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Agricultural and Forest Meteorology 239 (2017) 108–117

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology

journa l homepage: www.e lsev ier .com/ locate /agr formet

esearch paper

stimating maize yield potential and yield gap with agro-climaticones in China—Distinguish irrigated and rainfed conditions

aohua Liu a, Xinping Chen a,∗, Qingfeng Meng b,∗, Haishun Yang c, Justin van Wart c

Center for Resources, Environment and Food Security, China Agricultural University, Beijing 100193, ChinaCollege of Agronomy and Biotechnology, China Agricultural University, Beijing 100193, ChinaDepartment of Agronomy and Horticulture, University of Nebraska-Lincoln, Lincoln, NE 68583-0915, USA

r t i c l e i n f o

rticle history:eceived 21 September 2016eceived in revised form 14 February 2017ccepted 26 February 2017

eywords:ield potentialield gap

rrigatedianfedgro-climatic zoneuffer zone

a b s t r a c t

Understanding yield potential (Yp) and yield gap (Yg) in current intensive maize (Zea mays L.) productionis essential to meet future food demand with the limited resources. In this study, we used the agro-climaticzones (CZs) and the reference weather stations (RWS) buffer zones, together with the Hybird-Maizemodel to estimate maize Yp in the four maize-growing-regions of China under both irrigated and rainfedconditions. In irrigated maize areas, we got 70 RWS buffer zones, and total maize area in the RWS bufferzones covered 67% of the whole irrigated maize area. In rainfed maize areas, we got 106 RWS bufferzones, which covered 51% of the whole rainfed maize area. As a result, the average Yp was 14.2 t ha−1

and farmers have achieved 58% of Yp. The average water-limited yield potential (Yw) was 10.7 t ha−1

and farmers have achieved 65% of Yw. Further analysis for four maize-growing-regions showed thatprecipitation was a limiting factor for Yw to fully achieve Yp except in Southwest China (SW), whereasthe average precipitation was more than 653 mm during maize growing season. The ratio between Ywand Yp (Yw/Yp) was 51% in Northwest China (NW), and around 80% in both Northeast China (NE) and

North China Plain (NCP). The comparison of Yp in different regions showed the low Yp in NE was dueto low temperature while Yp in both NCP and SW were limited by low solar radiation. In conclusion,our findings highlight the efficiency and importance to estimate Yp, Yw and Yg by the upscaling methodwith CZs and RWS buffer zones. Meanwhile, the comparison of Yp, Yw and Yg in different regions wasimportant to improve maize production in future in China.

© 2017 Elsevier B.V. All rights reserved.

. Introduction

Maize has become the largest cereal food crop in China since013, and the maize production was 215 Mt in 2014, whichccounted for more than one-third of China’s cereal production andas responsible for 21% of the global maize output (FAO, 2016).ith the economic growth and changing diet, demand for maize

n China by 2030 is estimated to be 47% higher than now (Chent al., 2014). Until the middle 1990s, China’s maize yield increasedn a near-linear fashion, but has stagnated at around 5.0 t ha−1 since

Abbreviations: Yp, yield potential; Yw, water-limited yield potential; Yg, yieldap; Ya, actual farmers’ yield; YgI, the difference between Yp and Ya; YgR, the differ-nce between Yw and Ya; CZs, agro-climatic zones; RWS, reference weather station;E, Northeast China; NW, Northwest China; NCP, North China Plain; SW, South-est China; GYGA-ED, Global Yield Gap Atlas Extrapolation Domain; GDD, growing

egree days.∗ Corresponding authors.

E-mail addresses: chenxp@cau.edu.cn (X. Chen), mengqf@cau.edu.cn (Q. Meng).

ttp://dx.doi.org/10.1016/j.agrformet.2017.02.035168-1923/© 2017 Elsevier B.V. All rights reserved.

1995 (Meng et al., 2013). However, high-yielding experiments haveshowed that the maize yield was higher than 15 t ha−1 at 159 sites inChina from 2006 to 2010 (Chen et al., 2012). Hence, understandingthe yield potential (Yp) and yield gap (Yg) in the current intensivemaize production is essential to meet the future food demand.

