Research Articles

Optimizing field management to promote the ecologicalization of agriculture in loess hilly-gully region, China

  • HUANG Yunxin , 1, 2 ,
  • LI Yurui 1, 2, 3 ,
  • LIU Yansui , 1, 2, *
  • 1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • 2. College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 101408, China
  • 3. National Observation and Research Station of Earth Critical Zone on the Loess Plateau in Shaanxi, Xi’an 710061, China
*Liu Yansui (1965‒), Professor, E-mail:

Huang Yunxin (1993‒), E-mail:

Received date: 2022-07-27

  Accepted date: 2022-11-24

  Online published: 2023-05-11

Supported by

National Natural Science Foundation of China(41931293)

The National Key Research and Development Program of China(2017YFC0504701)


Scientific field management is an important path to realize ecological production and sustainable development of agriculture. As the main content of field management, nitrogen (N) management is the key to balance the economic and ecological benefits of agricultural production. In the loess hilly-gully region, for the fragile ecological and social system, ecologicalization of agricultural production is an important direction to promote sustainable agricultural development. However, irrational fertilization has been one of the main constraint factors, hindering the ecologicalization of local agriculture. In order to solve the problem and prove the practical significance of field management to ecologicalization of agriculture, this study aimed at evaluating the effects of different N fertilization rates and timing using Root Zone Water Quality Model (RZWQM) and then optimizing the N management. Experiments were conducted from 2018 to 2019 in Yangjuangou watershed, loess hilly-gully region, to calibrate and validate the model. The root mean square error (RMSE) of soil water content, nitrate N concentration, above-ground biomass, leaf area index ranged from 10.5-13.5 mm, 2.96-3.80 mg·kg–1, 730.3-1273.9 kg·ha–1 and 0.26-0.38, respectively, with the agreement index (d) between observed and simulated values ranging between 0.88 to 0.98. Simulation results showed that N leaching in semi-arid areas was also quite high due to concentrated rainfall and loose soil, which had previously been neglected. When the fertilization rate decreased by 35% (applying the chemical fertilizer at rate of 245.7 kg N ha–1) of typical fertilization (applying the chemical fertilizer at rate of 378.0 kg N ha–1), the leaching and residual N decreased by 72.2%-75.4% and 35.6%-50.9%, respectively, while NUE increased by 41.5%-45.2% with no reduction in maize yield. Additionally, compared with applying additional N at seedling stage in one batch, applying at seedling and jointing stages in two batches further decreased N leaching and improved NUE. Thus, a 35% reduction of typical fertilization rate combined with applying additional N at seedling and jointing stages is recommended. From the perspective of N management, this study demonstrated optimizing field management can play a positive role in the ecologicalization of agriculture, and more field management measures should be explored.

Cite this article

HUANG Yunxin , LI Yurui , LIU Yansui . Optimizing field management to promote the ecologicalization of agriculture in loess hilly-gully region, China[J]. Journal of Geographical Sciences, 2023 , 33(5) : 1055 -1074 . DOI: 10.1007/s11442-023-2119-5

