Summer maize growth under different precipitation years in the Huang-Huai-Hai Plain of China

Agricultural and Forest Meteorology 285–286 (2020) 107927

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Summer maize growth under different precipitation years in the HuangHuai-Hai Plain of China Peijuan Wanga, Dingrong Wua, Jianying Yanga, Yuping Maa, Rui Fengb, Zhiguo Huoa,c,

T

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a

State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081, China Shenyang Institute of Atmospheric Environment of CMA, Shenyang 110166, China c Collaborative Innovation Center of Meteorological Disaster Forecast, Early-Warning and Assessment, Nanjing University of Information Science & Technology, Nanjing 210044, China b

A R T I C LE I N FO

A B S T R A C T

Keywords: Process-based RS-P-YEC model Growth condition Summer maize Huang-huai-hai Plain

Maize (Zea mays L.) is one of the important crops for meeting the high food demand for both humans and animals in the world. Promptly monitoring and accurately assessing growth of summer maize, a major crop in the Huang-Huai-Hai Plain (the HHH Plain) in China, is regularly conducted for estimating national yield and assessing food security. In this study, the process-based Remote-Sensing-Photosynthesis-Yield Estimation for Crops (RS-P-YEC) model, driven by remote sensing products, meteorological observations, phenophases of summer maize, and some auxiliary parameters, was used to simulate daily net primary productivity (NPP) of summer maize in the HHH Plain from June to September in 2000–2017. Summer maize growth at the county scale level (characterized by NPP) under different precipitation years was evaluated along West-East, NorthSouth, and Northwest-Southeast transects in the HHH Plain. Results showed that with increasing accumulative precipitation during the summer maize growing season, maize growth exhibited the characteristics of a downward opening parabolic curve for a dataset including all site-years and for single year datasets. The best simulated maize growth and actual observed yield generally occurred when accumulative precipitation during the summer maize growing season was between 300 and 500 mm. Furthermore, summer maize growth was reduced in years with growing season accumulative precipitation less than 300 mm or greater than 500 mm as seen in the analysis of data from several stations under different precipitation levels along the three transects. This study confirmed that NPP simulated with the RS-P-YEC model, driven by remote sensing products and ground-based meteorological observations, is a good indicator for monitoring and evaluating summer maize growth under different precipitation levels in the HHH Plain. As such, the evaluation results will be helpful for forecasting yield across broad geographical areas, and for assessing national food security.

1. Introduction Maize plays a critical role in meeting the high food demand for both humans and animals (Haarhoff and Swanepoel, 2018). Worldwide, the harvested maize area reached 1.97 × 108 ha in 2017, which was second only to the harvested wheat area of 2.19 × 108 ha (FAO, 2018). Also in 2017, maize was the crop of greatest worldwide production with a value of 1134.7 Tg, which was much higher than wheat (Triticum aestivum L.) (771.7 Tg) and rice (Oryza sativa L.) (769.7 Tg) in second and third places (FAO, 2018). Of all of the countries of the world, China harvested the greatest area of maize, accounting for 17.7% of the world maize production area. China was second only to the United States in total maize production, accounting for 18.6% of the world maize production (FAO, 2018).

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Maize production in China can be classified by planting periods as spring maize, summer maize, and autumn maize. The HHH Plain is a concentrated planting area of summer maize in China. The area harvested and the production of summer maize in the HHH Plain accounted for 31.86% and 30.68% of the total in China, respectively (China Statistical Yearbook, 2018). Therefore, given the major proportion of summer maize in this region, early and accurately monitoring of summer maize growth in the HHH Plain is fundamental and would provide major assistance and data to policy makers and grain marketing agencies for planning exports or imports (Mkhabela et al., 2011). Since the 1970s, with the launch of the first Landsat satellite and the development of remote sensing technology, remotely sensed images have been widely used in many applications, such as land use and land

Corresponding author. Chinese Academy of Meteorological Sciences, No. 46, Zhongguancun South Street, Haidian District, Beijing 100081, China. E-mail addresses: [email protected] (P. Wang), [email protected] (Z. Huo).

https://doi.org/10.1016/j.agrformet.2020.107927 Received 27 May 2019; Received in revised form 11 January 2020; Accepted 30 January 2020 0168-1923/ © 2020 Published by Elsevier B.V.

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Fig. 1. The location of the Huang-Huai-Hai Plain and its meteorological and agro-meteorological stations.

research regarding NPP has focused on simulation modeling (Liu et al., 1997; Chen et al., 1999; Prieto-Blanco et al., 2009; Tian et al., 2010; Sun et al., 2017), spatial-temporal distribution characteristics (Wang et al., 2017b), carbon storage and carbon balance (Riedo et al., 2000; Hao et al., 2017), and carbon use efficiency (Liu et al., 2019). Recently, some research studies have paid more attention to the responses of NPP to phenological dynamics (Wang et al., 2017a), to land use and land cover changes (Yu et al., 2009; Li et al., 2018), and to rainfall pulses (Fan et al., 2016). Moreover, NPP also is related to aboveground biomass, and can easily distinguish the better and good growth conditions of crops due to its broader ranges. A recent study proposed NPP as a better indicator to monitor and evaluate the growth of spring maize by virtue of its ability to outperform other vegetation indices, such as NDVI and EVI (Wang et al., 2018). Previous studies have proven that the process-based RS-P-YEC model is suitable for simulating the growth of three major grain crops, i.e., wheat (Wang et al., 2011), maize (Yao et al., 2015; Wang et al., 2018), and rice (Yao et al., 2017). Therefore, the objective of this study was to simulate NPP in order to evaluate differences in summer maize growth due to differences in growing season precipitation. To reach this goal, this study first simulated the time series of NPP for summer maize in the HHH Plain using the RS-P-YEC model driven by satellite-derived leaf area index products and ground-based meteorological observations, and then monitored and compared summer maize growth during the growing season under different precipitation years. Several typical stations along three transects in the HHH Plain (West-East, North-South, and Northwest-Southeast) were selected according to their summer maize growing season accumulative precipitation (AccuPrec). By comparing the characteristics of daily NPP at stations along each transect, the growth of summer maize under wet, normal, and dry years was evaluated for the HHH Plain.

