jurandir zullo junior jurandir@cpa.unicamp.br effective use of agrometeorological systems and...

Jurandir ZULLO JuniorJurandir ZULLO Junior jurandir@cpa.unicamp.brjurandir@cpa.unicamp.br

Effective use of agrometeorological Effective use of agrometeorological systems and informationsystems and information

Expert Meeting on Weather, Climate and Farmers Geneva, November 15-Expert Meeting on Weather, Climate and Farmers Geneva, November 15-18, 200418, 2004

Presentation Outline

Brazilian Agricultural Zoning

Agrometeorological operational systems in Brazil – regional and national

Effects of climate change (IPCC) in Brazilian Agricultural Zoning

Agricultural Zoning

Agribusiness GDP in 2003: US$ 165 billion (30% GDP - US$ 500 billion)

Agribusiness Exportations: US$ 30 billion (40%) 30% of jobs Grain production in 2003: 122 millions tons

Program coordinated by the Department of Agriculture and EMBRAPA (National Institute for Agricultural Research) since 1995

Federal Farm Credit PolicyUS$ 8 billion (US$ 2.5 billion for small farmers)

Main Objective Decrease the climate risks

dry spells during flowering and grain-filling (60%) excessive rain during the harvest period (30%)

Product

Planting Calendars

Crops: Corn, Soybean, Beans, Rice (Wheat, Sorghum,

Fruits, Coffee)

Periods: Dekad (10 days), Oct to Dec (Jan to Dec)

Counties / Municipal districts

Macro-regions: Central, Southeast and South

(Northeast)

Risk: less than or equal to 20%

25% - 50%50% - 75%

75% - 100%

Probability of 10-days dry spellsJanuary - BAHIA

25% - 50%50% - 75%

75% - 100%

Probability of 10-days dry spellsFebruary - BAHIA

Methodology

Simulation of cumulative water balance for different soil types, planting dates, cycle lengths, ground stations

Decision criteria / Risk Analysis:a) WRSI during reproductive stage Threshold Value

(80% of all simulated years) WRSI: Water Requirement Satisfaction IndexWRSI = actual / maximum evapotranspiration

b) Excessive rain during harvest50mm accumulated in 3 days of a 5-days period

Preliminary Data Collection

Length of four growth stages (Initial, Vegetative, Reproductive and Maturity) for early and normal cultivars

Length of total growing period

Crop coefficient (Kc)

Soil water holding capacity

Rainfall

Evapotranspiration

Initial

Vegetative Stage

Reproductive Stage

Maturity

LENGTH OF GROWTH STAGES

RAINFALL

CLIMATERISK ZONING

PETCROP

COEFFICIENT

COMPUTE ZONING

CUMULATIVE WATER BALANCE

SOIL TYPE PLANTING

soybeansoybean

600.000 SIMULATIONS600.000 SIMULATIONS

corncorn

2.500.000 SIMULATIONS2.500.000 SIMULATIONSbeansbeans

500.000 SIMULATIONS500.000 SIMULATIONSricerice

wheatwheat

Soybean

Early Cultivar

Clay Soil

Dekad: Oct, 1st

Goiás (341.000km2)

F AV O R Á V E L

D E S F AV O R Á V E L

I N T E R M E D I Á R I A

5 0 k m

Z O N E A M E N T O A G R O C L IM Á T IC O D A C U LT U R A D A SO JA N O E S TA D O D E G O IÁ S

C I C L O : P R E C O C E S O L O : T IP O 3 S E M E A D U R A : 0 1 /1 0 a 1 0 /1 0

M A A /F IN AT E C /E M B R A PA -C N P S o/D N A E E /IN M E T

Soybean

Early Cultivar

Clay Soil

Dekad: Nov, 1st

Goiás (341.000km2)

F AV O R Á V E L

5 0 k m

C I C L O : P R E C O C E S O L O : T IP O 3 S E M E A D U R A : 0 1 /11 a 1 0 /11

M A A /F IN AT E C /E M B R A PA -C N PS o/D N A E E /IN M E T

Soybean

Early Cultivar

Clay Soil

Dekad: Dec, 1st

Goiás (341.000km2)

