model (Derry et al. 2006).
all meaningful work can be "banked" at a relevant point within the conceptual framework. Effective intersectoral and multi-staged communication of risk lies at the hub of the model, which will involve the development of communication path- ways and a common dialogue between scientists, managers and communities. The Hyderabad workshop was seen to provide opportunities for such collaboration on a regional level.
In terms of the model some envisaged policy-related strategies are:
• The assistance of agricultural development by anticipating short-term climatic variations, in order to improve economic yield, and hence security relating to food supply with positive outcomes on socioeconomic conditions and popula- tion health
• The provision of a suitable framework for policy modification in the anticipa- tion of important, short-term climatic change, enabling the incorporation of proactive intervention in agricultural practice
• The exploration of new approaches to managing crop diseases and the applica- tion of pesticides and herbicides to ensure economic use, and prevent overuse, as an important component in human health and aquatic ecosystem protection
• The encouragement of multilateral agricultural risk communication and dia- logue between all stakeholders in the agrometeorological process
16.2 Conclusions
In addressing risks and uncertainties for integrated pest management, Australian researchers have concluded that more needs to be known about the complex rela-
tionships between climate and pest cycles relevant to local place. In this regard, collaborative activity is required between scientists, risk managers, government and local farmers to determine best practice approaches for addressing pest man- agement, with the aim of achieving economically-sound and ecologically-sustain- able outcomes.
Research results relating to Sclerotinia rot in Australian canola and stripe rust in wheat offer useful practical findings for the development of pest management systems elsewhere. A major focus of Australian research is the optimization of nat- ural controls relating to informed planting strategies, and the minimization of pes- ticide application through the prediction of climatic influences, which can in turn lead to optimal effectiveness in the control of disease agents. Technology transfer is, however, a highly specialized area which has resulted in errors in the past, and which must therefore be treated with circumspection.
The relationship between macro- and microclimate, and the effects on the cycles of disease agents, needs special attention if quantity of applied pesticide is to be mi- nimised, while optimising disease control outcomes.
While improvements in meteorological and crop-pest monitoring and model- ing will remain important, a sound understanding of local economic, ecological and social realities is essential if the effectiveness and accountability of interven- tions is to be assured.
Acknowledgements
Support from the World Meteorological Organization (WMO), Asia-Pacific Net- work (APN), Australia-India Council, University of Western Sydney and New South Wales Department of Primary Industries (NSW DPI) in carrying out this research is appreciated.
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CHAPTER 17
Coping Strategies with Agrometeorological Risks and Uncertainties for Drought Examples in Brasil
o.Brunini, Y. M. T. da Anunciacao,1.T.G. Fortes, P.1. Abramides, G.C. Blain, A. P. C. Brunini, J. P. de Carvalho
17.1
Introduction
The 1997-1998 El-Nino caused an extreme drought in the northeastern region with considerable losses for agriculture, livestock, water resources and society. Re- gionally, the impact of these anomalies can be striking. In the southeastern region, for example, in the State of Sao Paulo in the El Nino period, the effects caused by this phenomenon were quite different with above average rainfall in months like May and June. This situation can be observed, as indicated by the rainfall anom- alies represented by the monthly Standardized Precipitation Index (SPI) for the month of May in 1998(Figure 17.1).The occurrence of these anomalies lead the State Government to create a task force involving the various sectors of society, such as, research institutes, universities and the civil defense, to propose mitiga- tion measures.
21'1---+--+-:
I!IO"
Fig. 17.1. Monthly precipitation anomalies as indicated by the monthly SPI (SPI-1)for the month of May 1998 in the State of Sao Paulo.
The National Meteorological Institute (INMET) determines the occurrence of droughts by means of the SPI, and also in deciles and the monthly deviation in precipitation compared to the climatological standard from 1961 to 1990. Studies have shown that 18 to 20 years of drought occurs every 100 years. The frequency of the drought occurrence in the Brazilian northeast is associated with the frequency of the El-Nino and of the Atlantic Ocean dipole; and the frequency of the drought occurrence in the southern region is associated with the frequency of the La-Nifia, The areas affected by the drought vary in intensity, extension and time duration.
When a drought situation is confirmed through precipitation anomaly indices, technical material is prepared containing the precipitation monitoring for the af- fected region with a climate prognosis for the following quarter and this material is forwarded to the federal authorities in order to support the Brazilian government emergency actions. In the northeastern region of Brazil, there are several institu- tions and technical and technological infrastructure to detect drought. A limiting factor to ease detection of drought and corresponding mitigating actions is the lack of training and capacity to define the applicable methodologies.
In addition to the National Meteorological Institute, some States of the Federa- tion developed specific studies for droughts to support not only agriculture, but also the civil defense activities and water resources planning and studies. An ex- ample is the State of Sao Paulo, through its Integrated Agrometeorological Infor- mation Center (CIIAGRO), and the Drought and Hydrometeorological Adversities Mitigation and Monitoring Center (INFOSECA). In this aspect, the assessments of the drought conditions and prognosis are prepared and distributed to farmers, ru- ral cooperatives and other sectors of society.
