COPING STRATEGIES WITH AGROMETEOROLOGICAL RISKS … · COPING STRATEGIES WITH AGROMETEOROLOGICAL RISKS AND UNCERTAINTIES FOR DROUGHT EXAMPLES IN BRASIL Agronomic Institute – CIIAGRO

NATIONAL METEOROLOGICAL INSTITUTE NATIONAL METEOROLOGICAL INSTITUTE -- SÃO PAULO STATE GOVERNMENT SÃO PAULO STATE GOVERNMENT --–– BRAZILBRAZIL

COPING STRATEGIES WITH COPING STRATEGIES WITH AGROMETEOROLOGICAL RISKS AND AGROMETEOROLOGICAL RISKS AND

UNCERTAINTIES FOR DROUGHTUNCERTAINTIES FOR DROUGHTEXAMPLES IN BRASILEXAMPLES IN BRASIL

Agronomic InstituteAgronomic Institute –– CIIAGRO CIIAGRO andand NationalNationalMeteorologicalMeteorological InstituteInstitute –– BrazilBrazil


O. Brunini, P. L. O. Brunini, P. L. AbramidesAbramides, G. C. , G. C. BlainBlain,,A. P. C. Brunini, J. P. de CarvalhoA. P. C. Brunini, J. P. de Carvalho

AgronomicAgronomic InstituteInstitute –– CNPq CNPq –– CIIAGRO CIIAGRO –– FUNDAG FUNDAG –– SãoPauloSãoPauloStateState GovernmentGovernment -- Av. Barão de Av. Barão de ItapuraItapura, 1481 13020, 1481 13020--901 901 ––

Campinas Campinas –– SPSP-- Brasil Brasil –– [email protected]@iac.sp.gov.br

Y. M. T. da AnunciaY. M. T. da AnunciaçãçãooL. T.G. Fortes,L. T.G. Fortes,

NationalNational MeteorologicalMeteorological InstituteInstitute –– Eixo Monumental Via Eixo Monumental Via -- S1S1Sudoeste Sudoeste –– 7068070680--900 900 –– Brasília Brasília –– DF DF -- BrazilBrazil


CLIMATE OF SOME REGIONS


Seasonal variation of the Palmer Drought Severity Seasonal variation of the Palmer Drought Severity Index the month of October in the regions of Index the month of October in the regions of AssisAssis and and

Votuporanga Votuporanga in the State of in the State of SãoSão Paulo.Paulo.


The 1997The 1997--1998 El1998 El--NiñoNiño caused an extreme drought caused an extreme drought in the northeastern region with considerable losses in the northeastern region with considerable losses

for agriculture, livestock, water resources and for agriculture, livestock, water resources and society. society.

In the southeastern region, for example, in the In the southeastern region, for example, in the State of State of SSããoo Paulo in the ElPaulo in the El NiNiññoo period, the period, the

effects caused by this phenomenon were quite effects caused by this phenomenon were quite different with above average rainfall in months different with above average rainfall in months

like May and June.like May and June.


Monthly precipitation anomalies as indicated by the monthly Monthly precipitation anomalies as indicated by the monthly SPI (SPISPI (SPI--1)1)

for the month of May in 1998 in the State of for the month of May in 1998 in the State of SãoSão Paulo.Paulo.

AGROMETEOROLOGY ADVISORY SYSTEMAGROMETEOROLOGY ADVISORY SYSTEM


The Ministry of Agriculture at Federal level and the The Ministry of Agriculture at Federal level and the Agricultural Secretariat of the Agricultural Secretariat of the SãoSão Paulo State Paulo State

GovernemntGovernemnt , at State level, apply the reports and , at State level, apply the reports and bulletins of drought monitoring in the bulletins of drought monitoring in the

Agricultural Activity Assurance Program Agricultural Activity Assurance Program (PROAGRO) and as a subsidy to the (PROAGRO) and as a subsidy to the

Agricultural/Livestock Expansion and Agricultural Agricultural/Livestock Expansion and Agricultural Insurance (FEAP)Insurance (FEAP)

for the federal , and for for the federal , and for SãoSão Paulo State Paulo State government, respectivelygovernment, respectively. .


Regionally, there are programs that involve research Regionally, there are programs that involve research institutions and the community in order to minimize risks for institutions and the community in order to minimize risks for

agriculture during drought situations. agriculture during drought situations.

There is already some specific forecast for drought, using the There is already some specific forecast for drought, using the climate prognosis indicating beforehand if there is a climate prognosis indicating beforehand if there is a

probability of precipitation remaining below or above the probability of precipitation remaining below or above the normal. normal.


