Indonesia

Indonesia is the world's fourth most populous country with about 261 million people in 2016 (UN, DESA, 2016). With the total land area of about 192 million ha, about 52% of total harvested area of food crops (14 million ha) is located in Java. Indonesia is the third rice (GRiSP, 2013) and fifth maize world's producer (Ministry of Agriculture, 2015). Average rice and maize yields are 5.0 and 4.4 tons per harvested hectare, respectively (Table 1). Average yields have increased during the past two decades at a rate 37 (rice) and 147 (maize) kg ha-1 y-1. GYGA-Indonesia focuses on irrigated and lowland rainfed rice and irrigated and rainfed maize. Upland (non-flooded) rice was not included in our analysis because it accounts for only 10% of total rice production in Indonesia.

Table 1. National average total production, harvested area, and average yield of rice, maize, soybean, and cassava during the time period between 2011 and 2015. Source: Indonesian National Statistics, 2016 (https://www.bps.go.id).

Crop

Total production (Mt)

Harvested area (Mha)

Average yield (t/ha)

Rice

70.5

13.7

5.2

Maize

18.8

3.9

4.9

Soybean

0.9

0.6

1.5

Cassava

23.5

1.1

22.1

Description of climate, soils, and cropping patterns

The tropical and wet climate of Indonesia is favorable to grow multiple crops in the same piece of land within the same year. Average temperature is around 26-280C, with total annual rainfall ranging from 1,000 mm in East Nusa Tenggara and Palu Valley to more than 3,000  mm in most parts of Sumatra, South Kalimantan, West Java, Central Java, and South Sulawesi. Dominant agricultural soils are infertile Inceptisols, especially in Java. Other soils include Ultisols (lowlands in West Java, Central and South Sumatra, and parts of Kalimantan), Vertisols (Central and East Java, Bali, West and East Nusa Tenggara), and Oxisols (Sumatra, parts of West Java, and Kalimantan).

Cropping systems are very diverse and include different ecosystems (upland, lowland, swamps, tidal), and water regimes (rainfed and irrigated), irrigated systems are most common for lowland (flooded) rice systems. Intercropping maize with cassava/beans/vegetables is practiced in the uplands of some regions of Central Java while relay cropping is still common in East Nusa Tenggara. Cropping intensity (for annual crops) varies from 1 to 3 crops per year. An example for maize- and rice-based cropping systems in Indonesia is shown for Central Java and Yogyakarta (Figure 1). In this region, October coincides with the onset of the rainy season for most areas and the rainy season continues until the end of March.

Figure 1. Scheme showing cropping sequences in Central Java and Yogyakarta. White and gray boxes correspond to rainfed and irrigated crops, respectively.

Selection of buffers and source of weather data

A total of 36 buffers were selected for Indonesia. These buffers account for 99% (irrigated rice), 94% (lowland rainfed rice), 58% (irrigated maize), and 89% (rainfed maize) of cropland area. These coverage values were calculated considering the climate zones where the selected buffers where located. We used the national meteorological station network of the Meteorology Climatology and Geophysics Agency (BMKG) as the main source of weather data. Daily weather data were available for the last 10-18 years for 28 buffers, including daily solar radiation, maximum and minimum temperature, humidity, and precipitation, but no daily weather data existed for 8 buffers; hence, NASA-POWER were used to generate weather data for them (NASA-POWER; NASA, 2012). Quality control and filling/correction of the weather data was performed based on correlations between the target Reference Weather Stations (RWS) and one to two adjacent weather stations following van Wart et al. (2013). Number of corrections/filled data was always lower than <3% for all variables.

Figure 2. Selected weather stations and associated source of weather data.

Selection of dominant soils in each buffer

Soil map was overlaid with the buffer zones to identify the dominant agricultural land area. Lowland area was identified based upon distribution of the lowland rice area. The 1:5,000 scale rice field maps used to calculate the lowland area were developed by the Data and Information Center Ministry of Agriculture (PUSDATIN, 2011); Director General Facilities and Infrastructure Ministry of Agriculture (Ditjen PSP, 2013). The lowland rice areas were divided into irrigated and rainfed lowland. The rice area outside Java was only classified as lowland. Upland areas was identified by using Landuse Map developed by the Indonesia Ministry of Land and Spatial Planning, at scale 1:250,000 (ATR, 2012). Soil texture, soil depth, and slope for dominant soil identified in each buffer were derived from Indonesian Center for Agricultural Land Resources Research and Development (2013)

Average farmer yield data

Annual data on average farmer yields and harvested area were retrieved from the time period between 2010 and 2015 (https://www.bps.go.id). Regency-level data on harvested area and average yield were retrieved for maize in lowland rice. For each buffer-year-crop, we estimated average farmer yield by averaging the yields reported for those regencies that overlap with the location of the buffers, weighted by the harvested area reported for each regency. For this calculation, we only included buffer that have >30% of their total area inside a given buffer zone (except for a few cases in which all regencies have <30% area inside the buffer). Because GYGA requires yields disaggregated by water regime, we estimated yields and areas for irrigated and rainfed lowland rice and maize based on the official statistics together with information obtained from local agronomist and extension agents. For each buffer and year, we estimated average on-farm yield for each crop and water regime.