cover change mapping, crop growth condition monitoring and yield estimation, forest fire detection, and global ecosystem carbon cycle research (Wang et al., 2014a). The electromagnetic signals measured by satellite remote sensing instruments can be directly linked to photosynthesis, stomatal resistance, and evapotranspiration, thereby facilitating the retrieval of information regarding crop status (Tucker and Sellers, 1986; Duveiller et al., 2012). A pragmatic way of analyzing the information available in the various spectral bands provided by the satellite is to combine the spectral band data algebraically into vegetation indices (such as Ratio Vegetation Index (RVI), Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), Perpendicular Vegetation Index (PVI), and Soil-Adjusted Vegetation Index (SAVI)) (Duveiller et al., 2012). Vegetation indices were designed to be sensitive to vegetation status while minimizing the effect of other influencing factors (e.g., the sun zenith angle, soil water content, different background soil reflectance, or atmospheric effects) (Duveiller et al., 2012; Zhang and Zhang, 2016). Many research studies have demonstrated that NDVI or peak NDVI during the period between flowering and milky ripe growth stages is the most effectively and extensively used parameter in statistically correlating and forecasting crop growth and yield (Bolton and Friedl, 2013; Shuai et al., 2013; Son et al., 2014; Zhang and Zhang, 2016). Evidently, the advantages of NDVI or peak NDVI for crop growth monitoring or yield forecasting vary with crop type and geographic location because parameters in statistical models are dependent on the quality of NDVI in representing crop growth and crop types in a given region (Doraiswamy et al., 2004; Rembold et al., 2013). Moreover, it becomes difficult to differentiate the crop growth condition between very good and average in areas of dense canopy where NDVI becomes saturated at values above 0.6 for corn and soybean [Glycine max (L.) Merr.] (Doraiswamy et al., 2004; Huete et al., 2006; Rocha and Shaver, 2009; Zhang and Zhang, 2016; Wang et al., 2018). Net primary productivity (NPP) of vegetation canopies is one of the key parameters in terrestrial ecosystems, and directly represents photosynthesis, respiration, and carbon sequestration. A large amount of 2

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2.2. Methodology 2.2.1. RS-P-YEC model The RS-P-YEC model used in this study is a process-based remote sensing model built on the Boreal Ecosystem Productivity Simulator (BEPS) (Liu et al., 1997; Chen et al., 1999). According to the horizontally homogeneous and vertically laminar characteristics of crops, the RS-P-YEC model has been successfully developed to simulate the growth and yield of crops, including wheat (Wang et al., 2011) in North China, maize (Yao et al., 2015; Wang et al., 2018) in Northeast China, and rice (Yao et al., 2017) in the middle and lower reaches of the Yangtze River of China. The applications of the RS-P-YEC model have covered the three major crops over the main grain producing areas in China. Furthermore, NPP has been confirmed to be a better indicator for monitoring and evaluating growth of grain crops because of its greater capacity to reflect the physiological characteristics of crops compared with other commonly used vegetation indices (e.g., NDVI and EVI) that are confined to narrow ranges (i.e., 0–1) and suffer from saturation limitations (Zhang and Zhang, 2016; Wang et al., 2018). Therefore, daily NPP, also called aboveground biomass for crops, was simulated using the RS-P-YEC model, and further used to monitor and evaluate the growth of summer maize in the HHH Plain. 2.2.2. Summer maize growth at county-level Daily NPP of summer maize was simulated at pixel scale, and then aggregated to county-level values according to the type of land use coverage (LUC) and county area (Eq. (1)). m

NPPcounty =

n

∑i = 1 ∑ j = 1 (NPPi, j × Pi, j × LUCi, j ) m

n

∑i = 1 ∑ j = 1 (Pi, j × LUCi, j )

(1) −2

); NPPi, j is where, NPPcounty is aggregated NPP at county level (g C m simulated NPP with the RS-P-YEC model at pixel scale (g C m − 2); Pi, j and LUCi, j are switch factors with values of 0 and 1, in which Pi, j=1 or 0 denotes the pixel is inside or outside this county, and LUCi, j=1 or 0 means the type of land use coverage is or isn't crop. Subscripts of i and j represent the position at the ith line and the jth column, respectively; m and n are total lines and columns in the study area.

Fig. 2. Three transects and their typical stations in the HHH Plain. Red dots, green crosses, and red squares are the stations in West-East, North-South, and Northwest-Southeast transects, respectively. Raoyang is a shared station both in West-East and North-South transects.

