 17( 4): 1217–1227

[6] RayD K, RamankuttyN, MuellerN D, WestP C, FoleyJ A. Recent patterns of crop yield growth and stagnation. Nature Communications, 2012, 3( 1): 1293

[7] LiuY, WenC, LiuX. China’s food security soiled by contamination. Science, 2013, 339( 6126): 1382–1383

[8] QiuT, LuoB. Do small farms prefer agricultural mechanization services? Evidence from wheat production in China.. Applied Economics, 2021, 53( 26): 2962–2973

[9] RenC, JinS, WuY, ZhangB, KanterD, WuB, XiX, ZhangX, ChenD, XuJ, GuB. Fertilizer overuse in Chinese smallholders due to lack of fixed inputs. Journal of Environmental Management, 2021, 293 : 112913

[10] DuanJ, RenC, WangS, ZhangX, ReisS, XuJ, GuB. Consolidation of agricultural land can contribute to agricultural sustainability in China. Nature Food, 2021, 2( 12): 1014–1022

[11] RenC, LiuS, van GrinsvenH, ReisS, JinS, LiuH, GuB. The impact of farm size on agricultural sustainability. Journal of Cleaner Production, 2019, 220 : 357–367

[12] WangS, BaiX, ZhangX, ReisS, ChenD, XuJ, GuB. Urbanization can benefit agricultural production with large-scale farming in China. Nature Food, 2021, 2( 3): 183–191

[13] ZhuangM, LiuY, YangY, ZhangQ, YingH, YinY, CuiZ. The sustainability of staple crops in China can be substantially improved through localized strategies. Renewable & Sustainable Energy Reviews, 2022, 154 : 111893

[14] ZhangQ, ChuY, XueY, YingH, ChenX, ZhaoY, MaW, MaL, ZhangJ, YinY, CuiZ. Outlook of China’s agriculture transforming from smallholder operation to sustainable production. Global Food Security, 2020, 26 : 100444

[15] ZhangX H, ZhangR, WuJ, ZhangY Z, LinL L, DengS H, LiL, YangG, YuX Y, QiH, PengH. An emergy evaluation of the sustainability of Chinese crop production system during 2000–2010. Ecological Indicators, 2016, 60 : 622–633

[16] ZhangG, WangX, ZhangL, XiongK, ZhengC, LuF, ZhaoH, ZhengH, OuyangZ. Carbon and water footprints of major cereal crops production in China. Journal of Cleaner Production, 2018, 194 : 613–623

[17] ZhangD, ShenJ, ZhangF, LiY, ZhangW. Carbon footprint of grain production in China. Scientific Reports, 2017, 7( 1): 4126

[18] GuB, LeachA M, MaL, GallowayJ N, ChangS X, GeY, ChangJ. Nitrogen footprint in China: food, energy, and nonfood goods. Environmental Science & Technology, 2013, 47( 16): 9217–9224

[19] NationalBureau of Statistics of China (NBSC). Announcement of grain production data in 2020. Beijing: NBSC, 2020. Available at NBSC website on April 22, 2022

[20] WuJ, GaoX J. A gridded daily observation dataset over China region and comparison with the other datasets. Chinese Journal of Geophysics, 2013, 56( 4): 1102–1111

[21] WuJ, GaoX, GiorgiF, ChenD. Changes of effective temperature and cold/hot days in late decades over China based on a high resolution gridded observation dataset. International Journal of Climatology, 2017, 37( S1): 788–800

[22] XuY, GaoX, ShenY, XuC, ShiY, GiorgiF. A daily temperature dataset over China and its application in validating a RCM simulation. Advances in Atmospheric Sciences, 2009, 26( 4): 763–772

[23] ZhouF, BoY, CiaisP, DumasP, TangQ, WangX, LiuJ, ZhengC, PolcherJ, YinZ, GuimberteauM, PengS, OttleC, ZhaoX, ZhaoJ, TanQ, ChenL, ShenH, YangH, PiaoS, WangH, WadaY. Deceleration of China’s human water use and its key drivers. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117( 14): 7702–7711

