METHOD OF RICE AREA MONITORING AND FORECASTING PRODUCTION TO SUPPORT

FOOD SELF-SUFFICIENCY IN INDONESIA

BY

BUDI WARYANTO

CENTRAL FOR AGRICULTURAL DATA AND INFORMATION SYSTEMS (CADIS) MINISTRY OF AGRICULTURE

INDONESIA

Background 1

JAVA 13.21 million ha

SUMATERA 47.36 million ha

BORNEO 54.96 million ha

CELEBES 19.92 million ha

PAPUA 43.30 million ha

INDONESIA IN THE WORLD MAP

LARGES ISLANDS

34 PROVINCES 514 DISTRICTS/CITIES 7,071 SUB-DISTRICTS INA

DESCRIPTION AMOUNT

TOTAL GDP (US$) 670.34 Billion

GDP Agriculture Sector 13.08 %

TOTAL Agric. Export (US$) 28.04 Billion

TOTAL Agric. Import (US$) 17.59 Billion

ECONOMIC INDICATORS 2015

10.45

DESCRIPTION AMOUNT

POPULATION 354.9 Million

POP. Growth 2010-2015 1.38 %

Wetland Area 2014 (Ha) 8.11 Million

Agric. Workers 37.75 Million

DOMESTIC RESOURCES 2015

R E S O U R C E S

2014-2019, GOVERN. FOCUSED TO PRODUCE

NO GOVERN. INTERVENTION FOR OTHER COMMODITIES

Therefore PROCESS AND OUTPUT OF ALL COMMODITIES

SHOULD BE MONITORED

NEED METHOD

Main Topic

Legal aspects of management of agricultural statistics - Generally

2

LEGAL ASPECTS OF THE IMPLEMENTATION OF STATISTICAL IN INDONESIA

ACT. No : 16/1997 Provides the

legal basis for statistical activities

(BPS Statistics-INA)

• Basis StatisticsBPS-Ina

• Sectoral StatisticsMOA

• Specific StatisticsNGO

Gov. Regulation (PP) No: 51/1999

Regulate 3 aspects:

Manage the collection of statistics for BPS Statistics Indonesia, MOA and NGO in detail

Act. No : 32/2004

Autonomy for provincial and district government institutions, including data management

Has a com-mand line

to the Prov. & Distr.

No Command line to the

Prov. & Distr.

DIFFERENCES ORGANIZATION OF BPS STAT. IDONESIA & MOA

MANAGING AGRICULTURAL STATISTICS IN MOA

• Upstream :

Agricultural Input, Machineries, Natural

Resources and Capital Resources

• On-farm :

– Food crops

– Horticulture

– Estate crops

– Livestocks

• Down-stream :

– Processing, Trading, GNP, Farmer’s Term of

Trade, Investment

• Supporting Data:

– Human resources

– Technology resources

Focus on this presentation

Agricultural data reporting mechanism; Case of food crops data

3

• Area Planted, Area Harvested, Productivity, Pest and Disease, Land Use and Machinery for Primary Crops

• Paddy, Maize, Soybeans, Cassava, Peanut, Mungbeans, Sweet Potatoes

• Monthly, Quarterly, Yearly

• Available Data : 1970 – 2015 *)

SCOPE OF FOOD CROP STATISTICS

*) Preliminary Figure

CALCULATION OF PRODUCTION

PRODUCTION = HARVESTED AREA (Ha) X PRODUCTIVITY (TON/HA)

METHOD COMPLETE REPORT (M) MESUREMENT SURVEY (Q)