2.2.3. Summer maize growth along three transects The growth of summer maize was evaluated along the following three transects in the HHH Plain: 1) West-East transect in the northern HHH Plain; 2) North-South transect in the middle of the HHH Plain; 3) Northwest-Southeast transect in the southern HHH Plain. Three stations were selected along the West-East (red dots) and North-South (green crosses) transects, and five stations were chosen along NorthwestSoutheast (red squares) transect (Fig. 2). Fig. 2 also shows the pattern of increasing precipitation during the summer maize growing season from northwest to southeast. Recent research has shown that water requirement during the summer maize growing season ranged between 300 and 500 mm for different cultivation practices and different climate regions (Kamali and Nazari, 2018; Liu et al., 2013; Xiao et al., 2008). Therefore, three different precipitation years, including dry (less than 300 mm), normal (300–500 mm), and wet (more than 500 mm) in West-East and NorthSouth transects were selected at each station according to AccuPrec from June to September from 2000 to 2017. For the NorthwestSoutheast transect, based on AccuPrec from June to September in only 2013, the five stations selected were Zhengzhou with AccuPrec of less than 300 mm, Shangqiu with 300–400 mm, Bozhou of 400–500 mm, Bengbu of 500–600 mm, and Suzhou with more than 600 mm.

2. Study area, methodology, and data 2.1. Study area The HHH Plain includes all of Tianjin City and parts of Hebei Province, Henan Province, Shandong Province, Anhui Province, Jiangsu Province, and Beijing City (Fig. 1), covering a total area of about 3.5 × 105 km2. The climate is characterized as temperate continental monsoon, with hot and rainy summers and cold and dry winters. The accumulative temperature of consecutive daily air temperatures ≥10℃ is about 3700–4700 ℃•d per year, increasing from the north to the south across the HHH plain. The thermal conditions are suitable for the winter wheat – summer maize rotation cropping system. Annual precipitation decreases from 1000 mm in the southeast of the study area to 600 mm in the northwest, transitioning from humid to semi-humid. Approximately 60–70% of the annual precipitation occurs during the summer maize growing season, lasting from June to September. The HHH Plain is one of the largest plains in China, and is widely recognized as a predominant corn production area. Therefore, changes in maize acreage and yield can have direct impacts on both the national economy and food security of China.

2.3. Data The data used in this study included in-situ observations (e.g., meteorological data and agro-meteorological data), remotely sensed 3

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(1 − β × hTmax ) is the drop in relative humidity over the course of the day, where hTmax is the relative humidity at the time of maximum temperature, β is a parameter that varies with site that can be calculated as β = max {1.041, 23.753 × ΔT /(Tmean + 273.16)} , where ΔT is the mean annual temperature ranging between Tmax and Tmin, and Tmean is the average temperature; and Q0 is the total daily solar radiation at the top of the atmosphere. All of these parameters can be calculated from the basic meteorological observations and geographical data described above.

products, soil data, and some auxiliary data. Details regarding the data and their processes are described below. 2.3.1. Agro-meteorological observations Phenophases of summer maize at 28 phenological stations, agrometeorological observations at 37 agro-meteorological stations, and meteorological data at 56 meteorological stations in the HHH Plain were acquired from the National Meteorological Information center of the China Meteorological Administration (http://idata.cma/) (Fig. 1). Phenophases of summer maize used in this study included dates of planting (V0), stem elongation (V6), tasseling (VT), milk (R3), and physiological maturity (R6). Limited by the availability of in-situ records, phenological observations were collected primarily from 1990 to 2010. Dates of the earliest planting and the latest physiological maturity during each of the observation years at each station were averaged, and DOY153 (day of year) and DOY275 were found to be the dates of earliest planting and latest physiological maturity. Therefore, the summer maize growing season in the HHH Plain was set at approximately 1 June to 31 September, and these dates were used as the simulation period in the RS-P-YEC model. Agro-meteorological observations mainly refer to the ground-based yield and ratio of grain yield to stem biomass at physiological maturity. Limited by the availability of observations, the yield was collected from 2000 to 2013, and the ratio of grain yield to stem biomass at physiological maturity was obtained from 2010 to 2017. Harvest index (HI), which was defined as the ratio of grain yield to aboveground biomass, was calculated based on the observed ratio (Eq. (2)) (Wang et al., 2018).

HI = a/(1 + a)

2.3.3. Remotely sensed products The Moderate Resolution Imaging Spectrometer (MODIS) products, including 8-day leaf area index (LAI, MOD15A2H) and annual land cover types (LC, MCD12Q1) at 500 m spatial resolution in the Collection 6 MODIS, were downloaded via the Earth Observing System Data and Information System (EOSDIS) of National Aeronautics and Space Administration (NASA) (https://earthdata.nasa.gov/). MOD15A2H is the MODIS LAI product inversed with both the MODIS Surface Reflectance Product (MOD09GA) and the Land Cover Product (MCD12Q1) from DOY049 in 2000 to the present.1 MCD12Q1 is the MODIS LC product including five legacy classification schemes (IGBP, UMD, LAI, BGC, and PFT) at annual time steps from 2001 to the present.2 In this study, the classification scheme of Plant Functional Types (PFT) was used and the crops were denoted with the value of “7″. All LAI and LC products were re-projected to Albers Conical Equal Area (ACEA) projection, and then merged, resized, and mosaicked to get the dataset that was exactly consistent with the study area. 2.3.4. Soil data Soil is the storage reservoir that provides water for plants. Soil available water capacity (AWC) is the volume of soil water that could be available to plants. It is commonly estimated as the amount of water held in the soil between field capacity and permanent wilting point with corrections for salinity, texture, and rooting depth (Liu et al., 1997; Zhou et al., 2005). The soil AWC data were estimated on the basis of a soil texture map by using statistical methods described by Zhou et al. (2005). Generally, the initial soil moisture at planting is adequate for the emergence of summer maize in the HHH Plain. Therefore, the initial soil moisture was set to 75% of AWC.