[24] NationalDevelopment ReformCommission (NDRC). National Agricultural Product Cost-benefit Data Compilation. Beijing: China Statistics Press, 2013 ( in Chinese)

[25] NationalBureau of Statistics of China (NBSC). China Energy Statistical Yearbook. Beijing: China Statistics Press, 2013 ( in Chinese)

[26] NationalBureau of Statistics of China (NBSC). China Statistical Yearbook. Beijing: China Statistics Press, 2013 ( in Chinese)

[27] NationalBureau of Statistics of China (NBSC). China Rural Statistical Yearbook. Beijing: China Statistics Press, 2013 ( in Chinese)

[28] ChinaAssociation of Agricultural Machinery Manufacturers (CAAMM). China Agriculture Machinery Industry Yearbook. Beijing: China Statistics Press, 2013 ( in Chinese)

[29] Ministryof Water Resources of the People’s Republic of China (MWRC). Bulletin of First National Census for Water. Beijing: MWRC, 2011 ( in Chinese)

[30] OdumH T. Environmental accounting: emergy and environmental decision making. New York: Wiley, 1996

[31] BrownM T, UlgiatiS. Emergy assessment of global renewable sources. Ecological Modelling, 2016, 339 : 148–156

[32] LiangD, LuX, ZhuangM, ShiG, HuC, WangS, HaoJ. China’s greenhouse gas emissions for cropping systems from 1978−2016. Scientific Data, 2021, 8( 1): 171

[33] YanX, AkiyamaH, YagiK, AkimotoH. Global estimations of the inventory and mitigation potential of methane emissions from rice cultivation conducted using the 2006 Intergovernmental Panel on Climate Change Guidelines. Global Biogeochemical Cycles, 2009, 23( 2): GB2002

[34] XiaL, TiC, LiB, XiaY, YanX. Greenhouse gas emissions and reactive nitrogen releases during the life-cycles of staple food production in China and their mitigation potential. Science of the Total Environment, 2016, 556 : 116–125

[35] ZhanX, AdalibiekeW, CuiX, WiniwarterW, ReisS, ZhangL, BaiZ, WangQ, HuangW, ZhouF. Improved estimates of ammonia emissions from global croplands. Environmental Science & Technology, 2021, 55( 2): 1329–1338

[36] LiuX, LiW, ZhaoC, MinQ, YangX, LiuM. High-quality development of modern smart ecological agriculture. Strategic Study of CAE, 2022, 24( 1): 38–45

[37] NorthrupD L, BassoB, WangM Q, MorganC L S, BenfeyP N. Novel technologies for emission reduction complement conservation agriculture to achieve negative emissions from row-crop production. Proceedings of the National Academy of Sciences of the United States of America, 2021, 118( 28): e2022666118

[38] LiuX, VitousekP, ChangY, ZhangW, MatsonP, ZhangF. Evidence for a historic change occurring in China. Environmental Science & Technology, 2016, 50( 2): 505–506

[39] BassoB, AntleJ. Digital agriculture to design sustainable agricultural systems. Nature Sustainability, 2020, 3( 4): 254–256

[40] TaoF, PalosuoT, ValkamaE, MäkipääR. Cropland soils in China have a large potential for carbon sequestration based on literature survey. Soil & Tillage Research, 2019, 186 : 70–78

[41] LiS, ZhuY, LiX. Analysis of the decisions of farmers working different sized farms to constantly use soil testing formula fertilizer. Journal of Resources and Ecology, 2018, 9( 2): 146–153

[42] XiaL, LamS K, ChenD, WangJ, TangQ, YanX. Can knowledge-based N management produce more staple grain with lower greenhouse gas emission and reactive nitrogen pollution? A meta-analysis.. Global Change Biology, 2017, 23( 5): 1917–1925

