Developing climate-smart agricultural systems in the North China Plain

Agriculture, Ecosystems and Environment 291 (2020) 106791

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Agriculture, Ecosystems and Environment journal homepage: www.elsevier.com/locate/agee

Developing climate-smart agricultural systems in the North China Plain a,b

Yue Xin

a,b,c,

, Fulu Tao

*

T

a

Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, 100049, China c Natural Resources Institute Finland (Luke), FI-00790, Helsinki, Finland b

ARTICLE INFO

ABSTRACT

Keywords: Climate change Climate adaptation and mitigation Conservation tillage Greenhouse gas emission Nitrogen leaching Soil organic carbon

Developing climate-smart agricultural systems is essential for climate change adaptation and mitigation. In the past decades, the typical winter wheat- summer maize rotation system in the North China Plain (NCP) has produced high yield, but the overuse of nitrogen fertilizer and over-pumping of groundwater for irrigation have caused severe environmental problems. It is necessary to develop climate-smart agricultural systems through a comprehensive multiple-objective assessment and optimization of alternative cropping systems and agronomic managements. Here, with the agricultural system model of APSIM, eight alternative cropping systems at four typical sites across the NCP under two climate change scenarios and two tillage managements were comprehensively evaluated in terms of crop yield, water use efficiency (WUE), nitrogen use efficiency (NUE), evapotranspiration (ET), groundwater recharge (GWR), N2O emission, N leaching, surface soil organic carbon (SOC), and carbon footprint (CF). The results showed that under both baseline and future climate scenarios, the currently dominant winter wheat- summer maize rotation system had the largest ET, N leaching and N2O emission, a medium crop yield, WUE, and SOC, however a low NUE, and GWR. The rotation/intercropping systems could have higher grain yields, while the monoculture cropping systems could have advantage on water conservation. Maize had relatively higher yield, WUE, NUE, GWR, and SOC, and lower N loss and CF than wheat and soybean because it could have a high yield without irrigation. The optimized winter wheat-summer maize rotation system, with the optimal irrigation, fertilizer and cultivar, had the greatest advantage over other seven systems with the highest yields, WUE, NUE, high ET, GWR, and SOC, and the lowest N losses and CF. Compared with conventional tillage, each cropping system would have a little bit less negative response to future climate change with conservation tillage. The study demonstrated a useful framework to develop climate-smart agricultural systems and sustainable agricultural strategies to meet the challenges of global climate change, which can be widely applied to other cropping systems and regions.

1. Introduction Global agricultural production and food security are facing the challenges of increasing population and food demands, climate change, land and water resource shortage, especially in the developing countries (Chen et al., 2014a,b; Cui et al., 2018; Ehrhardt et al., 2018; Kang and Eltahir, 2018; Thornton et al., 2014; Xiao et al., 2017; Zhang et al., 2017). In the meantime, agricultural production system is one of the major sources of greenhouse gas (GHG) emissions, water consumption, and agricultural non-point pollution (IPCC, 2013, 2014). Climate change would have a major impact on crop growth, productivity, and resource use efficiency through affecting agricultural production system processes and resource use (Ma et al., 2010; IPCC, 2014; Zhao et al.,

2015a,b). Agricultural production system could have a large potential in climate change adaptation, mitigation, and attainment of the United Nations Sustainable Development Goals (SDGs) (IPCC, 2014; Smith et al., 2014; United Nations, 2015). The North China Plain (NCP) is one of major agricultural production regions in China, accounting for more than 60 % wheat production and 30 % maize production in China (Liang et al., 2011; Yuan and Shen, 2013). Because precipitation and land surface water resources are limited, groundwater has been the major source of irrigation in the NCP, especially for winter wheat, which has caused severe groundwater overexploitation (Xiao et al., 2017). In addition, excessive nitrogen (N) fertilizer application in the NCP has caused a series of negative environmental effects (Chen et al., 2014a,b; Huang et al., 2011; Ju et al.,

⁎ Corresponding author at: Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographical Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China. E-mail address: [email protected] (F. Tao).

https://doi.org/10.1016/j.agee.2019.106791 Received 30 July 2019; Received in revised form 8 November 2019; Accepted 9 December 2019 Available online 09 January 2020 0167-8809/ © 2019 Elsevier B.V. All rights reserved.

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Fig. 1. Locations of the study sites.

2009; Liu et al., 2013). The N overuse contributes to high emissions of N2O and other GHGs, a high N leaching, and consequently groundwater and soil pollution, besides the unnecessary economic expenditure for farmers (Zheng et al., 2006; Ju et al., 2009; Dai et al., 2015; Li et al., 2016; Rutkowska et al., 2018). Climate change would have both positive and negative impacts on agricultural production, GHG emissions, and N leaching in the NCP (Tao and Zhang, 2013; IPCC, 2014). How the agricultural systems respond and adapt to, and mitigate climate change, has become of a key concern (Tao and Zhang, 2010; IPCC, 2014; Smith et al., 2014; Zhang et al., 2015; Zhao et al., 2015a,b). Extensive studies have been conducted to investigate the water use (Cao et al., 2013), nitrogen use (Zhao et al., 2015a,b), GHG emissions (Ren et al., 2018), water and fertilizer managements (Xin and Tao, 2019), tillage (Dai et al., 2013), and climate change impact and adaptation (Tao and Zhang, 2010) of agricultural systems in the NCP. These studies have shown that appropriate management practices can be adopted to manage some of the problems with agricultural production and environment in the NCP. For example, shifts of sowing dates, cultivars, and cropping density could increase crop productivity (Ren

et al., 2018; Xin and Tao, 2019; Zhang et al., 2015). Regulating the irrigation scheduling of winter wheat, together with utilizing straw mulching with winter wheat and summer maize, could reduce soil evaporation, increase irrigation water use efficiency (WUE), and consequently prevent the decline of the groundwater table in the NCP (Zhang et al., 2003). Growing legume could control the accumulation of residual nitrate and reduce the risk of N leaching significantly (Yao et al., 2018). By adopting knowledge-based optimum N fertilization techniques, a better N balance can be achieved without sacrificing crop yields but significantly reducing environmental risk (Chen et al., 2014a,b; Ju et al., 2009). Reducing fertilizer application rate, optimizing rotation schemes, and adopting conservation tillage could reduce GHG emissions and carbon footprint (CF) (Cheng et al., 2011; Gan et al., 2014; Zhang et al., 2016). However, most of previous studies have focused on a certain single cropping system such as maize or wheat system, and on one or few indictors of a cropping system such as crop yield, WUE or nitrogen use efficiency (NUE). Many previous studies on climate change impacts have mainly focused on crop phenology, yield, and water use, however 2