1 Introduction

Agriculture production is the cornerstone of social and economic development, especially for rural areas (Liu and Li, 2017a; Kanter et al., 2018). Over the past few decades, production pattern relying on high-intensity inputs has boosted the growth of agricultural production, promoting the rural development (Erisman et al., 2008). However, with the continuous increase of input intensity, increase in input has showed limited effect on output increase, while greatly increased the environmental and economic costs of agricultural production (Bindraban et al., 2015), which has hindered the sustainable development of agriculture. Thus, ecologicalization of agriculture which is conducive to environmental and ecological benefits attracted people’s attention (Rosa-Schleich et al., 2019). To promote the ecologicalization of agriculture, modern field management of which scientific application of chemical materials is an important practice is recommended (Reganold and Wachter, 2016; Liu et al., 2020). Rational application of N fertilizer which is an important content of modern field management, offers a path to realize win-win of environmental benefit and economic benefit (Wang et al., 2015; Houser, 2021).
It has been widely accepted that incorrect N fertilization will bring environmental problems and low utilization efficiency, which poses great challenges to agricultural sustainable production (Erisman et al., 2008; Jat et al., 2012; Zhang et al., 2015). For farmland ecosystem, excessive N fertilization will cause soil problems such as soil acidification and soil compaction, which in turn impairs soil productivity. Meanwhile, excessive use of N can also lead to non-point source pollution which may damage the safety of soil and water resources (Robert et al., 2008; Liu et al., 2020). Economically, excessive use of N will greatly increase the cost of agricultural production (Houser, 2021), which weakens farmer’s willingness to engage in agriculture. Thus, many studies have focused on optimizing N management (Islam et al., 2016). Jayasundara et al. (2007) found that the use of judicious N rates improved uptake efficiency of N by crops, while also reduced both fertilizer and soil N losses without sacrificing yields. Wang et al. (2019) found optimizing the way of farmers’ typical fertilization can save fertilizer and increase the yield. A five-year field experiment showed that using integrated management optimized N fertilizer application rate which had highest NUE and yield (Dong et al., 2020). Yu et al. (2006) found that a reduction of 25% of current water and N applications in the North China Plain reduced N leaching by 24%-77% with crop yield reduction of 1%-9%, and Fang et al. (2018) found that potential savings of more than 30% of the N application rates from 300 to 200 kg N ha−1 could reduce about 60% of the N leaching without compromising crop yield. Besides, it has been reported that the timing of application also affected the effect of N application and using more frequent N applications can increase NUE and reduce N leaching (Robertson and Vitousek, 2009). Other studies also explored the effects of field managements such as fertilizer application placement, irrigation and tillage to optimize the NUE (Quemada and Gabriel, 2016; Dong et al., 2020; Mezbahuddin et al., 2020). Although there have been many studies on N management, due to the great differences in natural conditions and agricultural production methods among regions, regional studies are still needed to evaluate the status of N management in different regions and put forward corresponding optimization schemes.
In loess hilly-gully region, where is ecologically fragile, ecologicalization of agriculture has been widely regarded as the development direction of regional agriculture by which can realize coordinated development of agricultural production and ecology (Qu et al., 2021). Since 1999, the Grain for Green Project (GGP) has greatly improved the eco-environment, while also intensified the contradictions between human and land and between human and grain for the massive decrease of cropland (Chen et al., 2015). Thus, measures such as building silt arrester, gully land consolidation were carried out to control soil erosion while also construct high quality cropland to supplement cropland (Liu and Li, 2017b; Li et al., 2021b). After years of comprehensive management, it witnessed significant changes of the spatial form of production-ecology, forming a spatial pattern of vegetation restoration on the hill and agricultural production in the gully (Cao et al., 2019). The formation of gully agriculture has laid a good foundation for the sustainable development of regional agriculture (Qu et al., 2021). Improving agricultural production efficiency and promoting ecologicalization of gully agriculture have become a feasible way to coordinate the development of regional agricultural production and ecological conservation (Guo and Liu, 2022). However, our survey showed that some problems have become obstacles to the ecologicalization of agriculture there, among which irrational N fertilization was the prominent one. Farmers there tended to use a large amount of chemical fertilizer to increase production (Wu et al., 2018). As a result, the use of chemical fertilizer has increased significantly. For example, in Yan’an, the average intensity of chemical fertilizer application of 2018 and 2019 was 558.49 kg·ha‒1 and 554.27 kg·ha‒1, respectively, which was about 2.5 times of the internationally recognized maximum fertilization rate of 225 kg·ha‒1 (Yang, 2012). The increase of chemical fertilizer application not only greatly increased the cost of agricultural production, but also caused potential pollution risk to regional water and soil environment, hindering the coordinated development of agriculture production and ecology (Wu et al., 2018). Although there have been studies on the N management trying to optimize the N fertilizer application in this region (Fan et al., 2005), these studies focused more on the efficiency of N use and its effect on water use, crop growth (Kang et al., 2000; Ding et al., 2015; Wang et al., 2015), and less on its environmental effects because the N leaching there was regarded to be small for the low annual rainfall (Yang et al., 2015). At the same time, these studies rarely involved the gully cropland. Thus, it is urgent to assess current N management of the gully agricultural production and optimize N management if necessary.
The RZWQM2 provides an effective tool to simulate the effects of different cropland managements on the processes in the soil and the growth of crops (Ma et al., 2012). A lot of studies have shown that this model can simulate and evaluate N related variables accurately under different irrigation and fertilization scenarios when the model was in concert with field observations (Saseendran et al., 2007). Ding et al. (2020) simulated nitrate concentration under different tillage scenarios using RZWQM with the RMSE and d value ranging from 5.3-10.3 mg·kg‒1 and 0.71-0.84, respectively, and found N leaching decreased by 55.6% under no-tillage compared with conventional tillage. Jeong and Bhattarai (2018) simulated significant decrease in N loss when timing and placement of fertilization was optimized. Esmaeili et al. (2019) suggested that RZWQM was beneficial to assess the potential long-term performance of agricultural management. Considering the good applicability of the model, we decided to use the RZWQM2 in our study based on a two-year field experiment.
Aiming at providing optimal N management measures for regional gully agricultural production, the objectives of this paper were as follows: (1) calibrating and validating the RZWQM model through experimental observation data; (2) simulating and evaluating the effects of regional typical N fertilization rate; and (3) simulating the effects of different rates and timing of fertilization in this region and recommending an optimized fertilization scheme to the regional N management.

2 Methods and materials

2.1 Practical framework for optimizing N application

Agriculture is the link between socioeconomic system and ecological environment system, providing a variety of service functions while also bringing some unexpected output. Ecologicalization of agriculture emphasizes to achieve as much expected output as possible with as little cost as possible, and field management gives the path to achieve the goal through “evaluation-feedback-control-optimization” mechanism (Figure 1). When the output of agricultural production deviates from the expected goal (evaluation), the feedback mechanism helps to find the reasons (feedback), and the corresponding more scientific field management will adopted to adjust the system (control and optimization). This study focused on N management in gully farmland, which is a part of field management, and use the practical framework of “evaluation-feedback-control-optimization” to optimize the N management.
Figure 1 Practical framework for achieving ecological agricultural goal through field management

