1. 5th Annual Meeting of MIRSA-2 project
Greenhouse Gas Mitigation in Irrigated Rice Paddies in Southeast Asia (Part 2)
September 2, 2017
SSPN paper summary: Indonesia (Alternate wetting and drying reduces methane emission from a rice paddy in Central Java, Indonesia without yield loss)
The manuscript has been reviewed 10 July 2017  recommended to accept as a paper published in special section for rice GHG, but it needs revisions
Prihasto Setyanto, Ali Pramono, Terry Ayu Adriany, Helena Lina Susilawati, Takeshi Tokida, Agnes T. Padre, Kazunori Minamikawa

2. Content ABSTRACT INTRODUCTION MATERIALS AND METHODS
2.1 Experimental Site
2.2 Experimental Design and Rice Cultivation
2.3 Measurements
2.4 Statistical Analysis
RESULTS
3.1 Weather and irrigation water
3.2 Rice growth and grain yield
3.3 CH4 and N2O emissions
3.4 GWP, yield-scaled GWP, and water productivity
DISCUSSION
4.1 Rice productivity and water saving under AWD
4.2 GHG emission reduction through AWD
CONCLUSION

3. Abstract
Alternate wetting and drying (AWD) is a possible option in regulating methane (CH4) and nitrous oxide (N2O) emissions from irrigated rice field, but there is limited information on its feasibility under locally environmental conditions, especially for tropical region.
The aim was to investigate the feasibility of AWD in terms of rice productivity, greenhouse gas (GHG) emission, and water use both in wet and dry seasons (WS and DS).
The treatments of water management were (1) (CF), (2) AWD), and (3) site- specific AWD with different criteria of soil drying (AWDS).
The grain yield did not significantly differ among the three treatments both in WS and DS.
AWD and AWDS significantly reduced the total water use (irrigation + rainfall) as compared to CF.
The CH4 emissions under AWD and AWDS were 35% and 38% smaller than that under CF, respectively.
The seasonal total N2O emission did not significantly differ among the treatments.
The results indicate that AWD is a promising option to reduce GHG emission as well as water use without sacrificing rice productivity in this field.

4. Introduction
Rice cultivation is a major source of a potent GHG, methane (CH4), contributing to about 11% of the global anthropogenic CH4 emissions (Ciais et al. 2013).
The availability of water resources will decline by 15–54% in 2025 compared to 1990 in many Asian countries (Gleick 1993)
AWD reduced of CH4 and N2O emissions (Hadi et al. 2010, Linguist et al. 2014, Liang et al. 2016, . Xu et al. 2015),
Reduced irrigation water showed without significant yield loss or even higher yield than continuous flooded (Siopongco et al. 2013, Liang et al. 2016, Xu et al )
Setyanto (2004) reported that intermittent and pulse irrigation reduced CH4 emission compared to the conventional continuous flooding in Central Java, Indonesia,
There is limited information on the feasibility of AWD as a way to achieve less GHG emissions, including CH4 and N2O, save water and maintain rice yields in local situation, especially for tropical region.

5. Objectives
to investigate the feasibility of AWD under the local settings of climate and agricultural practices in Indonesia.
Here we show AWD is an effective option to reduce GHG emission without rice yield penalty in an Indonesian rice paddy.

6. Materials and Methods
The study was conducted at an experimental paddy field of Indonesian Agricultural Environment Research Institute (IAERI) during 6 consecutive rice growing seasons from 2013 to 2016.
The three treatments were arranged in RCBD with three replications
continuous flooding at 5 cm (CF),
re-flooding 5 cm every when the surface water level naturally declined to 15 cm below the soil surface (AWD),
site-specific AWD (AWDS). In WS and DS in the first year (i.e., WS1 and DS1), multiple drainage 7 days before 1st and 2nd fertilization was implemented as AWDS, whereas in the second and third years (i.e., WS2, DS2, WS3, and DS3), re-flooding 5 cm every when the water level reached 25 cm below the soil surface was implemented as AWDS.
3. Irrigation water was supplied from water reservoir, embung, equipped with a water pump and connected with PVC pipe to distribute water to the plots
4. The total broadcasting fertilization rates were 120 kg N ha-1 as urea, 60 kg P2O5 ha-1 as super phosphate, 90 kg K2O ha-1 as potassium chloride, and 5 ton ha-1 of farmyard manure (12.4% organic carbon and 1.3% nitrogen  (N))