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many other important indicators such as N leaching, soil organic carbon (SOC), GHG emissions, and CF have been rarely investigated jointly (Porter et al., 2019). In addition, the studies on climate change adaptations of agricultural system have mainly investigated few adaptation options such as shifts of sowing date and cultivars (IPCC, 2014), more dramatic adaptation options such as shifts of cropping system and conservation tillage under future climate change scenarios have scarcely been investigated and quantified. Furthermore, the studies on climate change adaptation and mitigation have often been separated, although they are actually interlinked closely with each other. The integrated studies on climate change impact, adaptation and mitigation have been strongly recommended (Ewert et al., 2015; IPCC, 2014; Porter et al., 2019). To address these interlinked challenges of food security, climate change adaptation and mitigation, climate-smart agriculture (CSA) has been proposed as an integrative approach that explicitly aims for three objectives: sustainably increasing agricultural productivity to support equitable increases in farm incomes, food security and development; adapting and building resilience of agricultural and food security systems to climate change at multiple levels; and reducing GHG emissions from agriculture (https://csa.guide/). To accelerate implementation of CSA, climate-smart cropping systems and agricultural managements (e.g., Xin and Tao, 2019) need to be developed through a comprehensive multiple-objective assessment that jointly accounts for crop productivity, adaptation and resilience to climate change, as well as climate change mitigation (Pretty et al., 2018). Along this line, this aims of this study are to: 1) have a comprehensive multiple-objective assessment on eight alternative cropping systems in the NCP in terms of crop grain yield, WUE, evapotranspiration (ET), groundwater recharge (GWR), NUE, N leaching, N2O emission, SOC in the topsoil (0−20 cm), and CF at four NCP sites; 2) assess the responses of the eight alternative cropping systems to future climate change; and 3) investigate the roles of tillage managements on the eight cropping systems under the baseline conditions and future climate change scenarios. For these purposes, a series of agricultural system model simulations are first conducted with eight alternative cropping systems and two tillage managements at four sites under the baseline (2000–2009) conditions and two climate change scenarios in the future (2050–2059). Then, an integrated assessment on the eight cropping systems is conducted to develop climate smart agricultural systems in the NCP.

scenarios given by the Representative Carbon Pathway (RCP): 2.6 and 8.5. RCP 2.6 and RCP 8.5 represented the different radiative forcing levels in the atmosphere up to 2.6 and 8.5 Wm−2, respectively, by the 21 st century (van Vuuren et al., 2011; Zhang et al., 2017). The five GCMs applied here included GFDL_ESM2M, HadGEM2_ES, IPSL_CM5A_LR, MIROC_ESM_CHEM, and NorESM1_M. These GCMs were detailed in Zhang et al. (2017). The GCMs provided the simulated climate data for 2000–2009 and the projected climate scenarios for 2050–2059 under the emission scenarios of RCP 2.6 and RCP 8.5. These GCMs performed reasonably well in reproducing baseline climate in China (Liang et al., 2018; Zhang et al., 2017). These datasets were bias-corrected for climate change impact studies using the methods in Hempel et al. (2013). To eliminate the discrepancies between the observed and GCMs-simulated weather data for the baseline period, we followed Estes et al. (2013) to construct climate change scenarios data for APSIM model. Firstly, for climate variables of Tmin and Tmax, the GCMs-simulated absolute changes in daily Tmin and Tmax values between the baseline (2000–2009) and future period (2050–2059) were first computed, and then added to the observed daily Tmin and Tmax in the corresponding dates, respectively, for each day during 2000–2009, to derive the daily Tmin and Tmax for 2050–2059. For climate variables of daily precipitation and solar radiation, the GCMs-simulated relative changes in monthly precipitation and solar radiation between the baseline and future period were first computed, and then multiplied with the observed daily precipitation and solar radiation in the corresponding months, respectively, for each day during 2000–2009, to derive the daily precipitation and solar radiation for 2050–2059. Crop experimental data included crop key phenological dates, yield and yield components, and agronomic management practices. The experimental management practices were nearly same as the local farmers’ practices (Table 1). The information on crop field and soils was described in Xin and Tao (2019). 2.3. APSIM model and its parameterization APSIM model is a typical component-driven mechanism model. It simulates multiple crop responses to climate change and management options (Gaydon et al., 2017; Hochman et al., 2017; Keating et al., 2003). The model has been widely used to predict crop growth, soil water and nitrogen dynamics in the NCP (Chen et al., 2010; Sun et al., 2015; Xiao and Tao, 2014; Zhao et al., 2015a,b). In the study, we used local field trial data including planting spacing, sowing density, sowing date, fertilization, and irrigation to calibrate the crop cultivar parameters in APSIM model. The validation results of APSIM for maize, wheat and soybean at the four sites were shown in Fig. S1.

2. Materials and methods 2.1. Study area

2.4. Simulation settings

The study was conducted at four agro-meteorological experiment sites in the NCP, including Nanyang, Xinxiang, Taian, and Luancheng (Fig. 1). The winter wheat-summer maize rotation system is the representative cropping system in this region. Soybean is a main industrial crop in the NCP. Winter wheat is usually sown in the middle of October and harvested in the beginning of June. Summer maize and soybean are usually sown in the middle of June and harvested in the beginning of October. The experiment sites have contrasting geographical and climatic conditions, as detailed in Xin and Tao (2019).

