Analyzing Adaptation Strategies for Maize Production Under Future Climate Change in Guanzhong Plain, China

Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543 https://doi.org/10.1007/s11027-020-09935-0

ORIGINAL ARTICLE

Analyzing adaptation strategies for maize production under future climate change in Guanzhong Plain, China

Qaisar Saddique1,2,3 & Huanjie Cai 1,2,3 & Jiatun Xu1,2,3 & Ali Ajaz 4 & Jianqiang He1,2,3 & Qiang Yu5 & Yunfei Wang 1,2,6 & Hui Chen1,2,3 & Muhammad Imran Khan 7 & De Li Liu8,9 & Liang He10

Received: 18 September 2019 /Accepted: 15 October 2020 /Published online: 29 October 2020 # Springer Nature B.V. 2020

Abstract Agricultural adaptation is crucial for sustainable farming amid global climate change. By harnessing projected climate data and using crop modeling techniques, the future trends of food production can be predicted and better adaptation strategies can be assessed. The main objective of this study is to analyze the maize yield response to future climate projections in the Guanzhong Plain, China, by employing multiple crop models and determining the effects of irrigation and planting date adaptations. Five crop models (APSIM, AquaCrop, DSSAT, EPIC, and STICS) were used to simulate maize (Zea mays L.) yield under projected climate conditions during the 2030s, 2050s, and 2070s, based on the combination of 17 General Circulation Models (GCMs) and two Representative Concentration Pathways (RCPs 6.0 and 8.5). Simulated scenarios included elevated and constant CO2 levels under current adaptation (no change from current irrigation amount, planting date, and fertilizer rate), irrigation adaptation, planting date adaptation, and irrigation-planting date adaptations. Results from both maize-producing districts showed that current adaptation practices led to a decrease in the average yield of 19%, 27%, and 33% (relative to baseline yield) during the 2030s, 2050s, and 2070s, respectively. The future yield was projected to increase by 1.1–23.2%, 1.0–22.3%, and 2–31% under irrigation, delayed planting date, and double adaptation strategies, respectively. Adapta- tion strategies were found effective for increasing the future average yield. We conclude that maize yield in the Guanzhong Plain can be improved under future climate change conditions if irrigation and planting adaptation strategies are used in conjunction.

Keywords Multiple crop models . Climate change . Adaptation . Maize yield . Planting date . Irrigation

Supplementary Information The online version of this article (https://doi.org/10.1007/s11027-020-09935-0) contains supplementary material, which is available to authorized users.

* Huanjie Cai [email protected]

Extended author information available on the last page of the article 1524 Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543

1 Introduction

Climate change is caused by natural and anthropogenic activities, with the latter considered being the main cause of recent global warming (IPCC 2007). Globally, the average temperature is reported to have increased by 0.78 °C between 1850 and 2012 (IPCC 2013). Furthermore, the IPCC (AR5) report showed that the average annual temperature will keep rising between 2016 and 2035 (Stocker 2013)andCO2 concentration will reach 560 ppm by 2050 (IPCC 2014). Agriculture is one of the sectors most affected by climate change (Godfray et al. 2011;Hoetal.2018) as agricultural systems directly respond to climatic variables, e.g., temperature, precipitation, CO2 concentration, solar radiation, etc. These climatic variables can have subsequent effects on crop phenology and yield and can induce changes in current agricultural management practices such as irrigation, fertilizer use, and planting date (Li et al. 2011; Tao et al. 2006; Lobell et al. 2011). Several studies have discussed the possibilities of decrease in crop yields worldwide due to increase in temperature associated with climate change (Alcamo et al. 2007;Duncanetal.2015; Ruane et al. 2013), while elevated CO2 concentration can increase crop production and help reduce the drought stress by increasing water productivity (Leakey et al. 2019). Staple food sources such as cereal crops (e.g., wheat, rice, maize, etc.) in many parts of the world will also face dire consequences of climate change (Gammans et al. 2017;Lobelland Field 2007;Ozdogan2011; Parry et al. 2004), and an overall decline in yields—up to − 22% by 2080—has been projected. Among the abovementioned crops, maize is important not only as an edible crop but also as a source of biofuels. China is the world’s second largest maize growing country after USA, and studies have shown that Chinese maize production would face considerable decline under future climate change scenarios (Tao and Zhang 2011;Wang et al. 2014). Maize has been among the most prominent crops in China since the early twentieth century with almost 27% share in China’s cereal economy. Shaanxi province is located in the northwest maize agroecological region of China (Meng et al. 2006), and more than 50% of its cereal production comes from Guanzhong Plain (Shen et al. 2020). Decline in maize yield has been predicted in the region due to climate change–induced higher seasonal temperature (Saddique et al. 2020). Adaptation of management practices could be the way forward for mitigating the threats to maize yield associated with changing future climate trends in the Guanzhong Plain (Lin et al. 2017; Tao et al. 2016). Agricultural adaptation is an important technique to mitigate negative climate change impacts on crop production (Hoffmann and Sgro 2011;Lobell2014). There are several adaptation strategies to ensure the sustainability of agriculture amid changing climate, e.g., changing irrigation amount, variation in planting dates, etc. Irrigation adaptation has been found as a useful strategy to sustain and improve crop production under higher crop water demands due to climate change (Babel and Turyatunga 2014; Msowoya et al. 2016). For example, in Portugal, summer maize yield is projected to decrease with increasing temperature that can be improved by providing additional irrigation (Yang et al. 2017). In addition, changing the planting date to diminish the adverse effects of climate change can have a positive impact on maize production (Laux et al. 2010; Sultana et al. 2009). Wang et al. (2012) found that changing planting and harvesting times, to counter adverse impacts of climate change in the North China Plain, can increase wheat-maize rotation yield by 4–6%. The effect of climate change on agricultural production can be evaluated using the projected climate data coming out of a variety of GCMs (Baron et al. 2005, Bregaglio et al. 2017, Rao et al. 2015,Saddiqueetal.2020,Rahmanetal.2018). Crop simulation models, such as APSIM Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543 1525