7. Table 1. Soil chemical and physical properties.
Property
Organic carbon (g kg-1)
5.30
Total nitrogen (g kg-1)
0.47
Total phosphorous (g kg-1)
0.02
Total potassium (g kg-1)
0.004
Cation exchange capacity (cmol (+) kg-1)
5.16
Active iron (g kg-1)
3.6
Manganese (g kg-1)
0.06
Soil texture
Loam
Sand (%)
48.6
Silt (%)
28.8
Clay (%)
22.5
The paddy soil is classified as silt loam, Aeric Endoaquepts (Soil Survey Staff 2010)

8. Table 2. Field management practices in the six rice growing seasons.
WS1
DS1
WS2
DS2
WS3
DS3
Plowing
-12 DAS
-5 DAT
-1 DAS
-7 DAT
-2 DAS
-3 DAT
Rice stubble incorporation
Yes
Manure application
-1 DAT
Crop establishment
0 DAS
(2013 Oct 21)
0 DAT
(2014 Mar 12)
(2014 Nov 17)
(2015 Apr 4)
(2015 Nov 13)
(2016 Apr 1)
1st inorganic fertilization
30 DAS
11 DAT
26 DAS
7 DAT
23 DAS
9 DAT
2nd inorganic fertilization
46 DAS
38 DAT
36 DAT
51 DAS
37 DAT
3rd inorganic fertilization
59 DAS
56 DAT
61 DAS
64 DAT
67 DAS
58 DAT
Harvest
132 DAS
(2014 Mar 2)
107 DAT
(2014 Jun 27)
129 DAS
(2015 Mar 26)
110 DAT
(2015 Jul 23)
(2016 Mar 21)
104 DAT
(2016 Jul 14)
WS, wet season; DS, dry season; DAS, days after sowing; DAT, days after transplanting.

9. The CH4 and N2O fluxes were measured by a closed chamber method
The CH4 and N2O fluxes were measured by a closed chamber method. The size of chamber was 50 cm length × 50 cm width × 100 cm height and covered four rice hills
Irrigation water usage (ton ha-1) was estimated by multiplying the flow rate in the pipe by the time of irrigation.
The grain yield of harvested rice (14% moisture content) was measured from 6-m2 sampling in each plot.
We conducted an analysis of variance (ANOVA) using a split-plot design, where cropping season (CS) was treated as the whole-plot factor and treatment (water management: CF, AWD, AWDS) as the split-plot factor, with three replications.

10. Results and Discussion

11. Figure 1. Seasonal variations in daily rainfall (a, b, c), daily maximum and minimum air temperatures (d, e, f), mean surface water level (g, h, i), CH4 flux (j, k, l), and N2O flux (m, n, o) for three water management practices in the 1st, 2nd, and 3rd wet seasons (WS). Error bars for CH4 and N2O fluxes indicate the standard error (n = 3). Vertical dotted lines indicate the application of nitrogen fertilizer. Grey areas in b and e indicate the lack of data observation.

12. Figure 2. Seasonal variations in daily rainfall (a, b, c), daily maximum and minimum air temperatures (d, e, f), mean surface water level (g, h, i), CH4 flux (j, k, l), and N2O flux (m, n, o) for three water management practices in the 1st, 2nd, and 3rd dry seasons (DS). Error bars for CH4 and N2O fluxes indicate the standard error (n = 3). Vertical dotted lines indicate the application of nitrogen fertilizer. Grey areas in c and f indicate the lack of data observation.