The validated APSIM model was then applied at the four experiment sites to simulate the dynamics of different cropping systems with variable climate and tillage management options in this region. For each cropping system, APSIM model was run with the baseline climate and 10 bias-corrected future climate scenarios (5 GCMs × 2 emission scenarios). CO2 concentration for the baseline, RCP 2.6, and RCP 8.5 scenarios was set to be 350, 443, and 541 ppm, respectively. In order to select the optimal cropping system for the NCP, eight alternative cropping systems were taken into account and compared comprehensively. They were continuous wheat monoculture (CW), continuous maize monoculture (CM), continuous soybean monoculture (CS), wheat-soybean rotation (WS), maize-soybean intercropping (MS), conventional winter wheat-summer maize rotation (WM), conventional winter wheat-summer maize rotation without irrigation (WM-NI), optimized winter wheat-summer maize rotation (WM-OPT). Finally, conventional tillage and conservation tillage were applied to explore the better ways to enhance soil carbon storage. The conventional tillage (CT) treatment was to plow with residue incorporation and prepare the

2.2. Data For the baseline period, the observed daily meteorological data, as well as crop experimental data from 2000 to 2009 at the four sites, were obtained from the Chinese Meteorological Administration (CMA). The daily meteorological data included precipitation, minimum and maximum temperature, and sunshine hours. Daily solar radiation for each site was estimated from sunshine hours using the Angstrom equation (Prescott, 1940). For the future period (2050–2059), we used an ensemble of five global climate models (GCMs) under two emission 3

Irrigation Fertilizer Irrigation Fertilizer

Irrigation Fertilizer

CM

WS

4

Irrigation Fertilizer

Irrigation Fertilizer

Irrigation Fertilizer

WM

WM-NI

WM-OPT

– 100 kg N ha−1 for wheat

3*80 mm for wheat and 1*80 mm for maize 100 kg N ha−1 for wheat

1*80 mm – – 50 kg N ha−1 base fertilizer and 50 kg N ha−1 at flowering stage 3*80 mm for wheat 100 kg N ha−1 for wheat and 100 kg N ha−1for soybean 1*80 mm for maize 100 kg N ha−1for soybean

3*80 mm 100 kg N ha−1 base fertilizer

Xinxiang

a The eight alternative cropping systems are continuous wheat monoculture (CW), continuous maize monoculture (CM), continuous soybean monoculture (CS), wheat-soybean rotation (WS), maize-soybean intercropping (MS), conventional winter wheat-summer maize rotation (WM), conventional winter wheat-summer maize rotation without irrigation (WM-NI), optimized winter wheat-summer maize rotation (WM-OPT), respectively.

Irrigation Fertilizer

MS

CS

4*80 mm 3*80 mm 3*80 mm 120 kg N ha−1 base fertilizer and 210 kg N ha−1 at 130 kg N ha−1 base fertilizer 180 kg N ha−1 base fertilizer jointing stage 1*80 mm – – 240 kg N ha−1 at jointing stage 270 kg N ha−1 at jointing stage – – – – 50 kg N ha−1 base fertilizer and 50 kg N ha−1 at 50 kg N ha−1 base fertilizer and 50 kg N ha−1 at 50 kg N ha−1 base fertilizer and 50 kg N ha−1 at flowering stage flowering stage flowering stage 4*80 mm for wheat 3*80 mm for wheat 3*80 mm for wheat 130 kg N ha−1 for wheat and 100 kg N ha−1for 180 kg N ha−1 for wheat and 100 kg N ha−1for 330 kg N ha−1 for wheat and 100 kg N ha−1for soybean soybean soybean 1*80 mm for maize – – −1 −1 −1 −1 240 kg N ha for maize and 100 kg N ha for 270 kg N ha for maize and 100 kg N ha for 100 kg N ha−1for soybean soybean soybean 4*80 mm for wheat and 1*80 mm for maize 3*80 mm for wheat 3*80 mm for wheat 330 kg N ha−1 for wheat and 240 kg N ha−1for 130 kg N ha−1 for wheat and 270 kg N ha−1for 180 kg N ha−1 for wheat maize maize – – – 330 kg N ha−1 for wheat and 240 kg N ha−1for 130 kg N ha−1 for wheat and 270 kg N ha−1for 180 kg N ha−1 for wheat maize maize 3*80 mm for wheat −1 180 kg N ha for each crop (the ratio of base fertilizer and jointing topdressing are 2:3 and 1:1 for wheat and maize, respectively)

Irrigation Fertilizer

Taian

CW

Nanyang

Luancheng

Crop systema

Table 1 Information on the irrigation and fertilizer management practices for the eight alternative cropping systems at Luancheng, Nanyang, Taian, and Xinxiang.

Y. Xin and F. Tao

Agriculture, Ecosystems and Environment 291 (2020) 106791

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Fig. 2. Monthly mean precipitation (mm) and temperature (℃) of the three climate scenarios: Historical Baseline Climate, Future Climate (RCP 2.6, and RCP 8.5) at Luancheng, Nanyang, Taian and Xinxiang.

seedbed each year before crop sowing. The conservation tillage treatment was no tillage (NT) which was left undisturbed with residue retention without any seedbed preparation before seeding. For both CT and NT treatments, wheat and maize residues were not removed from the field, while soybean residues were removed. The total grain yield, WUE, ET, GWR, NUE, N leaching, N2O emission, SOC in the topsoil (0−20 cm), and CF were modelled to evaluate the cropping systems’ productivity and environmental consequence in response to different levels of irrigation and nitrogen inputs. CF has been an effective method to account for carbon sequestration and GHG emissions in agricultural systems. Based on the simulated results, the total yield, ET, GWR, and N loss by N leaching and N2O emission of the cropping system under each treatment, were derived. The WUE, NUE, and CF were calculated as:

WUE =

Yield ET + irrigation

(1)

NUE =

Yield Napplicationrate

(2)

