211---+--t-e
81ã lIO"
,--+---1---+---+---;20"
Fig.17.S. Variation ofSPI on a monthly scale (SPI-I) for the month of December 1963 in the State of Sao Paulo.
211---1f---_+_
81ã lIO" ....
48"
-SPl12
,--+---1---+---+---;20"
Fig.17.9. Variation ofSPI on and yearly scale (SPI-12) for the month of March 1964 in the State of Sao Paulo.
ff"r
20"
..
21 21-
0.0 -oJI
22"
".II
".6
4.0 2r
..2.6
24-
.,2lr
l52" a1' lIO" .... ... 4r 48" ... ...
Fig.17.10. Rainfall anomaly for the period October 2001 in Sao Paulo State based on the SPI val- ues (SPI-1).
SPI for the period 5eptember-Oclober.November 2005
• pori!!
..
Fig.17.11. Variation of the SPI for the months from September to November 2005, on a quarterly scale (SPI-3) for the State of Rio Grande do SuI, highlighting the location of the Auto-mated Me- teorological Stations.
~...
~MU"
=1Ud1...ta
Chapter17: Coping Strategies with Agrometeorological Risks 303
~ IIaIxa
~ ..
= AIta
Dt5tr~JtIlyso:lieplObiIlIllfdalle(%)~1lCOn8nc"de dlWCS!mr,"~a m8thhtitenca
~ AbaxoArJ"P16"I t.amAdilldillimidllm6c1Nihildn., ..~jllt6riclh .
,ureQi:>e9 IIacht.lndaa indlcllm I cnrfi,b'idade cax&viBJioIvide!£gEnes na figu'a:
Ohst.lbulqiiodeprobabilkl.adejo/.jdeoc:orr6nda de ctlUVtiemret~a mlllliahtitOrlca
§Acima dam6d1ahistDrica Pr611imaa ..m6d1ahlabarlca AbaIxo da m8dia hillt6ric.
AsregiOee hachuradas iodicama confiabilidade da prevls.'io(vIde legends na figural Fig. 17.12. Summary of the climatic prognosis corresponding to the December2005to February 2006,and January to March2006quarters, prepared by CPTEC/INPE and INMET.
(INPE) periodically carry out a climatic prognosis indicating if the meteorologi- cal conditions, especially precipitation, will be above or below the historic average (Figure 17.12). This trend is used by several institutions especially those dedicated to agricultural planning.
Figure 17.13 presents the prognosis for precipitation prepared for the State of Rio Grande do SuI by the 8th Meteorological District ofINMET, together with the Me- teorology Department of the Pelotas Federal University (CPPMetIUFPEL, 2005), for the months of Ianuary, February and March 2006.
For CIIAGRO and INFOSECA in the State of Sao Paulo, climatic prognosis is not carried out, but the INMET/INPE prognosis is used for agricultural purposes and planning. One of the uses is to establish a monthly prognosis of the SPI and its effect on agriculture. Since the climatic likelihood for the coming months was of precipitation below the average the SPI trend was projected as a function of the possibility of this event for the July-August quarter (Table 17.10). Since results indi- cate the persistency of the meteorological drought at least to the end of September in the regions comprising the states of Parana, Sao Paulo and Minas Gerais, pro- jecting unfavorable conditions for the sugarcane, coffee and citrus crop, and a de- lay for summer crops planting.