(2)

where a is the observed ratio of grain yield to stem biomass for summer maize at physiological maturity. Since there were only 1–3 in-situ ratio records at each agro-meteorological station for the collected years, all ground-based HI were averaged, and the average HI for summer maize in the HHH Plain was 0.5. In this paper, simulated NPP was converted to yield by multiplying HI so as to evaluate the reliability of the RS-P-YEC model by comparing with statistical or observed yields. 2.3.2. Meteorological data Meteorological data included daily maximum air temperature, minimum air temperature, average relative humidity, and precipitation from June to September in 2000 - 2017. All data were quality checked. Temperature and humidity values for days with missing data were computed as the average of the two values on the adjacent days at each station, and small precipitation amounts recorded as “32,700″ were filled with “0″. The average air temperature was calculated by averaging the maximum and minimum air temperature, and the daily air temperature range was calculated as the difference between the maximum and minimum air temperatures. Daily meteorological data at 56 meteorological stations were spatially interpolated over the HHH Plain based on the inverse distance weighting method, and the interpolation resolution was set to 500 m to be consistent with the remotely sensed products. The summer maize growing season AccuPrec values were calculated by summing daily precipitation from 1 June to 31 September. Solar radiation is also one of the most important factors affecting NPP. Because of the scarcity of radiation monitoring stations within the study area, daily solar radiation was calculated based on latitude, maximum temperature, minimum temperature, average temperature, relative humidity, and DEM data using Winslow's method (Winslow et al., 2001; Wang et al., 2014b):

Rs = τcf × D × (1 − β × hTmax ) × Q0

2.3.5. Auxiliary data In this study, some auxiliary data, e.g., latitude, longitude, and some statistical yields of summer maize from 2000 to 2014 with some missed data, were needed in the RS-P-YEC model. Both latitude and longitude were rasterized over the HHH Plain with spatial resolution of 500 m, the same as other input parameters for the RS-P-YEC model. Statistical yields of summer maize from several counties in the HHH Plain were also collected from the Ministry of Agriculture of China (MOA) in selected years to demonstrate the relationship between yield and accumulative precipitation. 3. Results 3.1. Evaluation of RS-P-YEC model Similar to prior research reported by Wang et al., 2018, the yield of summer maize in the HHH Plain was used to evaluate the RS-P-YEC 1 MODIS Collection 6 (C6) LAI/FPAR Product User's Guide. Updated on February 24, 2015. Also available via: https://lpdaac.usgs.gov/documents/2/ mod15_user_guide.pdf 2 Sulla-Menashe, D., Friedl, M.A., User Guide to Collection 6 MODIS Land Cover (MCD12Q1 and MCD12C1) Product. Updated on May 14, 2018. Also available via: https://lpdaac.usgs.gov/sites/default/files/public/product_ documentation/mcd12_user_guide_v6.pdf?_ga=2.255077704.1907922654. 1550131688-747303103.1540975900

(3)

where Rs is daily solar radiation at the Earth's surface; τcf is the cloudfree atmospheric transmissivity; D is the day-length correction; 4

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Fig. 3. Comparison between simulated and observed yield of summer maize in the HHH Plain. Yield simulations were from the RS-P-YEC model.

model due to the lack of ground-based NPP time series data. The yield simulated with the RS-P-YEC model was compared with observed yields at agro-meteorological stations, and the results are shown in Fig. 3. Validation of the RS-P-YEC model with 114 statistical yields produced a correlation coefficient (R) and root mean square error (RMSE) of 0.67 and 1087 kg hm−2, respectively. Generally, the RS-P-YEC model can be used to reliably evaluate or forecast maize yield by simulating summer maize growth over large areas of the HHH Plain. Fig. 4. Box plots of summer maize growing season (June-September) accumulative precipitation (AccuPrec) (a) and annual frequency distributions of AccuPrec grouped by three AccuPrec levels (below average, average, and above average) (b) for 56 meteorological stations in the HHH Plain from 2000–2017. In Fig. 4a, the upper and lower boundaries denoted with solid squares and triangles in the gray box represent the 25th and 75th percentiles, the solid line in the gray box represents the median, and the hollow squares and triangles at the ends of the whiskers represent the maximum and minimum precipitation, respectively. In Fig. 4b, the slash texture, solid gray, and solid black bar segments are below average, average, and above average precipitation with breakpoints of 300 mm and 500 mm, respectively.