CF =

(SCSend

2.5. Analysis For the baseline period, there are 160 sets of simulations for each site (10 years × 8 cropping systems × 2 tillage managements). For the future period, there are 1600 sets of simulations for each site (5 GCMs × 2 emission scenarios × 10 years × 8 cropping systems × 2 tillage managements). The annual average simulation results during 2000–2009 and 2050–2059 were firstly compared at each site. The mean of simulation results with the five GCMs was used to assess the changes between two tillage managements under two future climate scenarios of different cropping systems for a specific variable such as crop yield, WUE, ET, GWR, NUE, N leaching, N2O emission, SOC, and CF. In order to better compare the differences among the eight cropping systems under baseline condition, the variable values were normalized and plotted as a radar graph. The SPSS 19.0 software was used to conduct analysis of variance (ANOVA) to investigate the differences among climate scenarios, agricultural systems and tillage managements. In addition, a one-way ANOVA was used to test whether different agricultural systems could significantly affect the variables under each climate scenarios and tillage managements. In all the treatments, difference was considered to be statistically significant if P ≤ 0.05.

(3)

E N2O = N2 O × SCS =

into CO2. CF is the carbon footprint of each cropping system (kg CO2 -eq kg−1). Yield is the annual grain yield of each cropping system (kg ha−1).

SCSbegin ) 10

×

44 12

(4)

E N2 O + SCS Yield

(5)

3. Results

where, EN2O is the annual cumulative amounts of N2O emission from soil (kg CO2 -eq ha−1); the is the global warming potential (GWP) factors over a 100-year period, which is 298 for N2O (IPCC, 2013). SCS is the annual SOC change in the surface of 0−20 cm profile under the baseline (2000–2009) or future (2050–2059) climate (kg CO2 -eq ha−1). SCSend and SCSbegin SCSbegin are the soil carbon storage values of the 0−20 cm profile in 2009 and 2000 under baseline conditions, and 2059 and 2050 under future scenarios, respectively. 10 is the number of years of the experimental period; and 44 is the coefficient to convert C

3.1. Climate change scenarios The baseline climate and projected mean monthly temperature and precipitation for 2050–2059 at the four sites were shown in Fig. 2. At the four sites, compared with the baseline period, annual mean temperature would increase by 1.22–1.36 °C and 2.47–2.64 °C during the period of 2050–2059 under RCP 2.6 and RCP 8.5, respectively. Across

12

5

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Fig. 3. Changes in grain yield (t ha−1), water use efficiency (WUE, kg mm−1), nitrogen use efficiency (NUE, kg kgN−1), evapotranspiration (ET, mm), groundwater recharge (GWR, mm), N leaching (kg ha−1), N2O emission (N2O, kg ha−1), and soil organic carbon (SOC, kg ha−1) of eight alternative cropping systems under baseline, RCP 2.6 and RCP 8.5 with conventional tillage and conservation tillage at Luancheng. The eight alternative cropping systems are continuous wheat monoculture (CW), continuous maize monoculture (CM), continuous soybean monoculture (CS), wheat-soybean rotation (WS), maize-soybean intercropping (MS), conventional winter wheat-summer maize rotation (WM), conventional winter wheat-summer maize rotation without irrigation (WM-NI), optimized winter wheat-summer maize rotation (WM-OPT), respectively. Different lowercase letters indicate significant differences between systems at P ≤ 0.05.

the four sites, annual precipitation would increase from 9 % to 20 % and from 20 % to 31 % under RCP 2.6 and RCP 8.5, respectively. The annual precipitation would be significantly different among the four sites. It would be the lowest at Luancheng and highest at Nanyang. In addition, precipitation would increase mainly in summer.

3.2. Performances of different cropping systems under baseline conditions with different tillage managements The results of ANOVA showed climate change scenarios, different cropping systems and tillage managements could have significant impacts on various indicators of cropping systems (Table S1). Under the 6

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Fig. 4. Changes in gain yield (t ha−1), water use efficiency (WUE, kg mm−1), nitrogen use efficiency (NUE, kg kgN−1), evapotranspiration (ET, mm), groundwater recharge (GWR, mm), N leaching (kg ha−1), N2O emission (N2O, kg ha−1), and soil organic carbon (SOC, kg ha−1) of eight alternative cropping systems under the baseline, RCP 2.6 and RCP 8.5 with conventional tillage and conservation tillage at Nanyang. Different lowercase letters indicate significant differences between systems at P ≤ 0.05.

7

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Fig. 5. Relative changes (0–1) in yield (t ha−1), water use efficiency (WUE, kg mm−1), nitrogen use efficiency (NUE, kg kgN−1), evapotranspiration (ET, mm), groundwater recharge (GWR, mm), N leaching (N2O, kg ha−1), N2O emission (kg ha−1), and soil organic carbon (SOC, kg ha−1) of eight alternative cropping systems under baseline conditions with conventional tillage (a, b, c, d) and conservation tillage (e, f, g, h) at Luancheng (a, e), Nanyang (b, f), Taian (c, g) and Xinxiang (d, h).