The Ministry of Agriculture at Federal level and the Agricultural Secretariat of the Sao Paulo State Government, at State level, apply the reports and bulletins of drought monitoring in the Agricultural Activity Assurance Program (PROAGRO) and as a subsidy to the Agricultural/Livestock Expansion and Agricultural Insur- ance (FEAP) for the federal, and for Sao Paulo State government, respectively.
The immediate results of these actions are a reduction in the request for cov- erage for climatic events and the reduction of risks in Meteorological Adversities upon agriculture, in addition to the monitoring of the insurance operations and the agrometeorological management ofPROAGRO and FEAP (sources: www.agri- cultura.gov.br; www.agricultura.sp.gov.br.)
Regionally, there are programs that involve research institutions and the com- munity in order to minimize risks for agriculture during drought situations. In the northeastern region, the state governments have mechanisms of their own to aid the population, such as distribution of water and foodstuff. In the drought areas, communities are supported by the federal and state governments and NGOs that orient the population. In order to improve health and reduce infant mortality, effi- cient methods of collecting and storing water by means of rural cisterns, underwa- ter reservoirs and desalinization units are being applied.
In the northeastern and southern regions, regional forums for the quarterly climate prognosis for the rainy seasons are held. There is no specific forecast for drought, but the climate prognosis indicates beforehand if there is a probability of precipitation remaining below or above the normal. The State of Ceara, through its Secretariat of Rural Development and the Ceara Meteorological Foundation indi-
Chapter 17: Coping Strategies withAgrometeorological Risks 283 cates the beginning of the sowing time by means of the climate prognosis, and the drought probability studies using real-time monitoring of precipitation and soil moisture content.
In the southeastern region, the government of the State of Sao Paulo implement- ed the Drought and Hydrometeorological Adversities Mitigation and Monitoring Center (INFOSECA), which is subordinated to the Instituto Agron6mico (Agron- omy Institute). The work performed by INFOSECA along with the activities car- ried out at CIIAGRO is pioneering in Brazil the agrometeorological monitoring of drought and its effect on agricultural activities (source: http://ciiagro.iac.sp.gov.br - www.infoseca.sp.gov.br).With its major territorial portion restricted to the equa- torial humid or tropical areas, the effects of meteorological adversities on the Bra- zilian territories, and most notably drought, are very distinct. An assessment of the Humidity Index as proposed by Thornthwaite and Mather (1955) is presented on Figure 17.2, involving some states in the southern, northeastern and midwest- ern states.
In general, the macroclimatic characteristics indicate humid climate conditions for the states in the southeastern and midwestern regions. Nevertheless, even for humid regions, the climatic oscillations cause, in specific years, a drought condi- tion that is highly unfavorable to crops. This statement is supported by the monthly variation of the SPI for the areas of Campinas and Ribeirao Preto in the State of Sao Paulo for the month ofJanuary (Figure 17.3). Even though the month of Ianuary normally presents high rainfall indices, on specific years a meteorological drought occurs. This phenomenon has an elevated consistency with values that are high- ly unfavorable and prejudicial to crops. The same aspect can be observed by the monthly variation of the Palmer Drought Severity Index for the locations ofVotu- poranga and Assis during the month of October (Figure 17.4).
Figure 17.4 further indicates an incisive factor which is the higher incidence of dry periods in the month of October in the last 15 years, shifting the beginning of planting of the summer crops to early November. This relationship with the PDSI, as well as with the SPI oscillations support the importance of monitoring and prog- nosis of drought in Brazil from the meteorological, hydrological and agronomic standpoints, with greater focus on the socio-economic effects of this meteorologi- cal adversity.
The methodologies and parameters used at federal level by the National Meteo- rological Institute (INMET) and at a state level by the Integrated Agrometeorologi- cal Information Center - CIIAGRO, and by the Drought and Hydrometeorological Adversities Mitigation and Monitoring Center (INFOSECA), of the Agricultural Secretariat of the State of Sao Paulo are described below.
...
CLIMATE INDEX BASED ON WATER BALANCE
..
..
...-
- - .<--....
... _ ...
... ,._
11 _ . . .
• 1 ,.'1 ....
_ , . 11 . . .
...
• >--
1:8,000,000
o50 100 200 300 ~
..' 52' ...
..
Fig.17.2. Macroclimatic characteristics of some states in the southern, southeastern, midwest- ern and northeastern states based on the climatic classification proposed by Thornthwaite- Mather (1955),
Chapter17: Coping Strategies with Agrometeorological Risks 285
I .Campinas • RibeiriJio Prato I
I I
)~ I I . .