HARVESTED AREA PRODUCTIVITY

1 Unit Data Collection • All Sub-Districts

1 Unit Data Collection • Household & plots of crops.

Ex: 158 thousand sample

2 Method - Complete reports

2 Method • Statistical approach two-

stage sampling 3 Tools 3 Tools

Guide book Guide book Tools to measuring

THE METHODOLOGY FOR CALCULATING PRODUCTION

BPS CENTRAL

MOA

-DG Foodcrops

-CADIS

AGRICULTURE OFFICE

PROVINSI

AGRICULTURE OFFICE

DISTRICT

Agriculture

Officer Sub-Distc

Pest Cont-

rol Official

BPS

PROVINCE

BPS

DISTRICT

Statistics Officer

Sub District

EXTENSION

WORKERS

VILLAGE

OFFICIAL

FIELD / FARMERS

Dam

ag

e D

ata

Remarks: Coordination

Reporting

Data collection

DATA REPORTING MECHANISM

H A R V E S T E D

P R O D U C T I V

1. Prod. (Q1) = Harvested Area (Q1) x Productivity (Q1)

2. Prod. (Q2) = Harvested Area (Q2) x Produktivity (Q2)

3. Prod. (Q3) = Harvested Area (Q3) x Produktivity (Q3)

4. PRODUCTION JAN–DEC = ∑Production (Q1+Q2+Q3)

5. HAREVESTED AREA JAN-DEC = ∑Harvested Area (Q1 + Q2 + Q3)

PRODUCTION-INA = ∑PRODUKTIAL ALL PROVINCES

METHOD OF CALCULATION FOOD CROP PRODUCTION

PUBLICATION OF OFFICIAL DATA

1. March (t) = Preliminary Figures (t-1)~Q1+Q2+Q3 2. July (t) = Fixed Figures (t-1) and First Forecast (t) ~ Q1+Forecast (Q2+Q3) 3. Nov. (t) = Second Forecast (t)~Q1+Q2+Forecast Q3

Note: Forecasting method is simple

Uraian 2013 2014 2015

(Preliminary Figures)

Growth

2014 Over 2013

2015 Over 2014

% %

(1) (2) (3) (4) (5) (6)

1. Harvested Area (ha) Paddy 13 835 252 13 797 307 14 115 475 -0.27 2,31

Maize 3 821 504 3 837 019 3 786 815 0.41 -1.31

Soybean 550 793 615 685 613 885 11.78 -0.29

2. Produktivity (Ku/ha) Paddy 51.52 51.35 53.39 -0.33 3.97

Maize 48.44 49.54 51.79 2.27 4,54

Soybean 14.16 15.51 15.69 9.53 1.16

3. Production (ton) Paddy 71 279 709 70 846 465 75 361 248 -0.61 6,37

Maize 18 511 853 19 008 426 19 611 704 2.68 3.17

Soybean 779 992 954 997 963 099 22.44 0.85

EXAMPLES OF OFFICIAL DATA

The issues and challenge of managing agricultural data

4

ISSUES AND

CHALANGES

HUMAN

RESOURCES

REPORTING

FACILITIES

METHODOLOGY

1. Productivity data collected using statistical methods, but not, for harvested area data

2. Improved methods for forecast data

1. Publication of the official data released every quarterly, currently required monthly data

2. Information technology makes it possible to accelerate the data

INSTITUTIONAL

1. The effect of autonomy for data management

2. The focus of the organization is not uniform across provinces / districts

1. A limited persons of field officers

2. Different knowledge of field officers

3. Mutations field officers very quickly

Improvement: the management an methods of agricultural statistics

4

METHODOLOGY

Development of harvest area measurements using “Area Frame Sampling (AFS)”: Case for Paddy

Production = Harvested Area x Productivity

IMPROVEMENT

Definition: 1. AFS a sampling approach that uses the land as a unit of

observation (Developed by BPPT Indonesia) 2. The sample unit in a grid, line, or point 3. Objective: to estimate the area by extrapolation from the

sample to the population in a short period (rapid estimate)

A

Agency for Assessment and Application of Technology (BPPT)

1998-2012

STAGES TO BUILD FSA

Development “FSA”

• Stratification Area • Sample size

determination • Extraction of the

Sample Segments

Preparation Survey • Setup Tool survey

(Map, Photos satellite imagery, GPS)

Field Survey • Observe and record

the land cover and the growth phase of rice

Sending the survey

Processing and presentation of data

STUDY & IMPELEMENTA-TION 2015-2018

BPPT-BPS Stat Ina-MOA

BY

FSA Studies in Two Districts - 2015

West Java Province

Indramayu District

Garut District

Food Crops Area Stratification

Four Strata: S-1: Irrigated Land S-2: Rainfed Land S-3: Dry Land S-0: Not Farmland

1

By Probability Proporsional to Size (PPS)

300 m x 300 m

6 km

6 k

m

The stages of sample preparation

1. Creating grid size: 6 km x 6 km

2. Creating Sub-grid/Segment: 300 m x 300m

3. Selection Segment (1): 5% (20 Segment per grid) by ‘SRS’ with a threshold distance of ≥ 1 km

Grid

Indramayu District

Build Grid and Segments 2

Footer 23

Map of Segments (5 %) Overlay : Map of Strata and

Map of Segments

Total Segment (West Java)

Strata-1: 3,799 Segment Strata-2: 1,446 Segment Strata-3: 3,062 Segment Total: 8,307 Segment

Selection Segment (2): 1% With SRS

3

FSA in the District: Indramayu and Garut

Flat Area: District : 31 Segment: 186

Mountainous Area: District : 42 Segment: 169

Indramayu

Garut

4

30

0 m

300 m

Indramayu

The Point of Observa-tion (Ex: Indramayu District)