Yield potential is defined as the yield of an adapted crop varietywhen grown with optimal water and nutrients management andwithout yield losses due to biotic and abiotic stresses (Evans, 1993;van Ittersum and Rabbinge, 1997; Fischer, 2015). Yg is the differ-ence between Yp and actual farmers’ yield (Ya) (Lobell et al., 2009).Estimating Yp and Yg can help assess the status of current farm-ers’ yield relative to Yp and the possible space for yield gain in thefuture (Lobell et al., 2009; Hochman et al., 2013). In addition, theanalysis of spatial distribution of maize yield gap can help revealmajor yield limiting factors and thus effective efforts will be madeto increase yield efficiently (Aggarwal and Kalra, 1994; Naab et al.,

2004; Bhatia et al., 2008; Mueller et al., 2012).

Recently water scarcity has already been a critical issue inthe world (Rijsberman, 2006; McLaughlin and Kinzelbach, 2015).

B. Liu et al. / Agricultural and Forest Meteorology 239 (2017) 108–117 109

Fig. 1. Selected buffer zones on the harvest areas in the three irrigated maize-growing-regions in China: Northeast China (NE), Northwest China (NW), North China Plain(NCP). (a) Distribution of weather stations with 30 years of weather data since 1985 and harvested areas (hectare) of irrigated maize, agro-climatic zones (CZs) delineatedb athert ther sc

Edpt(mdac2cain

oAdemmdF

ased on the GYGA protocol. (b) Buffer zones with a 100 km radius surrounding a wehe homogeneity of the agricultural climate in each buffer zones (d) Reference weaovered maize harvest areas from big to small.

stimating yield potential separately for irrigated and rainfed con-itions is important for evaluating the impact of water on foodroduction. Crop yield obtained with no other manageable limi-ation apart from water supply is the water-limited yield potentialYw) (Lobell et al., 2009; Fischer, 2015). Irrigation is the essential

easure to increase Yw to fully achieve Yp. However, it is oftenifficult to distinguish irrigated crop areas with the rainfed ones in

large spatial scale. The crop distribution map of the global majorrops harvested area is often used in global studies (Mueller et al.,012), but often at the cost of precision. At the region scale, typi-al locations are often used to represent both irrigated and rainfedreas (Grassini et al., 2009; Liu et al., 2016). Therefore, estimat-ng crop Yp and Yw separately at a high precision raster map isecessary.

Yield potential for several crops has been estimated in previ-us studies in various scales including global, regional and farm.

grid-based approach is generally used in the global studies withatasets on climate, soil, agricultural land use and general crop cal-ndars (van Ittersum et al., 2013). The advantage for this global

ethod is that it provides a framework for upscaling. However,any details are ignored because of the large scale. For example, it

oesn’t distinguish irrigated and rainfed crops (Licker et al., 2010;oley et al., 2011), or explicitly describe the management informa-

station. (c) Overlap the buffer zones with the agro-climatic zones in order to ensuretation (RWS) buffer zones which selected from the buffer zones according to their

tion (cropping systems, planting date, cultivar maturity, plantingdensity, etc.) (Neumann et al., 2010; Mueller et al., 2012), or ensurerepresentation of the weather data with model or the yield ceil-ing with empirical approaches (Foley et al., 2011). In comparison,regional scale studies have the advantage of location-specific envi-ronmental conditions and management information, which resultsin more locally relevant results. However, the region studies usu-ally ignore the upscaling process. For example, Grassini et al. (2009)applied 18 sites to estimate the Yp in Western Corn-Belt of US. Menget al. (2013) used 50 sites to estimate maize Yp of the whole Chinawith all sites being high-yielding fields from the published litera-tures. The method to select the sites and their representativenessfor a region is worth of further discussion. In order to improve therepresentation for a region, appropriate upscaling methods shouldbe further considered on the region scale studies.

