**2. Investigating the possibility of improving crop forecast**

Several studies indicate that Southern Region of Brazil is the main area of the country affected by interannual climate variability, as commented above. As the climate predictability tends to be better in ENSO years over regions affected by this phenomenon, it is important to investigate the potential role of precipitation climate forecast as a partial substitute for the usual climatological data in crop forecast models.

The Southern Region of Brazil is included in the areas with low climatic risk for soybean production (AGRITEMPO, 2007), participating with 32% of the total production of soybeans in Brazil (IBGE, 2009). The Rio Grande do Sul State (RS) is the second main producer state of this region, being its rainfall regime strongly affected by impacts ENOS (Grimm et al.,1998; Coelho et al., 2002; Berlato & Cordeiro, 2005). Thus, were studied productivity cases on a municipality of RS in three years corresponding to different phases of ENSO: 2005/2006 harvest (neutral year), 2006/2007 harvest (El Niño year) and 2007/2008 harvest (La Niña year). The municipality evaluated is located in the interest area and has longs historical data series, being a reliable reference for studies on agricultural productivity.

#### **2.1 Model description**

The FAO model proposed by Doorenbos & Kassam (1979) was applied to estimate the crop agricultural productivity. This is an empirical model that includes the following components: soil, with its water balance; plant, with its development, growth and yield processes; and atmosphere, with its thermal regime, rainfall and evaporative demand. This model correlates the relative yield drop to the relative evapotranspiration deficit, being formulated by the Equation (1). Therefore, it is necessary to first estimate the potential productivity (Yp) that represents the maximum crop yield in suitable conditions and then estimate YR accounting the relative water deficiency that is weighed by a crop sensitivity factor for the water deficit.

$$\text{YPR/YP} = 1 - \text{ky.}\,\text{ (1 - ER/EP)},\tag{1}$$

being: YR the actual productivity;

YP the potential productivity;

ER the actual crop evapotranspiration;

EP the potential crop evapotranspiration;

 ky the productivity penalization coefficient per water deficit, variable with the crop phenological stage.

The actual crop evapotranspiration (ER) is determined by the sequential water balance based on daily temperature and precipitation data. The potential evapotranspiration (EP), or maximum crop evapotranspiration, is given by the product between the reference evapotranspiration (ETo), and the crop coefficient (Kc) for each phenological stage, as recommended by FAO, considering temperature information in its estimate. The Thornthwaite equation is a simpler method for estimating ETo, since it just requires mean temperature data. As there are limitations of this method to some climatic conditions, Camargo et al. (1999) proposed an adjust of Thornthwaite method using the concept of an effective temperature, which is a function of the local thermal amplitude. In this work was used the adjusted Thornthwaite's method was used, also considering the effective temperature corrected by photoperiod (Pereira et al., 2004). The penalization coefficient ky is an empirical adjustment factor that is specific for each crop, each phenological stage and each region of Brazil, considering the regional particularities of the varieties and the used production systems, according to values recommended by FAO (Assad et al., 2007). The potential productivity Yp represents the maximum value that can be obtained in each region and presupposes in its estimate that the phyto-sanitary, nutritional and water crop requirements are met and that the productivity is conditioned only by crop characteristics and the environmental conditions that are represented by solar radiation, photoperiod and air temperature.

The actual productivity (YR) calculation by the Equation 1 is normally made with the daily data obtained in surface stations or by climatology data. In the studied cases, one considered the values listed in the Table 1 for the ky and Kc coefficients were considered. Using extended weather forecast data may allow the actual productivity (YR) estimates to be made with the same anticipation and accuracy of meteorological models. The more accurate and anticipated will the productivity estimate be, more useful and strategic it will be.


Table 1. Values of ky and Kc coefficients per phenological stage that are considered in calculating the actual productivity.

#### **2.2 Data**

42 Soybean Physiology and Biochemistry

this region, being its rainfall regime strongly affected by impacts ENOS (Grimm et al.,1998; Coelho et al., 2002; Berlato & Cordeiro, 2005). Thus, were studied productivity cases on a municipality of RS in three years corresponding to different phases of ENSO: 2005/2006 harvest (neutral year), 2006/2007 harvest (El Niño year) and 2007/2008 harvest (La Niña year). The municipality evaluated is located in the interest area and has longs historical data

The FAO model proposed by Doorenbos & Kassam (1979) was applied to estimate the crop agricultural productivity. This is an empirical model that includes the following components: soil, with its water balance; plant, with its development, growth and yield processes; and atmosphere, with its thermal regime, rainfall and evaporative demand. This model correlates the relative yield drop to the relative evapotranspiration deficit, being formulated by the Equation (1). Therefore, it is necessary to first estimate the potential productivity (Yp) that represents the maximum crop yield in suitable conditions and then estimate YR accounting the relative water deficiency that is weighed by a crop sensitivity

ky the productivity penalization coefficient per water deficit, variable with the crop