[43] XiaL, LamS K, WolfB, KieseR, ChenD, Butterbach-BahlK. Trade-offs between soil carbon sequestration and reactive nitrogen losses under straw return in global agroecosystems. Global Change Biology, 2018, 24( 12): 5919–5932

[44] TracyS R, NagelK A, PostmaJ A, FassbenderH, WassonA, WattM. Crop improvement from phenotyping roots: highlights reveal expanding opportunities. Trends in Plant Science, 2020, 25( 1): 105–118

[45] AguilarJ J, MooreM, JohnsonL, GreenhutR F, RogersE, WalkerD, O’NeilF, EdwardsJ L, ThystrupJ, FarrowS, WindleJ B, BenfeyP N. Capturing in-field root system dynamics with RootTracker. Plant Physiology, 2021, 187( 3): 1117–1130

[46] SchifferZ J, ManthiramK. Electrification and decarbonization of the chemical industry. Joule, 2017, 1( 1): 10–14

[47] CuiZ, ZhangH, ChenX, ZhangC, MaW, HuangC, ZhangW, MiG, MiaoY, LiX, GaoQ, YangJ, WangZ, YeY, GuoS, LuJ, HuangJ, LvS, SunY, LiuY, PengX, RenJ, LiS, DengX, ShiX, ZhangQ, YangZ, TangL, WeiC, JiaL, ZhangJ, HeM, TongY, TangQ, ZhongX, LiuZ, CaoN, KouC, YingH, YinY, JiaoX, ZhangQ, FanM, JiangR, ZhangF, DouZ. Pursuing sustainable productivity with millions of smallholder farmers. Nature, 2018, 555( 7696): 363–366

[48] LacosteM, CookS, McNeeM, GaleD, IngramJ, Bellon-MaurelV, MacMillanT, Sylvester-BradleyR, KindredD, BramleyR, TremblayN, LongchampsL, ThompsonL, RuizJ, GarcíaF O, MaxwellB, GriffinT, OberthürT, HuygheC, ZhangW, McNamaraJ, HallA. On-Farm Experimentation to transform global agriculture. Nature Food, 2022, 3( 1): 11–18

[49] ZhangW, CaoG, LiX, ZhangH, WangC, LiuQ, ChenX, CuiZ, ShenJ, JiangR, MiG, MiaoY, ZhangF, DouZ. Closing yield gaps in China by empowering smallholder farmers. Nature, 2016, 537( 7622): 671–674

[50] ChambersJ M, WybornC, RyanM E, ReidR S, RiechersM, SerbanA, BennettN J, CvitanovicC, Fernández-GiménezM E, GalvinK A, GoldsteinB E, KlenkN L, TengöM, BrennanR, CockburnJ J, HillR, MuneraC, NelJ L, ÖsterblomH, BednarekA T, BennettE M, BrandeisA, Charli-JosephL, ChattertonP, CurranK, DumrongrojwatthanaP, DuránA P, FadaS J, GerberJ D, GreenJ M H, GuerreroA M, HallerT, Horcea-MilcuA I, LeimonaB, MontanaJ, RondeauR, SpierenburgM, SteyaertP, ZaehringerJ G, GrubyR, HuttonJ, PickeringT. Six modes of co-production for sustainability. Nature Sustainability, 2021, 4( 11): 983–996

Acknowledgements

This work is funded by State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex (SCAPC202102) and the National Natural Science Foundation of China (71904189).

Compliance with ethics guidelines

Jie Yan, Yize Liu, Rui Zhang, Chenhui Cui, Yingying Zheng, and Minghao Zhuang declare that they have no conflicts of interest or financial conflicts to disclose. All applicable institutional and national guidelines for the care and use of animals were followed.

RIGHTS & PERMISSIONS

The Author(s) 2022. Published by Higher Education Press. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0)