2.2 Field experiment and data

The experimental site of this study is located on the gully cropland in Yangjuangou watershed (109°31'‒109°71'E, 36°42'‒36°82'N), Yan’an city, where a total of 27.84 ha land has been reclaimed in an engineering named Gully Land Consolidation Project (GLCP) in 2014. The watershed with an altitude ranging from 1050 m to 1295 m, belongs to a typical loess hilly-gully region of the Loess Plateau. The annual average temperature ranges from ‒7.2℃ to 26.4℃ and the annual average precipitation is about 500 mm, of which about 79% of the rainfall occurs during June to September (Figure 2). The soil type of cropland is mainly loessal soil with the percentage of clay (<0.002 mm), silt (0.002-0.02 mm) and sand (0.02- 2 mm) is 5%, 70.8%, 24.2%, respectively. Single-season maize is the main agricultural production type, which is usually planted in early May and harvested in early October, and most crops in the region are rain-fed.
Figure 2 Daily precipitation and temperature during the study period of the experimental site
The experimental plots were built in 2017 on the cropland constructed in GLCP. Three treatments were designed to calibrate and validate RZWQM2, including control group (R0), a 10% reduction of control group (R10) and a 20% reduction of control group (R20) (Table 1). The same treatment was replicated three times on three plots randomly, each of which the size was 20 m2 (5 m×4 m). Before plating, rotary cultivator and disc harrowing was applied. The fertilization timing and amount over 2 years are presented in Table 1. The maize cultivar was “Ningyu 218” and the planting density was 49000 plants per hectare with a 45 cm row spacing. To ensure the normal growth of maize, 20 mm irrigation water was applied each year before sowing, and another 20 mm irrigation water was applied in seedling stage. The harvesting was on October 3 and September 30 for 2018 and 2019, respectively.
Table 1 Different fertilization treatments over two years for calibration and validation
Treatments Fertilization management
2018 2019
R0 2018‒05‒02: 60 kg N ha‒1, 17.65 kg P ha‒1, 88.24 kg K ha‒1
2018‒06‒05: Urea, 179.68 kg N ha‒1
2018‒07‒02: Urea, 143.75 kg N ha‒1
2019‒04‒17: 60 kg N ha‒1, 17.65 kg P ha‒1, 88.24 kg K ha‒1
2019‒07‒09: Urea, 143.75 kg N ha‒1
2019‒08‒01: Urea, 143.75 kg N ha‒1
R10 2018‒05‒02:54 kg N ha‒1, 15.89 kg P ha‒1, 79.42 kg K ha‒1
2018‒06‒05: Urea, 161.71 kg N ha‒1
2018‒07‒02: Urea, 129.38 kg N ha‒1
2019‒05‒02:54 kg N ha‒1, 15.89 kg P ha‒1, 79.42 kg K ha‒1
2019‒06‒05: Urea, 129.38 kg N ha‒1
2019‒07‒02: Urea, 129.38 kg N ha‒1
R20 2018‒05‒02: 48 kg N ha‒1, 14.12 kg P ha‒1, 70.59 kg K ha‒1
2018‒06‒05: Urea, 143.74 kg N ha‒1
2018‒07‒02: Urea, 115 kg N ha‒1
2019‒04‒17: 60 kg N ha‒1, 17.65 kg P ha‒1, 88.24 kg K ha‒1
2019‒07‒09: Urea, 115 kg N ha‒1
2019‒08‒01: Urea, 115 kg N ha‒1
The soil data, crop data, and meteorological data needed for the model were obtained in two years. Soil volumetric water content at the depths of 0-10, 10-20, 20-30, 30-40, 40-60, 60-80 cm were measured using TDR (TRIME-PICO-IPH TDR Soil Moisture Meter, IMKOMicromodultechnik) about once a week and soil water content was calculated by formulation (1).
$SWC=\underset{i=1}{\overset{6}{\mathop \sum }}\,{{W}_{i}}\times {{h}_{i}}\times 10$
where the SWC means soil water content, Wi is the volumetric water content, hi is the thickness of soil layer.
Meteorological data were obtained from an automatic meteorological station. Soil samples were taken at the depths of 0-10, 10-20, 20-30, 30-40, 40-60, 60-80 cm monthly. Five sample points were collected for each plot by S-shape sampling method and the samples were mixed evenly to obtain a 500 g mixed sample for each depth, so that there were three mixed samples for each treatment. Soil nitrate N (NO3-N) concentration and soil organic matter were measured by dual-wavelength spectrophotometry and potassium chromate volumetric analysis method respectively. Soil mechanical composition was measured using Bettersize2000 laser particle analyzer. For each year, three samples were taken at the same intervals by cutting rings for each plot during the growing period and after harvest, and then dried for 48 hours at 105℃ to calculate the soil bulk density. Five maize plants were randomly sampled in each plot and then dried to a constant weight to determine above-ground biomass. Leaf area index was measured using leaf area meter (Yaxin-1241). Above-ground biomass and leaf index were determined monthly. Maize yield of each plot was determined at filling and maturity stages by sampling five plants randomly.

2.3 Description of RZWQM2

RZWQM2 is a process-oriented agricultural system crop and environmental management model developed by USDA Agricultural Systems Research Institute (Imran et al., 2007). The model integrates and considers the effects of physical, biological and chemical processes in the root zone on crop growth, including physical module, chemical module, nutrient module, crop growth module, pesticide module and management module (Ma et al., 2006). The model is one-dimensional and is intent to simulate the overall soil water and N balance for environmental assessment. It can simulate the impacts of soil-crop-nutrient management practices on soil water, crop production, water quality (Cameira et al., 2014). The crop simulation modules (CSM) integrated in RZWQM2 can simulate the growth and development of crops (Jones et al., 2003) and parameter estimation software embedded in RZWQM2 can help calibrate the parameters automatically. Combining the detailed soil water, N, and management modules with the detailed crop modules (Ma et al., 2006), RZWQM2 provides an effective tool to simulate the effects of different field managements.