METHODOLOGIES TO ASSESS PRECIPITATION METHODOLOGIES TO ASSESS PRECIPITATION ANOMALY AND DROUGHTANOMALY AND DROUGHT

1.1. METEOROLOGICAL INDICES METEOROLOGICAL INDICES

2.2. AGROMETEOROLOGICAL INDICESAGROMETEOROLOGICAL INDICES


METEOROLOGICAL INDICESMETEOROLOGICAL INDICES

1.1. SPI SPI -- Standardized Precipitation IndexStandardized Precipitation Index

2. Palmer Drought Severity Index Adapted To The 2. Palmer Drought Severity Index Adapted To The State Of State Of SãoSão Paulo Paulo -- PdsiPdsi AdapAdap

3. 3. DecileDecile MethodMethod

4. 4. QuantileQuantile MethodMethod


The final expression for K =(22,8KThe final expression for K =(22,8K’’)/)/ΣΣDKDK’’PDSIadapiPDSIadapi = (= (ZiZi /0,94) + 0,15*/0,94) + 0,15*(PDSIadapi(PDSIadapi--1)1)

Extremely Dry≤ - 3,00Severely Dry-2,00 a-2,99Moderately Dry-1,00 a -1,99Slightly Dry -0,51 a -0,99Near Normal0,50 a -0, 50Slightly Wet 0,51 a 0,99Moderately Wet1,00 a 1,99Severe Wet2,00 a 2,99Extremely Wet≥ 3,OOCategoriesPDSI adap


Numeric scale indicating the estimated probability through the historical values of precipitation, in order to verify if a specific recorded rainfall

value is smaller, equal to or larger than the historical case.


AGROMETEOROLOGICAL INDICESAGROMETEOROLOGICAL INDICES1.1. Actual Actual EvapotranspirationEvapotranspiration Standardized Index Standardized Index

(IPER)(IPER)

2. Crop Moisture Index 2. Crop Moisture Index (CMI)(CMI)

3. Crop Development As A Function Of Soil 3. Crop Development As A Function Of Soil Moisture Moisture

4. Soil Water Supply Conditions And Water Stress 4. Soil Water Supply Conditions And Water Stress On A CropOn A Crop


ACTUAL EVAPOTRANSPIRATION ACTUAL EVAPOTRANSPIRATION STANDARDIZED INDEX (IPER)STANDARDIZED INDEX (IPER)

Blain andBlain and BruniniBrunini (2006), and based on the SPI (2006), and based on the SPI methodology ,this index begins with the adjustment methodology ,this index begins with the adjustment of the beta probability density function to the series of the beta probability density function to the series

of water balance in a ten days step. of water balance in a ten days step.

After this phase, the cumulative probability of a After this phase, the cumulative probability of a given estimated value for ETR is calculated. given estimated value for ETR is calculated.


Considering that the beta distribution is defined Considering that the beta distribution is defined in the interval [0 and 1] and that the de average in the interval [0 and 1] and that the de average temperatures in the State of temperatures in the State of SãoSão Paulo do not Paulo do not allow allow decendialdecendial ETR values above 100mm, the ETR values above 100mm, the following variable transformation was chosen:following variable transformation was chosen:

ETR’’ = ETR/100 ETR’’ = ETR/100

ETR’’ actual ETR’’ actual evapotranspirationevapotranspiration variable variable transformed so that 0 < ETR’’ < 1transformed so that 0 < ETR’’ < 1


-8,0

-6,0

-4,0

-2,0

0,0

2,0

4,0

1 /1 / 05 29 /1 / 05 26 /2 / 05 26 /3 / 05 23 /4 / 05 21 /5 / 05 18 /6 / 05

IPER

CAMPINAS MOCOCA PINDORAMA RIBEIRÃO PRETO


Arbitrary correspondence between the IPER values and the droughtcategories to address the crop water requirements

Extremely DryIPER< -2,0 Severely Dry-1,1 to 1,99

Moderately Dry-0,5 to -1,0Near NormalIPER > -0,5

CATEGORIESIPER VALUES


CROP DEVELOPMENT AS A FUNCTION OF SOIL CROP DEVELOPMENT AS A FUNCTION OF SOIL MOISTUREMOISTURE

CWDF = DAAS/DISPMAXCWDF = DAAS/DISPMAX0 < ≡ CWDF < = 10 < ≡ CWDF < = 1

DAAS = 0 implies in CWDF = 0DAAS = 0 implies in CWDF = 0DAAS = DISPMAX indicates that CWDF = 1DAAS = DISPMAX indicates that CWDF = 1


(CWDI), considers the average characteristics of soil (CWDI), considers the average characteristics of soil moisture and the crop.moisture and the crop.