III d
~1/ LI I I II I
'II' 'I 'I I' .' 1 I1J'r 11
r I I I I II I
I .
2,5
i 2
11' :
s 0,5
! 0
,Iio~ -0,5
1Dl -2,5_1~:-2
-3
1110 1113 191. 1919 1972 1975 1178 1181 1184 1187 1118 1193 1118 1911 2182 2085
Year
Fig. 17.3. Seasonal variation of the Standardized Precipitation Index (SPI) on a monthly scale (SPI-I) for the month of Ianuary in the regions of Campinas and Ribeirao Preto in the State of Sao Paulo,
I IIAssis .. votuporanga I
•
I I •
• I I. • • • III I I
ã'1' .' 1'1' , I 'II 'I'II • 'I 'r'I '1'1'I 'I'" 1'1'1' 1I1-lr . 'I' 111--'ã111.11 I' i I
1 III •
If 5
I 4
f: t-:
l:l
J ~:
-4
1970 1973 1976 1979 1982 1985 1988 1991 1994 1997 2000 2OlI3 2OlI6 v_
Fig.17.4. Seasonal variation of the Palmer Drought Severity Index (PDSI) for the month of Oc- tober in the regions of Assis and Votuporanga in the State of Sao Paulo,
17.2
Methodologies toAssess Precipitation Anomaly and Drought 17.2.1
Meteorological Indices 77.2.7.7
SPI Standardized Precipitation Index
The Standardized Precipitation Index (SPI), proposed by McKee et al. (1993), cor- responds to the number of standard deviations that the observed accumulated pre- cipitation deviates from the climatological average, for a determined period of time.
The State of Sao Paulo (Brunini et al. 2000, INFOSECA 2005), Pernambuco (San- tos and Anjos 2001) as well as INMET have been monitoring droughts through the SPI, presenting results that enable the use of the information to anticipate and mitigate adverse effects.
Itis common to see in literature an association between a range of values for the SPI and the qualitative assessment of precipitation observed during the corre- sponding period. The most frequent association is suggested by IRI (2005), as per Table 17.1.
Calculation of the index begins with the adjustment of the gamma probability density function to the monthly rainfall series. After this phase, the accumulated probability of the occurrence for each monthly total observed is estimated. The normal inverse function (Gaussian) is applied to this probability and the result is the SPI.
In this method, precipitation can be totalized in several scales (1 to 72 months).
When the time scale used is small (1, 2 or 3 months, for example), the SPI moves frequently above or below zero, observing the meteorological drought regime. As the assessment scale increases (12 or 24 months, for example) the SPI responds slower to changes in precipitation observing the hydrological drought regime.
Table 17.1. Arbitrary correspondence between the SPI values and the climate categories (adapted by Mckee et al., 1993)
Chapter 17: Coping Strategies withAgrometeorological Risks 287 The gamma probability density function (GPDF) assumes distinct forms, ac- cording to the variation of a. Values for this parameter inferior to 1 indicate a strong asymmetric distribution (exponential form) with g(x) tending to infinite when x tends to O. In the case of a = 1 the function intercepts the vertical axis in
~for x=O. The increase in the magnitude of this parameter reduces the asymmet- ric degree (deviation from the mode) of the distribution (the probability density is displaced to the right). Values foragreater than 1 result in a GPDF with the maxi- mum point (mode) in~*(a-I). An increase in the ~parameter stretches the GPDF to the right, lowering its height and reducing the probability of the occurrence of the mode value. Similarly, as the density is compressed to the left (reduction of the
~magnitude) and the height of the function becomes greater, the probability of the event increases.
Thus, the spatial variation of a and~in a state or country, indicate which are the regions with greatest degree of asymmetry in the temporal distribution of precipi- tation (rainfall irregularity). Considering the phenomenon of drought, anomalies in relation to environmental conditions of each area, these regions are at a greater risk of being subjected to meteorological droughts.
17.2.1.2
Palmer Drought Severity Index Adapted totheState ofSao Paulo - Pdsi Adap
The most important step of the PDSI is the calculation of precipitation, "Cli- matologically Appropriate Existing Conditions" (P)which can be understood as the amount of monthly precipitation necessary for a given area to remain under normal climatic conditions. This parameter is calculated as described by Palmer (1965). For the calculation of the monthly water anomaly (d), the precipitation ob- served in the month (Pi) is compared toPin the same period.
d=Pi - P (1)
As Palmer (1965) developed a standardized index compared to different locations at any period of time, it needs to be standardized (weighted) on a regional ba- sis (Karl 1986). Thus, Palmer (1965) developed the climatic characterization factor designated by the letterK.
12
K= 17.67*K'/LDK'
;=!
Where,
K' = 1.5loglO [(T+2.8)/(D)] +0.5
T - the ratio between the demand and supply of water in a region, and D - the monthly average of the absolute values for d.
(2)
(3)