(Agricultural Production Systems Simulator; Keating et al. 2003), AquaCrop (Vanuytrecht et al. 2014), DSSAT-CERES (Decision Support System for Agrotechnology Transfer-Crop Estimation through Resource and Environment Synthesis; Jones et al. 2003), EPIC (Environ- mental Policy Integrated Climate; Williams et al. 1989), and STICS (Simulateur multiple disciplinaire pour les Cultures Standard; Brisson et al. 2003) used with GCM-projected climatic data can be used to identify the impact of climate change on crop yield (Cortes et al. 2013; Ewert et al. 2015; Jones and Thornton 2003). The crop models, in order to simulate closer to field conditions, must be rigorously calibrated with growth and yield data (Rahman et al. 2017). Many factors can cause uncertainty in the results, such as greenhouse gas (GHG) emission scenarios, the choice of GCM used for the projection, data downscaling techniques, model structure, complexities in atmospheric modeling, and uncertainties in climate and crop models used (Rosenzweig et al. 2013, 2014; Ruane et al. 2013;Assengetal.2015;Rahmanet al. 2018). Several studies in the past focused on a single or fewer crop and climate models (e.g., Chisanga et al. 2020; Liang et al. 2019;Kassieetal.2015). However, to assess the comprehensive impact of climate change adaptation practices, multiple crop models along with multiple climate models with different climate scenarios are desirable (Araya et al. 2015a; Kassie et al. 2015). Only a handful of studies have evaluated the influence of a changing climate on maize yield in Chinese production areas where the temporal increase in temperature has been noted (Khan et al. 2017; Liu et al. 2018), up to 1.2 °C since 1960 (Piao et al. 2010). In many studies, maize production regions in China are included in global-scale simulations, but the output from these regions has not been precisely discussed. For example, Ray et al. (2014) investigated the relationship between climate and crop yield at a global scale between 1979 and 2008 and showed that almost 32% of global yield variability can be explained by climate variability. A recent study in Northeast China focused on late-maturing cultivars and change in planting dates for maize in the context of future climate change and found an increase in yield (Lin et al. 2017). However, such agricultural impact assessments have not considered irrigation adaptation in association with plant water requirement. Furthermore, the interaction among adaptation strategies for maize production in China by employing multiple climate models and crop models is not well documented. Therefore, an immediate need exists to study crop management adaptation options for sustainable maize production in China under future climate conditions. The objectives of this study are (1) to determine the main trends of future maize yields in the Guanzhong Plain by using multiple crop models and multiple climate model projections and (2) to determine the effects of implementing irrigation and planting date adaptation practices on maize yield in two main maize-producing districts (Yangling and Jinghui Qu) of Guanzhong Plain in Shaanxi Province, China.