13. Table 3. Seasonal CH4 and N2O emissions, GWP, rice grain yield, yield-scaled GWP, total water use, and water productivity as affected by cropping season and water management.
Treatment
CH4
(kg CH4 ha-1)
N2O
(kg N2O ha-1)
GWP
(Mg CO2 ha-1) DS WS CF
385
515
0.810
1.13
13.34
17.86
AWD
243
341
0.711
1.32
8.48
11.98
AWDS
244
311
0.690
1.22
8.51
10.92
Season means
291
389
0.737
10.11
13.59
Treatment means
450 A
0.971 A
15602 A
292 B
1.014 A
10232 B
278 B
0.954 A
9718 B
P value
Source of variation df
Block 2
Cropping season (CS) 5
****
Dry or Wet (DW)1 1
***
Dry season (DS)1 **
Wet season (WS)1
Main plot error 10
Treatment (T)
0.541
T × CS
0.288
T × DW2
0.418
0.482
0.461
T × DS2 4
0.312
T × WS2
0.208 *
Split-plot error 24
Means with different letters indicate significant difference at the 5% level. 1Sub-division of variation among cropping seasons. 2Sub-division of variation among T × CS interactions. * p<0.10, ** p<0.05, *** p<0.01, **** p<0.001.

14. Table 3.. Treatment Grain yield (Mg ha-1) Yield-scaled GWP
Grain yield
(Mg ha-1)
Yield-scaled GWP
(Mg CO2 Mg-1 grain)
Water use
(m3 ha-1)
Water productivity
(kg grain m-3) DS WS CF
5.12
6.87
2.68
2.61
6635
12521
0.832
0.574
AWD
5.20
1.65
1.75
6270
11831
0.888
0.602
AWDS
4.96
6.67
1.73
1.62
6146
11776
0.852
0.585
Season means
5.09
6.80
2.02
1.99
6350
12043
0.857
0.587
Treatment means
5.99 A
2.64 A
9578 A
0.703 A
6.04 A
1.70 B
9051 B
0.745 A
5.81 A
1.68 B
8961 B
0.718 A
P value
Source of variation df
Block 2
Cropping season (CS) 5
****
Dry or Wet (DW)1 1
0.236
Dry season (DS)1
Wet season (WS)1
0.117
Main plot error 10
Treatment (T)
0.303
0.476
T × CS
0.853 *
0.261
0.706
T × DW2
0.972
0.475
0.639
0.910
T × DS2 4
0.753
0.402
0.257
T × WS2
0.518
0.102
0.112
0.868
Split-plot error 24
Means with different letters indicate significant difference at the 5% level. 1Sub-division of variation among cropping seasons. 2Sub-division of variation among T × CS interactions. * p<0.10, ** p<0.05, *** p<0.01, **** p<0.001.

15. Supplemental Table S1. Seasonal CH4 and N2O emission, GWP, grain yield, yield-scaled GWP, total water use, and water productivity as affected by water management in the six cropping seasons.
Season/
Treatment
CH4
(kg CH4 ha-1)
N2O
(kg N2O ha-1)
GWP
(Mg CO2 ha-1)
Grain yield
(Mg ha-1)
Yield-scaled GWP
(Mg CO2 Mg-1 grain)
Water use
(m3 ha-1)
Water productivity
(kg m-3)
WS1 CF
250
0.99
8.78
6.81
1.29
13790a
0.49
AWD
160
1.01
5.75
6.85
0.84
13340b
0.51
AWDS
159
0.68
5.59
6.36
0.89
13490ab
0.47
DS1
300
1.48
10.62
5.70
1.90
8737
0.66
167
1.24
6.05
5.77
1.04
8655
0.67
253
1.39
9.02
5.60
1.61
8414
WS2
597a
1.60
20.79a
7.26
2.86a
9408
0.77
323b
2.22
11.65b
6.94
1.68b
9258
0.76
221b
2.17
8.15b
6.82
1.19c
9182
0.75
DS2
432a
14.83a
4.73
3.16a
4324
1.12
303ab
0.63
10.49ab
4.94
2.10ab
4261
1.16
236b
0.36
8.13b
4.32
1.89b
4309
WS3
699
0.80
24.01
6.53
3.68
14365a
0.45b
539
0.72
18.55
6.83
2.72
12896b
0.53a
553
0.81
19.04
2.79
12656b
0.54a
DS3
425a
0.46
14.57a
2.96a
6842
260b
0.26
8.91b
4.89
1.82b
5894
244b
0.33
8.39b
4.97
1.69b
5714
0.88
Different letters within the same column of each cropping season indicate significant differences at p < 0.05 by Tukey’ HSD test (n = 3).