baseline conditions with conventional tillage, the eight alternative cropping systems were significantly different at each site (Figs. 3 and 4 and Figs. S2–3). Among the eight investigated cropping systems, across the four sites, the currently dominant WM cropping system had the largest ET, N leaching, and N2O emission, a medium crop yield, WUE, SOC, and NUE, however the lowest GWR (Fig. 5). The ET, N leaching and N2O emission could reach up to 751.8 mm, 158.7 kg ha−1 and 27.5 kg ha-1, respectively, and the GWR could be as low as -127.6 mm (Figs. 3 and 4 and Figs. S2–3). The WM-NI cropping system could have a large N2O emission, a medium yield, WUE, GWR, ET, and SOC, a low N leaching, and the lowest NUE across the four sites. The NUE of WMNI cropping system could be as low as 14.1 kg kgN-1. As for monoculture cropping systems, CW cropping system had a large N leaching and N2O emission, a medium ET, GWR, and SOC, while a low yield, WUE, and NUE. CM cropping system showed a large GWR and N2O emission, a medium WUE, N leaching, and SOC, a low yield, ET, and NUE. CS cropping system also had a large GWR, a medium NUE, a low N leaching, N2O emission, however the lowest yield, WUE, ET, and SOC (Fig. 5). Its yield, WUE, ET, and SOC could be as low as 2.0 t ha-1, 4.4 kg mm-1, 402.2 mm, and 3.7 kg ha−1 (Figs. 3 and 4 and Figs. S2–3). In addition, WS cropping system could have a large ET, a medium yield, N leaching, and N2O emission, and yet a low WUE, GWR, NUE, and SOC. On the contrary, MS cropping system could have a large GWR and SOC, a medium yield, WUE, NUE, ET, N leaching, and N2O emission (Fig. 5). The most prominent was that the WM-OPT cropping system had the largest yield, WUE, and NUE, which ranged from 21.3–22.8 t ha-1, 23.6–26.2 kg mm-1, and 59.3–69.2 kg kgN-1, respectively, and the lowest N leaching and N2O emission which ranged from 6.2–34.8 kg ha−1 and 1.8 to 6.0 kg ha-1, respectively (Figs. 3–5 and Figs. S2–3). However, the system had a large ET, a medium SOC, and a low GWR. Under the baseline conditions with conservation tillage, the eight alternative cropping systems also had significant differences (Figs. 3 and 4 and Figs. S2–3). The ranking of the eight cropping systems could be changed in comparison with conventional tillage across the four sites (Fig. 5). Compared with conventional tillage, yield changes with conservation tillage were different for the cropping systems; the largest yield change across four sites was 35 %, 23 %, -2 %, 0.1 %, 2 %, 14 %, 4 %, and -1 %, respectively, for WM, WM-NI, CW, CM, CS, WS, MS, and WM-OPT (Table S2). In comparison with conventional tillage, for the eight cropping systems with conservation tillage, WUE could increase

by up to 39 %, 21 %, 6 %, 1 %, 2 %, 23 %, 3 %, and 4 %, respectively, owing to decrease in ET. Correspondingly, SOC could increase by up to 93 %, 68 %, 91 %, 7 %, 7 %, 699 %, 7 %, and 51 %, respectively, (Table S2). 3.3. Performances of different cropping systems under future climate scenarios with different tillage managements With conventional tillage, the eight cropping systems would have significantly different responses to future climate change, and consequently their rankings would change, in comparison with the baseline conditions (Figs. 3 and 4, Figs. S2–3 and Tables S3-6). The projected climate change impacts have a similar trend for RCP 2.6 and RCP 8.5 scenarios although the latter could result in larger impacts (Figs. 3 and 4 and Figs. S2–3). The currently dominant WM cropping system would have the largest ET, N leaching and N2O emission, a medium crop yield, WUE, and SOC, however a low NUE, and GWR. The ET, N leaching, and N2O emission would increase by up to 17 % (16 %), 25 % (80 %), and 65 % (114 %) under RCP 2.6 (RCP 8.5), respectively, while yield, WUE, NUE, SOC would decrease by 11 % (18 %), 25 % (31 %), 39 % (43 %), 40 % (48 %) under RCP 2.6 (RCP 8.5), respectively, compared with those under baseline conditions with conventional tillage. WM-NI cropping system would have a large WUE, a medium yield, GWR, ET, N2O emission and SOC, a low N leaching, and the lowest NUE across the four sites. Comparing with the baseline conditions with conventional tillage, yield, WUE, NUE, ET, N leaching, and N2O emission would increase under the future climate scenarios. Under both RCP 2.6 and RCP 8.5 emission scenarios, only the yields of WM-NI cropping system would increase and its overall ranking would improve (Figs. 3 and 4 and Figs. S2–3). Especially at Taian, yield of WM-NI cropping system would almost catch up with the yield of WM cropping system (Fig. S2). CW cropping system would have a large N leaching and N2O emission, a medium ET, GWR, and SOC, yet a low yield, WUE, and NUE. CM cropping system would have a large GWR, a medium WUE, N leaching, N2O emission, and SOC, a low yield, ET, and NUE. In contrast, CS cropping system would have the lowest yield, WUE, NUE, ET, N2O emission, and SOC with a large GWR and low N leaching. For these monoculture systems, yield and SOC would decrease while ET and N2O emission would increase under the future climate scenarios. WS cropping system would have a large ET, a medium yield, N leaching, and 8

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ranged from 0.14 to 0.35 kg CO2 -eq kg−1, which was lower than that of WM-NI across the four sites. The CF of WM-NI was the largest among all the eight systems at Xinxiang (0.17 kg CO2 -eq kg−1). The CF of CW was the largest among the eight systems at Taian (0.40 kg CO2 -eq kg−1) while the CF of CS was the largest at Luancheng (0.37 kg CO2 -eq kg−1) and Nanyang (0.56 kg CO2 -eq kg−1). The CF of CM, WS, and MS cropping systems was moderate. In contrast, the CF of WM-OPT was the lowest across the four sites ranged from -0.01 to 0.04 kg CO2 -eq kg−1. Compared with conventional tillage, conservation tillage could reduce CF (Fig. 6). Under the baseline with conservation tillage, the currently dominant WM cropping system had a medium CF ranging from 0.10 to 0.24 kg CO2 -eq kg−1. Compared with conventional tillage, its ranking among the eight systems decreased. The CF of WM-NI cropping system was close to that of WM except for Nanyang, nevertheless conservation tillage made a big difference between the two systems under the baseline. The CF of CW was still the largest among the eight systems at Taian (0.37 kg CO2 -eq kg−1). The CF of CS was the largest at Luancheng (0.34 kg CO2 -eq kg−1), Nanyang (0.56 kg CO2 -eq kg−1), and Xinxiang (0.15 kg CO2 -eq kg−1). In addition, the CF of WMOPT was the lowest across the four sites ranging from -0.01 to 0.02 kg CO2 -eq kg−1. Future climate change would increase CF of the cropping system, and the CF among the cropping systems could be significantly different under both RCP 2.6 and RCP 8.5 (Fig. 6, Tables S7). Compared with the baseline with conventional tillage, the CF of WM would be increased by 5–59 % and 22–123 %, respectively, under RCP 2.6 and RCP 8.5, except Nanyang (Fig. 6, Tables S7). It would become larger than that of WM-NI under RCP 8.5. What’s the important was that the future CF of monoculture cropping systems would increase faster than that of multiple cropping systems. The CF of CS would become the largest at all the sites, reaching up to 1.22 and 1.80 kg CO2-eq kg−1, respectively, under RCP 2.6 and RCP 8.5, followed by CW and CM. WS and MS would rank in the middle. At each site, WM-OPT would have the lowest CF among all systems.