2 Materials and methods

2.1 Study area

The Guanzhong Plain is located in northwest China (34° 18′ N to 34° 64′ N and 108° 04′ Eto 109° 50′ E) (Fig. 1). The study area is in a sub-humid to semi-arid climate zone with a mean annual temperature of 13.1 °C, with minimum and maximum air temperatures ranging between − 8 and 42 °C, respectively. The total annual sunshine duration averages 2196 h, 1526 Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543 with an average precipitation of 550 mm. Summer maize is extensively cultivated in this region, with annual grain production of 3.4 billion kg (Shaanxi Provincial Bureau of Statistics, http://www.shaanxitj.gov.cn/). Maize is grown in this region between mid-June and the beginning of October with a growing season of 105 days. The dominant soil type in the region is calcareous Eum-Orthic Anthrosol (Udic Haplustalf according to the USDA system), with a loamy texture capable of high agricultural production (Raza et al. 2018;SAIEID1982). Maize was planted at the Yangling experimental site on 19 June 2012, 23 June 2013, 20 June 2014, and 15 June 2015, and at Jinghui Qu on 11 June 2012, 13 June 2013, 14 June 2014, and 15 June 2015. Plant population was six plants per m−2 in rows spaced 50 cm apart at both sites. At Yangling, a specific amount of fertilizer was used before planting (180 kg N ha−1), whereas −1 −1 at Jinghui Qu it ranged between 50 and 180 kg N ha . Moreover, 120 kg ha of P2O5 was applied at both sites. The most common and widely adopted maize variety in this region is Wuke 2 (Xu et al. 2019). During the course of the experiment, this maize variety, planted in both the experimental field and farmers’ fields, matured over a range of 105 to 120 days.

2.2 Experimental and farmer field data

For this study, the crop and management data were collected from four experimental fields in Yangling district and four farmer fields in the Jinghui Qu irrigation district for four consecutive years (2012, 2013, 2014, and 2015). Data included planting date, fertilizer application rate, grain yield, aboveground biomass, phenology, soil texture, and irrigation water applied. The Yangling experiment consisted of nine irrigation treatments (each replicated three times) using the partial orthogonal experimental design method. Four successive phenology phases during the summer maize growing season were selected (V3 (three leaves)–V8 (eight leaves), V8

Fig. 1 Location of the two study areas (Jinghui Qu and Yangling), along with locations of the meteorological stations (Jingyang and Wugong, respectively) Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543 1527

(eight leaves)–V11 (eleven leaves), V11 (eleven leaves)–VT (tasseling), and VT (tasseling)– R6 (physiological maturity or black layer)). The nine irrigation treatments (2012 and 2013) (Table B2, supplementary material) were comprised of timing combinations of three irrigation levels. Those three irrigation levels were CK, i.e., the control treatment irrigated to meet 100% of crop evapotranspiration; 80% of CK; and 60% of CK. Crop evapotranspiration was determined by a lysimeter weighing system (with an accuracy of 0.021 mm). Soil moisture was measured by ThetaProbe ML2x sensors (Delta-T Devices Ltd., Cambridge, UK), which were installed at depths of 20, 40, 60, 80, 100, 200, and 250 cm. The ThetaProbe sensors determine volumetric soil water content over a range of 0 to 100%. When the soil moisture in the CK treatment dropped to 65% of field capacity, the irrigation was applied. Precipitation was excluded from these plots with a moveable rain-out shelter so that the effects of irrigation timing and amount could be assessed through the measurements of the soil water balance. In the Jinghui Qu farmer fields, different levels of irrigation were employed. In 2012 and 2014, irrigations were applied at VE (emergence) and V11 (eleven leaves). In 2013, irrigation was applied only at VE (emergence), and in 2015, irrigation was applied at VE (emergence), (V10) ten leaves, and R1 (silking) (supplementary Table B3).

2.3 Modeling

2.3.1 Model calibration, validation, and ensemble

In this study, five process-based crop models (APSIM, AquaCrop, CERES-Maize, EPIC, and STICS) were used to predict maize yield in the Guanzhong Plain on the basis of ensemble modeling. These five models were calibrated with data from the full irrigation treatment (CK) from both the experimental site and the farmer field site during the 4 years of the study (2012–2015). The purpose of the model calibration was to improve model accuracy for future long simulation periods. After calibration, the model simulation performance was validated for different irrigation treatments’ experiment field (T2–T9) and farmer field (T2–T5) in 2012 and 2013. Statistical performance indices for the validation results indicated adequate simulation accuracy for the five models. The main purpose of using ensemble crop models in this study was to increase the robustness in predicting the maize yield response to climate change (Wallach et al. 2016) rather than to compare models. All these maize models have been successfully applied at the field level for crop growth, development, and yield prediction (Ahmadi et al. 2015; Bregaglio et al. 2017;Caveroetal.2000; Dejonge et al. 2012; Holzworth et al. 2014; Saddique et al. 2019). Further details regarding crop model descriptions, calibrations, and evaluations can be found in the supplementary material (Appendices A, B, and C). The results of the individual models have been presented in the supplementary material.

