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Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model
Journal of Integrative Agriculture 2014, 13(3): 577-587
March 2014
RESEARCH ARTICLE
Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model ZHA Yan1, WU Xue-ping1 , HE Xin-hua1, 2, ZHANG Hui-min1, GONG Fu-fei1, CAI Dian-xiong1, ZHU Ping3 and GAO Hong-jun3 1
Key Laboratory of Crop Nutrition and Fertilization, Ministry of Agricultural/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China 2 School of Plant Biology, University of Western Australia, Crawley, WA 6009, Australia 3 Jilin Academy of Agricultural Sciences, Changchun 130033, P.R.China
Abstract Increasing basic farmland soil productivity has significance in reducing fertilizer application and maintaining high yield of crops. In this study, we defined that the basic soil productivity (BSP) is the production capacity of a farmland soil with its own physical and chemical properties for a specific crop season under local environment and field management. Based on 22-yr (1990-2011) long-term experimental data on black soil (Typic hapludoll) in Gongzhuling, Jilin Province, Northeast China, the decision support system for an agro-technology transfer (DSSAT)-CERES-Maize model was applied to simulate the yield by BSP of spring maize (Zea mays L.) to examine the effects of long-term fertilization on changes of BSP and explore the mechanisms of BSP increasing. Five treatments were examined: (1) no-fertilization control (control); (2) chemical nitrogen, phosphorus, and potassium (NPK); (3) NPK plus farmyard manure (NPKM); (4) 1.5 time of NPKM (1.5NPKM) and (5) NPK plus straw (NPKS). Results showed that after 22-yr fertilization, the yield by BSP of spring maize significantly increased 78.0, 101.2, and 69.4% under the NPKM, 1.5NPKM and NPKS, respectively, compared to the initial value (in 1992), but not significant under NPK (26.9% increase) and the control (8.9% decrease). The contribution percentage of BSP showed a significant rising trend (P<0.05) under 1.5NPKM. The average contribution percentage of BSP among fertilizations ranged from 74.4 to 84.7%, and ranked as 1.5NPKM>NPKM>NPK≈NPKS, indicating that organic manure combined with chemical fertilizers (1.5NPKM and NPKM) could more effectively increase BSP compared with the inorganic fertilizer application alone (NPK) in the black soil. This study showed that soil organic matter (SOM) was the key factor among various fertility factors that could affect BSP in the black soil, and total N, total P and/or available P also played important role in BSP increasing. Compared with the chemical fertilization, a balanced chemical plus manure or straw fertilization (NPKM or NPKS) not only increased the concentrations of soil nutrient, but also improved the soil physical properties, and structure and diversity of soil microbial population, resulting in an iincrease of BSP. We recommend that a balanced chemical plus manure or straw fertilization (NPKM or NPKS) should be the fertilization practices to enhance spring maize yield and improve BSP in the black soil of Northeast China. Key words: spring maize, long-term fertilization, basic soil productivity, black soil, DSSAT model
INTRODUCTION With a cropping area of 7.4 million ha and a population
over 109.3 millions, the black soil (Typic hapludoll) region of northeastern China plays a crucial role in food security to the regional and whole country. However, the long-term intensive maize
Received 9 October, 2013 Accepted 18 December, 2013 ZHA Yan, Tel: +86-10-82108665, E-mail: [email protected]; Correspondence WU Xue-ping, Tel: +86-10-82108665, E-mail: [email protected]; ZHANG Hui-min, Tel: +86-10-82105039, E-mail: [email protected] © 2014, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(13)60715-7
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cropping with all straw harvested for rural fuel and unsustainable land use practices over the last 20 yr, has resulted in great decreases of soil organic carbon (SOC), and rapid declines of soil fertility and crop productivity (Yu et al. 2006; Xu et al. 2010; Kou et al. 2012). To meet an ever-increasing grain demand, a further exploration of the productivity potential of farmland while improving its soil fertility is urgently required. The improvement of soil productivity depends on the optimal field management such as rational tillage, irrigation and fertilizer application, and the increase of basic soil productivity (BSP) with its own physical and chemical properties under the same climate condition. In previous studies, BSP was generally characterized by crop yields from the long-term no-fertilization control or the contribution percentage of BSP (Yields in the no-fertilization control treatment/Yields in a corresponding fertilization×100%) (Huang et al. 2006; Zhang et al. 2009; Ma et al. 2012; Zeng et al. 2012). In fact, soil nutrients keep continuous depletion under long-term no-fertilization control, so the yield of nofertilization could not really reflect actual changes of BSP under long-term different fertilizations. Unfortunately, at present, the conception of BSP has not been clearly defined. In order to accurately characterize the BSP and explore the changes of BSP, we define BSP as the productive capacity of a farmland soil with its own physical and chemical properties. Under the same field management, there are mainly two contributions, i.e., external fertilizer application and BSP, to crop yield. Therefore, the contribution of BSP would mainly determine the crop yield under the same contribution of external fertilizer application. In other words, the crop yield could be increased with the increase of BSP even the contribution of external fertilizer application remain unchanged. Kunzová and Hejcman (2010) summarized that over 50 yr, the annual wheat yield was decreased under the no-fertilization control than under the different farmyard manure and mineral fertilizers in the Caslav crop rotation experiment in the Czech Republic. They claimed that such a yield decrease was due to a reduced natural fertility in the non-fertilized soil than in the respective fertilized soil (Geyic Phaeozem, Chernozem or sandy loamy
ZHA Yan et al.
Cambisol) (Kunzová and Hejcman 2009). In contrast, under a high natural fertility, winter wheat production was stable and even increased without fertilizer input over 50 yr in Geyic Phaeozem soil (Kunzová and Hejcman 2010). In addition, crop yields in both the 150 yr long-term Rothamsted and 90 yr long-term Dikopshof fertilizations had also been maintained at >1 000 kg ha-1 without fertilizer input (Jenkinson 1991; Schellberg and Huging 1997). These results indicate that BSP is a reliable soil fertility index to maintain crop production. The change of BSP is a comprehensive process of matter and energy conversion, such as atmosphere deposition, nitrogen fixation, mineral weathering and humification, etc. Yield by BSP is an integrated index to express BSP. However, in most long-term experiments, there are no measured yields by BSP under different fertilizations. The decision support system for agro-technology transfer (DSSAT) crop growth model, which integrates the effects of soil, weather, management, genetics, and pests on daily growth, has been widely used to simulate growth, development, and yields of >26 crops growing on a uniform area of land, as well as variations in soil water, carbon, and nitrogen that take place under the cropping system over time (Soler et al. 2007; Thorp et al. 2008; Timsina et al. 2008; Yang et al. 2009; Dzotsi et al. 2010; Liu et al. 2011a; He et al. 2012; Deligios et al. 2013). The model has also predicted the long-term trend in potential yield of spring maize of Northeast China (Yang et al. 2010, 2011; Liu et al. 2012). According to the definition of BSP in this paper, the DAAST model could well simulate crop yields by BSP under local field management and no external fertilizer input for a specific crop season. Therefore, the DSSAT model may effectively resolve the problem of obtaining the yields by BSP under different fertilization treatments. In this study, the DSSAT-CERES-Maize model (ver. 4.0) was employed to simulate the yield by BSP of spring maize and analyze the change characteristics of BSP after long-term fertilizations in the black soil region of northeast China. Our aims were to develop the concept of BSP and then to explore the factors and fertilization practices that could affect the increase of BSP and the productive potential of farmland.
© 2014, CAAS. All rights reserved. Published by Elsevier Ltd.
Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model
an respectively annual increase of 3.1, 4.6, 6.0, and 4.1%. In addition, significant higher average yields by BSP between fertilizations ranked as 1.5NPKM (8 178 kg ha-1)>NPKM (7 245 kg ha-1)≈NPK (7 011 kg ha-1)≈NPKS (6 976 kg ha-1)>the control (3 405 kg ha-1). Over the 20-yr fertilization under NPK, NPKM, 1.5NPKM, and NPKS, four periods could be divided to show the change of maize yields with the BSP: a rapid increase period from 1992 to 1995, with an similar annual increase of 18.8, 19.0, 20.9, and 18.4%, respectively; a yield fluctuation period from 1996 to 2003 with a varied 5 606-8 580 kg ha-1 yield production; a relatively flat yield rising period from 2004 to 2008, with an annual increase from 6.9 to 10.7%; and a yield decrease period from 2009 to 2011, with an annual decrease from 1.4 to 3.1% (Fig. 2). Compared with data between the first three fertilization years (1992 to 1994) and the last three fertilization years (2009 to 2011), the average crop yields by BSP were significantly increased by 62.3, 46.7 and 39.2% (P<0.05) under 1.5NPKM, NPKM, and NPKS, respectively, but not significant under both NPK with a 26.9% increase and the control with a 8.9% decrease (Table 1).
RESULTS Yield evaluation The spring maize yields after different fertilizations (1990-2011) were simulated by the DSSAT model. The results showed that the normalized root mean square error (RMSE) and the index of agreement (d) of spring maize ranged from 9.70 to 13.46% and 0.849 to 0.929, respectively, over the 22 yr long-term fertilizations (Fig. 1).
Trends of yield by basic soil productivity of spring maize Yields of BSP of spring maize were significantly lower under the control than under any fertilization treatments (Fig. 2). Simulated yield by BSP of spring maize under NPK, NPKM, 1.5NPKM, and NPKS showed a similar increasing trend while a decreasing trend under the control over fertilization years. Compared to the year of 1992, after 20-yr of fertilization, maize yields by BSP was increased by 53.4, 78.0, 101.2, and 69.4%, respectively, with
Simulated yield (kg ha-1)
Simulated yield (kg ha-1)
A
B
12 000
C
12 000 y=0.632x+3 187 R2=0.772 n=22
11 000 10 000 9 000 8 000
11 000 10 000 9 000 RMSE=9.70% d=0.906
7 000 6 000 6 000
8 000
10 000
12 000
12 000 11 000
y=0.680x+2 124 R2=0.754 n=22
10 000 9 000
8 000
RMSE=13.27% d=0.862
7 000 6 000 6 000
8 000
10 000
12 000
6 000 6 000
E
F
6 000
12 000
9 000
5 000
y=0.536x+4 188 R2=0.521 n=22
3 000
8 000
RMSE=11.41% d=0.901
7 000 6 000 6 000
4 000
y=0.734x+1 180 R2=0.886 n=20
8 000
10 000
12 000
1 000 1 000
2 000
3 000
8 000
10 000
12 000
10 000
y=0.785x+ 366 R2=0.864 8 000 n=108
RMSE=10.87% d=0.929
2 000
RMSE=13.46% d=0.849
7 000
D 11 000
y=0.564x+3 314 R2=0.699 n=22
8 000
12 000 10 000
579
4 000
5 000
6 000
RMSE=13.23% d=0.872
4 000 2 000 2 000
4 000
6 000
8 000
10 000 12 000
Measured yield (kg ha ) -1
Fig. 1 Relationships between measured and simulated yields of spring maize over 22-yr long-term fertilizations (1990-2011). A, NPK treatment. B, NPKM treatment. C, 1.5NPKM treatment. D, NPKS treatment. E, control. F, five treatments (NPK, NPKM, 1.5NPKM, NPKS and control).
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ZHA Yan et al.
580 NPK
NPKM
1.5NPKM
NPKS
CK
10 000 9 000 8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000
NPK Contribution percentage of BSP(%)
Simulated yield by BSP (kg ha-1)
11 000
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
NPKM
1.5NPKM
NPKS
100 90 80 70 60 50 40
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 Year
Year
Fig. 2 Simulated yields by basic soil productivity of spring maize under long-term fertilizations (1992-2011).
Fig. 3 Contribution percentage of basic soil productivity under long-term fertilizations (1992-2011).
Table 1 Simulated yield by BSP of spring maize in different periods under different fertilizations1)
available potassium (Fig. 4).
Treatments Control NPK NPKM 1.5NPKM NPKS 1)
Mean yield (kg ha-1 yr-1) (1992-1994) 3 922±1 139 a, x 5 702±1 065 a, x 5 640±1 029 a, x 5 908±1 238 a, x 5 631±996 a, x
Mean yield (kg ha-1 yr-1) (2009-2011) 3 574±743 d, x 7 239±502 c, x 8 273±276 b, y 9 590±176 a, y 7 838±267 bc, y
Data were expressed as means±SE. Letters a, b, c between treatments for the same period within a column indicated a significant difference at P<0.05 level. Letters x, y between periods for the same treatment within a row indicated a significant difference at P<0.05 level.
Contribution percentage of basic soil productivity The contribution percentage of BSP showed a general increase trend but fluctuated with time over the 20-yr fertilizations under all fertilization treatments, except a significant increase under 1.5NPKM (P<0.05) (Fig. 3). The average contribution percentage of BSP between fertilizations ranged from 74.4 to 84.7%, and ranked as 1.5NPKM>NPKM>NPK≈NPKS.
Analysis of correlation between soil nutrient and basic soil productivity The contribution percentage of BSP was significantly correlated with the soil organic matter (SOM), total nitrogen, total phosphorus or available phosphorus (R2=0.08-0.124, n=56-68). In contrast, there are no correlations between the contribution percentage of BSP and available nitrogen, total potassium, or
DISCUSSION Basic soil productivity characterization BSP was a comprehensive index in evaluating soil basic fertility (Gong et al. 2013). Some researchers used the control yield (Zhu et al. 1997b; Xie et al. 2002; Jiang 2011), or yields under no nitrogen application (Meng et al. 2010; Wang et al. 2011), or contribution percentage of BSP as an index to characterize BSP (Xu et al. 2006; Tang and Huang 2008). The contribution percentage of BSP was expressed as the ratio of crop yields under nofertilization versus under fertilization (Zhu et al. 1997a; Huang et al. 2006; Zhang et al. 2009), or the ratio of crop yield under no-fertilization versus under suitable fertilization (Tang and Huang 2008) or versus the average yield among inorganic and organic combination treatments (Huang 2006). Studies have shown that the contribution percentage of BSP showed a fluctuating but constant decreasing trend over 27-yr to the early rice and late rice in yellow paddy soil in southern China (Wang et al. 2010). Meanwhile, the contribution percentage of BSP decreased from 79% in 1988 to 42% in 1996 (after 8 yr) in the black soil region of northeast China (Gao et al. 2009). The reason for such declining was that they used the crop yields under no-fertilization as numerator to calculate the
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100 90
80
80
70
70 y=0.736x+57.94 R2=0.124** n=68
60 50 40 15
25
35
45
55
50
Soil organic matter content (g kg-1) Contribution percentage of BSP (%)
C
B
90
100
100
150
200
250
0.8
Soil available N content (mg kg-1)
Soil total N content (g kg-1)
E
D
1.3
y=9.753x+63.89 60 R2=0.08* 50 n=68 40 1.8 2.3 2.8
F
G
100
90
90
80
80
70
70
60
y=0.075x+73.86 R2=0.10* n=56
50 40
0
50
100
150
200 0.3
Soil available P content (mg kg-1)
60
y=11.88x+68.43 R2=0.10* n=56 0.8
1.3
1.8
2.3
Soil total P content (g kg-1)
50 0
100
200
300
Soil available K content (g kg-1)
5
15
Effects of different fertilizations on basic soil productivity The trends of BSP varied from each other among
35
Soil total K content (g kg-1)
Fig. 4 Relationships between contribution percentage of basic soil productivity and soil nutrient contents. differences at P<0.05 and P<0.01 levels, respectively.