Two questions should be considered when upscaling locationsto a large spatial scale: (1) the homogeneity of the climate, and(2) acquire observed location-specific data. However, the challengeof using a bottom-up approach is the time, expense and access to

acquire observed data, e.g., weather data, soil data and crop man-agement data. The Global Yield Gap Atlas Extrapolation Domain(GYGA-ED) aims to estimate the yield gap for major food cropsbased on locally observed data. The GYGA-ED approach is used in

110 B. Liu et al. / Agricultural and Forest Meteorology 239 (2017) 108–117

Fig. 2. Selected buffer zones on the harvest areas in the four rainfed maize-growing-regions in China: Northeast China (NE), Northwest China (NW), North China Plain (NCP),Southwest China (SW). (a) Distribution of weather stations with 30 years of weather data since 1985 and harvested areas (hectare) of rainfed maize, agro-climatic zones(CZs) delineated based on the GYGA protocol. (b) Buffer zones with a 100 km radius surrounding a weather station. (c) Overlap the buffer zones with the agro-climatic zonesin order to ensure the homogeneity of the agricultural climate in each buffer zones. (d) Reference weather station (RWS) buffer zones which selected from the buffer zonesaccording to their covered maize harvest areas from big to small.

Fig. 3. Irrigated yield potential (Yp, a) and water-limited yield potential (Yw, b) in the four maize-growing-regions in China: Northeast China (NE), Northwest China (NW),North China Plain (NCP), and Southwest China (SW). n represents the number of the selected reference weather station (RWS) buffer zones. Each point is a 30-year averagefor a given location from 1985 to 2014. Solid and dashed lines in the boxes indicate medians and means, respectively. Upper and lower box boundaries indicate upper andlower quartiles. Whisker caps indicate 95th and 5th percentiles. Circles indicate outliers.

rest Meteorology 239 (2017) 108–117 111

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Table 1Maize management information for the four maize-growing-regions: NortheastChina (NE), Northwest China (NW), North China Plain (NCP), Southwest China (SW).

Planting Date Maturity date Planting density(1000 ha−1)

NE Apr.25–May.5 Sep.15–Sep.26 75

B. Liu et al. / Agricultural and Fo

his study, because with this method using of agro-climatic zonesCZs) and reference weather station (RWS) buffer zones can get aood balance to minimize the data volume with the goal of maxi-um appropriate representativeness of the specific locations. The

Zs is constructed from a matrix of three categorical variables:1) GDD with base temperature of 0 ◦C, (2) temperature season-lity (quantified as the standard deviation of monthly averageemperatures), and (3) an aridity index (annual total precipitationivided by annual total potential evapotranspiration) (van Wartt al., 2013). A buffer zone circle with 100 km radius centeredn a weather station was used in the GYGA-ED approach, whichas considered as the appropriate coverage area (van Bussel et al.,

015).The objectives of this study were to (1) use the CZs and RWS

uffer zones to estimate Yp, Yw and Yg in four maize-growing-egions of China under both irrigated and rainfed conditions, (2)se the GYGA-ED approach to upscale the results from locationso the region, and (3) use 30 years weather data including solaradiation, temperature and precipitation to analyze the variationor Yp among four maize-growing-regions.

. Materials and methods

.1. Hybrid-Maize model

The Hybrid-Maize Model was used to estimate Yp and Yw. It is process-oriented model that simulates maize development androwth on a daily time step under non-limiting or water-limitedonditions (Yang et al., 2006). It features temperature-drivenaize development, vertical canopy integration of photosynthe-

is, organ-specific growth respiration, and temperature-sensitiveaintenance respiration (Yang et al., 2004). The model has been

ested and widely used in the USA (Grassini et al., 2009; Setiyonot al., 2011; van Wart et al., 2013) and China (Bai et al., 2010; Chent al., 2011; Meng et al., 2013). Model input includes daily weatherata (i.e., solar radiation, maximum and minimum temperatures,recipitation, wind speed, and relative humidity), hybrid maturity,owing date, and planting density. When estimating Yw, major soilroperties were required, including maximum rooting depth, soilexture, and bulk density.

.2. Data sources

Data of maize harvested area was obtained from China’sational Bureau of Statistics (NBS). The data were at the county

evel in 2009. We used the SPAM2005 (http://mapspam.info) toistinguish irrigated and rainfed areas. Daily weather data forhe period from 1985 to 2014 for a total of 756 stations werebtained from China meteorological Administration (http://data.ma.cn) (Fig. 1a). Average maize growing degree days (GDD) wasalculated from the weather data for the maize growing seasonrom 1985 to 2014. Soil texture data, including proportion oflay and silt, were retrieved from ISRIC-World soil information,

ISE international soil profile dataset (http://www.isric.org). Cropanagement information including sowing and maturity date,

nd planting density were provided by local agronomic expertsTable 1).