2.3.2 Future climate data

Simulated downscaled climate data were obtained from the output of the MarkSim model. MarkSim provides a coarse-scale GCM that outputs to a 0.5° × 0.5° latitude/longitude grid resolution using stochastic downscaling and climate typing techniques (Jones and Thornton 2013). MarkSim produces daily solar radiation as well as maximum and minimum temperature using the methodology explained and adopted by Richardson (1981), while the precipitation data downscaling is based on the third-order Markov stochastic model. The model has been applied to downscale GCM outputs in different climate zones around the 1528 Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543 world (Corbeels et al. 2018; Nouri et al. 2017;Shirsathetal.2017). In this study, the daily climate data for RCP 6.0 and RCP 8.5 during the study periods of the 2030s (2020–2039), the 2050s (2040–2059), and the 2070s (2060–2079) were downloaded for 17 GCMs with their mean ensemble in 99 replications over both Yangling and Jinghui Qu study regions. The GCMs are detailed in Table 1.TheCO2 concentration selected for the baseline was 380 ppm, and in RCPs 6.0 and 8.5, these concentrations ranged from 380 to 650 ppm and 380 to 866 ppm, respectively, during 2020–2070.

2.3.3 Baseline simulations

The calibrated models were run to simulate average maize yield with a CO2 concentration of 380 ppm and baseline climate data (1961–1990). The management practices specified in the models to obtain the simulated baseline yields were determined from previous and current experiments, interviews with farmers, and agronomist knowledge from different field sites. According to currently available data, a planting date of 14 June, irrigation water 60% of plant available water capacity (PAWC), and fertilizer application rate of 180 kg N ha−1 were selected for the two study sites for the average baseline yield simulation. In addition, 70%, 80%, 90%, and 100% of PAWC baseline yields were estimated to compare with corresponding future simulations, as well as different planting dates (15 May to 24 July) were simulated under baseline climate data.

Table 1 Detailed description of General Circulation Models (GCMs) used in the study (institutions from Jones 2013)

Sr Model Institutions Resolution, No. Lat.° × Long °

1 BCC-CSM 1.1 Beijing Climate Center, China Meteorological Administration, 2.8125 × 2.8125 China 2 BCC-CSM 1.1(m) Beijing Climate Center, China Meteorological Administration, 2.8125 × 2.8125 China 3 CSIRO-Mk3.6.0 CSIRO and the Queensland Climate Change Center of 1.8750 × 1.8750 Excellence, Australia 4 FIO-ESM The First Institute of Oceanography, China 2.8120 × 2.8120 5 GFDL-CM3 Geophysical Fluid Dynamics Laboratory, USA 2.0 × 2.5 6 GFDL-ESM2G Geophysical Fluid Dynamics Laboratory, USA 2.0 × 2.5 7 GFDL-ESM2M Geophysical Fluid Dynamics Laboratory, USA 2.0 × 2.5 8 GISS-E2-H NASA Goddard Institute for Space Studies, USA 2.0 × 2.5 9 GISS-E2-R NASA Goddard Institute for Space Studies, USA 2.0 × 2.5 10 HadGEM2-ES Met Office Hadley Centre, UK 1.2414 × 1.8750 11 IPSL-CM5A-LR Institute Pierre-Simon Laplace, France 1.8750 × 3.7500 12 IPSL-CM5A-MR Institute Pierre-Simon Laplace, France 1.2587 × 2.5000 13 MIROC-ESM Atmosphere and Ocean Research Institute, National Institute for 2.8125 × 2.8125 Environmental Studies and Japan Agency for Marine Earth Science and Technology, Japan 14 MIROC-ESM-CHEM Atmosphere and Ocean Research Institute, National Institute for 2.8125 × 2.8125 Environmental Studies and Japan Agency for Marine Earth Science and Technology, Japan 15 MIROC5 Atmosphere and Ocean Research Institute, National Institute for 1.4063 × 1.4063 Environmental Studies and Japan Agency for Marine Earth Science and Technology, Japan 16 MRI-CGCM3 Meteorological Research Institute, Japan 1.1250 × 1.1250 17 NorESM1-M Norwegian Climate Centre, Norway 1.8750 × 2.5000 Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543 1529

2.3.4 Adaptation scenarios

For future climate conditions, the five calibrated models were applied to analyze the effect of four adaptations: (1) current adaptation with constant and elevated CO2 concentration (no change from current production practices, i.e., 14 June planting date, 200 mm irrigation amount or 60% of PAWC, and fertilizer amount 180 kg N ha−1); (2) irrigation adaptation— four irrigation treatments that were based on automatic irrigation mode in the model, i.e., 70%, 80%, 90%, and 100% of PAWC; (3) planting date adaptations, planting date varied at 10-day intervals from 15 May to 24 July; and (4) combined (double) adaptations of irrigation amount and planting date. The aforementioned adaptation treatments were selected to evaluate their potential to mitigate negative climate change effects on maize yield with the ultimate goal of being able to recommend an optimum adaptation strategy.