- 2 7 . 0 - r - - - ,
Above Normal Slightly Above
Normal Normal Slightly Below
Normal Below Normal
/'11ta~~
~!?12.0 -32.0
-JJ.5 -31.5
-32.5 -33.0 -30.5 -29.5 -29.0
-31.0 -28.0 -27.5
-30.0 -28.5
-3~ã~58.0 -57.0 -56.0 -55.0 -5+.0 -53.1> -52.0 -51.0 -50.0 --49.0 -48.1>
Fig.17.B. Prognosis for precipitation in the State of Rio Grande do Sui in the months from Janu- ary to March 2006. The areas represented in white indicate rainfall in the climatological average, yellow below average and blue above-average (source: www.inmet.gov.br/climatologia/cond-cli- ma/bol-dez200S.pdf
17.3.2
Agrometeorological Aspects of Drought
Several institutions in Brazil try to make quantification and the monitoring of drought from a meteorological and agronomic standpoint. Some examples are the Ceara Meteorological Foundation (FUNCEME), National Meteorology Institute (INMET) and the National Space Research Institute (INPE). Nevertheless, few of these institutions routinely consider the assessment and characteristics of this phe- nomenon towards agriculture and civil defense, embracing agronomy soil charac- teristics and crop behavior.
With regards to the SPI, several assessments have been made specifically for the southern region of Brazil, the INMET has tried to compare the behavior of the crops with the SPI values. Figure 17.14 shows the behavior of soybean yield and the SPI index for six months (SPI-6) calculation based on data from the Passo Fundo (soybean producing region) and Santa Maria, for the period ranging from Octo- ber to March. The estimation for the index for the October 2005 and March 2006 semester, and the forecast for the next crop, performed by CONAB, are indicat-
Chapter 17: Coping Strategies withAgrometeorological Risks 305 Table 17.10. Estimated monthly values for the Standardized Precipitation Index (SPI-1) in relation to the prognosis of rainfall
ed with distinct colors and highlighted by the oval (source production - CONAB 2005). The results highlight the importance of analyzing crop yield and rainfall patterns.
Concerning the State of Sao Paulo, the adverse effect of these precipitation anomalies has been assessed for some crops. For example, the assessment of sugar- cane yield in the Ribeirao Preto region, demonstrated a good correlation between the SPI values in 9-month scales (Figure 17.15) and sugar yield. Normally the grow- ing period for the crop is from September to May, in which accumulation and the increment of dry matter is directly influenced by the climate and in such a case. The SPI for May with the 9-month recurrence (SPI-9) adequately reflects the water con- ditions in this soil for this crop. But for maize in the off-season cropping in the As- sis region when planting is performed between January and March, it is observed that the averaged SPI on a monthly scale adequately reflects the water conditions for this crop. This relationship is presented on Figure 17.16, and a good relationship between the SPI and the productivity levels can be observed.
Another parameter that is adequately related to the agricultural production is the Palmer Drought Severity Index (PDSI). The relationship between the average PDSI adap values and maize yield in the State of Sao Paulo is presented in Figure 17.17, indicating the potential of this easily used index. These results are quite will correlated to overall maize grain production in the State, and the same figures were observed in the2005/2006crop growing season.
Passe Fundo
- - yield -+-SPI
<1:1... .d,J, .s)p df ~ .~ &- ..s/i1 .sR> .$> .t\... .f!,l, ~ .c:t .@ .iP
SANTAMARIA
/~.,.
..
.-~..
- - yield -+- SPI
Fig. 17.14. Comparison between soybean yield and the values for the SPI on a six-month scale (SPI-6) for the State of Rio Grande do SuI, considering the period from October/OS to March/06.
17.3.3
Drought Monitoring and Mitigation Center
The State of Sao Paulo, through the Agronomic Institute (lAC) in a partnership with the State Extension Service Agency CATI created the INFOSECA (Drought and Hydrometeorological Adversities Mitigation and Monitoring Center), an op- erational system that brings immediate reports of the actions and effects of meteo- rological adversities upon agriculture and proposes ways of monitoring and miti- gating the negative impact of these adversities, most notably, drought.
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 is available at the site:
www.infoseca.sp.gov.br.
Chapter17: Coping Strategies with Agrometeorological Risks 307
>;
0,5 1\1E
*
0 ~:::s
-0,5 ~ a::II)
•
Cl
-1 l!
