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

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

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.

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. 2015) 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.

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.

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))

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)

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.

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.

Results and Discussion

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.

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.

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.

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.

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).

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.

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.

(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.

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

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.

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.

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.