Ya was obtained from farm surveys conducted during007–2008. The farm surveys included face-to-face interviews witharmers and the questionary was designed to capture relevant datae.g., grain yield, planting density, harvest date, fertilizer and irriga-

ion management). For irrigated maize, a total of 1988 sets of dataere obtained with 693 in the NE, 214 in the NW, and 1081 in NCP,hich covered 33 RWS buffer zones in total. For the rainfed maize,

total of 2049 sets of data were obtained with 736 in NE, 229 in

NW Apr.20–May.1 Sep.20–Oct.1 75NCP Jun.10–Jun.15 Sep.25–Oct.1 90SW Feb.10–Apr.10 Jun.20–Aug.20 60–70

NW, 963 in NCP, and 121 in SW, which covered the 46 RWS bufferzones in total.

2.3. Upscaling method

RWS buffer zones of irrigated maize in each region were selectedfollowing the protocol described in Van Wart et al. (2013) and vanBussel et al. (2015). Firstly, the distribution of irrigated maize areaand weather stations were drawn on the China map, and CZs weresuperimposed on top of the map (Fig. 1a). Secondly, circles with100 km radius surrounding all weather stations were drawn as thebuffer zones (Fig. 1b). Thirdly, overlap the buffer zones with theCZs in order to ensure the homogeneity of the agricultural climatein each buffer zones (Fig. 1c). Finally, selecting the reference bufferzones (RWS) in each region according to their covered harvestedmaize area from big to small until the total harvested area in bufferzones became greater than 50% of each region (Fig. 1d). The selectedbuffer zones were the RWS buffer zones. The similar process forrainfed maize was shown in Fig. 2.

In total, we selected 70 RWS buffer zones for the irrigated maizeand 106 for the rainfed maize. China is a large country with awide distribution of maize area and great variation of climate. Inorder to reduce the variation of climate effect on Yp and Yw, wegrouped the RWS buffer zones to four maize regions according tothe agro-ecological conditions: Northeast China (NE), North ChinaPlain (NCP), Northwest China (NW), and Southwest China (SW)(Figs. 1a, 2a). While rainfed maize was grown in all four regions,irrigated maize was grown in NE, NW, and NCP, but not in SW dueto relative adequate precipitation in this region.

Then we used crop areas to identify dominant soil types in eachRWS. Put the soil type, maize harvested area and RWS in a map.Calculated the coverage maize harvested area of each soil type ineach RWS. Selected the dominant soil types according to their cov-ered maize harvested area. The disciplines were: (1) More than 50%coverage of maize harvested area in each RWS; (2) Selected the soiltype if maize harvested area in it was more than 10%; (3) No morethan three soil types were selected in each RWS.

For each irrigated RWS buffer zone, we run the Hybrid-Maizemodel to estimate its Yp using 30 years weather data and maizemanagement data. For rainfed maize, Yw was simulated for each ofits dominant soil types, and then averaged using the harvest areaas the weight of each soil type. We then aggregated Yp or Yw in theRWS buffers to the CZ level using the maize harvested area in eachRWS as its weight in the aggregation. Finally, we aggregated Yp orYw to the region level using the harvest area in each CZ as its weightin the aggregation. Similarly, Ya was aggregated from each RWS tothe region level. For each region, the yield gap between Yp and Ya(YgI), and the yield gap between Yw and Ya (YgR) were calculated.

3. Results

3.1. Selected RWS buffer zones and crop area coverage

The total maize harvested area in China was 27.15 Mha, with10.26 Mha under irrigated conditions and 16.89 Mha under rainfed

112 B. Liu et al. / Agricultural and Forest Meteorology 239 (2017) 108–117

Fig. 4. Growing season climate date for irrigated maize in the three regions: Northeast China (NE), Northwest China (NW), North China Plain (NCP). (a) Minimum temperature.(b) Maximum temperature. (c) Mean temperature, (d) Cumulative precipitation, (e) cumulative solar radiation. (f) Growing degree days (GDD). n represent the number of theselected reference weather station (RWS) buffer zones. Each point is a 30-year average for a given location from 1985 to 2014. Solid and dashed lines in the boxes indicatemedians and means, respectively. Upper and lower box boundaries indicate upper and lower quartiles, while whisker caps indicate 95th and 5th percentiles, and circlesindicate outliers.