contribution percentage of BSP. The control yield gradually decreased with the continuous depletion of soil nutrient (Wang et al. 2000; Sui et al. 2005; Liu et al. 2011b). In this study, the control yield also decreased 8.9% over 20 yr (Fig. 2). Therefore, using the control yield to calculate the contribution percentage of BSP under various fertilizations could not accurately reflect the changes of BSP. Our study indicated that the application of the calibrated DSSAT model could well simulate yield by BSP of spring maize and resolved the problems that were derived from the measured control yield to characterize BSP. In this study, the contribution percentage of BSP was expressed as the ratio of crop yield by BSP under different fertilizations vs. measured yield in corresponding fertilization. The contribution percentage of BSP increased under NPK, NPKM, 1.5NPKM and NPKS treatments, and ranked as 1.5NPKM>NPKM>NPK≈NPKS (Fig. 3).
25
*
and
**
40
Contribution percentage of BSP (%)
A
100
581 Contribution percentage of BSP (%)
Contribution percentage of BSP (%)
Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model
indicate significant
different fertilizations compared with their 20-yr data. The result showed that after 20-yr fertilization, simulated yields by BSP of spring maize significantly increased under NPKM, 1.5NPKM, and NPKS, while decreased 8.9% under the control (Fig. 2 and Table 1). The simulated average yields by BSP of spring maize (1992 to 2011) under NKP, NPKM, 1.5NPKM and NPKS were significantly higher than that of the control and were 2.06, 2.13, 2.40 and 2.05 times that of the control, respectively (Fig. 2). At the end of experiment (2009 to 2011), the yield of BSP of spring maize under NPK, NPKM, 1.5NPKM and NPKS increased 64.1, 88.9, 118.1 and 80.3% compared with that of the control, respectively. The yield of BSP under NPKM, 1.5NPKM and NPKS were significantly higher than that of the NPK and increased 14.3, 32.5 and 8.3% compared with that of NPK, respectively (Table 1). These results indicated that the manure or straw combined with chemical fertilizer application could more effectively increase the yields by BSP than that of single chemical fertilizer as a whole. Our results corresponded with the study on BSP of Shajiang black soil over 22-yr long-term fertilization (Cao et al. 2008).
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of the experiment (in 1990), application of farmyard manure plus inorganic fertilizer significantly increased the concentration of total phosphorus and available phosphorus with 72-148% and 10.7-21.3 times, respectively, in 2005 (Fig. 5-C and D), and increased the concentration of total nitrogen with 10-21% in 2008 (Fig. 5-B). Thus, the increase of soil N and P caused by inorganic fertilizer combined with organic fertilizer (Gao et al. 2011; Alijani et al. 2012) were main reasons for BSP increasing. The BSP change is a complicated result of the soil physical and chemical properties, microbiology, as well as management practices. Except the effects of soil nutrient on BSP, improvement of the soil physical properties (Zhao and Zhou 2011), and structure and diversity of soil microbial population (Kong et al. 2008; Wang et al. 2008; Wei et al. 2008; Sradnicka et al. 2013) caused by increasing the application of manure or straw combined with chemical fertilizer were other main reasons that lead to the increase of BSP.
Effects of soil nutrient on basic soil productivity The reasons of BSP differences mainly lie in soil nutrient caused by fertilization management. The relationship between soil nutrient and BSP proved that soil nutrient, especially SOM was the key factor that affects BSP in black soil (Fig. 4), which corroborated with observation from Shajiang black soil in north Anhui Province (Zhang et al. 2005). Soil with high SOM had a high yield of BSP (e.g., 1.5NPKM, NPKM), whereas soil with a relative low SOM had a low one (e.g., the control) (Fig. 2 and Fig. 5-A). SOM showed a significant linear increasing trend under NPKM and 1.5NPKM over 20-yr long-term fertilization. In 2008, the SOM increased 21.7, 65.0, 65.0 and 25.9% under NPK, NPKM, 1.5NPKM and NPKS treatments, respectively, compared with the control. It has been believed that SOM plays vital roles in soil structure, water holding capacity, nutrient transformations and cycling (Galantini and Rosell 2006; Bhattacharyya et al. 2011; Zhao and Zhou 2011), and SOM could thus improve BSP. Total nitrogen, total phosphorus and available phosphorus also play important role in BSP increasing in black soil. Compared to the values at the beginning NPK
SOM (g kg-1)
45
NPKM
3
35 30 25 20
C
1.7 1.2 0.7 0.2
1990 1992 1994 1996 1998 2000 2002 2004 Year
B
2.5 2 1.5 1 0.5 0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 Available phosphorus (mg kg-1)
Total phosphorus (g kg-1)
2.2
NPKS
1.5NPKM
A
40
15
In this study, applying the calibrated DSSAT model well simulated yields by BSP of spring maize and
Total nitrogen (g kg-1)
50
CONCLUSION
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
D
250 200 150 100 50 0
1990
1992
1994
1996
1998
2000
2002
2004
Year
Fig. 5 Dynamic changing of SOM (A), total nitrogen (B), total phosphorus (C) and available phosphorus (D) under different fertilizations in black soil.
© 2014, CAAS. All rights reserved. Published by Elsevier Ltd.
Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model
resolved the problems that using the control yield to characterize BSP. Simulated yields by BSP of spring maize under NPKM, 1.5NPKM, and NPKS showed a significant increasing trend, but not significant under both NPK with a 26.9% increase and the control with a 8.9% decrease over 20-yr fertilization. The average contribution percentage of BSP between fertilizations ranged 74.4 to 84.7%, and ranked as 1.5NPKM>NPKM>NPK≈NPKS. This study showed that SOM was the key factor in various nutrient factors that affecting BSP in black soil, and total nitrogen, total phosphorus and available phosphorus also played important role in BSP increasing. Compared with the inorganic fertilizer, the balanced mineral application combined with manure or straw application (e.g., NPKM and NPKS) not only increased the concentrations of soil nutrients, but also
583
improved the soil physical properties, and structure and diversity of soil microbial population, which resulted in an increase of BSP.
MATERIALS AND METHODS General situation of study region The long-term experiment was started in the 1990’s in the experimental plot of Jilin Academy of Agricultural Sciences in Gongzhuling City, Jilin Province, China (124°48´34´´E and 43°30´23´´N, 220 m a.s.l). The site is in a flat area with an annual temperature of 4-5°C, an annual rainfall of 450-600 mm (70% in June-August), a frost-free period of 125-140 d, an effective accumulated temperature of 2 6003 000°C, and an annual amount of evaporation of 1 2001 600 mm. The soil was a black soil. The basic physiochemical characteristics before treatments are listed in Table 2.