Crop system data and modeling

Management practices for each RWS buffer zone were retrieved from local agronomists and extension agents. Requested information included: dominant crop sequences, ecosystems (upland/lowland), water regime (rainfed/irrigated) and proportion of each of them to the total harvested area, average sowing/transplanting dates, dominant cultivar name and maturity, and actual and optimal plant population density. Reported dates of sowing in the Atlas correspond to mid-point of the (20-day) sowing window reported by country agronomist.

Simulations of widespread varieties for each region were performed using Maize-Hybrid (Yang et al., 2004, 2017) for maize and ORYZA V3 (Li et al. 2013, 2017) for rice. Models were calibrated for Inpari 32 (rice) and Pioneer 21 (maize) cultivars using available experimental data from Indonesia (Witt et al. 2006; Pasuquin et al. 2012, 2014; Agustiani et al., in preparation). These two cultivars are amongst the dominant ones across Indonesia. While local rice varieties are still important in some buffers (e.g., South Kalimantan, Lampung), experimental data to calibrate model parameters for these varieties are not available. Simulations assumed no limitations to crop growth by nutrients or biotic stresses.

For irrigated crops, we assumed no water limitation, while simulation of rainfed crops considered rainfall and soil properties influencing the soil water balance. Despite technically not irrigated, lowland rainfed rice still remains flooded (or at least under soil saturation) during most of the growing season; hence, to a large degree it can be considered non-water limiting (Haefele and Bouman, 2009). Still a portion of the lowland rainfed rice area is drought prone, especially at late stages (Boling et al., 2004, 2008). There is a high uncertainty in relation with groundwater depth across sites, seasons, and landscapes. Hence, we simulated water-limited yield potential for lowland rainfed rice (including tidal and swampy rice) for three scenarios of groundwater depth during the entire crop cycle (shallow, medium, and deep). For these simulations, we assumed a puddled clay soil with Ksat = 2 mm/d and bund height of 25 cm. We assumed that each groundwater scenario accounts for an equal share of the lowland rainfed rice area in each buffer.

For each buffer-year-crop-water regime combination, each crop cycle (in each sequence) x soil type combination was simulated, and then weighted by their relative proportion to retrieve an average yield potential for each buffer zone. Our simulations only included crop sequences that account for >3% of the (rice + maize) area in each buffer. The yield gap was calculated as the difference between the yield potential (or water-limited yield potential for rainfed crops) and average on-farm yield.

A recorded seminar about GYGA-Indonesia is available at: https://youtu.be/sOlIHvGKECw

References

Agraria dan Tata Ruang (ATR). 2012. Tata Ruang Wilayah. (Website: http://tataruang.atrbpn.go.id, citation on November 18, 2017).

Agustiani, N., S. S. Girsang, V.E.Aristya, D. Kuntjoro, Syafrudin, J.I.R. Edreira, N. Deng, F. Agus, and P. Grassini.  Evaluating two crops models for simulating rice and maize yield potential in the tropical environment of Indonesia (in preparation).

Badan Meteorologi, Klimatologi, dan Geofisika. Trend Curah Hujan di Indonesia. (Website: http://www.bmkg.go.id/iklim/ trend-curah-hujan.bmkg, citation on November 12, 2017).

Ditjen PSP. 2013. Potensi alih fungsi lahan akibat tidak ditetapkan LP2B dalam RTRW kabupaten/kota. Bahan tayang Ditjen PSP. Direktorat Jenderal Prasarana dan Sarana Pertanian, Jakarta.

GRiSP (Global Rice Science Partnership). 2013. Rice almanac, 4th edition. Los Baños (Philippines): International Rice Research Institute. 283 p.

Indonesian National Statistics. Statistical Yearbook of Indonesia. (Wesbite: https://www.bps.go.id, citation on November 12, 2017).

Indonesian National Statistics. 2016. Statistical Yearbook of Indonesia. BPS-Statistics Indonesia p 193-280

Indonesian National Statistics. 2010. Statistical Yearbook of Indonesia. BPS-Statistics Indonesia p 179-270

Li.T., O. Angeles, M. Marcaida III, E. Manalo, M.P. Manalili, A. Radanielson, S.Mohanty. 2017. From ORYZA2000 to ORYZA (v3): An improved simulation model for rice in drought and nitrogen-deficient environments. Agricultural and Forest Meteorology. 237-238. 246-256p.

Li. T., A.K. Raman, M. Marcaida III, A. Kumar, O. Angeles, A.M. Radanielson. 2013. Simualtion of genotype performances across a large number of environments for rice breeding using ORYZA2000. Field Crop Research 149 (2013) 312-321.