3.2. Precipitation characteristics for 56 meteorological stations during the summer maize growing season for 2000–2017 Box plots of summer maize growing season AccuPrec for 56 meteorological stations in the HHH Plain from 2000–2017 are shown in Fig. 4a. The distribution of AccuPrec among the 56 stations was compared for each year, and the solid line in each gray box represented the median AccuPrec, which was equivalent to the 50th percentile of all observations. The differences in maize growing season AccuPrec were quite large, and the spatial distribution was heterogeneous. For example, the median AccuPrec was the highest in 2003 (712 mm), while the AccuPrec in the 25th percentile and the maximum AccuPrec in 2003 were only ranked in third and second places, respectively, in these 18 years. Moreover, the minimum AccuPrec (213 mm) was ranked in ninth place. The median AccuPrec in 2001 and 2002 were relatively low, with values of less than 400 mm. In particular, the maximum AccuPrec in 2002 (708 mm) was still slightly less than the median AccuPrec in 2003 (712 mm). The AccuPrec of more than 75% of the stations in the HHH Plain was greater than 300 mm from 2000–2017 (except for the years of 2001 and 2002), which may be enough to satisfy the water requirement of summer maize during its growing season (Kamali and Nazari, 2018; Liu et al., 2013; Xiao et al., 2008). According to the growing season water requirement of summer maize reported by others (Kamali and Nazari, 2018; Liu et al., 2013; Xiao et al., 2008), two precipitation breakpoints (300 mm and 500 mm) were selected to classify precipitation levels. As a result, AccuPrec was divided into three levels (less than 300 mm, between 300 and 500 mm, and more than 500 mm), which were subsequently denoted as dry, normal, and wet years, respectively. Percentage of meteorological stations for these three AccuPrec levels is shown for each year in Fig. 4b. The majority of the stations received average or above average AccuPrec from 2000–2017, whereas more than 20% of the 56 stations received less than 300 mm AccuPrec in 2001, 2002, and 2014. Especially in 2002, about 45% of the stations had less than 300 mm AccuPrec, exhibiting the most widely distributed drought during the summer maize growing season in the past 18 years.

The two years having the greatest percentage of stations in each of the three precipitation categories were selected for analysis. Those six years were 2001 and 2002 for dry, 2009 and 2015 for normal, and 2003 and 2005 for wet condition. The frequency distributions of AccuPrec at 100 mm intervals for the three precipitation levels and all site-years are shown in Fig. 5. The results demonstrate that the two years selected for the dry condition designation had a significant left shift in distribution, where the AccuPrec of nearly 30% of the stations in those two years was concentrated in the range of 200–300 mm. As AccuPrec increased, the frequency of stations in the two dry years gradually decreased from more than 20% with AccuPrec in the 300–500 mm range to less than 10% for AccuPrec > 500 mm. Correspondingly, the peak frequencies for normal and wet years had an obviously right shift compared with dry years, with the predominant ranges of 300–500 mm and 500–600 mm for normal and wet years, respectively. For all site-years (blue line), there was an apparent left skew in the frequency distribution as also seen for the distributions of dry and normal years. The maximum frequency for all site-years was found in the 400–500 mm range, the same as observed for the normal precipitation years.

3.3. Characteristics of NPP under different precipitation years When similar field management practices and varieties are used, growth of summer maize is primarily determined by meteorological factors. The HHH Plain is one of the predominant corn production areas 5

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Fig. 7. Average net primary production (NPP) and number of site-years in different AccuPrec intervals. The dotted line was the fitted quadratic polynomial curve for average NPP.

Fig. 5. Frequency distributions of maize growing season accumulative precipitation (AccuPrec) for the two years having the greatest percentage of meteorological stations in each of the three precipitation categories (histogram), and all site-years during the entire study period (2000–2017) (blue line) for the HHH Plain, China.

precipitation across the Yellow River Basin during the summer months from 1981 to 2013 (Zhao et al., 2019). Moreover, annual precipitation variation was relatively large, as seen for AccuPrec at Zhengzhou in Henan Province ranging from 632 mm in 2003 to 135 mm in 2013. Therefore, AccuPrec during the summer maize growing season is the predominant factor affecting the growth of summer maize in the HHH Plain. The number of site-years and the average NPP of summer maize for different AccuPrec intervals are shown in Fig. 7. The AccuPrec in the 400–500 mm interval had the greatest number of site-years. As expected, average NPP was greatest for this same AccuPrec range. Average NPP increased as AccuPrec increased from 0 to 500 mm, and NPP decreased as AccuPrec increased to values greater than 500 mm. The relationship between average NPP and AccuPrec was found to be well represented (R2 = 0.89) by a quadratic polynomial equation. The curve showed a value of maximum NPP produced at about 400–500 mm of AccuPrec. This result was consistent with other studies (Kamali and Nazari, 2018; Liu et al., 2013; Xiao et al., 2008).

3.4. Summer maize growth along three transects 3.4.1. West-East transect across the northern HHH Plain Three stations from west to east (Shijiazhuang, Raoyang, and Huanghua) were selected along the West-East transect in the northern part of the HHH Plain (Fig. 2). Average AccuPrec during the summer maize growing season was calculated for each station over the past 30 years (1986–2015). Considering both average AccuPrec and the summer maize water requirement during the growing season, the three consecutive years of 2012 (more than 500 mm), 2013 (300–500 mm), and 2014 (less than 300 mm) at Shijiangzhuang and Huanghua were chosen to represent wet, normal, and dry levels, respectively. AccuPrec in 2012 at Raoyang was 446 mm, which was near the 2013 value of 418 mm. Therefore, 2005 with AccuPrec of 575 mm was selected to denote the wet condition at Raoyang rather than 2012. AccuPrec values for the years selected for the three different precipitation levels at each station are listed in Table 1. The temporal dynamics of NPP under the three different AccuPrec levels and the 5-year average (2013–2017) at the three selected stations along the West-East transect in the northern HHH Plain are shown in Fig. 8, along with statistical yields in three different precipitation years and annual AccuPrec during the maize growing season, with wet, normal, and dry levels displayed in blue, black, and red colors. Generally, summer maize growth under the normal AccuPrec level was better than growth under both wet and dry levels, especially during VTR6 stages from the first ten days of August to late September at all three stations. Moreover, average 2013–2017 maize growth was reduced from that observed in the normal precipitation year (particularly at