CWDI = [{(CWDF/0.40)CWDI = [{(CWDF/0.40)--1}]1}]

CriticalCWDI < -0,75Severe-0,75 <= CWDI < -0,5

Harmfully-0,5 <= CWDI < -0,25Not favorable-0,25 <= CWDI < 0,0Reasonable0,0 <= CWDI < 0,5Favorable0,5 <= CWDI < 1,0

Good1,0 <= CWDI <= 1,5

CONDITIONS RELATED TO THE PLANT WATER SATISFACTION

INDEX

SOIL MOISTURE INDEX


SOIL WATER SUPPLY CONDITIONS AND WATER SOIL WATER SUPPLY CONDITIONS AND WATER STRESS ON A CROP STRESS ON A CROP

Z1 (25 cm) = potato, onion,Z1 (25 cm) = potato, onion,

Z2 (50 cm) = beans, peanuts, corn, sorghumZ2 (50 cm) = beans, peanuts, corn, sorghum

Z3 (75 cm) = soybean, citrus, sugarcane, cottonZ3 (75 cm) = soybean, citrus, sugarcane, cotton

Z4 (100 cm) = coffee , citrus, sugarcaneZ4 (100 cm) = coffee , citrus, sugarcane


Arbitrary relationship between the Crop Water Stress Index Arbitrary relationship between the Crop Water Stress Index (CWSI) and the plant agrometeorological development (CWSI) and the plant agrometeorological development

conditionsconditions

Critical0,8 <= CWS <= 1,0Not Favorable0,6 <= CWS < 0,8

Reasonable0,4 <= CWS < 0,6Ordinary0,2 <= CWS < 0,4Favorable0,1 <= CWS <= 0,2

Good0 = CWS < 0,1

PLANT DEVELOPMENT CONDITIONS

CROP WATER STRESS INDEX


-1,5

-1

-0,5

0

0,5

1

1,5

Adamantina

Andradina

Ara çatubaAraraquara

Bebedouro

CampinasFranca

Ilha Solte

ira

Jaboticabal

Mococa

Registro

Ribeirão PretoTaquaritu

baTarum

ãVotuporanga

CWDI

cwdi-25 cwdi-75


METEOROLOGICAL ASPECTS OF METEOROLOGICAL ASPECTS OF DROUGHT MONITORING AND DROUGHT MONITORING AND

PREDICTION PREDICTION



Rainfall anomaly for the period October 2001 in São Paulo State based on the SPI values

(SPI-1).


CIIAGRO and INFOSECA in the State of São Paulo, climatic prognosis is not carried out, but the INMET/INPE prognosis are used for agricultural purposes and planning.

-0,63-0,66-0,69Uberaba-MG-0,72-0,84-0,96Paranavaí-PR-0,75-0,87-0,99Maringá -PR-0,78-0,9-1,02Joaquim Távora -PR-0,81-0,93-1,05Cambará-SP-0,40-0,58-0,75São José do Rio Preto -SP-0,43-0,60-0,78Ribeirão Preto -SP-0,46-0,63-0,81Piracicaba -SP-0,49-0,66-0,84Jaú -SP-0,52-0,69-0,86Catanduva -SP-0,55-0,72-0,89Araçatuba -SPSeptemberAugustJulyLocality

ESTIMATED MONTHLY VALUES FOR THE SPI ( SPI-1)


AGROMETEOROLOGICAL ASPECTS OF DROUGHT

CROP YIELD

WATER MANAGEMENT

IRRIGATION PURPOSES


DECREASING PRODUCTIVITY FOR SUGARCANE AND THE SPI VALUES ON THE NINE-MONTH SCALE (SPI-9)

FOR THE RIBEIRÃO PRETO – SP, REGION.

0,6

0,8

1

1,2

1997

1998

1999

2000

2001

2002

2003

2004

2005

crop calendar- year

Rel

ativ

e Yi

eld

-1,5

-1

-0,5

0

0,5

1

Ave

rage

SPI

Val

ues:

(oct

-may

)

crop yield SPI


YIELD OF THE OFF SEASON MAIZE AND THE AVERAGE MONTHLY VALUES OF SPI


DROUGHT MONITORING AND MITIGATION CENTER

The State of São Paulo, through the Agronomic Institute (IAC) in a partnership with the State Extension Service Agency

