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Frontiers of Agricultural Science and Engineering

ISSN 2095-7505

ISSN 2095-977X(Online)

CN 10-1204/S

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Front. Agr. Sci. Eng.    2014, Vol. 1 Issue (3) : 223-235    https://doi.org/10.15302/J-FASE-2014025
RESEARCH ARTICLE
Modeling of the overwintering distribution of Puccinia striiformis f. sp. tritici based on meteorological data from 2001 to 2012 in China
Xiaojing WANG1,Zhanhong MA1,Yuying JIANG2,Shouding SHI3,Wancai LIU2,Juan ZENG2,Zhiwei ZHAO1,Haiguang WANG1,*()
1. College of Agriculture and Biotechnology, China Agricultural University, Beijing 100193, China
2. National Agro-Tech Extension and Service Center, Ministry of Agriculture, Beijing 100125, China
3. China Animal Agriculture Association, Beijing 100028, China
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Abstract

Wheat stripe rust caused by Puccinia striiformis f. sp. tritici occurs widely in China and seriously affects wheat production. Global warming could profoundly impact the incidence and prevalence of low-temperature diseases such as stripe rust. Studies on the effects of temperature on the distribution of overwintering stripe rust could help us understand the incidence and prevalence of the disease and could also provide support for monitoring, forecasting and developing control strategies. An exponential model and a spherical model of the ordinary Kriging method in the ArcGIS platform were used to predict the overwintering regions of stripe rust based on the probability that the average temperature of the coldest month from December to February was higher than -6 or -7°C from 2001 to 2012. The results showed that the areas with a probability between 70% and 90% were transition regions for the overwintering of stripe rust. Based on annual mean temperature of the coldest month from December to February for 2001 to 2012, overwintering distribution of stripe rust was likewise evaluated. The boundary for overwintering of stripe rust was consistent with the areas where the probability was predicted to be 70% to 90% for the overwintering distribution of stripe rust, but the boundary was shifted northward toward Beijing in North China. Some areas in Xinjiang, including Akto, Pishan, Hotan and Yutian, were also predicted to be suitable for the overwintering of stripe rust.
Keywords stripe rust      wheat      overwintering      geospatial distribution      geographic information system      climate change     
Corresponding Author(s): Haiguang WANG   
Online First Date: 12 December 2014    Issue Date: 27 January 2015
 Cite this article:   
Xiaojing WANG,Zhanhong MA,Yuying JIANG, et al. Modeling of the overwintering distribution of Puccinia striiformis f. sp. tritici based on meteorological data from 2001 to 2012 in China[J]. Front. Agr. Sci. Eng. , 2014, 1(3): 223-235.
 URL:  
https://academic.hep.com.cn/fase/EN/10.15302/J-FASE-2014025
https://academic.hep.com.cn/fase/EN/Y2014/V1/I3/223
Fig.1  Meteorological stations used for the modeling of overwintering distribution of Puccinia striiformis f. sp. tritici in China
Fig.2  Growing areas of winter wheat and selected historical overwintering locations of stripe rust in China
Power P>-6°C P>-7°C
R Mean RMS R Mean RMS
1 0.9291 0.0059 0.1568 0.9164 0.0071 0.1612
2 0.9395 0.0066 0.1564 0.9276 0.0082 0.1616
3 0.9471 0.0061 0.1604 0.9350 0.0082 0.1661
4 0.9525 0.0053 0.1654 0.9400 0.0078 0.1713
Tab.1  Comparison of evaluation parameters after interpolation with different powers of IDW
OP P>-6°C P>-7°C
R Mean RMS R Mean RMS
1 0.6042 ???0.00002 0.2798 0.6177 ???0.00003 0.2802
2 0.6141 -0.0001 0.2716 0.6247 -0.0001 0.2697
3 0.8357 -0.0001 0.2323 0.8271 -0.0001 0.2347
4 0.8226 ??0.0002 0.2196 0.8224 ??0.0002 0.2196
Tab.2  Comparison of evaluation parameters after interpolation with different orders of GPI
OP Kernel function P>-6°C P>-7°C
R Mean RMS R Mean RMS
1 Ex 0.9470 ??0.0009 0.1496 0.9387 ??0.0019 0.1531
PO5 0.8978 -0.0015 0.1639 0.8854 -0.0029 0.1668
G 0.9210 ??0.0001 0.1552 0.9069 -0.0004 0.1588
Ep 0.8701 -0.0041 0.1770 0.8584 -0.0038 0.1828
Q 0.8791 ??0.0019 0.1727 0.8696 -0.0035 0.1755