F AV O R Á V E L

5 0 k m

C I C L O : P R E C O C E S O L O : T IP O 3 S E M E A D U R A : 0 1 /1 2 a 1 0 /1 2

M A A /F IN AT E C /E M B R A PA -C N P S o/D N A E E /IN E M E T

Soybean

Early Cultivar

Clay Soil

Dekad: Nov, 1st

Minas Gerais (588.000km2)

50º 18 ' 44º 57 ' 39º 37 '

21º 55 '

18º 02 '

14º 10 '

C IC L O : P R E C O C E S O L O : T IP O 3 S E M EA D U R A : 01 /11 a 10 /11

M AA/FINATE C/EM B RAPA-CNPSo-CPAC/DNAEE /INM ET

F A V O R Á V E L

D E S F A V O R Á V E L

ZO NEAMENTO AG RO C LIMÁTIC O DA C ULTURA DA SO J ANO ESTADO DE MINAS G ERAIS

Soybean

Early Cultivar

Clay Soil

Dekad: Dec, 1st

Minas Gerais (588.000km2)

50º 18 ' 44º 57 ' 39º 37 '

21º 55 '

18º 02 '

14º 10 '

C IC L O : P R E C O C E S O L O : T IP O 3 S E M EA D U R A : 01 /1 2 a 1 0/12

M AA/FINATE C/EM B RAPA-CNPSo-CPAC/DNAEE /INM ET

F A V O R Á V E L

D E S F A V O R Á V E L

ZO NEAMENTO AG RO C LIMÁTIC O DA C ULTURA DA SO J ANO ESTADO DE MINAS G ERAIS

www.inmet.gov.brwww.inmet.gov.br

www.agritempo.gov.www.agritempo.gov.brbr

Soil Water Content – São Paulo – Sep/16 – Soil Water Content – São Paulo – Sep/16 – 20/200420/2004

Soil Water Content – São Paulo – Oct/15 – Soil Water Content – São Paulo – Oct/15 – 19/200419/2004

Maximum Temperature – São Maximum Temperature – São PauloPaulo

Rainfall – São PauloRainfall – São Paulo

Frost Risk – Frost Risk – ParanáParaná

www.cpa.unicampwww.cpa.unicamp.br.br

Results

Organization of federal farm credit policy and

insurance

Decrease of agricultural losses due to climate events

(Insurance debt = US$ 637 million accumulated in 5

years)

Even self-financed farmers are using the zoning

Increase: productivity, production and use of

technology

Losses of US$ 33 millions in 2002: farmers growing

corn during fall and winter in unsuitable (high risk)

Frauds decrease and inhibition

Availability of data for agricultural planning

Demand for new crops, regions and periods

Questions

What can happen to agricultural zoning in next decades ?

Will the suitable areas change in the case of new climate conditions ? (decrease ?, increase ?, migrate ?)

Methodology

Prepare new zonings simulating different climate conditions according to IPCC report (2001)

Climate scenarios: Current, Increase of 1°C, 3°C and 5.8°C in Temperature and Increase of 15% in precipitation

Climate Conditions Suitable for Coffee Production

Average Annual Temperature: 18°C to 22°C (up to 23°C)

Annual Water Deficit: 0mm to 100mm (up to 150mm)

Absolute Minimum Temperature: > 1°C (Frost Risk)

Total Area: 249.000km2

Further Works

Simulations for other crops

Simulations for other states

Use of other models and analysis of climate

change

Assessment of climate change

Jurandir ZULLO JuniorJurandir ZULLO Junior jurandir@cpa.unicamp.brjurandir@cpa.unicamp.br

Effective use of agrometeorological Effective use of agrometeorological systems and informationsystems and information

Agricultural Losses - Seasons 91/92 and 92/93Crop S. PAULO BAHIA NORTHEAST

Rice 30% 34% 47%

Beans 21% 34% 41%

Corn 16% 37% 71%

Soybean 16% 29% -

I rrigated Soybean

- - 32%

Cotton 22% 22% 81%

Factors / causes:

Dry spells during reproductive stage (flowering and grain-filling) - 60%

Excessive rain during harvest - 30%

(Planting after the first rainfalls of spring)

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