2.4 Statistical analysis

The models were evaluated using the coefficient of determination (R2), d-index of agreement (Willmott et al. 1985), and normalized root mean square error (nRMSE) between simulated and observed data. The d-index is a dimensionless, sum-of-squares–based index with values ranging from 0 (no agreement between observed and simulated data) to 1 (perfect agreement between observed and simulated data). We defined four categories of nRMSE to evaluate the relationship between simulated and observed data: nRMSE < 10% was considered excellent, 10% < nRMSE < 20% was considered good, 20% < nRMSE < 30% was considered fair, and nRMSE > 30% was considered poor (Anothai et al. 2013).

3 Results and discussion

3.1 Future changes in temperature and precipitation

The average maximum temperature (May–October) is predicted to be 2.6 °C warmer at Yangling and 3.0 °C warmer at Jinghui Qu, relative to baseline, for both RCPs during the three time periods (2030s, 2050s, 2070s) (Fig. 2a). Similarly, the average minimum temperature (May–October) is predicted to be 2.9 °C and 3.4 °C warmer at Yangling and Jinghui Qu, respectively (Fig. 2b). We noted that the changes in minimum temperature were greater than the changes in maximum temperature, which is consistent with the changes in temperature predicted for this region by Liu et al. (2018). These results are also similar to those reported by Zhang et al. (2011) who stated that both Tmax and Tmin are increasing in the northwest region of China. Furthermore, seasonal average rainfall amount of both RCPs varies during 2030s, 2050s, and 2070s at both sites. Multiple climate model mean ensemble values show a slight increase (2.0–5.3%) in rainfall at both sites (Fig. 3). The trend of precipitation is ascending in both scenarios, which is broadly in line with Cao et al. (2010) and Sun et al. (2018).

3.2 Maize yield with elevated and constant CO2 levels

For RCPs (6.0 and 8.5) in three future periods (2030s, 2050s, and 2070s), the maize yield decreased both under increased and constant CO2 concentration scenarios (Figs. 4 and 5). Overall, the decline in maize yield (with increased CO2) ranged 15.3–28% and 19–32.5% for 1530 Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543

Fig. 2 Changes in a maximum (Tmax)andb minimum (Tmin) temperature expected in 2030s, 2050s, and 2070s under RCPs 6.0 and 8.5 relative to the baseline period (1961–1990)

RCPs 6.0 and 8.5, respectively. These results were consistent with the findings of Zhang and Zhao (2017) who estimated 7–18% decrease under future climate change by 2039 using the APSIM model and multiple GCMs in North China. Also, 10–30% reduction in maize yield was predicted by Wang et al. (2011) using CERES-Maize and ensemble of climate scenarios. The percentage decline in maize yield gradually increased temporally for both CO2 scenarios, though this decline was greater when CO2 concentration did not increase with time (Fig. 6). At both sites during the 2030s, 2050s, and 2070s, the projected decline in maize yield under increasing CO2 scenario was 10–17%, 8.9–14.7%, and 10.1–12.9% smaller compared with no increase in CO2 under RCP 6.0, respectively. Furthermore, the reduction in maize yield under increasing CO2 was 5–19.8%, 19–21.5%, and 10–18.8% smaller as to no increase in CO2 under and RCP 8.5, during 2030s, 2050s, and 2070s, respectively. These results corroborate the findings of Lin et al. (2017) who reported that the consideration of CO2 increment slightly offset the adverse effects of future climate change. In addition, Durand et al. (2018) found through crop simulation modeling that elevated CO2 levels are less likely to increase maize production with the exception for drought periods, when increased CO2 helps in increasing the plant’s water productivity. We speculate that the damped positive impact of CO2 on overall maize production could be most likely due to future

Fig. 3 Changes in precipitation expected in 2030s, 2050s, and 2070s under RCPs 6.0 and 8.5 relative to the baseline period (1961–1990) Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543 1531

Fig. 4 Simulated ensemble model maize yield change (%) under increased CO2 scenario with 60% PAWC irrigation and planting date (14 June) adaptation (current adaptation) at a Yangling and b Jinghui Qu, China, during the 2030s, 2050s, and 2070s under RCPs 6.0 and 8.5. The middle line in the box plots represents the median, and the black X represents the average yield. The upper box boundary indicates the 75th percentile of the data set, and the lower box boundary indicates the 25th percentile. The lower and upper box plot whiskers represent the yield at the 10th and 90th percentiles, respectively temperature increases in the projected periods as described by Guo et al. (2010) and Chen et al. (2017). Both of the aforementioned studies reported that future maize yield was noticeably affected by increasing temperature. The findings of Xia et al. (2014) also support the preceding argument as they estimated 17.2% lower maize yield for China without considering increment in CO2 concentration and were able to simulate higher yields in future by employing reduced insolation scenarios.