Table 2The number of reference weather station (RWS) buffer zones maize areas in the entire China and the distribution in the four maize-growing-regions: Northeast China (NE),Northwest China (NW), North China Plain (NCP), Southwest China (SW).

Total areas (106 ha) RWS Buffer zones number Areas in selected buffer zones (106 ha) Ratio (%)

Irrigated NE 2.47 23 1.61 65NW 1.46 21 0.82 56NCP 6.33 26 4.48 71

Rainfed NE 7.02 26 3.64 52NW 2.05 31 1.05 51NCP 3.57 23 1.85 52SW 4.25 26 2.12 50

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atio: the ratio between the area in the selected buffer zones of a region and the to

onditions (Table 2). In total, we selected 70 RWS buffer zones forrrigated maize, with 23 in NE, 21 in NW, and 26 in NCP. For irrigated

aize, the area covered by the RWS buffer zones accounted for 65%,6% and 71% of the total harvest irrigated maize area in NE, NW,nd NCP, respectively. For the rainfed maize, we selected 106 RWSuffer zones, with 26 in NE, 31 in NW, 23 in NCP, and 26 in SW. Theoverage of the harvested area by the RWS buffer zones accountedor 52%, 51%, 52% and 57% of the total rainfed maize area in NE, NW,CP and SW, respectively.

.2. Yield potential and yield gap

For the whole China, the average Yp and Yw was 14.2 t ha−1 and0.7 t ha−1, respectively (Table 3). The average Ya under irrigatednd rainfed conditions was 8.2 t ha−1 and 7.0 t ha−1, respectively.herefore, the YgI was 6.0 t ha−1 for irrigated maize and YgR was

15.67 57

a in the region.

3.7 t ha−1 for rainfed maize. Farmers achieved 58% of Yp under irri-gated conditions and 65% to Yw under rainfed conditions.

Among the three regions of irrigated maize, NW had the highestaverage Yp (16.8 t ha−1) with the largest variation while NE and NCPhad a lower Yp along with smaller variation (Fig. 3a). The average Yain the three regions were similar and all were around 8.0–8.5 t ha−1.As a result, the highest average YgI was found in NW (8.4 t ha−1) andYa only achieved 50% of Yp. The average YgI in NE and NCP werearound 5–6 t ha−1, and Ya achieved around 60% of Yp (Table 3).

Among the four regions of rainfed maize, the average Yw in NEand NCP was at a similar level (11.7 t ha−1 and 11.1 t ha−1), whichwas far above those in both NW and SW (Fig. 3b). For Ya, SW hadthe lowest value of 5.1 t ha−1 while Ya in NE, NW, NCP was around7–8 t ha−1. The average Yg in NW was much lower than the other

Rthree regions, and the Ya achieved 85% Yw (Table 3). In contrast,the average YgR was highest in SW and Ya only achieved 53% Yw.

B. Liu et al. / Agricultural and Forest Meteorology 239 (2017) 108–117 113

Fig. 5. Growing season climate data for rainfed maize in the four regions: Northeast China (NE), Northwest China (NW), North China Plain (NCP), and Southwest China(SW). (a) Minimum temperature. (b) Maximum temperature. (c) Mean temperature, (d) Cumulative precipitation, (e) cumulative solar radiation. (f) Growing degree days(GDD). n represent the number of selected reference weather station (RWS) buffer zones. Each point is the 30-year average for a given location from 1985 to 2014. Solid anddashed lines in the boxes indicate medians and means, respectively. Upper and lower box boundaries indicate upper and lower quartiles, whisker caps indicate 95th and 5thpercentiles, and circles indicate outliers.

Table 3Irrigated yield potential (Yp), water-limited yield potential (Yw), actual farmers’ yield (Ya), yield gap between Yp and Ya (YgI), yield gap between Yw and Ya (YgR), and theYa relative to Yp and Yw (Ya/Yp, Ya/YW) in the whole China and the four maize-growing-regions: Northeast China (NE), Northwest China (NW), North China Plain (NCP),Southwest China (SW).