2.4 Model calibration and validation

Indices including soil water content, soil nitrate N content, above-ground biomass, leaf area index and crop yield were used to measure the accuracy of the model (Ma et al., 2012). Meteorological data of the test site were input, including precipitation, temperature, relative humidity, wind speed, radiation intensity and sunshine length. The data collected in 2018 and 2019 were used for calibration and validation, respectively. Calibration and validation of the model were carried out as follows:
(1) Soil water module. Soil data input are present in Table 2, among which mechanical composition, bulk density, porosity, initial soil moisture content and nitrate N content were measured, and saturated hydraulic conductivity, field water capacity for θ1/3 were obtained through iteration. Soil properties such as bulk density, porosity and saturated hydraulic conductivity were adjusted slightly to promise a high simulation accuracy.
Table 2 Soil hydraulic characteristic and other soil parameters for calibration
Soil depth (cm) Mechanical composition (%) Bulk density (g·cm‒3) Porosity Saturated hydraulic conductivity (cm·h‒1) Field capacity at 33 kPa (cm3·cm‒3) Soil organic matter (g·kg‒1
Clay Silt Sand
0-10 5.1 70.8 24.1 1.47 0.445 1.10 0.23 4.30
10-20 5.0 70.7 24.3 1.50 0.434 0.15 0.26 3.05
20-30 5.0 70.9 24.1 1.53 0.423 0.16 0.27 2.18
30-40 6.0 71.0 23.0 1.58 0.404 0.10 0.28 1.17
40-60 6.0 70.0 24.0 1.60 0.396 0.10 0.29 3.09
60-80 6.0 70.0 24.0 1.62 0.389 0.10 0.26 1.70
(2) Nutrient module. The initial soil organic matter content was input and the inter-pool transfer coefficient of soil organic matter was set according to the default values. The model was run 50 times with conventional management practices before simulation to get stable residue pools (Ahuja and Ma, 2011). The microbial parameters and coefficients associated with soil N conversion were manually calibrated (Table 3).
Table 3 Calibration parameters for nutrition module
Parameter Description Value range Calibration values
Kdent Death rate of aerobic heterotrophs 1×10‒36-1×10‒33 7.5×10‒35
Kdden Death rate of autotrophs 1×10‒34-1×10‒31 8×10‒32
ANIT Reaction rates of nitrification 1×10‒10-1×10‒8 8×10‒8
ADEN Reaction rates of denitrification 1×10‒14-1×10‒12 2×10‒13
AHYD Reaction rates of urea hydrolysis 1×10‒6-1×10‒3 6.5×10‒6
(3) Crop growth module. DSSAT4.0 embedded was used to simulate the maize growth. Select an existing maize cultivar and adjust its genetic parameters to obtain a new cultivar which has similar characteristic with the crop we plant. The parameters for crop growth module have been calibrated and validated in previous study (Huang et al., 2022).

2.5 Model performance criteria and NUE

Root mean square error (RMSE), agreement index (d) and mean relative error (MRE) were used to evaluate the model performance (Ding et al., 2020). The smaller the RMSE is, the closer the d value is to 1, and the closer the MRE is to 0, the better the simulation accuracy of the model is. The calculation formula of the indicators are as follows:
$RMSE=\sqrt{\frac{\mathop{\sum }_{1}^{n}~{{\left( {{P}_{i}}-{{M}_{i}} \right)}^{2}}}{n}}$
$d=1-\frac{\mathop{\sum }_{1}^{n}~{{\left( {{P}_{i}}-{{M}_{i}} \right)}^{2}}}{\mathop{\sum }_{1}^{n}~{{\left( \left| {{P}_{i}}-{{M}_{avg}}\left| + \right|{{M}_{i}}-{{M}_{avg}} \right| \right)}^{2}}}$
$MRE=\frac{1}{n}\underset{i=1}{\overset{n}{\mathop \sum }}\,\left| \frac{{{P}_{i}}-{{M}_{i}}}{{{M}_{i}}} \right|\times 100%$
where n is the number of measured or simulated values, Mi is the measured value, Pi is the simulated value, and Mavg is the average value of measured values.
The NUE was calculated by the formula (5) (Ding et al., 2020):
$\text{NUE}=\frac{Y}{~\left( {{N}_{vol}}+{{N}_{den}}+{{N}_{lea}}+{{N}_{up}} \right)}$
where NUE is N use efficiency, Y is the maize yield (kg·ha‒1), Nvol is N volatilization (kg·ha‒1), Nden N denitrification (kg·ha‒1), Nlea is N leaching and Nup is N uptake (kg·ha‒1).

2.6 Fertilization scenarios

According to local management practice, farmers there usually apply only chemical fertilizer to meet the nutrient requirements of crop growth. The field survey showed that the local typical fertilization included two stages (Table 4): applying potassium sulfate and diammonium hydrogen phosphate at the rate of 102 kg N ha−1, 30 kg P ha−1, 150 kg K ha−1 at the sowing stage, and then applying urea at the rate of 276 kg N ha‒1 at the jointing stage. We set the typical fertilization schedule as the control group and the reduction groups as the experimental groups. To obtain the alternative fertilization rates, we reduced the typical fer- tilization rate by a gradient of 5%, until it was reduced by 55%. With the typical fertilization as the control group, there were 12 fertilization rate scenarios in total. Besides, under the same fertilization rate, we also designed different fertilization timing of additional N application to find a better fertilization scheme (Table 4). Except base fertilizer, the additional N was applied at seedling stage in one batch (method 1) or at seedling and jointing stages in two batches (method 2). All fertilization scenarios were simulated using the validated model.
Table 4 Descriptions of typical fertilization, different fertilization rates and timing
Scenarios Descriptions
Typical fertilization Base fertilizer (May 1): 102 kg N ha−1, 30 kg P ha−1, 150 kg K ha−1 at sowing stage
Additional fertilizer (May 25): 276 kg N ha‒1 at the seedling stage
Reduction groups Base fertilizer /additional fertilizer: reduced by a gradient of 5% on the basis of typical usage, until it was reduced by 55%.
Timing Method 1:
Base fertilizer (May 1): sowing stage
Additional fertilizer (May 25): seedling stage (100% of additional N)
Method 2:
Base fertilizer (May 1): sowing stage
First Additional N (May 25): seedling stage (50% of additional N)
Second Additional N (July 2): jointing stage (50% of additional N)