Table 2 Chemical and physical properties of soil before treatments Soil layer (cm) 0-20 21-40 41-64 65-89 90-150
Organic matter (g kg-1) 22.8 15.2 7.1 6.8 6.3
Total N (mg g-1) 1.4 1.3 0.57 0.5 0.38
Total P (mg g-1) 0.61 0.59 0.44 0.43 0.40
Total K (mg g-1) 18.42 18.58 18.33 18.42 18.50
Available N (mg kg-1) 114 98 41 39 37
Olsen-P (mg kg-1) 11.79 6.77 3.14 1.83 1.79
Available K (mg kg-1) 158.33 150.83 154.17 157.50 155.83
pH 7.6 7.5 7.5 7.6 7.6
Bulk density (g cm-3) 1.19 1.27 1.33 1.35 1.39
Design of field experiment
Table 3 Rates of fertilizers application in different treatments each year (kg ha-1)
Five treatments or fertilizations were included in the experiment: (1) Control (no fertilizers); (2) NPK (nitrogen, phosphorus and potassium fertilizer); (3) NPKM (NPK plus organic manure); (4) l.5NPKM (1.5 times of NPKM); (5) NPKS (NPK plus 7 500 kg ha-1 straw). Spring maize with one crop per year was used as the experimental crop. The ratio of organic nitrogen to inorganic nitrogen was 7:3, with N:P2O5:K2O=l:0.5:0.5. A list of the fertilizer treatments used is given in Table 3. Phosphorus (calcium superphosphate, 12.5% P 2 O 5 ), potassium (potassium sulfate, 50% K2O) and one third of nitrogen fertilizer (urea, 45%N) were applied as the base fertilizer with sowing and the remaining two thirds of nitrogen fertilizer was topdressed at a depth of 10 cm below the topsoil before jointing. Organic manure (pig manure or cow manure) was used as the base manure and chopped (3-5 cm) straw was spread in furrows after topdressing in mid-July.
Treatment
Model selection and simulation method In this study, one of the DSSAT cropping system models, CERES-Maize (ver. 4.0) was selected for the yield simulation. The concrete steps of BSP simulation were
Control NPK NPKM 1.5NPKM NPKS
Inorganic fertilizer K2O N P2O5 0 0 0 165 82.5 82.5 50 82.5 82.5 75 123.8 123.8 112 82.5 82.5
Manure N 0 0 115 173 0
Straw N 0 0 0 0 53
Total amount of applied N 0 165 165 248 165
as follows: (1) the parameters of soil, weather, field management, and crop variety required for the model were inputted; (2) according to the actual measured yield of spring maize under NPK in the long-term experiment, the parameter of crop variety was calibrated using the “trial and error” method until the model simulation result was in accord with the actual measured value; (3) the yields under other fertilizer treatments (e.g., NPKM, 1.5NPMK and NPKS) were simulated by means of the calibrated parameter and then were tested for consistency with the actual measured values; (4) the yield by BSP for the year was simulated by setting no fertilization in the appropriate season only and was obtained using the same set of parameters, with a fixed fertilization in other years. For example, if the amount of a fertilizer in the n year is set to be zero only, with the fixed parameters including
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fertilization in the (n-1) year, the yield by BSP in the n year would be obtained when running yield simulation.
Model input date The simulation process of the DSSAT model is mainly performed via the four modules of weather, soil, genetic characteristics of crop, and field management. Meteorological data were from the database of the National Weather Service, including the daily weather data of Gongzhuling region, Jilin Province from 1990 to 2011. The daily weather data include daily solar radiation quantity (SRAD), daily maximum temperature (TMAX), daily minimum temperature (TMIN), and daily rainfall (RAIN). In the case of difficulty to obtain the daily solar radiation quantity (Q), based on the daily sunshine hours and relevant astronomical parameters, the Q value was estimated by means of the empirical formula (1):
Q=Q0(a+b S ) S0
(1)
Where, Q0 is astronomical radiation, S is actual measured sunshine hours, S0 is available sunshine hours, a and b are functions of S and S0 (Rietveld 1978). The soil profile input data in this study were directly obtained from the soil sample analysis in the 0-20 cm (the National Soil Fertility and Fertilizer Effects Longterm Monitoring Experiment Station 1998). The layered soil parameters include organic carbon, soil texture, bulk density, lower limit, drained upper limit, saturated soil water concentrations, cation exchange capacity, etc. The field management module include initial soil water concentration, crop cultivar, plant density, sowing date, harvest date, fertilization, etc. According to the actual measured yields of spring maize under NPK from 1990 to 2011, the parameter of crop variety was calibrated using the “trial and error” method (Li et al. 2001) until the model simulation was in accord with the actual measured value. After several calibrations, the parameter of crop variety of spring maize was finally determined (Table 4). The yields under NPKM, 1.5NPKM, NPKS, and the control were simulated by means of the calibrated parameter and then were tested for consistency with the actual measured values.
Calibration method In this study, the normalized root mean square error (RMSE) and index of agreement (d) were used to measure the relative degree of difference between the simulated value and actual measured value and to test the goodness of fit between them, respectively. The calculations are shown in the equations (2) and (3): n
RMSE=
∑ (Si - Ri )2 i =1
n
×
100 R
(2)
Table 4 Genetic parameters of spring maize Parameters Values
P1 260-280
P2 0.70-0.75
P5 850-950
G2 650-850
G3 PHINT 8.0-12.0 38.9-42.9
P1, degree days (base 8°C) from the emergence to end of juvenile phase; P2, photoperiod sensitivity coefficient (0-1.0); P5, degree days (base 8°C) from silking to physiological maturity; G2, potential kernel number; G3, potential kernel growth rate, mg/(kernel d); PHINT, degree days required for a leaf tip to emerge (phyllochron interval) (°C d).
n 2 (Si - Ri ) i∑ =1 d=1- n ∑ (|S´|-|R´|) 2 i =1 i i
(3)
Where, Ri is the actual measured value, Si is the simulated value, R is the average actual measured value, Si´=Si -R, Ri´=Ri -R, n is the number of simulated value samples. It is generally considered that, RMSE<10% is excellent; 10%30% is bad; and that as d value is closer to one, the consistency between the simulated value and actual measured value is better.
Soil sampling and analyses Soil samples were collected from the topsoil (0-20 cm) every year for all plots after harvest. Soil organic carbon concentration was measured by vitriol acid-potassium dichromate oxidation (Walkley and Black 1934). Total nitrogen and available nitrogen were determined by the method introduced by Lu (1999). Total phosphorus was measured by Murphy and Riley (1962), and available phosphorus (Olsen-P) by the Olsen-P method (Olsen et al. 1954). Total potassium was determined by Kundsen et al. (1982), and available potassium by Lu (1999). Soil bulk density was measured with iron ring (Lu 1999).
Data analysis The contribution percentage of BSP was calculated using the following equation: Contribution percentage of BSP (%)=Yield by BSP / Measured yield in corresponding fertilization×100 The Microsoft Excel 2007 software was applied for data processing and diagramming while the SAS 9.1 software was used for statistical analysis. Differences between yields by BSP between treatments or time periods were analyzed using the method of least significant difference (LSD) at P<0.05 or 0.01.
Acknowledgements We acknowledge all colleagues for their great efforts on the long-term experiments in black soil in Gongzhuling. The study was supported by the National 973 Program of China (2011CB100501), the National 863 Program of China
© 2014, CAAS. All rights reserved. Published by Elsevier Ltd.
Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model
(2013AA102901), the Special Fund for Agro-Scientific Research in the Public Interest, China (201203077), and the Science and Technology Project for Grain Production, China (2011BAD16B15).