5

• Make ID on Segment: Prov, Distr, Sub Distr & Random Code

• Each segment is plotted on a topographical map

• Each segment is equipped with satellite imagery photo

• Each of the selected segment is divided into sizes of 100 m x 100 m

• The midpoint used as an observation point (9 per segment)

Equipment for Survey 6

The field survey aims to observe and record the growth phase of paddy/land cover at any point, then send an SMS to the server

Kode Visualisasi

the

gro

wth

ph

ase

of

pad

dy

Kode Visualisasi

1

5

Early Vegetative (1-35 days) Land Preparation

2

6

Thend of the Vegetatif (35-55 days)

Crop failure

3

7

Generative phase (55 days) Others

4

8

Harvested Not Farmland

7

Innas: Uji Implementasi Kerangka Sample Area Jabar

7

Exsample Utara

A B C

Baris-1 3 8 4

Baris-2 8 2 3

Baris-3 8 8 3

Selatan

3

8

4

3

3 8

2

8

8

8

System Delivery / Receipt Data by SMS

3212010 384 823 883

•Waktu survey •Segmen •Jumlah Fase •Syntax

9

The formula to calculate the area of paddy

(Extrapolating from the sample to the population)

hn

i

ih

hh

pn

p1

1

h

h

n

i

hih

hp

ppn 1

22

1

1

pDA hhh

H

h

hAA1

nSE hp

2 100(%) x

p

SECV

h

ph

ip

hn

Ah

A

Where: is the proportion of crops in stratum h is the proportion of crops in the sample segments - i is the number of segments on the sample stratum h ih is all i sampled segments in stratum h H is the number of stratum at the sub district Dh is the area of a region in the stratum h is the crops area in stratum h is the total area in the entire sub-district

10

Example: Data From Indramayu District 11

Sub District

Harvested Area

FUTURE PLAN: Implementation AFS

No Description 2016 2017 2018

1 Study in 4 Districts (West Java)

.

2 Building SFA for all Java

.

3 Implementing SFA in All Java

.

4 Building SFA for all Outside Java

.

5 Implementing SFA in All Outside Java

.

12

REPORTING

FACILITIES

Speed up the flow of data from Sub-District to Center

Production = Harvested Area x Productivity

Speed up the flow of data

B

Objective: 1. Accelerate the planting and harvesting of data from the

regions to the center every month 2. Makes analysis and forecast as Early Warning Systems (EWS)

Official data published every quarterly changed into monthly

MOA

Office of Province

Office of District

Field staff/KCD

(sub District)

Supervision

Supervision

Supervision

Arsip SP (1)

FLOW DATA FROM SUB DISTRICT TO JAKARTA

Database SP

BPS Jkt

BPS Prov

BPS District

Field Staff/KSK (Sub Distrikct)

Supervision

Supervision

-Entry -Verification, Validation - Supervision

Database SP

Database SP

Database SP

Coordination

Send Data 20 (Java), 25 (Outside Java)

Note: Change regular Coordination

(Montly)

Central

Province

District

Send Data 10 (Java), 15 (Outside Java)

Send Data 5 (Java), 10 (Outside Java)

Send Data 10 (Java), 15 (Outside Java)

Quarterly

TIME SCEDULE

Uraian The Next Month,

At the latest reporting Java Outside Java

KCD ► KSK (Sub District) 1) 5 5 KSK ► BPS Stat. District 1) 6 10 BPS Dist. ►BPS Province 2) 15 15 BPS Prov, ► BPS Central 2) 20 20

BPS Central ► CADIS 3) 25 1) Copy: Form AS-Paddy; Form AS-Secondary Food Crops

2) File data from entry

3) File data

System to Accelerate Food Crops Data

• Row data (Acces file database)

• Every 25th of the month, BPS Stat. Ina send to CADIS

• CADIS make analysis

EXAMPLE 1: TARGET VS REALIZATION OUTPUT

39

EXAMPLE 2: TARGET VS REALIZATION OUTPUT by PROVINCES

Note: Jan-March Data

EXAMPLE 3: THE ESTIMATION OF RICE HARVESTED AREA

REALIZATION ESTIMATION Planted Area (t-3) * 0.9654

EXAMPLE 4: THE ESTIMATION OF RICE PRODUCTION (TON)

REALIZATION ESTIMATION

Rice Prod.= Esti. of Harv. Area X Productivity

No. Provinces January February March April May June July Jan-July 2016

1 1100000 Aceh 50,795 197,822 558,073 349,646 81,263 79,795 78,076 1,395,470

2 1200000 Sumatera Utara 463,917 514,407 445,026 296,772 258,173 329,552 179,372 2,487,220