•>
ô
0,6 """"""""""" """" """""" -1,5
....# '),<§''\i '),<§'.... '),<§''), '),'\i'\i"J crop calendar- year
Fig.17.15. Relationship between the decreasing productivity for sugarcane and the SPI values on the nine-month scale (SPI-9) for the Ribeirao Preto - SP, region.
4 1,6
- - Crop Yield -+-SPI
3,5 1,1 ';:
:I
'; 3 ~
1 ..
! 0,6 t
t .2.5 0,1 j:110i:
,I. 2 ..
II: • ...
-0,4 =
1,5 0<ã
-0,9
1990 1992 1994 1996 1998 2000 2002 2004
Crop C81end8r)I88F
Fig. 17.16. Relationship between the yield for the off season maize and the average monthly values for the (SPI-l).
The users have two basic lines of work, in other words, a user may analyze the effect of the drought from a fully meteorological as well as, an agrometeorologi- cal standpoint. The INFOSECA system has the purpose of processing and mak- ing available the agrometeorological information related to drought indices, and communicates agrometeorological warning and outlook of these adversities to the agribusiness. This system is based on agrometeorological parameters and relies on a management model and the direct data input via web from the meteorological stations. Furthermore, it has a module to provide information and counseling and real-time consulting via Internet. Meteorological data are collected (mainly, pre- cipitation, maximum and minimum air temperatures) from 130 locations in dif-
"..5I 0,8
~
1- 0,6
en en .Ema.Cl
= 0,4 Cle
liD 0,2
•
"lliE
a. 0
HID 3D)
.. , .. - •
700l
•
Maize Yield -KglHa
Fig. 17.17. Relationship between the yield of summer maize in the State of Sao Paulo and the aver- age values for the Palmer Drought Severity Index (PDSI) values during crop growing sea-son.
ferent regions of the State of Sao Paulo, that are recorded in to the CIIAGRO sys- tem. Data are consisted, assessed and transformed into agronomic parameters and displayed in the form of tables and maps of indices (SPI, Palmer, ETM/ETP and DI) and the agrometeorological indices (CMI, CWS, CWSI, IPER and Crop Develop- ment Index). A daily bulletin containing drought prognosis is supplied. The sys- tem was developed using the Sis Plant technology and is based on the HTML, ASP, VbScript and SQL languages. Communication of Web data and database server is performed via ODBC, using the MySQL database. Information is provided at mu- nicipal level and consolidated by Administrative Region, Regional Development Offices - EDR/CATI, Water Resource Management Units - UGRH and Regional Research Centers.
The study allows the analysis of the meteorological conditions and drought through the use of the universally adopted indices and introducing new analy- sis that take into account soil characteristics, crop evapotranspiration and the re- lationship between potential evapotranspiration and water availability in the soil and the development of the root system. In this aspect, results referring to the dif- ferent depths of root systems are also presented, as for crop with superficial root systems and consequently more sensitive water storage such as rice, beans, onions and deeper root systems, such as citrus, coffee and fruit.
Table 17.11 presents the average water stress conditions for the off-season maize crop with a root system at SOcm in the period ranging from March 1st to April 30th, 2006, as well as for the sugarcane crops during the same period, however with a the root system of 1 m deep.Itcan be noted that for sugarcane, the agrometeo- rological conditions were not considered critical, due to larger soil volume explo- ration by the sugar cane rooting system, however, for the maize crop, the situation was highly prejudicial.
Chapter 17: Coping Strategies withAgrometeorological Risks 309 Table 17.11. 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
17.3.4
Climatic Risk Zoning
One of the most important aspects of agrometeorology is to define the timeframe and location with probability of occurrence of drought and other adverse phenom- ena for specific crop development stage, or the climatic risk assessment for agricul- ture exploitation. Specifically considering drought, this assessment of water short- age probability is made by comparing the crop water demand and the water avail- ability in the ecosystem imposed by the rainfall precipitation regime. Crop Water Requirement Index (CWRI), can be defined as:
CWRI = (ETR/ETM) where:
ETR - actual crop evapotranspiration; and
ETM - maximum crop evapotranspiration, as defined by ETM=Kc.ETo
where:
Kc - crop coefficient
ETo - reference crop evapotranspiration
(25)
(26)
The studies that sought to quantify the climate-plant relationship and the risks of meteorological adversities are one of the basic tools used in the agricultural financ- ing programs. As examples we can name the PROAGRO at Federal Government level and the FEAP at the State of Sao Paulo Government level.