Fig. 6. The spatial distribution of average maize growing season accumulative precipitation (June to September) in 2000–2017 for the HHH Plain, China.

in China, and its thermal resources are adequate to fulfill the growing requirements of summer maize. However, the spatial distribution of water resources in the HHH Plain are heterogeneous, with average growing season AccuPrec during the study period decreasing from southeast to northwest (Fig. 6). This result is consistent with data from Zhao et al. (2019) who reported a similar finding for hourly mean 6

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Table 1 Maize growing season accumulative precipitation for years associated with three different precipitation levels at each station in the West-East transect in the northern HHH Plain, China. Site Name

Shijiazhuang Raoyang Huanghua

Table 2 Maize growing season accumulative precipitation for years associated with three different precipitation levels at each station in the North-South transect in the middle of the HHH Plain, China.

Precipitation from June to September (mm)

Site Name

Wet

Year

Normal

Year

Dry

Year

516 575 551

2012 2005 2012

436 418 413

2013 2013 2013

219 167 266

2014 2014 2014

Raoyang Shenxian Fuyang

Precipitation from June to September (mm) Wet

Year

Normal

Year

Dry

Year

575 578 670

2005 2005 2002

465 371 428

2008 2008 2004

213 160 286

2003 2002 2011

(2013), and lower in the below average AccuPrec year (2014) at Shijiazhuang (Fig. 8b). Similar results were observed at Raoyang with slightly worse growth in 2014 than in 2005 (Fig. 8c), which was consistent with statistical yield and the deviation between AccuPrec and the water requirement of summer maize (Fig. 8d). For Huanghua (Fig. 8e & f), the above average precipitation in 2012 had a larger detrimental effect on summer maize growth than the below average precipitation in 2014. Consecutive strong precipitation processes in late July caused oriental armyworm moths (Mythimna separata Walker) to land in Huanghua during their migration from the northeastern China to the south. At the same time, the climatic conditions (with lower temperature and higher relative humidity) provided suitable living conditions for the moth, and further promoted its infestation (Sun and

Huanghua), and slightly better than in both wet and dry years. As expected, summer maize growth in the dry or wet years showed relatively poorer growth compared with the normal precipitation year because the water supplies during the growing season were either insufficient or excessive. In other words, the closer that growing season AccuPrec was to 300–500 mm, the better maize growth was. From an individual station viewpoint, growing season AccuPrec in 2012 at Shijiazhuang was 516 mm, slightly more than the optimal water requirement of summer maize (Fig. 8b). Correspondingly, summer maize growth in 2012 was closer to 2013 (the average AccuPrec year) and the 5-year average (2013–2017), and better than in 2014 (the below average AccuPrec year with 219 mm) (Fig. 8a). These findings coincided with statistical yield. The yield was higher in the average AccuPrec year

Fig. 8. Temporal dynamics of NPP (left panels) in three different precipitation years and the 2013–2017 average, and histograms of annual maize growing season accumulative precipitation (AccuPrec, right panels) at three stations along the West-East transect in the northern HHH Plain, China. Histograms and lines in blue, black, and red colors denote three different AccuPrec levels (wet, normal, and dry conditions). Green lines in the NPP time-series panels are average NPP for 2013–2017. The red diamonds filled with black (right panels) are the annual statistical yields from the Ministry of Agriculture of China. 7

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Fig. 9. Temporal dynamics of NPP (left panels) in three different precipitation years and the 2013–2017 average, and histograms of annual maize growing season accumulative precipitation (AccuPrec, right panels) at three stations along the North-South transect in the middle of the HHH Plain, China. Histograms and lines in blue, black, and red colors denote three different AccuPrec levels (wet, normal, and dry conditions). Green lines in the NPP time-series panels are average NPP for 2013–2017. The red diamonds filled with black (right panels) are the annual statistical yields from the ministry of Agriculture of China.

colors, respectively. Summer maize growth at all three stations was best and the yield was the highest for the average precipitation year compared with the below average and above average years. Meanwhile, at all three sites along the North-South transect, maize growth was better with above average precipitation than with below average precipitation. The AccuPrec during the period of VE-V6 in 2003 at Raoyang station (Fig. 9b) was only 14 mm, which could not adequately meet the water requirement of summer maize. Poorer water supplies resulted in worse summer maize growth from the beginning of the growing period (Fig. 9a). Although the AccuPrec amounts during the periods of V6-VT and VT-R3 were greater than during the VE-V6 period, the water supplies were still less than the water requirement, especially since these two growth stage periods are the critical water requirement periods for maize (Zheng et al., 1994; Nielsen et al., 2009, 2010; Liu et al., 2013). Correspondingly, the yield in 2003 was the least among these three years. At Shenxian, the water supply during the maize growing season in 2002 was very low (Fig. 9d), and therefore summer maize growth and yield in the below average precipitation year were much less than the growth and yield in the average year (2008), the above average year (2005), and for the 2013–2017 average (Fig. 9c & 9d). Although AccuPrec during the periods of VE-V6 and VT-R3 at Fuyang station in 2011 were adequate for maize (Fig. 9f), low AccuPrec at V6-VT and R3R6 limited summer maize growth and consequently resulted in low final NPP (Fig. 9e) and yield (Fig. 9f).