3 Results

3.1 Model calibration and validation

Table 5 shows the comparison statistics between simulated values and observed values of soil water content, concentration of nitrate N, above-ground biomass and leaf area index for calibration and validation. The RMSE values of soil water content of the 80 cm soil profile ranged from10.5 and 14.06 mm for calibration and validation, respectively with d > 0.8. The RMSE values for calibration and validation of NO3-N concentration ranged from 2.38 and 3.8 mg·kg‒1 with d > 0.72. The RMSE values of above-ground biomass and leaf area index for calibration and validation ranged from 730.3 to 1273.9 kg·ha‒1 and from 0.26 to 0.47, respectively. The MRE values of soil water content, NO3-N concentration, above-ground biomass, and leaf area index were all acceptable, ranging from 5.4% to 35.3%. The observed maize yield in 2018 and 2019 of different treatments are presented in Figure 3. The RMSE values of maize yield for calibration and validation were 647.98 kg·ha‒1 and 434.67 kg·ha‒1, respectively with MRE being 3.8% and 2.8%. In all, the simulation results of different treatments over two years showed that all the modules of the model met the precision requirement for relevant simulation.
Table 5 Statistics for comparing simulated values to observed values for calibration and validation
Items Treatments Calibration Validation
Soil water content (mm) R0 10.50 0.85 5.4% 13.5 0.88 9.1%
R10 11.72 0.86 6.2% 14.06 0.82 6.6%
R20 12.55 0.81 6.7% 13.45 0.80 6.3%
Nitrate N concentration (mg·kg‒1) R0 2.38 0.83 14.4% 3.80 0.76 15.9%
R10 3.55 0.73 20.8% 3.42 0.80 16.0%
R20 3.71 0.72 21.6% 3.39 0.79 16.4%
Above-ground biomass (kg·ha‒1) R0 1273.9 0.98 8.0% 730.3 0.98 27.8%
R10 952.82 0.96 9.4% 987.84 0.96 35.3%
R20 835.21 0.96 9.5% 1126.30 0.95 33.3%
Leaf area index R0 0.26 0.98 16% 0.38 0.94 21.2%
R10 0.31 0.96 27.6% 0.47 0.92 27.0%
R20 0.40 0.93 29.6% 0.40 0.94 23.5%
Figure 3 Comparison of observed and simulated values of maize yield for 2018 and 2019. Error bars are standard errors.