NE NW NCP SW China

Irrigated Yp (t ha−1) 14.4 16.8 13.3 – 14.2Ya (t ha−1) 8.5 8.4 8.1 – 8.2YgI (t ha−1) 5.9 8.4 5.2 – 6.0Ya/Yp (%) 59 50 61 – 58

Rainfed Yw (t ha−1) 11.7 8.5 11.1 9.6 10.7−1

3

immtis(l

mt(a

Ya (t ha ) 7.6

YgR (t ha−1) 4.1

Ya/Yw (%) 65

.3. Analysis of climatic factors in different regions

For irrigated maize, growing season (i.e., from planting to phys-ological maturity) temperature was low in NE with an average

inimum temperature (Tmin), maximum temperature (Tmax),ean temperature (Tmean) of 14.3 ◦C, 25.3 ◦C, and 19.8 ◦C, respec-

ively (Fig. 4). As a result, total seasonal available GDD was also lown NE with an average of 1514. However, NE had relatively higholar radiation 2658 MJ m−2. NW also has a high solar radiation2860 MJ m−2). In comparison, solar radiation in NCP was relativelyow (1779 MJ m−2).

For rainfed maize across whole China, the precipitation duringaize season was higher than for the irrigated maize, while GDD,

emperature and solar radiation was similar in NE, NW and NCP

Fig. 5). In addition, SW had plentiful precipitation and high temper-ture. The average precipitation was as high as 653 mm, and Tmin,

7.2 7.7 5.1 7.01.3 3.4 4.5 3.785 69 53 65

Tmax, Tmean were averaged at 18.4 ◦C, 26.8 ◦C, 22.6 ◦C, respec-tively. Solar radiation in SW was relative low with 1853 MJ m−2.

3.4. Relationship between climatic variable and yield potential

The relationship between cumulative precipitation duringmaize growing season and Yp, and Yw was compared in each region(Fig. 6). Among four regions, similar Yp and Yw was found only inSW, which indicated water was not a limited factor for maize pro-duction in this region (Fig. 6d). In NW (Fig. 6b), the total averageprecipitation was 360 mm, and the difference between Yp and Ywwas 7.2 t ha−1. In both NE (Fig. 6a) and NCP (Fig. 6c), the total aver-age precipitation was around 450 mm and the difference betweenYp and Yw was 2.4 t ha−1 in NE and 2.2 t ha−1 in NCP.

In each region, Yp increased with the increase of total solar radi-ation during maize season in four regions (Fig. 7a). Yp in NCP andSW were relative low together with the low solar radiation. In com-parison, Yp in NE and NW was higher with the high solar radiation.

114 B. Liu et al. / Agricultural and Forest Meteorology 239 (2017) 108–117

Fig. 6. The relationship between cumulative precipitation during maize growing season and yield potential in the four maize-growing-regions: Northeast China (NE, a),N . The gt 1985c ces to

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orthwest China (NW, b), North China Plain (NCP, c), and Southwest China (SW, d)he rainfed yield potential. Each point is the 30-year average for a given location fromorrelation at *p < 0.05, **p < 0.01, and ***p < 0.001. (For interpretation of the referen

he effect of mean temperature on Yp was different with solar radi-tion (Fig. 7b). In NE and SW, the average mean temperature were9.8 ◦C and 22.6 ◦C, and the Yp increased with the mean temper-ture. However, there was no significant relationship between Ypnd mean temperature in NW and NCP, where the average meanemperature was 21.4 ◦C and 25.3 ◦C, respectively.

. Discussion

For the whole China, our study showed that Yp and Yw aver-ged at 14.2 t ha−1 and 10.7 t ha−1, YgI was 6.0 t ha−1 and YgR was.7 t ha−1 for irrigated and rainfed maize, respectively. Farmerschieved 58% and 65% of Yp and Yw, respectively (Table 3). Yieldotential for maize has been estimated in many other countries.s the biggest maize production country, USA also has the highest

p (Fischer et al., 2014; FAO, 2016). Van Wart et al. (2013) showedhat the Yp and Yw was 15.1 t ha−1 and 13.2 t ha−1 for USA maizeroduction, respectively. The high Yp in USA was mainly a benefit

rom the well-irrigated management in a highly suitable temper-

reen circles represent the irrigated yield potential while the red crosses represent to 2014. The black cross-shaped indicate the standard deviation. Asterisks indicate

colour in this figure legend, the reader is referred to the web version of this article.)