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Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model
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March 2014
RESEARCH ARTICLE
Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model ZHA Yan1, WU Xue-ping1 , HE Xin-hua1, 2, ZHANG Hui-min1, GONG Fu-fei1, CAI Dian-xiong1, ZHU Ping3 and GAO Hong-jun3 1
Key Laboratory of Crop Nutrition and Fertilization, Ministry of Agricultural/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, P.R.China 2 School of Plant Biology, University of Western Australia, Crawley, WA 6009, Australia 3 Jilin Academy of Agricultural Sciences, Changchun 130033, P.R.China
Abstract Increasing basic farmland soil productivity has significance in reducing fertilizer application and maintaining high yield of crops. In this study, we defined that the basic soil productivity (BSP) is the production capacity of a farmland soil with its own physical and chemical properties for a specific crop season under local environment and field management. Based on 22-yr (1990-2011) long-term experimental data on black soil (Typic hapludoll) in Gongzhuling, Jilin Province, Northeast China, the decision support system for an agro-technology transfer (DSSAT)-CERES-Maize model was applied to simulate the yield by BSP of spring maize (Zea mays L.) to examine the effects of long-term fertilization on changes of BSP and explore the mechanisms of BSP increasing. Five treatments were examined: (1) no-fertilization control (control); (2) chemical nitrogen, phosphorus, and potassium (NPK); (3) NPK plus farmyard manure (NPKM); (4) 1.5 time of NPKM (1.5NPKM) and (5) NPK plus straw (NPKS). Results showed that after 22-yr fertilization, the yield by BSP of spring maize significantly increased 78.0, 101.2, and 69.4% under the NPKM, 1.5NPKM and NPKS, respectively, compared to the initial value (in 1992), but not significant under NPK (26.9% increase) and the control (8.9% decrease). The contribution percentage of BSP showed a significant rising trend (P<0.05) under 1.5NPKM. The average contribution percentage of BSP among fertilizations ranged from 74.4 to 84.7%, and ranked as 1.5NPKM>NPKM>NPK≈NPKS, indicating that organic manure combined with chemical fertilizers (1.5NPKM and NPKM) could more effectively increase BSP compared with the inorganic fertilizer application alone (NPK) in the black soil. This study showed that soil organic matter (SOM) was the key factor among various fertility factors that could affect BSP in the black soil, and total N, total P and/or available P also played important role in BSP increasing. Compared with the chemical fertilization, a balanced chemical plus manure or straw fertilization (NPKM or NPKS) not only increased the concentrations of soil nutrient, but also improved the soil physical properties, and structure and diversity of soil microbial population, resulting in an iincrease of BSP. We recommend that a balanced chemical plus manure or straw fertilization (NPKM or NPKS) should be the fertilization practices to enhance spring maize yield and improve BSP in the black soil of Northeast China. Key words: spring maize, long-term fertilization, basic soil productivity, black soil, DSSAT model
INTRODUCTION With a cropping area of 7.4 million ha and a population
over 109.3 millions, the black soil (Typic hapludoll) region of northeastern China plays a crucial role in food security to the regional and whole country. However, the long-term intensive maize
Received 9 October, 2013 Accepted 18 December, 2013 ZHA Yan, Tel: +86-10-82108665, E-mail: [email protected]; Correspondence WU Xue-ping, Tel: +86-10-82108665, E-mail: [email protected]; ZHANG Hui-min, Tel: +86-10-82105039, E-mail: [email protected] © 2014, CAAS. All rights reserved. Published by Elsevier Ltd. doi: 10.1016/S2095-3119(13)60715-7
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cropping with all straw harvested for rural fuel and unsustainable land use practices over the last 20 yr, has resulted in great decreases of soil organic carbon (SOC), and rapid declines of soil fertility and crop productivity (Yu et al. 2006; Xu et al. 2010; Kou et al. 2012). To meet an ever-increasing grain demand, a further exploration of the productivity potential of farmland while improving its soil fertility is urgently required. The improvement of soil productivity depends on the optimal field management such as rational tillage, irrigation and fertilizer application, and the increase of basic soil productivity (BSP) with its own physical and chemical properties under the same climate condition. In previous studies, BSP was generally characterized by crop yields from the long-term no-fertilization control or the contribution percentage of BSP (Yields in the no-fertilization control treatment/Yields in a corresponding fertilization×100%) (Huang et al. 2006; Zhang et al. 2009; Ma et al. 2012; Zeng et al. 2012). In fact, soil nutrients keep continuous depletion under long-term no-fertilization control, so the yield of nofertilization could not really reflect actual changes of BSP under long-term different fertilizations. Unfortunately, at present, the conception of BSP has not been clearly defined. In order to accurately characterize the BSP and explore the changes of BSP, we define BSP as the productive capacity of a farmland soil with its own physical and chemical properties. Under the same field management, there are mainly two contributions, i.e., external fertilizer application and BSP, to crop yield. Therefore, the contribution of BSP would mainly determine the crop yield under the same contribution of external fertilizer application. In other words, the crop yield could be increased with the increase of BSP even the contribution of external fertilizer application remain unchanged. Kunzová and Hejcman (2010) summarized that over 50 yr, the annual wheat yield was decreased under the no-fertilization control than under the different farmyard manure and mineral fertilizers in the Caslav crop rotation experiment in the Czech Republic. They claimed that such a yield decrease was due to a reduced natural fertility in the non-fertilized soil than in the respective fertilized soil (Geyic Phaeozem, Chernozem or sandy loamy
ZHA Yan et al.
Cambisol) (Kunzová and Hejcman 2009). In contrast, under a high natural fertility, winter wheat production was stable and even increased without fertilizer input over 50 yr in Geyic Phaeozem soil (Kunzová and Hejcman 2010). In addition, crop yields in both the 150 yr long-term Rothamsted and 90 yr long-term Dikopshof fertilizations had also been maintained at >1 000 kg ha-1 without fertilizer input (Jenkinson 1991; Schellberg and Huging 1997). These results indicate that BSP is a reliable soil fertility index to maintain crop production. The change of BSP is a comprehensive process of matter and energy conversion, such as atmosphere deposition, nitrogen fixation, mineral weathering and humification, etc. Yield by BSP is an integrated index to express BSP. However, in most long-term experiments, there are no measured yields by BSP under different fertilizations. The decision support system for agro-technology transfer (DSSAT) crop growth model, which integrates the effects of soil, weather, management, genetics, and pests on daily growth, has been widely used to simulate growth, development, and yields of >26 crops growing on a uniform area of land, as well as variations in soil water, carbon, and nitrogen that take place under the cropping system over time (Soler et al. 2007; Thorp et al. 2008; Timsina et al. 2008; Yang et al. 2009; Dzotsi et al. 2010; Liu et al. 2011a; He et al. 2012; Deligios et al. 2013). The model has also predicted the long-term trend in potential yield of spring maize of Northeast China (Yang et al. 2010, 2011; Liu et al. 2012). According to the definition of BSP in this paper, the DAAST model could well simulate crop yields by BSP under local field management and no external fertilizer input for a specific crop season. Therefore, the DSSAT model may effectively resolve the problem of obtaining the yields by BSP under different fertilization treatments. In this study, the DSSAT-CERES-Maize model (ver. 4.0) was employed to simulate the yield by BSP of spring maize and analyze the change characteristics of BSP after long-term fertilizations in the black soil region of northeast China. Our aims were to develop the concept of BSP and then to explore the factors and fertilization practices that could affect the increase of BSP and the productive potential of farmland.
© 2014, CAAS. All rights reserved. Published by Elsevier Ltd.
Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model
an respectively annual increase of 3.1, 4.6, 6.0, and 4.1%. In addition, significant higher average yields by BSP between fertilizations ranked as 1.5NPKM (8 178 kg ha-1)>NPKM (7 245 kg ha-1)≈NPK (7 011 kg ha-1)≈NPKS (6 976 kg ha-1)>the control (3 405 kg ha-1). Over the 20-yr fertilization under NPK, NPKM, 1.5NPKM, and NPKS, four periods could be divided to show the change of maize yields with the BSP: a rapid increase period from 1992 to 1995, with an similar annual increase of 18.8, 19.0, 20.9, and 18.4%, respectively; a yield fluctuation period from 1996 to 2003 with a varied 5 606-8 580 kg ha-1 yield production; a relatively flat yield rising period from 2004 to 2008, with an annual increase from 6.9 to 10.7%; and a yield decrease period from 2009 to 2011, with an annual decrease from 1.4 to 3.1% (Fig. 2). Compared with data between the first three fertilization years (1992 to 1994) and the last three fertilization years (2009 to 2011), the average crop yields by BSP were significantly increased by 62.3, 46.7 and 39.2% (P<0.05) under 1.5NPKM, NPKM, and NPKS, respectively, but not significant under both NPK with a 26.9% increase and the control with a 8.9% decrease (Table 1).
RESULTS Yield evaluation The spring maize yields after different fertilizations (1990-2011) were simulated by the DSSAT model. The results showed that the normalized root mean square error (RMSE) and the index of agreement (d) of spring maize ranged from 9.70 to 13.46% and 0.849 to 0.929, respectively, over the 22 yr long-term fertilizations (Fig. 1).
Trends of yield by basic soil productivity of spring maize Yields of BSP of spring maize were significantly lower under the control than under any fertilization treatments (Fig. 2). Simulated yield by BSP of spring maize under NPK, NPKM, 1.5NPKM, and NPKS showed a similar increasing trend while a decreasing trend under the control over fertilization years. Compared to the year of 1992, after 20-yr of fertilization, maize yields by BSP was increased by 53.4, 78.0, 101.2, and 69.4%, respectively, with
Simulated yield (kg ha-1)
Simulated yield (kg ha-1)
A
B
12 000
C
12 000 y=0.632x+3 187 R2=0.772 n=22
11 000 10 000 9 000 8 000
11 000 10 000 9 000 RMSE=9.70% d=0.906
7 000 6 000 6 000
8 000
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12 000 11 000
y=0.680x+2 124 R2=0.754 n=22
10 000 9 000
8 000
RMSE=13.27% d=0.862
7 000 6 000 6 000
8 000
10 000
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6 000 6 000
E
F
6 000
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9 000
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y=0.536x+4 188 R2=0.521 n=22
3 000
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RMSE=11.41% d=0.901
7 000 6 000 6 000
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y=0.734x+1 180 R2=0.886 n=20
8 000
10 000
12 000
1 000 1 000
2 000
3 000
8 000
10 000
12 000
10 000
y=0.785x+ 366 R2=0.864 8 000 n=108
RMSE=10.87% d=0.929
2 000
RMSE=13.46% d=0.849
7 000
D 11 000
y=0.564x+3 314 R2=0.699 n=22
8 000
12 000 10 000
579
4 000
5 000
6 000
RMSE=13.23% d=0.872
4 000 2 000 2 000
4 000
6 000
8 000
10 000 12 000
Measured yield (kg ha ) -1
Fig. 1 Relationships between measured and simulated yields of spring maize over 22-yr long-term fertilizations (1990-2011). A, NPK treatment. B, NPKM treatment. C, 1.5NPKM treatment. D, NPKS treatment. E, control. F, five treatments (NPK, NPKM, 1.5NPKM, NPKS and control).
© 2014, CAAS. All rights reserved. Published by Elsevier Ltd.
ZHA Yan et al.
580 NPK
NPKM
1.5NPKM
NPKS
CK
10 000 9 000 8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000
NPK Contribution percentage of BSP(%)
Simulated yield by BSP (kg ha-1)
11 000
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010
NPKM
1.5NPKM
NPKS
100 90 80 70 60 50 40
1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 Year
Year
Fig. 2 Simulated yields by basic soil productivity of spring maize under long-term fertilizations (1992-2011).
Fig. 3 Contribution percentage of basic soil productivity under long-term fertilizations (1992-2011).
Table 1 Simulated yield by BSP of spring maize in different periods under different fertilizations1)
available potassium (Fig. 4).
Treatments Control NPK NPKM 1.5NPKM NPKS 1)
Mean yield (kg ha-1 yr-1) (1992-1994) 3 922±1 139 a, x 5 702±1 065 a, x 5 640±1 029 a, x 5 908±1 238 a, x 5 631±996 a, x
Mean yield (kg ha-1 yr-1) (2009-2011) 3 574±743 d, x 7 239±502 c, x 8 273±276 b, y 9 590±176 a, y 7 838±267 bc, y
Data were expressed as means±SE. Letters a, b, c between treatments for the same period within a column indicated a significant difference at P<0.05 level. Letters x, y between periods for the same treatment within a row indicated a significant difference at P<0.05 level.
Contribution percentage of basic soil productivity The contribution percentage of BSP showed a general increase trend but fluctuated with time over the 20-yr fertilizations under all fertilization treatments, except a significant increase under 1.5NPKM (P<0.05) (Fig. 3). The average contribution percentage of BSP between fertilizations ranged from 74.4 to 84.7%, and ranked as 1.5NPKM>NPKM>NPK≈NPKS.
Analysis of correlation between soil nutrient and basic soil productivity The contribution percentage of BSP was significantly correlated with the soil organic matter (SOM), total nitrogen, total phosphorus or available phosphorus (R2=0.08-0.124, n=56-68). In contrast, there are no correlations between the contribution percentage of BSP and available nitrogen, total potassium, or
DISCUSSION Basic soil productivity characterization BSP was a comprehensive index in evaluating soil basic fertility (Gong et al. 2013). Some researchers used the control yield (Zhu et al. 1997b; Xie et al. 2002; Jiang 2011), or yields under no nitrogen application (Meng et al. 2010; Wang et al. 2011), or contribution percentage of BSP as an index to characterize BSP (Xu et al. 2006; Tang and Huang 2008). The contribution percentage of BSP was expressed as the ratio of crop yields under nofertilization versus under fertilization (Zhu et al. 1997a; Huang et al. 2006; Zhang et al. 2009), or the ratio of crop yield under no-fertilization versus under suitable fertilization (Tang and Huang 2008) or versus the average yield among inorganic and organic combination treatments (Huang 2006). Studies have shown that the contribution percentage of BSP showed a fluctuating but constant decreasing trend over 27-yr to the early rice and late rice in yellow paddy soil in southern China (Wang et al. 2010). Meanwhile, the contribution percentage of BSP decreased from 79% in 1988 to 42% in 1996 (after 8 yr) in the black soil region of northeast China (Gao et al. 2009). The reason for such declining was that they used the crop yields under no-fertilization as numerator to calculate the
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100 90
80
80
70
70 y=0.736x+57.94 R2=0.124** n=68
60 50 40 15
25
35
45
55
50
Soil organic matter content (g kg-1) Contribution percentage of BSP (%)
C
B
90
100
100
150
200
250
0.8
Soil available N content (mg kg-1)
Soil total N content (g kg-1)
E
D
1.3
y=9.753x+63.89 60 R2=0.08* 50 n=68 40 1.8 2.3 2.8
F
G
100
90
90
80
80
70
70
60
y=0.075x+73.86 R2=0.10* n=56
50 40
0
50
100
150
200 0.3
Soil available P content (mg kg-1)
60
y=11.88x+68.43 R2=0.10* n=56 0.8
1.3
1.8
2.3
Soil total P content (g kg-1)
50 0
100
200
300
Soil available K content (g kg-1)
5
15
Effects of different fertilizations on basic soil productivity The trends of BSP varied from each other among
35
Soil total K content (g kg-1)
Fig. 4 Relationships between contribution percentage of basic soil productivity and soil nutrient contents. differences at P<0.05 and P<0.01 levels, respectively.