3.3 Effect of irrigation adaptation on maize yield

At Yangling, the ensemble model results indicated that the 70% and 80% of PAWC irrigation treatments had average maize yields below the baseline for 2030s that declined further below for 2050s and 2070s, and the same trend was noticed for Jinghui Qu (Fig. 7a–d). Our results demonstrated that the proposed 70% and 80% irrigation treatment adaptation could not meet the crop water requirement, presumably due to high soil moisture deficit or evapotranspiration rate resulting from increased temperature during the maize crop growth period under the

Fig. 5 Simulated ensemble model maize yield change (%) under constant CO2 scenario with 60% PAWC irrigation and planting date (14 June) adaptation (current adaptation) at a Yangling and b Jinghui Qu, China, during the 2030s, 2050s, and 2070s under RCPs 6.0 and 8.5. The middle line in the box plots represents the median, and the black X represents the average yield. The upper box boundary indicates the 75th percentile of the data set, and the lower box boundary indicates the 25th percentile. The lower and upper box plot whiskers represent the yield at the 10th and 90th percentiles, respectively 1532 Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543

Fig. 6 Simulated ensemble model maize yield change (%) with CO2 and without CO2 during the 2030s, 2050s, and 2070s relative to the baseline period under RCPs 6.0 and 8.5 projected future climate scenarios (Su et al. 2018, Okada et al. 2015;Zhaietal.2010). In addition, the 75th and 90th percentiles showed noticeable improvements in yield for Jinghui Qu for both RCPs 6.0 and 8.0 (2030s and 2050s) under 80% PAWC irrigation treatment, while Jinghui Qu had a slight improvement in yield for 2030s under RCP 6.0 and for 2050s and 2070s under RCP 8.5. The yield improvement simulated by the models with irrigation adaptation at both sites (compared with yields simulated with current adaptation) for RCP 6.0 ranged from 19 to 26%, 17 to 22%, and 16 to 21%, and for RCP 8.5, the yield increases ranged from 8.1 to 16.5%, 12 to 15%, and 9 to 22% (Fig. 7a–d) during the 2030s, 2050s, and 2070s, respectively. These results are in line with the findings of Wang et al. (2011) who found that the reduction in the maize yields under future climate change scenarios could be reverted by increasing the amount of irrigation. Using 90% and 100% of PAWC irrigations resulted in a greater increase in average maize yields at both sites relative to the baseline yield (Fig. 7e–h). Nevertheless, there was no significant difference between 90 and 100% irrigation treatments; therefore, we considered the 90% PAWC a more economical irrigation treatment with the current planting date of 14 June to cope with the adverse effect of climate on maize yield. This consideration compares well with Jazy et al. (2012), who found that higher water consuming irrigation treatment could be skipped or ignored if lower irrigation treatments have similar yield results. For 90% PAWC irrigation treatment under RCP 6.0, increases were 11.7–18.3%, 5.6– 10.6%, and 3.3–7.3% in comparison to baseline yield, and under RCP 8.5, increases were 2.6– 8.2%, 1.9–6.6%, and 1.1–4.3% during the 2030s, 2050s, and 2070s, respectively (Fig. 7e, f). Furthermore, these results corroborate those reported by Yu et al. (2018) as they found that irrigation not only reduces the negative impact of climate change and higher temperatures on maize yield in China, but also it is beneficial for increasing the yield. Moreover, this study showed that the optimum irrigation amount under future climate change conditions was about 95% higher relative to the current irrigation amount over the entire growing season. This irrigation amount essentially doubles the current irrigation amount (180–200 mm). These results are consistent with the results of Thomas (2008) and Wang et al. (2011) who reported Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543 1533

Fig. 7 Simulated ensemble model maize yield change (%) with irrigation adaptation (a, b 70%; c, d 80%; e, f 90%; and g, h 100% of PAWC), during the 2030s, 2050s, and 2070s under RCPs 6.0 and 8.5. The middle line in the box plots represents the median, and the black X represents the average yield. The upper box boundary indicates the 75th percentile of the data set, and the lower box boundary indicates the 25th percentile. The lower and upper box plot whiskers represent the yield at the 10th and 90th percentiles, respectively that the irrigation demand for maize in Guanzhong Plain and northern parts of China could vary between 300 and 400 mm under future climate change. The availability of additional water to fulfill the future irrigation needs of the region could be a point of concern for the growers and policy makers. The optimization of water resources allocation, nonetheless, along with the development of new river interlinking projects, improving water-use efficiency, flood water storage, and use of reclaimed water in the 1534 Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543

Fig. 8 Simulated ensemble model maize yield change (%) with planting date adaptation at Yangling, China, during the 2030s, 2050s, and 2070s under RCP 6.0 and RCP 8.5. The middle line in the box plots represents the median, and the black X represents the average yield. The upper box boundary indicates the 75th percentile of the data set, and the lower box boundary indicates the 25th percentile. The lower and upper box plot whiskers represent the yield at the 10th and 90th percentiles, respectively

Guanzhong region, as discussed by Zhu et al. (2018), may increase the future water supply ensuring sustainable maize production in the region.