Pan, 2013). The poorer weather conditions and moth infestation resulted in less maize growth in 2012 compared with 2013, 2014, and the 5-year average. Fortunately, effective pest control measures resulted in no significant reduction in maize production.3 3.4.2. North-South transect across the middle of the HHH Plain Raoyang, Shenxian, and Fuyang were the three stations analyzed along the North-South transect in the middle of the HHH Plain (Fig. 2). Raoyang was a shared station with the West-East transect, but the years selected for the three precipitation levels were different from the WestEast transect so as to easily differentiate one analysis from the other. Therefore, in this section, years at each station were chosen to produce three precipitation levels that were as different from each other as possible (see Table 2). Again, AccuPrec for the three different precipitation years at each station fell into the categories of more than 500 mm, between 300 and 500 mm, and less than 300 mm. Summer maize growth under the three different precipitation levels and the 2013–2017 average at the three stations along the North-South transect in the middle of the HHH Plain is shown in Fig. 9, along with three statistical yields and annual AccuPrec during the maize growing season at each station. Again, the time-course of NPP and precipitation for the wet, normal, and dry levels are displayed in blue, black, and red 3

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3.4.3. Northwest-Southeast transect across the southern HHH Plain The five stations selected for analyzing the effect of precipitation on maize NPP along the Northwest-Southeast transect in the southern HHH Plain were Zhengzhou, Shangqiu, Bozhou, Bengbu, and Suzhou (Fig. 10). AccuPrec values in 2013 for these five stations were 135 mm, 328 mm, 435 mm, 503 mm, and 651 mm, respectively. Precipitation during the 2013 maize growing season across most of the HHH Plain was generally good for summer maize, with AccuPrec being between 300–600 mm. Areas with less than 300 mm, denoted by dark red color in Fig. 10, were mainly concentrated in southwestern parts of the HHH Plain. There were small regions with greater than 600 mm scattered throughout the eastern parts of the HHH Plain. The quadratic relationship between NPP and AccuPrec at the five stations along the Northwest-Southeast transect (Fig. 11) was consistent with the relationship observed for the all site-years analysis (Fig. 7) discussed previously, with small differences in the regression coefficients. The peak NPP in 2013 was larger compared with the all siteyears analysis, and the AccuPrec corresponding to the peak NPP slightly shifted to the left due to the good water supplies in 2013. The AccuPrec where NPP reached its peak in 2013 (459 mm), was within the ideal growing season water requirements of summer maize that we specified earlier, ranging from 300–500 mm. Temporal dynamics of NPP and precipitation in 2013 at the five selected stations are presented in Fig. 12. The temporal distribution of precipitation in 2013 was relatively homogeneous during the summer maize growing season, and there was no long-term drought in the Northwest-Southeast transect in the southern HHH Plain. Summer maize growth at Zhengzhou was the poorest among the five stations due to the lowest precipitation. Almost no growth was observed after midAugust due to only 16 mm of precipitation from 13 August to the end of September. Summer maize at the other four stations had relatively better growth benefiting from abundant precipitation, especially during the two critical water requirement periods for summer maize. Although the AccuPrec at Suzhou exceeded the water requirements of summer maize, more than two-thirds of the precipitation received occurred during V6-VT and VT-R3 periods when the summer maize water requirement was greatest (Zheng et al., 1994; Nielsen et al., 2009 & 2010; Liu et al., 2013). With an adequate water supply for summer maize growth at Suzhou, NPP rapidly increased from mid-July to late-August.

Fig. 10. The spatial distribution of maize growing season accumulative precipitation from June to September in 2013, and five stations along the Northwest-Southeast transect in the southern HHH Plain, China.

4. Discussion and limitations Crop growth appears to be most strongly related to available resources, such as water and thermal resources. The thermal resource is generally not an inhibiting factor during the summer maize growing season in the HHH Plain, especially since air temperatures have significantly increased for the past several decades, as noted in several previous studies (Guo et al., 2013; Yang et al., 2015; Gao et al., 2019). However, when soil moisture contents go below or above certain thresholds, crop aboveground biomass and grain yields may be reduced (Liu et al., 2010; Holzman et al., 2014; Zhang et al., 2018a; White et al., 2019). Results from the present study provide indirect support for this conclusion, since the growth of summer maize was affected by both less precipitation and more precipitation at different site-years along the three transects. To further illustrate this, the relationships between maize grain yields and summer maize growing season AccuPrec at several stations and years are shown in Fig. 13. The fitted quadratic polynomial equations exhibited consistent downward opening parabolic curves (Fig. 11). For curves associated with data from Raoyang and from the Northwest-Southeast transect locations in 2013, the highest yields occurred when AccuPrec was between 300 and 500 mm (414 mm at Raoyang and 319 mm for the Northwest-Southeast transect in 2013). The high yields at both Shijiazhuang and Huanghua occurred when AccuPrec was 510 mm and 503 mm, respectively, which was very close to the upper limit of the optimal water requirement of summer maize.

Fig. 11. The relationship between NPP and maize growing season accumulative precipitation (AccuPrec) at five stations along the Northwest-Southeast transect in the southern HHH Plain, China, in 2013. The dotted line was the fitted quadratic polynomial curve.