ate region environment (Fischer et al., 2014). Moreover, the YgIand YgR was lower compared with China, which was 3.4 t ha−1 and3.5 t ha−1 under irrigated and rainfed conditions, respectively (VanWart et al., 2013). The low YgI and YgR in USA were the resultsof excellent crop varieties and advanced management technolo-gies (plant density, nitrogen management, precision planter, etc.)(Fischer et al., 2014). In Kenya, Tittonell et al. (2008) showed thatthe Yw was 5.4 t ha−1 while the YgR was 3.7 t ha−1, and farmersonly achieved 31% of the Yw. In this area, the severely degradedsoil and drought led to the low Yw (Fischer et al., 2014; Heisey andEdmeades, 1999). The high YgR in Kenya was mainly due to theshortage resources, backward agronomic management technologyand smallholder production system (Tittonell et al., 2008). In addi-tion, Brasil (the third maize production country) and Argentina (thefourth maize production country) had the similar Yw and YgR as

China. Affholder et al. (2013) and Aramburu Merlos et al. (2015)showed the Yw was 8.3 t ha−1 in Brail and 11.6 t ha−1 in Argentinaand the YgR was 3.7 t ha−1 and 4.8 t ha−1, respectively.

B. Liu et al. / Agricultural and Forest Meteorology 239 (2017) 108–117 115

Fig. 7. Relationship between yield potential (Yp) and cumulative solar radiation (a), and mean temperature (b) during maize growing season in the four maize-growing-r rth Chi ed ina , the r

itiprmda83st2i(atulalstton

trt(ivtetasl

egions: Northeast China (NE, red triangles), Northwest China (NW, blue squares), Nos the 30-year average for a given location from 1985 to 2014. The black cross-shapnd ***p < 0.001. (For interpretation of the references to colour in this figure legend

In this study, we analyzed both Yp and Yw with distinguishingrrigated maize and rainfed maize in the whole China. At the coun-ry level, the Yp was 33% (3.5 t ha−1) higher than the Yw (Fig. 3). Itndicated water has already become a big limiting factor for maizeroduction in China. However, we found varied results in differentegions. For example, there was nearly no restrictions of water for

aize production in SW with an average precipitation of 653 mmuring maize the growing season (Fig. 6d). However, when theverage precipitation dropped to 450 mm in NE and NCP, Yw was0% of Yp (Fig. 6a, c). While the average precipitation dropped to60 mm in NW, Yw was just 50% of Yp. Similarly, a recent studyhowed that the critical precipitation level was 462 mm to closehe gap between Yw and Yp in the China maize belt (Meng et al.,016). In addition, water scarcity was already a critical problem

n China, which was expected to become more sever in the futureRijsberman, 2006; Jiang, 2009). China maize production may have

big challenge under the most severe water scarcity. Meanwhile,he sensitivity of maize production to water has also increasednder climate change (Meng et al., 2016), which will make the prob-

em worse. We also found solar radiation and temperature werelso two important factors affecting Yp. Yp in NE was limited byow temperature while Yp in NCP and SW were limited by the lowolar radiation. Parry et al. (2007) showed that the north-high lati-udes have experienced among faster warming trend in the world,hus maize production in NE would benefit in future. However, thebserved significant decrease in solar radiation in NCP may have aegative impact on Yp (Meng et al., 2016).

As the largest cereal crop in China, closing maize Yg is essentialo increase grain production and meet future food demand. Firstly,educing the 3.5 t ha−1 difference between the Yp and Yw throughhe irrigation would be an effective measure. Meanwhile, Bu et al.2013) and Liu et al. (2010) showed that plastic film mulchingncreased maize yield by 20%–80% because of the effective conser-ation of available soil water and increase of soil temperatures inhe spring. Meanwhile, YgR between the Yw and Ya was still large,specially in SW (4.5 t ha−1). This is because agricultural produc-

ion is dominated by smallholder farmers in SW and farm size isn important factor to improve grain yield. In comparison, large-cale farm is common in NE which is benefit to its high Ya andow YgR. In additional, the hot and humid climate conditions in

ina Plain (NCP, yellow rhombuses), Southwest China (SW, green circles). Each pointdicate the standard deviation. Asterisks indicate correlation at *p < 0.05, **p < 0.01,eader is referred to the web version of this article.)