3.2 Soil N of different fertilization rates

N balance during crop growth in 2018 and 2019 under different N fertilizer managements were simulated by RZWQM2, and the average values were showed in Table 6. As shown in the table, the main N loss pathways were crop uptake and N leaching, while volatilization and denitrification of N were relatively less. Under the typical fertilization strategy (378.0 kg N ha‒1), the crop uptake N of two years contributed 57.4% to N loss. The N leaching contributed 35.5% to the N loss and accounted for more than 37.6% of the N application with an amount of 142.2 kg·ha‒1, which may pose potential threat to the environment. The NUE under typical fertilization management was just 22.6 and 23.3 kg·kg‒1 for 2018 and 2019, respectively, lower than that in many other practices (Abbasi et al. 2013; Ding et al., 2020). The results indicated that the typical fertilization management was environmentally unfriendly and inefficient.
Table 6 Average values of soil N during maize growing period under different N fertilization scenarios over two years
Application times Rates of N application (kg·ha‒1) Mineralization (kg·ha‒1) Denitrification (kg·ha‒1) Volatilization (kg·ha‒1) leaching (kg·ha‒1) Uptake by crop (kg·ha‒1) Residual (kg·ha‒1)
In one batch 378.0 44.58 16.03 10.99 139.24 229.86 29.35
359.1 (-5%) 44.58 13.95 9.49 118.72 229.86 30.69
340.2 (-10%) 44.58 13.82 8.79 109.27 229.86 26.41
321.3 (-15%) 44.58 13.12 8.30 95.15 229.86 21.99
302.4 (-20%) 44.58 10.09 7.65 73.37 229.86 26.36
283.5 (-25%) 44.58 8.22 7.14 59.34 229.86 22.04
264.6 (-30%) 44.58 6.87 6.58 50.15 229.86 17.46
245.7 (-35%) 44.64 4.81 6.01 36.52 226.47 14.57
226.8 (-40%) 45.39 2.58 5.49 23.16 210.80 31.89
207.9 (-45%) 46.31 1.66 4.99 18.70 198.41 33.89
189.0 (-50%) 45.08 1.49 4.52 16.63 175.78 39.30
170.1 (-55%) 47.87 0.83 4.02 14.32 166.23 35.88
In two batches 378.0 44.58 17.45 6.92 127.20 229.86 44.48
359.1 (-5%) 44.58 14.76 6.41 109.44 229.86 46.16
340.2 (-10%) 44.58 13.79 5.93 101.37 229.86 36.84
321.3 (-15%) 44.58 12.04 5.47 84.99 229.86 36.24
302.4 (-20%) 44.58 9.71 5.04 67.66 229.86 37.46
283.5 (-25%) 44.58 7.24 4.63 54.12 229.86 34.74
264.6 (-30%) 44.58 5.11 4.23 39.92 229.86 32.44
245.7 (-35%) 44.72 3.09 4.27 26.88 227.13 31.84
226.8 (-40%) 45.21 2.26 3.44 19.39 224.12 30.98
207.9 (-45%) 45.88 1.63 3.09 16.89 203.08 31.12
189.0 (-50%) 46.70 1.14 2.24 13.48 182.45 37.76
170.1 (-55%) 48.04 0.78 2.42 11.32 171.68 33.66
When additional N was added at seedling stage in one batch, as we decreased the rate of N fertilization by a gradient of 5% based on the typical application rate, the indexes of soil N changed. The average amount of N loss decreased from 400 kg·ha‒1 to 185.4 kg·ha‒1, with a decrease of 53.7% (Table 6). The proportion of applied N lost to environment decreased from 45.2% to 11.3%. The N leaching, which was the main loss path, showed an obvious downward trend and the proportion of N leaching to N loss decreased from 35.5% to 7.7%. Results of regression between N leaching and N fertilization rate showed in Figure 4 revealed that there was a significant positive correlation between N application rate and N leaching (R2>0.98, p<0.01). When the fertilization rate was low, the amount of N leaching was relatively small. However, when the application rate exceeded a certain threshold value, the N leaching increased rapidly. The average N uptake was keeping at the value of 229.86 kg·ha-1 until fertilization rate was reduced by 35%, but the proportion of N uptake to N loss increased constantly from 57.4% to 89.7% (Table 6). There showed a downward trend followed by an upward trend of residual N when the fertilization rate decreased (Figure 4). Volatilization and denitrification of soil N decreased obviously as the decrease in fertilization rate, with the reduction ranged from 9%-95.2% and 13.7%-63.4%, respectively (Table 6).
Figure 4 Simulation results of N leaching and residue in soil of different N application rates for 2018 (a) and 2019 (b). The solid line is the quadratic regression of N leaching, and the dotted line is the quadratic regression of the N residue.
When the fertilization rate decreased by 35%, the yield of maize did not suffer from a reduction while N leaching over two years decreased by 72.2%-75.4%, and residual N decreased by 35.6%-50.9%. At the same time, the crop uptake contributed more than 80% to N loss, while N leaching contributed less than 15%. Consequently, the NUE generally showed a trend of constant increase as the rate of N application decreased and increased by 5.7%-86.7% (Figure 5). The results indicated that reducing rate of fertilization can obviously mitigate the potential environmental risk caused by fertilization and improve the NUE in this area.
Figure 5 Simulation results of NUE of different N application schemes. NUE is abbreviation of N use efficiency.

3.3 Above-ground biomass and yield of different fertilization rates

As was shown in Figure 6, under the typical fertilization scenario, the above-ground biomass in 2018 and 2019 was 15,134 and 14,688 kg·ha‒1 respectively, and the corresponding yield was 9026 and 8798 kg·ha‒1. With the decrease in the rate of fertilization, the above-ground biomass and yield of maize firstly remained unchanged until the fertilization rate was reduced by 35%. However, if we continue to reduce the fertilization rate with a 5% gradient reduction on basis of a 35% reduction, the maize yield decreased by 1.1%-15.2% while above-ground biomass decreased by 1.1%-10.1%. Therefore, when reduce the application rate by 35%, it can not only save production cost but also promise the need of normal crop growth. It can also be induced from the result that the local typical rate of fertilization has exceeded crop growth requirement.
Figure 6 Simulated average yield and above-ground biomass of maize under different N application rates for two application methods

3.4 Effects of different fertilization timing

Simulation results showed that the frequency and timing of additional N application had effects on soil N. Compared with applying additional N in one batch at seedling stage (method 1), applying additional N in two batches at seedling and jointing stages (method 2) reduced N loss to the environment by 9.9% to 33.9%, with the proportion of applied N lost to environment ranging from 8.5%-40.0%; the volatilization and leaching of soil N decreased by 29.0%-50.3% and 9.3%-34.5%, respectively (Table 6). Applying additional N in two batches had no obvious additional effect on N mineralization and denitrification compared with method 1. When the reduction of fertilization rate was less than 35%, the N uptake by crops between two fertilization methods had no difference; however, when the reduction was more than 35%, the N uptake of method 2 was higher than that of method 1 (Table 6), indicating that applying additional N frequently can enhance the N supply capacity of the soil.
The dynamic variations of NO3-N concentration, leaching and denitrification of N were simulated, and the results of typical fertilization, method 1 with 35% reduction and method 2 with 35% reduction are shown in Figure 7. The NO3-N concentration under typical fertilization was far higher than those of reduction groups all the time (Figures 7e and 7f). In the 35% reduction scenarios, after the first N addition, the NO3-N concentration of method 1 was higher than that of method 2 until the second batch additional N of method 2 was applied. As for the N denitrification (Figure 7a, 7b), the values of typical fertilization were always higher than those of reduction groups. There was also difference between method 1 and method 2 in N denitrification. After the first additional N was applied, the N denitrification of method 1 was higher; however, when the second batch additional N of method 2 was applied, there was no obvious difference. The N leaching showed a similar intergroup relationship to that of N denitrification. It was also showed that there were synchronisms between N leaching and N denitrification and precipitation (Figure 7).
Figure 7 Simulated values of denitrification, N leaching and concentration for 2018 (a, c, e) and for 2019 (b, d, f). The red dots on the horizontal axis represent the fertilization timing of base fertilizer and additional N in turn.
There was also a threshold effect under method 2, and the threshold value of fertilization was the same as that of method 1(Figure 6). As for above-ground biomass and maize yield, when the reduction of fertilization rate was less or equal to 35%, there was no difference between two methods; however, when the reduction of fertilization exceeded 35%, both the above-ground biomass and yield of method 2 were higher than those of method 1 (Figure 6). The average NUE of two years with different rates under method 2 was 1.2%-6.5% higher than that of method 1 (Figure 5). Compared with typical fertilization, when reduce fertilization rate by 35% under method 2, the N leaching decreased by 81.1% while NUE increased by 53.3% with yield suffering from no compromise. Generally, comparison between two fertilization methods under different rates showed that applying additional N in two batches at seedling and jointing stages could not only ensure normal yield, but also further reduce N loss to environment and improve NUE.