Figure 17.18 presents the climatic risk zoning for the summer maize crop in the State of Sao Paulo (Brunini et al. 2001) used in the PROAGRO Agricultural Insur- ance Program.
44 44"
48 45
-f---"""-f---+---{!!4"
+---j---+--+--...,;z1ã
49"
51ã
21" f - - - - f - - - + - t ' - f
25"
22"f----+--H~...
Fig.17.18: Probability of water supply during the tasseling period of the maize crop in the State of Sao Paulo. Between the 1st and 10th of October (source: Brunini et aI200l).
One further step was taken by the government of the State of Sao Paulo in this system for the risk characterization, with the introduction of the "Sistema de Aval- iafilo de Riscos Climaticos e Monitoramento Agrometeorologico de Culturas"(Cli- matic Risk Assessment System and Agrometeorological Monitoring of Crops).
In this process, climatic risks related to drought are assessed as well as the prob- ability of addressing the water demand for any crop, be it annual or perennial. The likelihood of addressing the water requirement is made on the beta distribution
(~),that the best represents the agro-system being analyzed, since the ETR/ETM ratio has values betweenaand 1. Furthermore, following up on the evolution of the agrometeorological parameters and behavior is allowed. The study can be made for all critical phenological phases of the crop, and a subroutine allows that the soil volume for each the crop inferred by the root system is also inferred (climate Risk Evaluation and Crop Agrometeorology System).Information on the probabil- ity for meeting crop water requirements for each planting scheduling for each criti- cal phase of the crop is automatically inserted into the CIIAGRO, enabling the on- line assessment of climatic risks.
Table 17.12 indicates the probability of attending crop water requirements water for the off-season maize crop in the region of Palmital - SP,as well as the risk of oc- currence of frost or agricultural drought in the tasseling period.
Even though tables and charts allow the indication or the results of the occur- rence of adverse phenomena, and the response of a crop in a given region, they do
Chapter 17: Coping Strategies withAgrometeorological Risks 311 Table 17.12. Probability of attending crop water demands during specific phenological phases of the corn crop planted between 1-5 January, and the risk of high or low air temperature
Prob - Probability function
not provide the spatial visualization of these parameters or their degree of occur- rence in different time frames.
Inorder to make this information more readily understood by the general users and by the decision makers, these data are transformed into agrometeorological maps. Two basic tools were used for this- SURFER and ARG-GIS.
Figure 17.19 shows the water stress conditions for maize crop using the Surfer methodology. Note the differences as a function of the spatial variability and the topography of the state when the different types of soil are included.
On the other hand, with the use of the ARC-GIS, this information is more de- tailed, enabling the overlapping of other variables. Figure 17.20presents the same map with the water stress conditions for the maize in the ARC GIS system. In this case, minimum and maximum air temperatures lower than 16°C and higher than 32°C were superimposed on the map indicating restrictive areas due to thermal in- sufficiency or elevated temperatures, as well as the water supply.
112' 11ã 10"
Fig.17.19. Average condition of water stress on the maize crop in the State of Sao Paulo during the month of March 2006, by the Surfer system.
52' ..' ...
AGROCLIMATIC CONDITIONS FOR MAIZE CROP DEVELOPMENT
_on
• AdeqUate CWSIN>O.5endTMIN>140CtM1dTMAX<J20C
-- 52'
1:4,000,000
o25 50 100 160
..t,' ..'
Fig. 17.20. Average conditions of water stress on the maize crop in the State of Sao Paulo with overlapping of the areas with minimum air temperature below 14°C, by the ARC-GIS system.