a ’W ASR occurrence: Adapted: Anti-ASR Consortium (2016) · 2016. 11. 2. · CALEND TEST PREC a b...
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Please, visit: http://www.leb.esalq.usp.br/agmfacil/ Acknowledgements Supported by Performance of Asian soybean rust warning system in different Brazilian regions Gustavo C. Beruski 1* , Paulo C. Sentelhas 1 , Emerson M. Del Ponte 2 , Gil M.S. Câmara 3 , Ivan P. Araújo 4 , André B. Pereira 5 , João Victor Mattos 5 , Jéssica Jaguela 5 , Bruno Philipovsky 5 1 Department of Biosystems Engineering, ESALQ/University of São Paulo, Brazil; 2 Department of Plant Pathology, Federal University of Viçosa, Brazil; 3 Department of Plant Production, ESALQ/University of São Paulo, Brazil; 4 Department of Plant Pathology, Mato Grosso Foundation, Brazil; 5 Department of Soil Science and Agricultural Engineering, Ponta Grossa State University, Brazil. * Corresponding author e-mail: [email protected] / [email protected] INTRODUCTION MATERIAL AND METHODS RESULTS Soybean is the most important agricultural crop in Brazil. This crop is cultivated in large areas all over the country (Figure 1a). After 2001/02 growing season the Asian soybean rust (ASR) became a problem in all producing regions of the country, which remains till the present (Figure 1b). ASR is controlled by sequential applications of fungicides following a calendar-based system (Figure 1b), approach that considers only aspects of related to the crop, such as phenological phase, disregarding the influence of local weather conditions on the disease progress. Part of this problem is mainly related to the lack of weather data availability, limiting the use of disease warning systems based on these data. Based on that, the aim of this study was to evaluate the performance of Asian warning system, based on rainfall data in different Brazilian regions. Figure 1 – a) Soybean yield in Brazil in the crop seasons of 2014/15; b) ASR occurrence reported in soybean in the crop seasons of 2014/15; c) ASR management in Brazil and its main symptoms. Subtitles: Only municipalities that produce more than 5,000 tons. 5,000 – 50,000 50,001 – 100,000 100,001 – 400,000 400,001 – 1,926,930 State border Adapted CONAB/IBGE (2016) a ASR case recorded in voluntary soybean 1 2-5 6-10 11-20 >20 ASR occurrence: Adapted: Anti-ASR Consortium (2016) c The experimental data was obtained from three trials conducted in different Brazilian regions: All experiments were conducted in a row spacing of 0.45 m and a plant population of 270,000 plants ha -1 . The experiments were set in a randomized block design with four repetitions (Figure 2a,b,c). At each experimental site, an automatic weather station was installed close to the crop in order to monitor weather conditions (Figure 2d). a) Campo Verde – Mato Grosso State: b) Piracicaba – Sao Paulo State: c) Ponta Grossa – Parana State: Köppen climate classification – Aw Coordinates geographic – 15°24’S, 55°50’W Altitude – 689 meters Sowing date - 12 th December 2014 Köppen climate classification – Cwa; Coordinates geographic – 22°42’S, 47°30’W Altitude – 546 meters Sowing date - 12 th December 2014 Köppen climate classification – Cfb; Coordinates geographic – 25°05’S, 50°09’W Altitude – 969 meters Sowing date - 18 th December 2014 Figure 2 – a. Campo Verde experiment; b. Piracicaba experiment; c. Ponta Grossa experiment; d. Automatic weather station (Campbell Scientific Inc.). a b c d Treatments: - TEST - unsprayed check treatment; - CALEND - calendar-based sprays with a 14-day interval from R1 stage; - PREC system, with