3 1300000 Sumatera Barat 207,369 207,645 253,312 234,711 229,376 180,429 174,655 1,487,496

4 1400000 Riau 33,932 131,855 81,456 31,243 32,914 46,870 26,331 384,601

5 1500000 Jambi 15,662 26,587 120,716 140,946 91,550 104,718 38,990 539,169

6 1600000 Sumatera Selatan 66,354 406,780 1,094,626 861,175 84,772 326,217 476,888 3,316,812

7 1700000 Bengkulu 24,903 28,438 112,224 116,593 95,888 65,400 34,079 477,526

8 1800000 Lampung 22,286 133,743 721,704 930,497 195,670 297,958 208,161 2,510,020

9 1900000 Kep. Bangka Belitung 16,293 23,570 19,402 5,285 7,467 1,688 807 74,511

10 2100000 Kepulauan Riau 103 129 37 294 9 71 5 648

11 3100000 Dki Jakarta 748 37 392 599 478 295 533 3,081

12 3200000 Jawa Barat 226,619 347,307 1,696,781 1,699,895 743,483 863,343 923,682 6,501,111

13 3300000 Jawa Tengah 277,322 631,480 2,047,683 1,573,441 549,040 1,189,942 963,927 7,232,834

14 3400000 Di Yogyakarta 11,352 48,668 323,665 86,739 30,853 68,647 79,129 649,052

15 3500000 Jawa Timur 213,824 522,015 2,718,245 2,024,671 636,100 1,103,491 1,475,415 8,693,760

16 3600000 Banten 29,710 48,956 425,177 416,434 139,051 49,892 211,470 1,320,690

17 5100000 Bali 26,880 19,764 59,045 79,511 95,656 69,846 22,995 373,697

18 5200000 Nusa Tenggara Barat 32,536 35,703 355,138 746,938 393,586 62,164 168,066 1,794,132

19 5300000 Nusa Tenggara Timur 17,507 7,020 60,328 309,487 235,270 184,788 22,448 836,848

20 6100000 Kalimantan Barat 278,603 593,448 577,468 183,888 11,988 198,349 80,074 1,923,819

21 6200000 Kalimantan Tengah 27,045 94,065 296,482 262,151 74,234 194,803 175,928 1,124,707

22 6300000 Kalimantan Selatan 342 13,537 222,168 405,545 334,384 603,169 318,320 1,897,466

23 6400000 Kalimantan Timur 2,544 23,006 81,094 93,201 34,609 8,626 7,417 250,497

24 6500000 Kalimantan Utara 20,418 30,829 8,583 3,070 383 627 348 64,259

25 7100000 Sulawesi Utara 38,811 42,834 60,176 83,825 59,369 51,835 33,109 369,959

26 7200000 Sulawesi Tengah 23,762 50,137 83,423 136,817 118,564 135,307 30,600 578,609

27 7300000 Sulawesi Selatan 62,780 71,293 522,408 978,068 622,671 367,092 318,400 2,942,712

28 7400000 Sulawesi Tenggara 39,762 17,734 12,483 49,037 137,889 162,432 69,843 489,181

29 7500000 Gorontalo 4,738 20,398 72,026 24,773 17,491 6,554 10,555 156,535

30 7600000 Sulawesi Barat 24,760 14,221 53,756 66,695 95,367 139,476 5,676 399,950

31 8100000 Maluku 749 7,590 8,624 15,078 14,138 2,617 444 49,240

32 8200000 Maluku Utara 4,569 4,232 14,956 13,140 7,359 7,110 3,507 54,873

33 9100000 Papua Barat 438 250 141 346 1,370 39 162 2,747

34 9400000 Papua 886 3,417 5,050 4,133 59,292 34,880 272 107,930

2016 2,268,318 4,318,917 13,111,866 12,224,645 5,489,709 6,938,021 6,139,684 50,491,160

2015 3,021,470 6,020,255 12,586,838 11,426,706 5,608,616 5,641,688 6,184,860 50,490,433

Differences 2016 to 2015 -753,152 -1,701,338 525,028 797,939 -118,907 1,296,333 -45,177 727

FUTURE PLAN: Integration of database 2017 and 2018

Development of forecasting methods of production

Production = Harvested Area x Productivity

C

Dependent Variable

Independent Variable

Simultaneous Econometric Models

Objective: 1. Building a projection model uses several variables

simultaneously 2. Predict the data for the next 5 years

Description Number of equation

Supply Side Model

1. Harvested Area

2. Productivity

3. Import

4. Production

5. Total Supply

1 - 5

6 - 10

11 – 14

15 – 19

20 - 24

Demand Side Model

1. Consumption per capita (rice, maize,

soybean, cassava, peanut)