Ministry of Agriculture. 2015. Rencana Strategi Kementrian Pertanian Tahun 2015-2019. Kementrian Pertanian Republik Indonesia. (Website: http://www.pertanian.go.id/file/ RENSTRA_2015-2019.pdf, citation on 11 November 2017).

NASA, 2012. NASA-Agroclimatology methodology. Available at: http://power.larc.nasa.gov/common/AgroclimatologyMethodology/Agro1d0

National Aeronautics and Space Administration. Prediction of Worldwide Energy Resources. (Website: http://power.larc.nasa.gov/, citation on 12 November 2017).

Pasuquin, J.M., M.F. Pampolino, C.Witt, A. Dobermann, T. Oberthur, M.J. Fisher, and K. Inubushi. 2014. Closing yield gaps in maize production in Southeast Asia through site-specific nutrient management. Field Crop Research. 156. 219-230p.

Pasuquin J.M, S. Saenong, P.S. Tan, C. Witt, and M.J. Fisher. 2012. Evaluating N Management strategies for hybrid maize in Southeast Asia. Field Crops Research 134 (2012) 153-157.

Pusat Data dan Sistem Informasi Pertanian (PUSDATIN). 2011. Hasil Audit Lahan Baku Sawah, Hasil Update dan Paduserasi Hasil Update dan Paduserasi (Website: http://psp.deptan.go.id/index.php/page/lahan_audit, diakses 18 November 2017).

UN. DESA, 2016. World population prospects 2016. (Website: https://esa.un.org/unpd/wpp/Graphs/DemographicProfiles/, citation on 18 November 2017).

Wart, J.V., K.C. Kersebaum, S. Peng, M. Milner, K.G. Cassman. 2013. Estimating crop yield potential at regional to national scales. Field Crop Research. 143. 34-43p.

Witt, C., J.M. Pasuquin, and A. Dobermann. 2006. Towards a site-specific nutrient management approach for Maize in Asia. Better Crops. 90:2.28-31Yang, H., P. Grassini, K.G. Cassman, R. M. Aiken, and P. I. Coyne. 2017. Improvement to the Hybrid-Maize model for simulating maize yield in harsh rainfed environment.Field Crop Research. 204:180-190.

Yang, H.S., Doberman, A., Lindquist, J.L., Walters, D.T., Arkebauer, T.J., Cassman, K.G., 2004. Hybrid-maize-a maize simulation model that combines two crop modeling approaches. Field Crops Res. 87, 131–154.

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Country staff Country staff

Name

Affiliation

Picture below

Coordinators

 

 

Fahmuddin Agus

Indonesian Center for Agricultural Land Resources Research and Development (ICALRRD)

1

Anny Mulyani

 

 

Indonesian Center for Agricultural Land Resources Research and Development (ICALRRD)

2

Country Agronomists

 

 

Dwi Kuntjoro

ICALRRD

3

Nurwulan Agustiani

Indonesian Center for Rice Research (ICRR)

4

Syafrudin

Indonesian Cereals Research Institute (ICRI)

5

Khadijah EL Ramija Lubis

Assessment Institute of Agricultural Technology (AIAT) North Sumatra

 6

Siti Fatimah Batubara

AIAT North Sumatra

7

Setia Sari Girsang

AIAT North Sumatra

8

Via Yulianti

AIAT West Sumatera

9

Syahri

AIAT South Sumatera

10

Andarias Makka Murni

AIAT Lampung

11

Oswald Marbun

AIAT West Java

12

Hijriah Mutmainnah

AIAT Banten

13

Samijan

AIAT Central Java

14

Vina Eka Aristya

AIAT Central Java

15

Agung Iswadi

AIAT Jogyakarta

16

LY Krisnadi

AIAT East Java

17

Nurul Istiqamah

AIAT East Java

18

Rina Dirgahayu Ningsih

AIAT South Kalimantan

19

Aulia Dina Pramesti

AIAT South Kalimantan

20

Andi Yulyani Fadwiwati

AIAT Gorontalo

21

Rahmat Anasir

AIAT Gorontalo

22

Syamsyiah Gafur

AIAT Central Sulawesi

23

Andi Irma Damayanti

AIAT Central Sulawesi

24

Herniwati

AIAT South Sulawesi

25

Maintang

AIAT South Sulawesi

26

Hiryana Windiyati

AIAT West Nusa Tenggara

27

Baig Sri Ratna. E

AIAT West Nusa Tenggara

28

Evert Hosang, Ph.D.

AIAT East Nusa Tenggara

29

Ratri Ariani

Indonesian Soil Research Institute (ISRI) 

30

  1. Putu Wigena

ISRI

31

Didu Wahyudi

Indonesian Center for Agricultural Technology Assessment and Development (ICATAD)

 32

Yovita Anggita Dewi

ICATAD

33

Muhammad Maulana

Indonesian Center for Agricultural Socio Economic and Policy Studies

 34

Dedi Nursyamsi

Resource Person, Director of ICALLRD 

35