threshold for 50% severity cut-off. (Fungicide: Azoxistrobina + Benzovindiflupir, 150 g ha -1 ). Disease assessment was constantly made. At the end of the experiment the yield was evaluated. For all locations, it was observed that the TEST treatment exhibit higher disease levels (Figure 3a,b; Table 1). Analyzing two different approaches for controlling ASR, it was observed different sprays timing (Table 1), probably due to different meteorological conditions (Figure 4a, b, c, d, e, f). In all locations the disease warning system based on rainfall data proved to have a better performance in relation to the Calendar. In in Piracicaba and Ponta Grossa the system allowed to reduce the number of sprays and to keep the disease severity al least at the same level of the Calendar system. However, in Campo Verde the warning system was not effective for controlling ASR (Figure 5), which was mainly caused by the high disease pressure in this location during the experiment. Based on these results, we concluded that rainfall disease warning system for Asian soybean rust is a promising approach to rationalize sprays in this crop. CALEND TEST PREC a b Figure 3 – ASR severity assessment in Piracicaba (a) and Ponta Grossa (b), Brazil, using different treatments to control ASR during 2014/15 crop seasons. Treatments Spray numbers Final Severity Defoliation Sao Paulo State TEST 0 100.0 a - CALEND 5 44..0 b - PREC 3 51.5 b - Parana State TEST 0 71.3 a - CALEND 4 48.3 b - PREC 3 15.8 c - Mato Grosso State TEST 0 - 100.0 a CALEND 3 - 100.0 a PREC 5 - 68.7 b Table 1 – Number of sprays, final severity and defoliation obtained at three experimental areas, using different methodologies to control ASR. Tukey test (α = 0.05). Figure 4 – Weather regime at different field experiments: a) Rainfall; b) Mean air temperature and leaf wetness duration – Campo Verde; c) Rainfall; d) mean air temperature and leaf wetness duration – Piracicaba; e) Rainfall; f) mean air temperature and leaf wetness duration – Ponta Grossa. Brazil. 0 20 40 60 80 100 120 6/1 13/1 20/1 27/1 3/2 10/2 17/2 24/2 3/3 10/3 17/3 24/3 Rainfall (mm day-1) 0 5 10 15 20 25 0.0 3.0 6.0 9.0 12.0 15.0 18.0 21.0 24.0 27.0 30.0 6/1 13/1 20/1 27/1 3/2 10/2 17/2 24/2 3/3 10/3 17/3 24/3 Daily LWD (hour day -1 ) Mean air temperature (°C) 0 20 40 60 80 100 120 1/1 8/1 15/1 22/1 29/1 5/2 12/2 19/2 26/2 5/3 12/3 19/3 26/3 Rainfall (mm day -1 ) 0 5 10 15 20 25 0.0 3.0 6.0 9.0 12.0 15.0 18.0 21.0 24.0 27.0 30.0 1/1 8/1 15/1 22/1 29/1 5/2 12/2 19/2 26/2 5/3 12/3 19/3 26/3 Daily LWD (hour day -1 ) Mean air temperature (°C) 0 20 40 60 80 100 120 11/1 18/1 25/1 1/2 8/2 15/2 22/2 1/3 8/3 15/3 22/3 29/3 Rainfall (mm day -1 ) 0 5 10 15 20 25 0.0 3.0 6.0 9.0 12.0 15.0 18.0 21.0 24.0 27.0 30.0 11/1 18/1 25/1 1/2 8/2 15/2 22/2 1/3 8/3 15/3 22/3 29/3 Daily LWD (hour day -1 ) Mean air temperature (°C) b a d c f e 351.8* 61.9 120.2 2724.5 3541.7 3369.2 1212.9 2413.9 3003.7 0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000 TEST CALEND PREC TEST CALEND PREC TEST CALEND PREC CAMPO VERDE PIRACICABA PONTA GROSSA Yield (kg ha -1 ) * - Second columns for each treatments correspond the profitability of each field experiment. To determine the profitability was adopted a cost of 150 kg ha-1 for each spraying. † - Fungicide sprays required to control ASR. 0 † 3 5 0 5 3 0 4 3 Figure 5 – Yield and profitability obtained in the three experimental areas, using different methodologies for control de ASR. b
Transcript of a ’W ASR occurrence: Adapted: Anti-ASR Consortium (2016) · 2016. 11. 2. · CALEND TEST PREC a b...