16. The total water use was significantly reduced by AWD (by 5%) and AWDS (by 6%) compared to CF. The water productivity, WP was significantly affected only by cropping season. The WP in DS was 46% higher than that in WS. Increasing water productivity implies either to produce the same yield with less water or to obtain greater crop yield with the same water volume
The seasonal total CH4 emission was significantly affected by each of cropping season and water management. The greater CH4 emission in WS (at the latter stage) due to the greater rice biomass and the longer flooding periods.

17. There was no consistent seasonal pattern in N2O emission under AWD through the six cropping seasons. The seasonal total N2O emission was significantly affected by cropping season. Low N2O fluxes are found during flooded periods, whereas high N2O fluxes are found during temporal drained periods. Greater N2O emission was observed in WS because drained soil conditions as was in WS would enhance temporal N2O production and emission
Rice grain yield was significantly greater (by 33%) in WS than in DS (the different planting method and weather condition  higher nutrient, lower pest and disease incidences in the WS) and there was no significant effect of water management on the yield (AWD and AWDS implemented in this study were not so severe for sound rice growth that negative effects of soil drying). Rice grain yield ranged from 4.32 to 7.26 Mg ha-1
The GWP in WS was 34% greater than that in DS, and AWD and AWDS significantly reduced the GWP compared to CF.
The yield-scaled GWP was reduced by AWD and AWDS by 35% and 36%, respectively, compared to CF.

18. (a)
(f)
(c)
(e)
Plant Height
Number of tillers
Supplemental Figure S1. Seasonal changes in plant height and the number of tillers per hill under different water managements in the six cropping seasons. (a) WS1, (b) DS1, (c) WS2, (d) DS2, (e) WS3, and (f) DS3.

19. (b)
(d)
(f)
Plant Height
Number of tillers
Supplemental Figure S1..

20. Supplemental Table S2. The number of days in which surface water level declined to less than zero cm in the six rice growing seasons
Treatment
WS1
DS1
WS2
DS2
WS3
DS3 CF
AWD 20 31 38 41 25 29
AWDS 14 16 44 27 24
WS, wet season; DS, dry season.

21. Conclusions
The adoption of AWD to rice cultivation in Indonesia will be feasible because AWD can reduce GHG emission and water use without rice yield loss.
The success of AWD in WS would depend on the field location (lowland vs. upland).
The results of AWD and AWDS on non-significant difference in grain yield, there may be room for more severe soil drying being feasible.
Further study is needed to find out the optimum AWD threshold and schedule that lead to the improvement of rice productivity as observed in temperate region.

22. Acknowledgements
The research was funded by the Ministry of Agriculture, Forestry and Fisheries of Japan through the international research project "Technology Development for Circulatory Food Production Systems Responsive to Climate Change: Development of Mitigation Options for Greenhouse Gas Emissions from Agricultural Lands in Asia (MIRSA-2)."
We would like thank Prof. Kazuyuki Inubushi (Chiba University, Japan), Dr. Reiner Wassmann (IRRI, Philippines), and Dr. Kazuyuki Yagi (NARO, Japan) for their valuable comments on the earlier version of this manuscript.