SW increased the difficulty to control the biotic and abiotic stressin maize production. It is also difficult for management under thisclimate such as nutrient management with many rainy days. Gener-ally, further yield boost would come from optimized managementchoices, including variety, planting date, seeding rate, and fertil-izer management (Chen et al., 2011). For example, the integratedsoil-crop system management was designed to make maximumuse of solar radiation and temperatures, and synchronized nutri-ent supply and crop demand (Chen et al., 2011, 2014). Field resultsshowed the integrated soil-crop system management can increasewheat grain yield by 35% compared with farmers’ practices (Chenet al., 2014).

In this study, we used the GYGA-ED protocol to estimate Yp andYw for China maize production. The CZs scheme delineated har-vest areas based on the growing degree days, aridity index, andtemperature seasonality seems to strive a good balance betweenthe zone size and the number of zones, and keeping a better cli-matic homogeneity within the zones. The selection of the bufferzones according to their clipped harvested crop areas from big tosmall can ensure the representative of the RWS, while the mini-mum 50% harvested area coverage of buffer zones to the regioncan decrease the demand for data by a large margin. When the cov-erage ratio reached 50% of region, the weighted averaged Yp tendedto be stabilized (Fig. 8). Similar result was also found in USA maizeproduction (van Bussel et al., 2015). In comparison, the estimationof Yp and Yw in the past studies mainly relied on the gridded data oreven had no upscaling process. For example, Mueller et al. (2012)and Neumann et al. (2010) estimated the Yp and Yw at a 5 arc-minute by 5 arc-minute resolution at the global scale. Collectingthe weather data, agronomic data and soil data is time consumingand expensive. Meanwhile, the quality of the gridded-interpolatedaverage weather data, the accuracy of the crop management dataand soil data is an issue on a large scale. In addition, the non-uniformgeospatial distribution of crop area within a grid was ignored. Someother studies based on locally observed data have the advantage,but often at the cost of spatial scale. Grassini et al. (2009) used 18

sites to estimate the maize Yp in Western Corn-Belt of USA. Lu andFan (2012) used 43 sites to estimate the wheat Yp in NCP. Liu et al.(2016) used 55 sites to estimate the maize Yp in NE. In summary, thebottom-up GYGA-ED protocol in this study obtains a good balance

116 B. Liu et al. / Agricultural and Forest M

Fig. 8. Maize yield potential change with the coverage ratio of reference weathersz

bor

5

mgg1YrtpNpibao

A

ga(

R

A

A

A

B

B

efficiencies and response to fertilisers by maize across heterogeneous

tations buffer zones. The coverage ratio = area of reference weather stations bufferones/total maize area*100.

etween RWS buffer zones and CZs to minimize the data volumef weather, soils, and crop management with the goal of maximumepresentativeness of the specific locations.

. Conclusions

In this study, we used GYGA-ED protocol method together withodel simulations to estimate the maize yield potential and yield

ap in the four maize-growing-regions of China under both irri-ated and rainfed conditions. We found Yp and Yw averaged at4.2 t ha−1 and 10.7 t ha−1, respectively. The corresponding YgI andgR was 6.0 t ha−1 and 3.7 t ha−1, for irrigated and rainfed maize,espectively. Meanwhile, the analysis of the climate factors showedhat precipitation was the most important limiting factor to maizeroduction except in SW. Yp was limited by low temperature inE, and by low solar radiation in both NCP and SW. Using GYGA-EDrotocol method to estimate the Yp, Yw and Yg when upscal-

ng location to the large spatial region maintains a good balanceetween minimum data demand for weather, soils, and crop man-gement and the goal of maximum appropriate representativenessf the specific locations.

cknowledgements

This work was supported by the National Basic Research Pro-ram (973 Program) of China (No. 2015CB150402, 2015CB150401)nd the national key research and development program of ChinaNo. 2016YFD0300110).

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