4 Discussion

4.1 Effects of different fertilization rates

As shown in the Table 6, N lost to environment in the study was relatively high especially under the typical N management. As the main source of non-point source pollution, N leaching has received extensive attention except in arid and semi-arid areas (Yang et al., 2015). However, compared to the studies about Northern China, in which the N leaching accounted for 10.6% to 27.4% of the N application (Li et al., 2012; Ju and Zhang, 2017; Li et al., 2020), the N leaching under the typical fertilization in our study accounted for 37.6% of the N addition, demonstrating that N leaching in arid and semi-arid areas could not be ignored. Concentrated rainfall, loose soil structure and low clay content of the loess soil, make it easy for N to move along with water, which may be the external influencing factors for the high leaching there (Hoffmann et al., 2000; Yang et al., 2015). Considering the high N application rate and the significant positive correlation between N leaching and N application rate (Figure 5), over fertilization may be the direct reason for high N leaching. Meanwhile, the high fertilizer input also led to a high N residue in soil (Table 6), which would be a great potential leaching loss in fallow period (Dong et al., 2019). As we decreased the N fertilization rate, the denitrification as well as volatilization decreased simultaneously (Table 6), and excessive fertilization is thought to be the main reason (Leal et al., 2010; Jat et al., 2012). The results also showed that the fertilization rate had an effect on N mineralization. When the reduction of fertilization rate exceeded 35%, the N mineralization showed an increasing trend (Figure 5). The possible reason may be that when the soil available N decreases to a certain threshold, the mineralization of soil organic matter may accelerate to satisfy the demand of the growth of crops (Craine et al., 2007). This mechanism gives us an enlightenment to ensure the soil nutrient supply capacity by increasing soil organic matter while reducing the chemical N input.
It has been reported that N application improves plant growth within a certain range, and too little N may lead to low crop yield while too much N may lead to environmental pollution with the plants being less sensitive to N addition (Houser, 2021). In this study, there was a threshold effect of N fertilization rate. As we decreased the fertilization rate, the maize yield and above-ground biomass remained unchanged until the fertilization rate was 245.7 kg N ha-1 (a 35% reduction) (Table 6). Liu et al. (2020) also reported the same effect with a threshold value of 150 kg N ha‒1 in the North China Plain. The reason might be that when the fertilization rate was too high, the available N in soil far exceeded the demand to support normal crop growth and could not be absorbed by crops (Houser, 2021). Thus, when we decreased the fertilization rate in a certain range, yield and above-ground biomass did not change as well as the N uptake by crops (Table 6). Therefore, the way by increasing the fertilization rate to improve the maize yield was unreasonable.
In conclusion, decreasing the typical N fertilization rate is necessary to reduce the N loss, especially the N leaching which has been underestimated or even ignored for many years in arid and semi-arid region (Yang et al., 2015). A 35% reduction of the typical fertilization rate may be a suitable rate to meet the growth requirement of maize in loess hilly-gully region.

4.2 Effects of different fertilization timing

It has been reported that using more frequent N applications at critical stages during the growing season can increase N use efficiency and reduce environmental cost by reducing N input without harming yield (Robertson and Vitousek, 2009). In this study, compared with applying additional N at seedling stage in one batch, applying additional N at seedling and jointing stages in two batches improved the NUE, decreased N loss (Table 5 and Figure 5) and even improved maize yield when fertilization rate was low (Figure 6). Jeong and Bhattarai (2018) also found that optimizing the timing and frequency of fertilization further reduced N loss without compromising the corn yield under the same fertilization rate. Same result was also reported that there was a 21%-23% increase in NUE and a 3%-9% increase in yield when N loss decreased in frequent application compared with single N application at planting stage (Abbasi et al. 2013). The reason is that when N is applied in a high rate in one batch, the available N rises rapidly (Figure 7f) and is far greater than that crops can use quickly, thus the excessive N will become the source of N leaching and denitrification (Jat et al., 2012; Robertson et al., 2013; Houser, 2021). In our study, when additional N was applied at seedling stage in one batch, the period that the nutrient requirements are quite low, the high fertilization rate always results in higher nutrient concentrations (Figures 7e and 7f), resulting in high N loss (Wang et al., 2001). However, if N is applied more frequently at appropriate timing, it is more likely to ensure the synchronization of N demand and N supply, and we can expect efficiency gains in N use for each application and reduce N loss (Jat et al., 2012; Houser, 2021). Nevertheless, other research showed that frequent application of N fertilizer did not affect their performance and productivity (Liu and Wiatrak, 2011), and the reasons for the different results may be the different fertilization rates and methods, climate or other factors.