3.4 Effect of planting date adaptation on maize yield

Simulation results indicated that higher maize yields could be achieved by delaying the sowing and planting maize during the 4–24 July time period with 100–105 days to maturity (Figs. 8 and 9) under both RCP scenarios during all three time periods at both locations (Yangling and Jinghui Qu). Multiple crop models showed that planting dates starting from July 4 led to an increase in the average maize yield by 2.0–19.0% and 1.0–12% compared with the relative baseline yields for both RCP 6.0 and 8.5 scenarios, respectively, at both sites, during the 2030s, 2050s, and 2070s. The results confirm that the July planting dates are a good adaptation practice to protect maize yields against the yield declines that are likely due to climate change if the planting date were to remain at 14 June. On an average, the maize yield at Yangling was 6.8% greater than the baseline yield, while at Jinghui Qu the maize yield was 3.75% greater than the baseline yield (Figs. 8 and 9). Similar results were reported by Lin et al. (2017)who projected that maize yield could increase by 0.5 to 15.9% due to delayed planting date in Northeast China. Similarly, Deb et al. (2014) found that changing the planting date from the current planting schedule would increase maize yield by 5.0–22.5% in the future. A possible Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543 1535

Fig. 9 Simulated ensemble model maize yield change (%) with planting date adaptation at Jinghui Qu, China, during the 2030s, 2050s, and 2070s under RCP 6.0 and RCP 8.5. The middle line in the box plots represents the median, and the black X represents the average yield. The upper box boundary indicates the 75th percentile of the data set, and the lower box boundary indicates the 25th percentile. The lower and upper box plot whiskers represent the yield at the 10th and 90th percentiles, respectively reason of increased yield due to delayed planting dates could be the availability of higher temperatures in post-flowering periods as described by Lv et al. (2019)—they noted up to 25% improvement in the potential maize yield in China by extending the sowing period under future climate change scenarios. Furthermore, Wang et al. (2011) also discussed the impacts of delayed sowing of maize in Northeast China amid future climate change, as improved thermal conditions would extend the grain-filling duration. Moreover, availability of additional rainfall around flowering time in result of delayed planting can avoid probable water stress due to low soil moisture that maize can face if sowing time is not adjusted accordingly (Chen at al. 2013).

3.5 Effect of double adaptation (irrigation and planting date) on maize yield

Irrigation and planting date adaptations were simulated in conjunction to assess the synergy of both management practices. Ninety percent PAWC irrigation treatment was tested for multiple planting dates, and the results are shown in Figs. 10 and 11. At Yangling, the ensemble models simulated highest average maize yield that increased ranging between 3 and 31% relative to the baseline yield, with planting dates from 4 June to 24 July under the RCP 6.0 scenario 1536 Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543

Fig. 10 Simulated ensemble model maize yield change (%) with double adaptation “(withboth90% PAWC—irrigation adaptation and planting date adaptation)” at Yangling, China, during the 2030s, 2050s, and 2070s under RCPs 6.0 and 8.5. The middle line in the box plots represents the median, and the black X represents the average yield. The upper box boundary indicates the 75th percentile of the data set, and the lower box boundary indicates the 25th percentile. The lower and upper box plot whiskers represent the yield at the 10th and 90th percentiles, respectively during the 2030s and 2050s (Fig. 10). At Jinghui Qu, the ensemble model simulated average maize yield that was 2% and 18.5% greater than the baseline yield with planting dates between 14 June and 24 July under both RCP scenarios during the 2030s, 2050s, and 2070s (Fig. 11). These results led us to conclude that the use of both the irrigation adaptation and the planting date adaptation could complement each other. These findings partially support the results of Wang et al. (2011) who reported stable maize yields by employing combined adaptation, i.e., twofold irrigation and later sowing dates under future climate change. It is important to mention here that double (combined) adaptation and its effectiveness in improving maize crop yield under climate change are not well documented in the literature, and adaptation strategies have been studied separately, e.g., planting schedules, increasing irrigation amount, and alternative cultivars (Lin et al. 2017; Babel and Turyatunga 2014). Furthermore, there are very few studies reporting the interaction between irrigation strategies and different planting dates using ensemble crop models and GCMs for improving or maintaining the maize yield under future climate change conditions in China. Moving on, though 90% PAWC irrigation treatment was tested for the combined simulations, the growers could also possibly achieve improved yields with irrigation treatment lower than 90% PAWC. Furthermore, we assume that even if future plant breeding leads to improved cultivars better adapted to future climate change conditions, those improved maize cultivars would respond similarly to irrigation and later planting dates, as mentioned previously by Rurindal et al. (2015). Likewise, Araya et al. (2015b) and Kassie et al. (2015) found similar yield trends by using different cultivars under future climate change conditions. Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543 1537