N2O emission, and yet a low WUE, GWR, NUE, and SOC. On the contrary, MS cropping system could have a large GWR and SOC, a medium yield, WUE, ET, N leaching and N2O emission, yet a low NUE. Its WUE, NUE, and SOC would decrease under RCP 2.6 and RCP 8.5, while ET, GWR, N leaching, and N2O emission would increase. The WM-OPT cropping system would have a large ET and SOC, and a low GWR. Its yield, WUE, and NUE were the largest while N leaching and N2O emission were the lowest among the eight systems. Under the RCP 2.6 and RCP 8.5, its yield, WUE, NUE, and SOC would decrease, while ET, GWR, N leaching, and N2O would increase. Nevertheless, the WM-OPT cropping system still could keep the lowest N leaching (13.8–43.0 and 23.8-48.2 kg ha−1) and N2O emission (6.5–7.2 and 6.5-9.5 kg ha−1) among the eight systems under the RCP 2.6 and RCP 8.5 by adjustment of irrigation and fertilizer management in the future (Figs. 3 and 4 and Figs. S2–3). With conservation tillage, the eight cropping systems would also have significantly different responses to future climate change (Tables S3–6), but each cropping system would have a little bit less negative response than that with conventional tillage (Figs. 3 and 4, Figs. S2–3). For example, with conservation tillage, yield, WUE, NUE, and particularly SOC would decrease less, while ET and N leaching would increase less, with climate change (Figs. 3 and 4 and Figs. S2–3). In comparison with conventional tillage, SOC would increase by up to 179 % (148 %), 146 % (133 %), 144 % (110 %), 59 % (58 %), 57 % (57 %), 748 % (594 %), 53 % (50 %), and 124 % (108 %), respectively, across the four sites, under RCP 2.6 (RCP 8.5), for WM, WM-NI, CW, CM, CS, WS, MS, and WM-OPT, with conservation tillage. 3.4. Carbon footprint The CF changes of eight cropping systems at the four sites under the baseline and future climate scenarios with different tillage managements were shown in Fig. 6. Under the baseline with conventional tillage, the currently dominant WM cropping system had a median CF

Fig. 6. Carbon footprints (CF, kg CO2-eq kg−1) of eight alternative cropping systems under the baseline, and their changes under RCP 2.6, and RCP 8.5, with conventional tillage and conservation tillage at Luancheng, Nanyang, Taian, and Xinxiang. Different lowercase letters indicate significant differences between systems at P ≤ 0.05.

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Compared with the baseline with conservation tillage, the CF of current WM cropping system would increase by 16–88 % and 34–184 %, respectively, under RCP 2.6 and RCP 8.5 (Fig. 6). It would become larger than that of WM-NI except for Nanyang. The CF of CS cropping system would be the largest at all the sites, reaching up to 1.25 and 1.79 kg CO2 -eq kg−1, respectively, under RCP 2.6 and RCP 8.5, followed by CW, CM, WS and MS cropping systems. Finally, WM-OPT would still have the minimum CF mainly due to its high yield. In addition, the CF of WM-OPT with conservation tillage would be lower than that with conventional tillage in the future. Above all, these suggested that WMOPT cropping system with conservation tillage should be recommended for the NCP in the future. Compared with the current WM cropping system with conventional tillage, WM-OPT with conservation tillage would reduce CF by 87–92 % and 76–94 %, respectively, under RCP 2.6 and RCP 8.5.

crop production in the region. On the other hand, future climate change would advance phenological development stage, shorten the duration of grain-filling period, increase the risk of high temperature stress and drought stress, and consequently reduce crop yields if no adaptation were taken (Xiao and Tao, 2016; Chen et al., 2018a,b). Despite the geographical and meteorological conditions of the four sites were different, the results revealed a common shared response across the four sites that yield would decrease with the future climate change under both RCP 2.6 and RCP 8.5 without adaptation. Additionally, the WUE and NUE of crops would be affected by climate change because rising CO2 and climate change would affect crop water availability, ET, N uptake, and grain yield (Fang et al., 2010; Liu et al., 2016; Ma et al., 2010). The results showed that the WUE of crops would decrease with ongoing climate change as a result of decrease in yield. The ET would increase because a higher temperature and precipitation could lead to greater cumulative ET offsetting the shorter growing duration in consuming water through ET. The deeper surface water unabsorbed by the plant would be drained to the groundwater, which was considered as a kind of groundwater compensation (Yuan and Shen, 2013). The reduction of NUE in the future climate at the four sites mainly resulted from more N losses from N leaching and N2O emission, and decrease in grain yield. More N losses from N leaching and N2O emission led to soil, water, and air pollution (Ju et al., 2009; Liu et al., 2016). SOC stocks would decrease with increasing temperature and precipitation because higher temperatures and soil moisture generally result in higher belowground activity and therefore faster turnover of soil carbon (Wei et al., 2014). CF would increase in the future climate because the large increase in N2O emission, decrease in SOC and grain yield. Therefore, it is necessary to reduce N loss, enhance carbon sequestration, and reduce CF by optimizing agricultural management practices such as reducing fertilizer application rate and adopting conservation tillage (Iocola et al., 2017; Lugato et al., 2014). To mitigate the negative effects of climate change, the impacts of shifts of sowing dates, agronomic management practices, and crop cultivars on crop yield have been proposed and evaluated (e.g., Fang et al., 2010; Ren et al., 2018; Xin and Tao, 2019), however the roles of shifts of cropping system in climate change adaptation and mitigation have been less investigated. The currently dominant WM cropping system had the largest ET, N leaching and N2O emission, and the lowest GWR in the future. Therefore, this system needed to be improved or replaced in the NCP since it could aggravate resources shortage and environmental pollution. The WM-NI cropping system would increase yields and WUE with ongoing climate change due to projected increase in precipitation. Compared with current irrigated scheme, GWR would increase significantly, which would mitigate water shortage. The rotation/intercropping systems could have high annual yields, while monoculture cropping systems were beneficial for water conservation, under both baseline and future climate. Wheat and maize had relatively higher yields and ET than soybean, therefore, rotation/intercropping systems with wheat and maize would be better choice for the areas that needed to maintain a high yield. In general, the currently common irrigation management practice for wheat led to a high ET and crop yield, resulting in a low WUE and GWR, so the rotation systems with wheat using the common irrigation practice would not be suitable for the areas with severe water shortage. However, maize had a higher WUE than wheat and soybean due to a high grain yield without irrigation. Therefore, for the purposes of water conservation and productivity, the rotation/intercropping systems with maize would be a better option. Regarding to N losses through N leaching and N2O emission, the order of three crops was wheat > maize > soybean, which resulted from fertilizer/water consumption and species characteristic. Soybean had the lower SOC than wheat and maize. With climate change, the SOC ranking of different systems varied at different sites, due to the local soil properties, but the MS, WM, WM-NI and WM-OPT cropping systems still had advantage in terms of soil carbon sequestration. In addition, soybean had the highest CF under both baseline and future climate. In