2. National consumption

3. Demand of rice

4. Demand of maize

5. Demand of soybean

6. Demand of cassava

7. Demand of peanut

8. Supply and Demand (rice, Maize, Soybean,

Cassava, Peanut)

25 - 29

30 - 34

35 - 40

41 – 44

45 – 48

49 – 51

52 – 54

55 – 59

Supply and Demand equation

Eq 1. Harvested Area -Paddy

HAP = a0 + a1 HAPt-1) + a2 PrPaddy(t-1) + a3 PrMaize(t-1) +a4 PrSoybean(t-1) + µ1

Assumption of Parameter : a1, a2 > 0; a3, a4 > 0

Eq 6. Productivity - Paddy

YP = f0 + f1 YP(t-1) + f2 PrFertilizer(t-1) + f3 Tecnology + f4 D Policy + f5 Irigation +

f6 RLPPJ + µ6

Assumption of Parameter : f1, f2, f3, f4 , f5 > 0, f6 < 0

Eq 11. Import of rice

IB = ko + k1 Riceprod + k2 RiceCons + k3 PriceImport + k4 Pricedomestic + µ11

Assumption of Parameter : k2, k4 > 0 ; k1, k3 < 0

Eq 25. Rice Consumption (per cap/year)

RiceCons = o0 + o1 GDP + o2 Conspriceindex + o3 RiceCons(t-1) + µ12

Parameter estimasi yang diharapkan: o1, o3 > 0 ; o2 < 0

EXAMPLE. Equation

Etc…Eq 59.

The same variable

EXAMPLE. Analysis Procedures

1. Proc Syslin by SAS Software 2. Proc SimNlin by SAS Software 3. Forecasting with Simulation

EXAMPLE. The output of the production forecast modeling

losses Feed SeedNon Food

Industrylosses Animal Feed

Non Food

Industry

5.40 0.40 0.90 0.60 62.74 2.50 0.17 0.66

2011 65,756,904 3,550,873 263,028 591,812 394,541 60,956,650 38,244,202 956,105 65,015 252,412 36,970,670

2012 69,056,126 3,729,031 276,225 621,505 414,337 64,015,029 40,163,029 1,004,076 68,277 265,076 38,825,600

2013 71,279,218 3,849,078 285,117 641,513 427,675 66,075,835 41,455,979 1,036,399 70,475 273,609 40,075,495

2014 1) 70,846,465 3,825,709 283,386 637,618 425,079 65,674,673 41,204,290 1,030,107 70,047 271,948 39,832,187

2015 2) 75,550,895 4,079,748 302,204 679,958 453,305 70,035,680 43,940,385 1,098,510 74,699 290,007 42,477,171

2016 2) 77,245,271 4,171,245 308,981 695,207 463,472 71,606,366 44,925,834 1,123,146 76,374 296,511 43,429,804

2017 2) 79,370,274 4,285,995 317,481 714,332 476,222 73,576,244 46,161,736 1,154,043 78,475 304,667 44,624,550

2018 2) 81,495,277 4,400,745 325,981 733,457 488,972 75,546,122 47,397,637 1,184,941 80,576 312,824 45,819,296

2019 2) 83,620,280 4,515,495 334,481 752,583 501,722 77,516,000 48,633,538 1,215,838 82,677 320,981 47,014,041

Paddy

Production

(tonne)

Paddy

Production

(tonne)

Paddy (tonne)Rice

Production

(tonne)

Non Food Rice Utilization (tonne) Rice

Availibility

For

Consumption

(tonne)

Year

Paddy Production Forecast

FUTURE PLAN: Build projection models integrated with other commodities

1. Adding Commodities in to forecasting models

2. Adding variables are interconnected into the system simultaneously

3. Adding the simulation process on the independent variables

Conclusion 5

1. As a big country, the challenge for Indonesia should be able to do a self-sufficiency that have been planned. It requires data for monitoring.

2. Monitoring data needs to be important, both in terms of accuracy and speed of getting data

3. Improvements are being made, particularly in terms of methodology, namely: a) a method to collect planting area with “Area Frame Sampling”, b) accelerating the flow of data and c) develop methods of forecasting data.

4. Completion of the methodology will continue, with the cooperation among government agencies, such as: MOA, BPS Statistics Indonesia and BPPT

THANK YOU