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Fig. 12. Temporal dynamics of NPP and precipitation in 2013 at five selected stations along the Northwest-Southeast transect in the southern HHH Plain, China.

can typically express cultivar variation. Therefore, as our results agree with previous studies (Liu et al., 2018) and yield records (Fig. 13), we are confident that the main findings of this study are robust. An additional limitation of the study may be that the effects of irrigation on summer maize were not considered. Generally, summer maize in the HHH Plain is grown under rainfed conditions. Observations4 at 61 agro-meteorological stations within the HHH Plain included irrigation information for a total of 77 site-years from ten days prior to planting (V0) to physiological maturity (R6) in 2003–2010, comprising 15.78% of the total site-years. The average irrigation amount was about 830 m3/ha/y for the 77 site-years, which was equivalent to 83 mm of precipitation. The effects of irrigation on summer maize growth were not considered in the RS-P-YEC model, which may result in underestimated simulation results. Fortunately, no irrigation was used at the selected stations used in this study. Therefore, we believe that the relationships between simulated time-series NPP and different precipitation amounts in different years at the selected stations are valid. The last limitation of our study was that LAI used in this study was available at an 8-day interval. When simulating daily NPP with the RSP-YEC model on a certain day, we used LAI products on the nearest date assuming that the LAI did not change between these two days. However, previous studies have shown that summer maize LAI over the entire growing season exhibits an approximate downward opening parabolic curve relationship (Fang et al., 2008; Jiang et al., 2010; Houborg et al., 2016). LAI therefore should display smooth ascending and descending characteristics instead of a stepped curve. Fortunately, LAI changes within eight days are relatively small, and an alternative LAI estimation method using interpolated LAI values would not have significantly influenced the quantification of NPP. Thus, even though there are currently no dense time-series LAI products available, using 8day LAI as an input parameter is a practical way to simulate daily NPP of crops.

Fig. 13. Relationships between maize grain yields and summer maize growing season accumulative precipitation (AccuPrec) at several typical stations in selected years. The dotted lines in red, blue, and green were the fitted quadratic polynomial curves at Shijiazhuang, Raoyang, and Huanghua, respectively (the West-East transect). The black line was the fitted curve at five stations along Northwest-Southeast transect in the southern HHH Plain in 2013.

One of the limitations of this study was that the planting and physiological maturity dates were set to 1 June and 31 September, respectively, for the entire study area. However, the spatial extent of the study area is very large in both north-south and east-west directions, and the actual phenophases at different stations and in different years can vary greatly due to different thermal resources, water resources, rotational systems, cultivars, crop management practices, and so on (Rezaei et al., 2017; Zhang et al., 2018b; Wu et al., 2018 & 2019). This means that the simulated time-series NPP from June to September may include other crops to some degree, such as winter wheat in the northern HHH Plain, and canola in the southern HHH Plain. Using actual planting and physiological maturity dates observed at each agrometeorological station in individual years is, theoretically, the best approach for objectively simulating daily NPP, and for monitoring and assessing summer maize growth. However, this would require a more dense spatial distribution of phenological stations to record the actual phenophases so as to generate objective regional simulations. Another limitation of this study was that the actual summer maize cultivars planted in the HHH Plain were not considered. Summer maize growth, denoted by NPP time series, was influenced by more than just water supply. In fact, characteristics of specific cultivars have been shown to be an important parameter affecting crop growth (MartínezRomero et al., 2019). The adaptation of different cultivars to greater or lesser precipitation amounts can be different, which may lead to differing growth responses under the same water supplies for different cultivars (Martínez-Ferri et al., 2016; Singh et al., 2016). The water requirements of summer maize may increase correspondingly in regions with more precipitation. Ideally, the simulations performed in this analysis would have considered cultivar differences. However, cultivar records were missing from our datasets, and the structure of the RS-PYEC model did not consider the impacts of cultivar differences. As a matter of fact, crop LAI, an input parameter for the RS-P-YEC model,

5. Summary This study simulated daily NPP of summer maize using the RS-PYEC model to evaluate growth under different precipitation years along three transects in the HHH Plain. Results showed downward opening parabolic relationships between NPP and AccuPrec for all site-years and for single years. The analyses at several selected stations under different precipitation years along three transects also revealed that both above average and below average precipitation during the growing season were detrimental to summer maize growth. Better summer maize growth in the HHH Plain was observed when the accumulative precipitation during the growing season was between 300 and 500 mm, and worse growth when the AccuPrec was more than 500 mm or less 4 Data come from the “Data set of soil moisture in Chinese AgroMeteorological stations”

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than 300 mm. This result was consistent with previously determined optimal water requirements of summer maize. Moreover, the parabolic relationship between NPP and AccuPrec was also observed for the relationship between maize grain yield and AccuPrec. This study confirmed that NPP simulated using the RS-P-YEC model is a useful indicator to monitor and evaluate summer maize growth under different precipitation conditions in the HHH Plain, which further extends the applicability of the RS-P-YEC model. Moreover, the data sources in the RS-P-YEC model are available and easy to obtain via EOSDIS and local meteorological stations, thus allowing for regional growth condition evaluations and yield predictions. Simulated results will be helpful for crop management and policy planning activities, such as crop production layout according to local precipitation, cropping system adjustment, national food security, grain import and export situations, and so on.

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