Fig. 11 Simulated ensemble model maize yield change (%) with double adaptation “(withboth90% PAWC—irrigation adaptation and planting date adaptation)” at Jinghui Qu, China, during the 2030s, 2050s, and 2070s under RCPs 6.0 and 8.5. The middle line in the box plots represents the median, and the black X represents the average yield. The upper box boundary indicates the 75th percentile of the data set, and the lower box boundary indicates the 25th percentile. The lower and upper box plot whiskers represent the yield at the 10th and 90th percentiles, respectively

4Conclusions

The analysis of future climate change scenarios suggested less favorable conditions for maize growth and production in Guanzhong Plain, China. Our study found an overall increase in seasonal temperature of 2.8 °C and 3.3 °C under RCP 6.0 and RCP 8.5, respectively, for 2030–2070 at two locations in the Guanzhong Plain. The impact of future climate change on maize yield was assessed, and possible adaptation strategies/options were explored to offset the negative impacts on average maize yields using simulations of five cropping systems models. The results showed that both the CO2 concentration and temperature under different future climate change scenarios would affect the maize yield. The warmer future temperature would have a dominant negative effect on maize yield while future elevated CO2 could positively affect the maize yield. The average maize yield would decrease by 31–47% without CO2 fertilization, and under increased CO2 scenario, the decrease in yield ranged between 16 and 28%. As a potential adaptation, shifting the planting date of maize 20 days later than the current planting date would increase maize yield by 19% compared with the baseline yield. The yield would also be increased by employing irrigation at 90% PAWC threshold as compared with the baseline yield with current adaptation. Double adaptation (the combination of using irrigation and delaying planting date) has potential of not only mitigating the negative effect of climate change on maize yield, but also it could enhance the maize yield up to 31% relative to the baseline yield. The study provides valuable understanding and direction for 1538 Mitigation and Adaptation Strategies for Global Change (2020) 25:1523–1543 maize adaptation management options under climate change scenarios. Other maize growing regions in the world expecting warmer temperature in the future could also consider optimizing the irrigation and planting dates to utilize from the potential CO2 fertilization and for better climate change adaptation. One limitation of this study was the limited data availability to two locations in the Guanzhong Plain. Also, changing cultivar adaptation was not included in this study. Multiple sites and cultivars should be included in future studies. Furthermore, nitrogen fertilization adaptation could be explored for its effects on the sustainable maize production in the region.

Acknowledgments This research was jointly supported by National Key Research and Development Program of China (2016YFC0400201), the National Science Foundation of China (no. 51179162), and the Program of Introducing Talents of Discipline to Universities, China (B12007). The authors would like to thank Luca Doro and INRA team France for providing the assistance in calibration of the EPIC model and STICS model.

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Affiliations

Qaisar Saddique1,2,3 & Huanjie Cai 1,2,3 & Jiatun Xu 1,2,3 & Ali Ajaz 4 & Jianqiang He1,2,3 & Qiang Yu5 & Yunfei Wang1,2,6 & Hui Chen 1,2,3 & Muhammad Imran Khan7 & De Li Liu8,9 & Liang He10

Qaisar Saddique [email protected] Jiatun Xu [email protected]

Ali Ajaz [email protected] Jianqiang He [email protected]

Qiang Yu [email protected] Yunfei Wang [email protected]

Hui Chen [email protected] Muhammad Imran Khan [email protected]

De Li Liu [email protected]

Liang He [email protected]

1 Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100 Shaanxi, China 2 Institute of Water Saving Agriculture in Arid Regions (IWSA), Northwest A&F University, Yangling 712100 Shaanxi Province, China 3 College of Water Resources and Architectural Engineering, Northwest A&F University, Yangling 712100 Shaanxi, China 4 Oklahoma State University, Stillwater, OK, USA 5 School of Life Sciences, Faculty of Science, University of Technology Sydney, Broadway, Sydney, NSW 2007, Australia 6 University of Twente, Enschede, Netherlands 7 National Research Center of Fluid Machinery Engineering and Technology, Jiangsu University, Zhenjiang, China 8 NSW Department of Primary Industries, Wagga Wagga Agricultural Institute, Wagga Wagga, New South Wales 2650, Australia 9 Climate Change Research Centre and ARC Centre of Excellence for Climate System Science, University of New South Wales, Sydney, New South Wales 2052, Australia 10 National Meteorological Center, Beijing 100081, China