Constant 0.9394 -0.0004 0.1487 0.9302 -0.0007 0.1528
2 Ex 0.9383 ??0.0002 0.1563 0.9472 ??0.0009 0.1577
PO5 0.9288 ??0.0004 0.1601 0.9239 ??0.0023 0.1647
G 0.9502 -0.0007 0.1549 0.9561 -0.0004 0.1562
Ep 0.9193 ??0.0004 0.1665 0.9079 ??0.0013 0.1702
Q 0.9374 ??0.0173 0.4776 0.9161 ??0.0014 0.1644
Constant 0.9498 ??0.0001 0.1668 0.9454 ??0.0011 0.1624
3 Ex 0.9127 ??0.0001 0.1683 0.8818 -0.0024 0.1784
PO5 0.8951 -0.0010 0.1788 0.8965 ??0.0005 0.1791
G 0.9074 ??0.0001 0.1715 0.9033 ??0.0004 0.1719
Ep 0.8853 -0.0010 0.1841 0.8856 -0.0007 0.1842
Q 0.8966 -0.0002 0.1803 0.8926 ??0.0001 0.1801
Constant 0.8655 -0.0025 0.1919 0.8615 -0.0025 0.1932
Tab.3  Comparison of evaluation parameters after interpolation with different orders and functions of LPI
Function P>-6°C P>-7°C
R Mean RMS R Mean RMS
CRS 0.9705 0.0012 0.1727 0.9562 0.0031 0.1780
SWT 0.9769 0.0011 0.1593 0.9614 0.0026 0.1675
MF 0.9663 0.0015 0.1515 0.9521 0.0024 0.1578
IM 0.9745 0.0007 0.1775 0.9595 0.0030 01852
TPS 0.9825 0.0003 0.1627 0.9695 0.0022 0.1720
Tab.4  Comparison of evaluation parameters after interpolation with different functions of RBF
Model P>-6°C P>-7°C
R MS RMS ASE RMSS C/(%) R MS RMS ASE RMSS C/(%)
C 0.9639 ??0.0049 0.1492 0.1252 0.1987 0.70 0.9534 ??0.0056 0.1538 0.1320 1.1604 1.29
Sp 0.9646 ??0.0049 0.1496 0.1220 1.2398 0.45 0.9541 ??0.0059 0.1542 0.1294 1.1888 1.05
TSp 0.9650 ??0.0050 0.1498 0.1212 1.2500 0.39 0.9545 ??0.0060 0.1544 0.1285 1.1998 0.94
PSp 0.9670 ??0.0049 0.1509 0.1150 1.3412 0 0.9545 ??0.0064 0.1549 0.1250 1.2419 6.94
E 0.9666 ??0.0047 0.1509 0.1407 1.0954 0 0.9579 ??0.0071 0.1572 0.1368 1.1679 0
G 0.9099 -0.0045 0.1571 0.1891 0.8279 9.72 0.9014 -0.0062 0.1607 0.1912 0.8372 10.24
RQ 0.9378 -0.0104 0.1478 0.1388 1.0609 5.14 0.9266 -0.0142 0.1526 0.1451 1.0469 5.80
HF 0.9071 -0.0025 0.1594 0.2065 0.7692 13.70 0.8986 -0.0040 0.1627 0.2064 0.7855 14.14
K-B 0.9232 -0.0062 0.1520 0.1715 0.8832 6.92 0.9071 -0.0077 0.1587 0.1825 0.8665 8.70
J-B 0.9082 -0.0040 0.1576 0.1933 0.8124 10.36 0.9005 -0.0054 0.1613 0.1970 0.8160 11.03
St 0.9289 -0.0042 0.1502 0.1707 0.8752 6.84 0.9195 -0.0062 0.1550 0.1760 0.8755 7.64
Tab.5  Comparison of evaluation parameters after interpolation with different Kriging models
Method P>-6°C P>-7°C
R Mean RMS R Mean RMS
IDW 0.9291 ??0.0059 0.1568 0.9164 ??0.0071 0.1612
GPI 0.8357 -0.0001 0.2323 0.8271 -0.0001 0.2347
LPI 0.9394 -0.0004 0.1487 0.9302 -0.0007 0.1528
RBF 0.9663 ??0.0015 0.1515 0.9521 ??0.0024 0.1578
OK 0.9666 -0.0017 0.1509 0.9579 ??0.0026 0.1572
Tab.6  Comparison of evaluation parameters after five interpolation methods
Fig.3  Overwintering areas of stripe rust based on the annual probability of the critical temperature (exponential model). (a) The critical temperature was -6°C; (b) the critical temperature was -7°C.
Fig.4  Overwintering areas of stripe rust based on the annual probability of the critical temperature (spherical model). (a) The critical temperature was -6°C; (b) the critical temperature was -7°C.
Annual probability/% Exponential model Spherical model
P>-6°C P>-7°C P>-6°C P>-7°C
0–5 1 0 2 0
5–10 2 1 1 1
10–15 2 2 2 2
15–20 1 0 1 0
20–30 1 3 1 3
30–40 2 1 1 2
40–50 2 3 3 1
50–60 0 1 0 1
60–70 2 0 2 1
70–80 6 6 5 6
80–90 7 6 8 7
90–100 26 29 26 27
Total 52 52 52 52
Tab.7  Statistics of selected historical oversummering locations in the predicted overwintering regions based on the annual probability using an exponential model and a spherical model
Fig.5  Elevation interpolation using ordinary Kriging based on the standard regression equation. (a) Exponential model; (b) spherical model.
Fig.6  Elevation interpolation using ordinary Kriging based on the self-built regression equation. (a) Exponential model; (b) spherical model.
Fig.7  Local amplification (a) of Fig. 6(a). The counties circled by pink line were on the boundary for Pst overwintering.
Fig.8  Local amplification (b) of Fig. 6(a). The counties circled by pink line were on the boundary for Pst overwintering.
Fig.9  Local amplification (c) in Fig. 6(a). The counties circled by pink line were on the boundary for Pst overwintering.
Fig.10  Local amplification of Xinjiang in Fig. 6(a)
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