Please, visit:
http://www.leb.esalq.usp.br/agmfacil/ Acknowledgements Supported by
Performance of Asian soybean rust warning system in different Brazilian regions
Gustavo C. Beruski1*, Paulo C. Sentelhas1, Emerson M. Del Ponte2, Gil M.S. Câmara3, Ivan P. Araújo4, André B. Pereira5, João
Victor Mattos5, Jéssica Jaguela5, Bruno Philipovsky5
1Department of Biosystems Engineering, ESALQ/University of São Paulo, Brazil; 2Department of Plant Pathology, Federal University of Viçosa, Brazil; 3Department of Plant Production, ESALQ/University
of São Paulo, Brazil; 4Department of Plant Pathology, Mato Grosso Foundation, Brazil; 5Department of Soil Science and Agricultural Engineering, Ponta Grossa State University, Brazil.
* Corresponding author e-mail: [email protected] / [email protected]
INTRODUCTION MATERIAL AND METHODS
RESULTS
Soybean is the most important agricultural crop in Brazil. This crop is
cultivated in large areas all over the country (Figure 1a). After 2001/02
growing season the Asian soybean rust (ASR) became a problem in all
producing regions of the country, which remains till the present (Figure 1b).
ASR is controlled by sequential applications of fungicides following a
calendar-based system (Figure 1b), approach that considers only aspects of
related to the crop, such as phenological phase, disregarding the influence of
local weather conditions on the disease progress. Part of this problem is
mainly related to the lack of weather data availability, limiting the use of
disease warning systems based on these data.
Based on that, the aim of this study was to evaluate the performance of
Asian warning system, based on rainfall data in different Brazilian regions.
Figure 1 – a) Soybean yield in Brazil in the crop seasons of 2014/15; b) ASR occurrence reported in soybean in
the crop seasons of 2014/15; c) ASR management in Brazil and its main symptoms.
Subtitles:Only municipalities that produce more
than 5,000 tons.
5,000 – 50,00050,001 – 100,000100,001 – 400,000400,001 – 1,926,930State border
Adapted CONAB/IBGE (2016)
a ASR case recorded in voluntary soybean
1 2-5 6-10 11-20 >20ASR occurrence:
Adapted: Anti-ASR Consortium (2016)
c
The experimental data was obtained from three trials
conducted in different Brazilian regions:
All experiments were conducted in a row spacing of 0.45 m
and a plant population of 270,000 plants ha-1. The
experiments were set in a randomized block design with four
repetitions (Figure 2a,b,c). At each experimental site, an
automatic weather station was installed close to the crop in
order to monitor weather conditions (Figure 2d).
a) Campo Verde – Mato Grosso State:
b) Piracicaba – Sao Paulo State:
c) Ponta Grossa – Parana State:
Köppen climate classification – Aw
Coordinates geographic – 15°24’S, 55°50’W
Altitude – 689 meters
Sowing date - 12th December 2014
Köppen climate classification – Cwa;
Coordinates geographic – 22°42’S, 47°30’W
Altitude – 546 meters
Sowing date - 12th December 2014
Köppen climate classification – Cfb;
Coordinates geographic – 25°05’S, 50°09’W
Altitude – 969 meters
Sowing date - 18th December 2014
Figure 2 – a. Campo Verde experiment; b. Piracicaba experiment; c. Ponta Grossa
experiment; d. Automatic weather station (Campbell Scientific Inc.).
a b c d
Treatments:
- TEST - unsprayed check treatment;
- CALEND - calendar-based sprays
with a 14-day interval from R1 stage;
- PREC system, with threshold for
50% severity cut-off. (Fungicide: Azoxistrobina +
Benzovindiflupir, 150 g ha-1).
Disease assessment was constantly made. At the end of the
experiment the yield was evaluated.
For all locations, it was observed that the TEST treatment exhibit
higher disease levels (Figure 3a,b; Table 1).
Analyzing two different approaches for controlling ASR, it was
observed different sprays timing (Table 1), probably due to
different meteorological conditions (Figure 4a, b, c, d, e, f).