4. Discussion 4.1. Performance of alternative cropping systems and the underlying mechanisms In the present study, a comprehensive multiple-objective assessment was conducted on alternative cropping systems accounting for crop yield, WUE, NUE, ET, GWR, GHG emissions, N leaching, and SOC. There exist synergies and trade-offs between the multiple objectives. For example, high yields were usually companied with high ET, N leaching and N2O. A high GWR was linked with a high WUE and a low ET. The results identified the advantages and disadvantages of different cropping systems, as well as the synergies and trade-offs between different objectives. The currently dominant WM cropping system in the NCP had a medium crop yield while the largest ET, N leaching, and N2O emission, and the lowest GWR, mainly because of overuse of N fertilizer and irrigation water, and over-pumping of groundwater for irrigation (Cao et al., 2013; Ju et al., 2009; Ren et al., 2018). The WM-NI cropping system could have a large N2O emission and low NUE because the fertilizer might not be fully used by crop under water stress condition. CW cropping system had a large N leaching and N2O emission, however a low WUE and NUE because wheat required more N and irrigation water inputs than maize and soybean. CM cropping system showed a large GWR and N2O emission, however a low yield, ET, and NUE because maize growth period coincided with rain season and maize required less irrigation water inputs. CS cropping system had a large GWR, a low N leaching and N2O emission, and the lowest yield, WUE, ET, and SOC because soybean growth period also coincided with rain season and maize required less irrigation water inputs. In addition, soybean required less N inputs than wheat and maize because soybean have the ability to ‘fix’ nitrogen from the air into ammonia which can be used by the soybean plant (Carter and Tegeder, 2016). Because of having only one harvest per year, the monoculture systems including CW, CM, and CS usually had a lower total grain yield compared with the rotation systems which had more than harvest per year on average. WS cropping system could have a large ET, yet a low WUE, GWR, NUE, and SOC, because wheat required more N and irrigation water inputs. MS cropping system could have a large GWR and SOC, a medium yield, WUE, NUE, ET, N leaching, and N2O emission because it required much less N and irrigation water inputs. The WM-OPT cropping system had the largest yield, WUE, and NUE, however a large ET, a medium SOC, and a low GWR, because it adopted optimal crop sowing dates, planting density, cultivars, N and irrigation water inputs (Xin and Tao, 2019). 4.2. Developing climate-smart agricultural systems for different areas in the NCP Future climate change will increase temperature and precipitation during the period 2050–2059 in the NCP, which suggested that the projected increases could improve the thermal and water resource for 10

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general, the CF of monoculture cropping systems would increase faster than multiple cropping systems with ongoing climate change. The WMOPT cropping system was the best one to keep low CF. In short, the WM-OPT cropping system had the greatest advantage than other seven systems with the highest yield, WUE, NUE, high ET, GWR, and SOC, and the lowest N loss and CF, by optimizing irrigation, fertilizer and cultivar, which could mitigate the water shortage and environmental pollution in the NCP (Ren et al., 2018; Xin and Tao, 2019; Zhang et al., 2015). Our recommended optimal systems showed that the use of water and N could be 40 % less than current input of agricultural practices by the farmers in the NCP (Xin and Tao, 2019). In addition, a new attempt could be considered in the NCP to change the organic arable farming to agroforestry, which could reduce the N input, increase the productivity, and remain the N surplus within an optimal range (Lin et al., 2016). Above all, taking into account jointly crop productivity, climate change adaptation and mitigation, the WM-OPT cropping system could be the optimal option for the NCP. In addition, different cropping systems should be selected for a specific environment with a specific problem such as severe water shortage, severe environment pollution, or severe food shortage.

climate change on agricultural production systems and SOC decrease. CSA is an effective approach to transform and adjust agricultural systems to simultaneously ensure food security and mitigate environmental costs under future changing climate. A comprehensive multiobjective assessment and optimization of agricultural production systems is necessary to develop CSA for a certain environment. We concluded that the WM-OPT cropping system and conservation tillage should be recommended in the NCP, although it needs further evaluation in the field. In addition, the study demonstrates a useful modeling framework to develop sustainable agricultural strategies and CSA for agricultural production system to meet the challenges of global climate change. The modeling framework can identify the advantages and disadvantages of different cropping systems, as well as the synergies and trade-offs between multiple objectives, which is essential to develop climate-smart agricultural systems for a specific environment and can be widely applied to other cropping systems and regions. The modeling framework can provide guidance and strategies to develop CSA a specific environment with less labor, time, and money costs. Nevertheless, due to the uncertainties of agricultural system models, the results based on modeling need to be further tested and evaluated using field experiments.