CATI created the INFOSECA

(Drought and HydrometeorologicalAdversities Mitigation and Monitoring

Center),


DROUGHT MONITORING AND MITIGATION CENTER

The work of INFOSECA allows systematically following up on the evolution of drought conditions in the State, proposing mitigating and relief measures, as well as

physical and agronomic processes to bypass the problem. These processes may include future prognosis of the drought conditions, and it is available at the site:

www.infoseca.sp.gov.br




Average conditions of water stress for the off-season corn crop (Z=50cm) and for the sugarcane crop (Z=100cm) in the period ranging from March 1, 2006 to April 30, 2006

Harmfully0,23(Sugar cane-full development stage )100Jaboticabal

Extremely Severe-0,31(Maize -tasseling period) 50Jaboticabal

Extremely Severe-0,27(Maize -early stage) 25Jaboticabal

Good0,65(Sugar cane-full development stage) 100Guariba

No Favorable0,33(Maize- tasseling period stage ) 50GuaribaExtreme Severe0,03(Maize-early stage) 25Guariba

CondiçõesACWDIRooting DepthLocality


CLIMATIC RISK ZONING

Crop Water Requirement Index (CWRI), CWRI = (ETR/ETM

ETR – actual crop evapotranspiration ;andETM – maximum crop evapotranspiration, as defined by


Probability of water supply during the tasseling period of the maize crop in the State of São Paulo. Between the 1st and 10th

of October (source : Brunini et al 2001).


(CLIMATIC RISK ASSESSMENT SYSTEM AND AGROMETEOROLOGICAL MONITORING OF CROPS).

In this process, climatic risks related to drought are assessed as well as the probability of addressing the

water demand for any crop, being it annual or perennial. The likelihood of addressing the water requirement is

made on the beta distribution (β), that the best represents the agro-system being analyzed, since the

ETR/ETM ratio has values between 0 and 1.


Probability of attending crop water demands during specific phenologicalphases of the corn crop planted between January 1st and 5st, and the risk of

high or low air temperature

210099,980,01Ripening11/4

210099,980,01Ripening6/4

210099,980,01Ripening1/4

210099,960,03Tasselling6/3

210099,980,01Tasselling1/3

210099,980,01Tasselling26/2

210099,980,01Tasselling21/2

210099,980,01Tasselling16/2

130099,980,01Fast growing6/2

130099,980,01Fast growing1/2

60099,960,03Sowing6/1

60091,628,03Sowing1/1

Dry Spell Max-Days

Dry Spell Min-Days

Frost risk%

ProbETR/ETMp>=0,8

ProbETR/ETM0,7<p<0,8

Phenological StageDay/Month

Prob – Probability function


GEOINFORMATION SYSTEM

Two basic tools are used for this–SURFER and ARCIS.


Average condition of water stress on the maize crop in the State of SãoPaulo during the month of March 2006, by the Surfer system.


Average conditions of water stress on the maize crop in the State of São Paulo with overlapping of the areas with minimum air temperature below 14°C, by the Arc-Gis

system.

NATIONAL METEOROLOGICAL INSTITUTE NATIONAL METEOROLOGICAL INSTITUTE -- SSÃÃO PAULO STATE GOVERNMENT O PAULO STATE GOVERNMENT --–– BRAZILBRAZIL

- CONCLUSIONS

1--Drought is a constant phenomenon in agriculture in Brazil, thus requiring continuous prediction and monitoring to provide

valuable mitigating measures.

2--As a result of the territorial extension, the mitigating measures are not necessarily identical and must take into consideration the cultural aspects of

the population, the climate regime, and the agricultural exploitation.

3-The various indices presented have proven to be adequate for monitoring and mitigating the effects of drought, nevertheless, adjustments

are necessary for the use of these indices for each region and crop.For the PDSI, the parameters of the

equations should be estimated for each region in Brazil.


- CONCLUSIONS-cont

4--Every state should create a Drought Monitoring and Mitigation Center, subordinated to the State‘s Agricultural Secretariat. It should be the

responsibility of the National Meteorology Institute, in association with state agencies, to propose norms and to define standards and policy for the

monitoring and mitigation of drought on a regional and nationwide scale.

5--It should be understood that the drought phenomenon cannot be assessed and interpreted by only one field of expertise, but rather by a set of

specialists and institutions. Furthermore, it is extremely important that researchers and specialist in the areas of agronomy, agrometeorology,

meteorology, civil defense, agro extension service, and others, should be involved in the study of the drought phenomenon.


MUITO OBRIGADO - THANK YOU

http://ciiagro.iac.sp.gov.br

www.infoseca.sp.gov.br

COPING STRATEGIES WITH AGROMETEOROLOGICAL RISKS … · COPING STRATEGIES WITH AGROMETEOROLOGICAL RISKS AND UNCERTAINTIES FOR DROUGHT EXAMPLES IN BRASIL Agronomic Institute – CIIAGRO

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