In all locations the disease warning system based on rainfall data
proved to have a better performance in relation to the Calendar. In
in Piracicaba and Ponta Grossa the system allowed to reduce the
number of sprays and to keep the disease severity al least at the
same level of the Calendar system. However, in Campo Verde the
warning system was not effective for controlling ASR (Figure 5),
which was mainly caused by the high disease pressure in this
location during the experiment. Based on these results, we
concluded that rainfall disease warning system for Asian soybean
rust is a promising approach to rationalize sprays in this crop.
CALEND TEST PREC a b
Figure 3 – ASR severity assessment in Piracicaba (a) and Ponta Grossa (b), Brazil, using different
treatments to control ASR during 2014/15 crop seasons.
Treatments Spray numbers Final Severity Defoliation
Sao Paulo State
TEST 0 100.0 a -
CALEND 5 44..0 b -
PREC 3 51.5 b -
Parana State
TEST 0 71.3 a -
CALEND 4 48.3 b -
PREC 3 15.8 c -
Mato Grosso State
TEST 0 - 100.0 a
CALEND 3 - 100.0 a
PREC 5 - 68.7 b
Table 1 – Number of sprays, final severity and defoliation obtained at three experimental areas, using
different methodologies to control ASR. Tukey test (α = 0.05).
Figure 4 – Weather regime at different field experiments: a) Rainfall; b) Mean air temperature and leaf
wetness duration – Campo Verde; c) Rainfall; d) mean air temperature and leaf wetness duration –
Piracicaba; e) Rainfall; f) mean air temperature and leaf wetness duration – Ponta Grossa. Brazil.
0
20
40
60
80
100
120
6/1 13/1 20/1 27/1 3/2 10/2 17/2 24/2 3/3 10/3 17/3 24/3
Ra
infa
ll (
mm
da
y-1
)
0
5
10
15
20
25
0.0
3.0
6.0
9.0
12.0
15.0
18.0
21.0
24.0
27.0
30.0
6/1 13/1 20/1 27/1 3/2 10/2 17/2 24/2 3/3 10/3 17/3 24/3
Da
ily
LW
D (
ho
ur
da
y-1
)
Mea
n a
ir t
emp
era
ture
(°C
)
0
20
40
60
80
100
120
1/1 8/1 15/1 22/1 29/1 5/2 12/2 19/2 26/2 5/3 12/3 19/3 26/3
Ra
infa
ll (
mm
da
y-1
)
0
5
10
15
20
25
0.0
3.0
6.0
9.0
12.0
15.0
18.0
21.0
24.0
27.0
30.0
1/1 8/1 15/1 22/1 29/1 5/2 12/2 19/2 26/2 5/3 12/3 19/3 26/3
Da
ily
LW
D (
ho
ur
da
y-1
)
Mea
n a
ir t
emp
era
ture
(°C
)
0
20
40
60
80
100
120
11/1 18/1 25/1 1/2 8/2 15/2 22/2 1/3 8/3 15/3 22/3 29/3
Rain
fall
(m
m d
ay
-1)
0
5
10
15
20
25
0.0
3.0
6.0
9.0
12.0
15.0
18.0
21.0
24.0
27.0
30.0
11/1 18/1 25/1 1/2 8/2 15/2 22/2 1/3 8/3 15/3 22/3 29/3
Da
ily
LW
D (
ho
ur
da
y-1
)
Mea
n a
ir t
emp
eratu
re (°C
)
b a
d c
f e
351.8* 61.9 120.2
2724.5
3541.7 3369.2
1212.9
2413.9
3003.7
0
500
1000
1500
2000
2500
3000
3500
4000
4500
5000
TEST CALEND PREC TEST CALEND PREC TEST CALEND PREC
CAMPO VERDE PIRACICABA PONTA GROSSA
Yie
ld (
kg
ha
-1)
* - Second columns for each treatments correspond the profitability of each field experiment. To determine the profitability was
adopted a cost of 150 kg ha-1 for each spraying. † - Fungicide sprays required to control ASR.
0† 3
5
0
5
3
0
4
3
Figure 5 – Yield and profitability obtained in the three experimental areas, using
different methodologies for control de ASR.
b