4.3 Implications and limitations

Ecologicalization of agriculture is an important path to realize the coordinated development of agricultural production and ecology in loess hilly-gully region, and scientific field management is one of the effective practice measures. A 35% reduction of N fertilization rate was recommended and an optimized fertilization timing was also proposed, which gave the farmers a direct management reference. Reducing the N fertilization rate not only greatly reduced the potential environmental risks of agricultural production, but also brought obvious economic benefits. Our calculation showed that, according to the commonly used fertilizer types (compound fertilizer and urea) and the current market price, the economic cost of 1059 yuan per hectare can be saved under the optimized fertilization rate compared with typical N application, which greatly improves the economic benefits of agricultural production.
The minimum application rate recommended was 245.7 kg N ha‒1 which was still higher than the maximum fertilization rate of 225 kg·ha‒1. Nevertheless, we still think the optimized rate of 245.7 kg N ha‒1 is acceptable considering the nutrient content of the regional cultivated land is too low with the content of organic matter is only 1.17-4.30 g·kg‒1 (Table 2). However, it does not mean there is no space for further improvement. Applying chemical N fertilizer combined with organic fertilizer such as manure and plant straw can not only save production cost, but also help maintain a healthy soil environment. The fertilization timing was explored in this study but not sufficiently detailed. The demand of nutrient at different stages are different so that fertilization timing and quota should be tailored to the needs of the crop (Wang et al., 2001).
This study also proved that the N leaching in arid and semi-arid regions with concentrated precipitation is also quite high, which has not been valued in the past. Especially in places like loess hilly-gully region, where the soil structure is loose, the nutrients are more readily lost with water. To reduce the N leaching, we suggest increasing the frequency of N application and adopting a precise fertilization timing and rate according to crop requirements. Meanwhile, the use of slow release fertilizer also helps to reduce the leaching of N and ensure the sustainable nutrient supply (Jat et al., 2012). Additionally, other field management techniques such as no-tillage, minimal tillage and shallow tillage should be applied to reduce soil porosity, thus reducing N leaching.
Although an optimized scheme for N management of regional gully farmland was proposed, there are still some limitations. Firstly, due to the heterogeneity of precipitation, soil, terrain and other environmental conditions in large-scale geographic space, the optimized application rate may only be applicable in a small area. For example, where soils are poor, more nitrogen than recommended may be needed to ensure crop yields. Therefore, the spatial heterogeneity of environmental conditions should be fully considered, and the model parameters calibrated by site data should be modified in combination with regional conditions to obtain more locally targeted optimized schemes. Secondly, in this study, only N management was optimized, and management measures of potassium and phosphorus fertilizers, which are also important, were not explored. Reasonable combination of different fertilizers can help to reduce the total amount of fertilizer input and improve the yield (Sun et al., 2019). Thus, future studies should further explore the application methods of nitrogen, phosphate and potassium, combined application schemes, and the effects of different fertilizer combined application schemes in gully farmland. Additionally, more field management measures, such as application of organic fertilizer, soil improvement, irrigation, tillage methods need to be further explored and the interactions between different measures and their effects should be further explored (Liu et al., 2018; Ding et al., 2020; Srivastava et al. 2020).
The promotion of the optimized scheme is the key to realize the value of this study. It has been reported that the use strategy of chemical fertilizers is influenced by many factors such as population age, market price and farm size (Zhang et al., 2020), which is also the case in loess hilly-gully region. The high cost of fertilizer input has made the households have the subjective initiative to optimize fertilization. However, due to the concerns about the risk of production reduction and low level of education, farmers often choose to rely on their own experience rather than try new techniques, which always results in excessive fertilization. Thus, external intervention is needed to induce farmers to adopt new management methods. Firstly, government departments can recommend optimal fertilization scheme to farmers through regular technical training and field practice guidance, which can promote the acceptance and mastery of new fertilization methods by farmers. Secondly, moderate large-scale production should be promoted, which is conducive to the application of new agricultural production methods (Wu et al., 2018). What’s more, strengthening environmental protection education can promote farmers to adopt the optimized fertilization scheme (Aregay et al., 2018).

5 Conclusions

Scientific field management is important for the ecologicalization of agriculture in loess hilly-gully region, of which N management is the core. The RZWQM was used to evaluate the effects of different N management schemes. Calibration and verification results showed that RZWQM model can be used for simulation in loess hilly-gully region. Simulation results showed that the typical fertilization rate there was excessive and N leaching in arid and semi-arid region also needed attention. Reducing fertilization rate and applying frequently can not only greatly reduce the potential environmental risk of agricultural production, but also improve NUE. To be specific, a 35% reduction (applying the N fertilizer at rate of 245.7 kg N ha‒1) of typical fertilization (applying the N fertilizer at rate of 378.0 kg N ha‒1), can both ensure the normal growth of crops and increase the NUE by about 44%, while the N leaching decreased by 71.2%. Compared applying additional N at seedling stage in one batch, applying at seedling and jointing stages in two batches can further improve NUE and reduce N loss. The results indicate that rational N fertilization is an effective way to realize environment friendly agricultural production and promise normal grain production. What’s more, other field management measures should be explored to support the ecologicalization of agriculture in this region.
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