contribution percentage of BSP. The control yield gradually decreased with the continuous depletion of soil nutrient (Wang et al. 2000; Sui et al. 2005; Liu et al. 2011b). In this study, the control yield also decreased 8.9% over 20 yr (Fig. 2). Therefore, using the control yield to calculate the contribution percentage of BSP under various fertilizations could not accurately reflect the changes of BSP. Our study indicated that the application of the calibrated DSSAT model could well simulate yield by BSP of spring maize and resolved the problems that were derived from the measured control yield to characterize BSP. In this study, the contribution percentage of BSP was expressed as the ratio of crop yield by BSP under different fertilizations vs. measured yield in corresponding fertilization. The contribution percentage of BSP increased under NPK, NPKM, 1.5NPKM and NPKS treatments, and ranked as 1.5NPKM>NPKM>NPK≈NPKS (Fig. 3).
25
*
and
**
40
Contribution percentage of BSP (%)
A
100
581 Contribution percentage of BSP (%)
Contribution percentage of BSP (%)
Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model
indicate significant
different fertilizations compared with their 20-yr data. The result showed that after 20-yr fertilization, simulated yields by BSP of spring maize significantly increased under NPKM, 1.5NPKM, and NPKS, while decreased 8.9% under the control (Fig. 2 and Table 1). The simulated average yields by BSP of spring maize (1992 to 2011) under NKP, NPKM, 1.5NPKM and NPKS were significantly higher than that of the control and were 2.06, 2.13, 2.40 and 2.05 times that of the control, respectively (Fig. 2). At the end of experiment (2009 to 2011), the yield of BSP of spring maize under NPK, NPKM, 1.5NPKM and NPKS increased 64.1, 88.9, 118.1 and 80.3% compared with that of the control, respectively. The yield of BSP under NPKM, 1.5NPKM and NPKS were significantly higher than that of the NPK and increased 14.3, 32.5 and 8.3% compared with that of NPK, respectively (Table 1). These results indicated that the manure or straw combined with chemical fertilizer application could more effectively increase the yields by BSP than that of single chemical fertilizer as a whole. Our results corresponded with the study on BSP of Shajiang black soil over 22-yr long-term fertilization (Cao et al. 2008).
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of the experiment (in 1990), application of farmyard manure plus inorganic fertilizer significantly increased the concentration of total phosphorus and available phosphorus with 72-148% and 10.7-21.3 times, respectively, in 2005 (Fig. 5-C and D), and increased the concentration of total nitrogen with 10-21% in 2008 (Fig. 5-B). Thus, the increase of soil N and P caused by inorganic fertilizer combined with organic fertilizer (Gao et al. 2011; Alijani et al. 2012) were main reasons for BSP increasing. The BSP change is a complicated result of the soil physical and chemical properties, microbiology, as well as management practices. Except the effects of soil nutrient on BSP, improvement of the soil physical properties (Zhao and Zhou 2011), and structure and diversity of soil microbial population (Kong et al. 2008; Wang et al. 2008; Wei et al. 2008; Sradnicka et al. 2013) caused by increasing the application of manure or straw combined with chemical fertilizer were other main reasons that lead to the increase of BSP.
Effects of soil nutrient on basic soil productivity The reasons of BSP differences mainly lie in soil nutrient caused by fertilization management. The relationship between soil nutrient and BSP proved that soil nutrient, especially SOM was the key factor that affects BSP in black soil (Fig. 4), which corroborated with observation from Shajiang black soil in north Anhui Province (Zhang et al. 2005). Soil with high SOM had a high yield of BSP (e.g., 1.5NPKM, NPKM), whereas soil with a relative low SOM had a low one (e.g., the control) (Fig. 2 and Fig. 5-A). SOM showed a significant linear increasing trend under NPKM and 1.5NPKM over 20-yr long-term fertilization. In 2008, the SOM increased 21.7, 65.0, 65.0 and 25.9% under NPK, NPKM, 1.5NPKM and NPKS treatments, respectively, compared with the control. It has been believed that SOM plays vital roles in soil structure, water holding capacity, nutrient transformations and cycling (Galantini and Rosell 2006; Bhattacharyya et al. 2011; Zhao and Zhou 2011), and SOM could thus improve BSP. Total nitrogen, total phosphorus and available phosphorus also play important role in BSP increasing in black soil. Compared to the values at the beginning NPK
SOM (g kg-1)
45
NPKM
3
35 30 25 20
C
1.7 1.2 0.7 0.2
1990 1992 1994 1996 1998 2000 2002 2004 Year
B
2.5 2 1.5 1 0.5 0
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 Available phosphorus (mg kg-1)
Total phosphorus (g kg-1)
2.2
NPKS
1.5NPKM
A
40
15
In this study, applying the calibrated DSSAT model well simulated yields by BSP of spring maize and
Total nitrogen (g kg-1)
50
CONCLUSION
1990 1992 1994 1996 1998 2000 2002 2004 2006 2008
D
250 200 150 100 50 0
1990
1992
1994
1996
1998
2000
2002
2004
Year
Fig. 5 Dynamic changing of SOM (A), total nitrogen (B), total phosphorus (C) and available phosphorus (D) under different fertilizations in black soil.
© 2014, CAAS. All rights reserved. Published by Elsevier Ltd.
Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model
resolved the problems that using the control yield to characterize BSP. Simulated yields by BSP of spring maize under NPKM, 1.5NPKM, and NPKS showed a significant increasing trend, but not significant under both NPK with a 26.9% increase and the control with a 8.9% decrease over 20-yr fertilization. The average contribution percentage of BSP between fertilizations ranged 74.4 to 84.7%, and ranked as 1.5NPKM>NPKM>NPK≈NPKS. This study showed that SOM was the key factor in various nutrient factors that affecting BSP in black soil, and total nitrogen, total phosphorus and available phosphorus also played important role in BSP increasing. Compared with the inorganic fertilizer, the balanced mineral application combined with manure or straw application (e.g., NPKM and NPKS) not only increased the concentrations of soil nutrients, but also
583
improved the soil physical properties, and structure and diversity of soil microbial population, which resulted in an increase of BSP.
MATERIALS AND METHODS General situation of study region The long-term experiment was started in the 1990’s in the experimental plot of Jilin Academy of Agricultural Sciences in Gongzhuling City, Jilin Province, China (124°48´34´´E and 43°30´23´´N, 220 m a.s.l). The site is in a flat area with an annual temperature of 4-5°C, an annual rainfall of 450-600 mm (70% in June-August), a frost-free period of 125-140 d, an effective accumulated temperature of 2 6003 000°C, and an annual amount of evaporation of 1 2001 600 mm. The soil was a black soil. The basic physiochemical characteristics before treatments are listed in Table 2.