4.3. The roles of conservation tillage

Declaration of Competing Interest

In this study, compared with the conventional tillage, conservation tillage did not show advantage in term of crop productivity. Instead, we found the benefits of conservation tillage in mitigating the negative impacts of climate change and SOC decrease. For example, crop yield, WUE, NUE, and particularly SOC would decrease less, while ET and N leaching would increase less, with conservation tillage under future climate change. These are because conservation tillage could decrease soil disturbance and decomposition, leaving surface residue cover that can increase water retention, soil C and N, and potentially crop yield (Johnson et al., 2017). The roles of conservation tillage could be more pronounced in areas with relatively warmer or drier climates or lower nitrogen fertilizer inputs (Bai et al., 2019). Therefore, conservation tillage should be adopted widely in the NCP, although there are some different conclusions in some previous studies. For example, Dai et al. (2013) found that conservation tillage significantly reduced wheat yield after two years. On the contrary, the increase of maize yield happened with the application of no tillage in U.S (Ogle et al., 2012). The different impacts of conservation tillage on yields should result from climate variation, study duration, or soil properties especially soil moisture, and vice versa, this will affect residue C inputs to soils. Besides, conservation tillage significantly increased SOC of the surface soil at the four sites. Powlson et al. (2011) has previously shown the implement of conservation tillage since it could reduce the SOC decomposition. Moreover, conservation tillage limits the diffusion and number of air-filled pores in the soil, by which soil CO2 emission were little (Hu et al., 2015). However, in recent years, some researchers have questioned whether conservation tillage could increase SOC stocks, believing it was highly overemphasized in climate change mitigation (Baker et al., 2007; Du et al., 2017; VandenBygaart, 2016). In fact, SOC changes significantly varied in different layers, no matter under conservation tillage or conventional tillage (De Sanctis et al., 2012; Iocola et al., 2017). These questions emphasize the importance to assess entire soil profile to compare the effect of different tillage managements on soil carbon stocks.

No conflict of interest exits in the submission of this manuscript. This work was original research that has not been published previously. All the authors listed have approved the manuscript that is enclosed. Acknowledgements This study is supported by The National Key R&D Program of China (Project No. 2017YFA0604700, 2016YFD0300201, 2017YFD0300301) and the National Science Foundation of China (Project Nos. 41571088, 31761143006, 41977405). The phenological and climate data used are from the Chinese Meteorological Administration. FT was also partly supported by the Academy of Finland through projects AI-CropPro (decision no. 316172) and DivCSA (decision no. 316215). References Bai, X., Huang, Y., Ren, W., et al., 2019. Responses of soil carbon sequestration to climatesmart agriculture practices: a meta-analysis. Glob. Change Biol. 25, 2591–2606. https://doi.org/10.1111/gcb.14658. Baker, J.M., Ochsner, T.E., Venterea, R.T., Griffis, T.J., 2007. Tillage and soil carbon sequestration – what do we really know? Agric. Ecosyst. Environ. 118, 1–5. https:// doi.org/10.1016/j.agee.2006.05.014. Cao, G., Zheng, C., Scanlon, B.R., Liu, J., Li, W., 2013. Use of flow modeling to assess sustainability of groundwater resources in the North China Plain. Water Resour. Res. 49, 159–175. https://doi.org/10.1029/2012WR011899. Carter, A.M., Tegeder, M., 2016. Increasing nitrogen fixation and seed development in soybean requires complex adjustments of nodule nitrogen metabolism and partitioning processes. Curr. Biol. 26, 2044–2051. https://doi.org/10.1016/j.cub.2016. 06.003. Chen, C., Wang, E., Yu, Q., Zhang, Y., 2010. Quantifying the effects of climate trends in the past 43 years (1961–2003) on crop growth and water demand in the North China Plain. Clim. Change 100, 559–578. https://doi.org/10.1007/s10584-009-9690-3. Chen, Q.C., Tian, Y.C., Yao, X., Cao, W.X., Zhu, Y., 2014a. Comparison of five nitrogen dressing methods to optimize rice growth. Plant Prod. Sci. 17, 66–80. https://doi. org/10.1626/pps.17.66. Chen, X., Cui, Z., Fan, M., Vitousek, P., Zhao, M., Ma, W., Wang, Zhenlin, Zhang, Weijian, Yan, X., Yang, J., Deng, X., Gao, Q., Zhang, Q., Guo, S., Ren, J., Li, S., Ye, Y., Wang, Zhaohui, Huang, J., Tang, Q., Sun, Y., Peng, X., Zhang, J., He, M., Zhu, Y., Xue, J., Wang, G., Wu, Liang, An, N., Wu, Liangquan, Ma, L., Zhang, Weifeng, Zhang, F., 2014b. Producing more grain with lower environmental costs. Nature 514, 486+. https://doi.org/10.1038/nature13609. Chen, Y., Zhang, Z., Tao, F., 2018a. Impacts of climate change and climate extremes on major crops productivity in China at a global warming of 1.5 and 2.0 degrees C. Earth Syst. Dyn. 9, 543–562. https://doi.org/10.5194/esd-9-543-2018. Chen, Z., Xu, C.C., Ji, L., Fang, F.P., Chen, F., 2018b. Dynamic of carbon footprint and its composition for double rice production in Southern China during 2004-2014. Ying yong sheng tai xue bao = J. Appl. Ecol. 29, 3669–3676. https://doi.org/10.13287/j. 1001-9332.201811.027. Cheng, K., Pan, G.X., Smith, P., Luo, T., Li, L.Q., Zheng, J.W., Zhang, X.H., Han, X.J., Yan,

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