Table 2 Chemical and physical properties of soil before treatments Soil layer (cm) 0-20 21-40 41-64 65-89 90-150
Organic matter (g kg-1) 22.8 15.2 7.1 6.8 6.3
Total N (mg g-1) 1.4 1.3 0.57 0.5 0.38
Total P (mg g-1) 0.61 0.59 0.44 0.43 0.40
Total K (mg g-1) 18.42 18.58 18.33 18.42 18.50
Available N (mg kg-1) 114 98 41 39 37
Olsen-P (mg kg-1) 11.79 6.77 3.14 1.83 1.79
Available K (mg kg-1) 158.33 150.83 154.17 157.50 155.83
pH 7.6 7.5 7.5 7.6 7.6
Bulk density (g cm-3) 1.19 1.27 1.33 1.35 1.39
Design of field experiment
Table 3 Rates of fertilizers application in different treatments each year (kg ha-1)
Five treatments or fertilizations were included in the experiment: (1) Control (no fertilizers); (2) NPK (nitrogen, phosphorus and potassium fertilizer); (3) NPKM (NPK plus organic manure); (4) l.5NPKM (1.5 times of NPKM); (5) NPKS (NPK plus 7 500 kg ha-1 straw). Spring maize with one crop per year was used as the experimental crop. The ratio of organic nitrogen to inorganic nitrogen was 7:3, with N:P2O5:K2O=l:0.5:0.5. A list of the fertilizer treatments used is given in Table 3. Phosphorus (calcium superphosphate, 12.5% P 2 O 5 ), potassium (potassium sulfate, 50% K2O) and one third of nitrogen fertilizer (urea, 45%N) were applied as the base fertilizer with sowing and the remaining two thirds of nitrogen fertilizer was topdressed at a depth of 10 cm below the topsoil before jointing. Organic manure (pig manure or cow manure) was used as the base manure and chopped (3-5 cm) straw was spread in furrows after topdressing in mid-July.
Treatment
Model selection and simulation method In this study, one of the DSSAT cropping system models, CERES-Maize (ver. 4.0) was selected for the yield simulation. The concrete steps of BSP simulation were
Control NPK NPKM 1.5NPKM NPKS
Inorganic fertilizer K2O N P2O5 0 0 0 165 82.5 82.5 50 82.5 82.5 75 123.8 123.8 112 82.5 82.5
Manure N 0 0 115 173 0
Straw N 0 0 0 0 53
Total amount of applied N 0 165 165 248 165
as follows: (1) the parameters of soil, weather, field management, and crop variety required for the model were inputted; (2) according to the actual measured yield of spring maize under NPK in the long-term experiment, the parameter of crop variety was calibrated using the “trial and error” method until the model simulation result was in accord with the actual measured value; (3) the yields under other fertilizer treatments (e.g., NPKM, 1.5NPMK and NPKS) were simulated by means of the calibrated parameter and then were tested for consistency with the actual measured values; (4) the yield by BSP for the year was simulated by setting no fertilization in the appropriate season only and was obtained using the same set of parameters, with a fixed fertilization in other years. For example, if the amount of a fertilizer in the n year is set to be zero only, with the fixed parameters including
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fertilization in the (n-1) year, the yield by BSP in the n year would be obtained when running yield simulation.
Model input date The simulation process of the DSSAT model is mainly performed via the four modules of weather, soil, genetic characteristics of crop, and field management. Meteorological data were from the database of the National Weather Service, including the daily weather data of Gongzhuling region, Jilin Province from 1990 to 2011. The daily weather data include daily solar radiation quantity (SRAD), daily maximum temperature (TMAX), daily minimum temperature (TMIN), and daily rainfall (RAIN). In the case of difficulty to obtain the daily solar radiation quantity (Q), based on the daily sunshine hours and relevant astronomical parameters, the Q value was estimated by means of the empirical formula (1):
Q=Q0(a+b S ) S0
(1)
Where, Q0 is astronomical radiation, S is actual measured sunshine hours, S0 is available sunshine hours, a and b are functions of S and S0 (Rietveld 1978). The soil profile input data in this study were directly obtained from the soil sample analysis in the 0-20 cm (the National Soil Fertility and Fertilizer Effects Longterm Monitoring Experiment Station 1998). The layered soil parameters include organic carbon, soil texture, bulk density, lower limit, drained upper limit, saturated soil water concentrations, cation exchange capacity, etc. The field management module include initial soil water concentration, crop cultivar, plant density, sowing date, harvest date, fertilization, etc. According to the actual measured yields of spring maize under NPK from 1990 to 2011, the parameter of crop variety was calibrated using the “trial and error” method (Li et al. 2001) until the model simulation was in accord with the actual measured value. After several calibrations, the parameter of crop variety of spring maize was finally determined (Table 4). The yields under NPKM, 1.5NPKM, NPKS, and the control were simulated by means of the calibrated parameter and then were tested for consistency with the actual measured values.
Calibration method In this study, the normalized root mean square error (RMSE) and index of agreement (d) were used to measure the relative degree of difference between the simulated value and actual measured value and to test the goodness of fit between them, respectively. The calculations are shown in the equations (2) and (3): n
RMSE=
∑ (Si - Ri )2 i =1
n
×
100 R
(2)
Table 4 Genetic parameters of spring maize Parameters Values
P1 260-280
P2 0.70-0.75
P5 850-950
G2 650-850
G3 PHINT 8.0-12.0 38.9-42.9
P1, degree days (base 8°C) from the emergence to end of juvenile phase; P2, photoperiod sensitivity coefficient (0-1.0); P5, degree days (base 8°C) from silking to physiological maturity; G2, potential kernel number; G3, potential kernel growth rate, mg/(kernel d); PHINT, degree days required for a leaf tip to emerge (phyllochron interval) (°C d).
n 2 (Si - Ri ) i∑ =1 d=1- n ∑ (|S´|-|R´|) 2 i =1 i i
(3)
Where, Ri is the actual measured value, Si is the simulated value, R is the average actual measured value, Si´=Si -R, Ri´=Ri -R, n is the number of simulated value samples. It is generally considered that, RMSE<10% is excellent; 10%
Soil sampling and analyses Soil samples were collected from the topsoil (0-20 cm) every year for all plots after harvest. Soil organic carbon concentration was measured by vitriol acid-potassium dichromate oxidation (Walkley and Black 1934). Total nitrogen and available nitrogen were determined by the method introduced by Lu (1999). Total phosphorus was measured by Murphy and Riley (1962), and available phosphorus (Olsen-P) by the Olsen-P method (Olsen et al. 1954). Total potassium was determined by Kundsen et al. (1982), and available potassium by Lu (1999). Soil bulk density was measured with iron ring (Lu 1999).
Data analysis The contribution percentage of BSP was calculated using the following equation: Contribution percentage of BSP (%)=Yield by BSP / Measured yield in corresponding fertilization×100 The Microsoft Excel 2007 software was applied for data processing and diagramming while the SAS 9.1 software was used for statistical analysis. Differences between yields by BSP between treatments or time periods were analyzed using the method of least significant difference (LSD) at P<0.05 or 0.01.
Acknowledgements We acknowledge all colleagues for their great efforts on the long-term experiments in black soil in Gongzhuling. The study was supported by the National 973 Program of China (2011CB100501), the National 863 Program of China
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Basic Soil Productivity of Spring Maize in Black Soil Under Long-Term Fertilization Based on DSSAT Model
(2013AA102901), the Special Fund for Agro-Scientific Research in the Public Interest, China (201203077), and the Science and Technology Project for Grain Production, China (2011BAD16B15).
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