The actual crop evapotranspiration (ER) is determined by the sequential water balance based on daily temperature and precipitation data. The potential evapotranspiration (EP), or maximum crop evapotranspiration, is given by the product between the reference evapotranspiration (ETo), and the crop coefficient (Kc) for each phenological stage, as recommended by FAO, considering temperature information in its estimate. The Thornthwaite equation is a simpler method for estimating ETo, since it just requires mean temperature data. As there are limitations of this method to some climatic conditions, Camargo et al. (1999) proposed an adjust of Thornthwaite method using the concept of an effective temperature, which is a function of the local thermal amplitude. In this work was used the adjusted Thornthwaite's method was used, also considering the effective temperature corrected by photoperiod (Pereira et al., 2004). The penalization coefficient ky is an empirical adjustment factor that is specific for each crop, each phenological stage and each region of Brazil, considering the regional particularities of the varieties and the used production systems, according to values recommended by FAO (Assad et al., 2007). The potential productivity Yp represents the maximum value that can be obtained in each region and presupposes in its estimate that the phyto-sanitary, nutritional and water crop requirements are met and that the productivity is conditioned only by crop characteristics and the environmental conditions that are represented by solar radiation, photoperiod and

The actual productivity (YR) calculation by the Equation 1 is normally made with the daily data obtained in surface stations or by climatology data. In the studied cases, one considered

YR/YP = 1 – ky . (1 – ER/EP), (1)

series, being a reliable reference for studies on agricultural productivity.

**2.1 Model description** 

factor for the water deficit.

phenological stage.

air temperature.

being: YR the actual productivity; YP the potential productivity;

 ER the actual crop evapotranspiration; EP the potential crop evapotranspiration; In this study, we used forecast and observed precipitation daily data in Passo Fundo/RS (28.23ºS; 52.31ºW) and observed temperature daily data, both from October 20 to February 21 of years: 2005/2006, 2006/2007, 2007/2008, related harvest periods according drought risk sowing date recommended (AGRITEMPO, 2007). This is a municipality of Rio Grande do Sul State (RS), is located in southern Brazil, presenting humid subtropical local climate (Cfa) according to the Koppen's classification.

The climatologic values of daily precipitation that were calculated for each year's month, on the basis of a 40-year observation period (1961 to 2000). Were also analyzed precipitation historical data to found the precipitation thresholds associated with the range that each precipitation tercile for the coming 3-month season. These values was used to represent qualitatively the precipitation climate forecast.

The climate forecasts of precipitation were obtained by consensus seasonal forecast developed monthly at CPTEC, based in forecasts of CPTEC-COLA AGCM compared with results of other models climate, being presented by maps on the CPTEC web. As the precipitation seasonal forecast are available by tercile maps displays the probability of occur above normal, normal and below normal precipitation. The use of tercile probabilities provides both the direction of the forecast relative to climatology, as well as the uncertainty of the forecast. The probability that any of the three outcomes will occur is one-third, or 33.3%. Recall that for each location and season, the tercile correspond to actual precipitation ranges, based on the set of historical observations. Thus, were used the values of observed precipitation thresholds to represent the forecasts precipitation of the category forecasts most likely. Based on values of seasonal precipitation were obtained forecast daily precipitation. These values were updated monthly to each new climate forecast, as well as is possible on real-time situation.

The observed soybean productivity data was obtained by the Brazilian Geographical and Statistical Institute (IBGE) for harvests studied.

#### **2.3 Simulations**

The simulations were developed considering the possibility of its real-time replicating. Thus to evaluate the possible contribution of precipitation climate forecasts, the actual productivity was estimated in three different ways, changing only the precipitation data set, as follows:


Thus, to accomplish the second and third types of simulation was necessary to process the crop forecast model 125 times, that corresponds to the cycle day number, since the observed data is updated daily.

### **3. Results and discussion**

In regard to climatology precipitation in the studied region is well known that the Rio Grande do Sul State presents a double peak in the wet season: from summer to spring and then to late winter, presenting a phase discontinuity (Grimm et al, 1998). In the location of Passo Fundo, at the northern part of the state of Rio Grande do Sul, the peak rainy season is the austral spring, with the largest volume in September (206 mm) and the lowest in April (118 mm). In general, the precipitation in Passo Fundo is well distributed throughout the year. There is a marked seasonal cycle of temperatures in Passo Fundo, with maximum temperatures around 28 ºC (17 °C) in January (July) and minimum temperatures near 17 ºC (7 ºC) in January (July).

Whereas, that the suitable productivity estimate is reached by using the data observed throughout the period, that is, simulating a yield forecast model with the data observed throughout the crop cycle period, this type of simulation was used as basis comparison. The estimated soybean productivity for Passo Fundo using observed meteorological data was of 2588 Kg/ha in 2005/2006, 2525 Kg/ha in 2006/2007 and 2652 Kg/ha in 2007/2008. The verified soybean productivity was of 2500 kg/ha, 3000 kg/ha and 2450 kg/ha for the harvest of 2005/2006, 2006/2007 and 2007/2008, respectively (IBGE, 2008). The estimate productivity using data observed was better adjusted to 2005/2006 and 2007/2008 harvest than 2006/2007 period.

It is important to estimate the productivity estimate gain at different forecast periods, using whatever precipitation forecasts are available in a real-time application. To develop realtime productivity estimates in different periods of the crop cycle, there are observed data until the day of estimative, 3-month season of climate forecast and climatology for the remainder. Thus, various soybean productivity estimates were made in Passo Fundo, assuming that such estimates had been made in different crop periods, considering that there were observed data up to the beginning of the process, completed by forecasts and climatology from the end of the precipitation forecast period until harvest. Results are presented in form of graphics in Figures 2 to 4.

When comparing the results of the productivity estimated by the observed precipitation with that based on the precipitation climate forecasts throughout the crop cycle and with the climatological precipitation, it is verified that the estimate based on the precipitation forecasts is closer to the observed productivity than the estimate based on the climatological rainfall to

i. A suitable model simulation for the productivity estimate, using the observed

ii. Productivity estimates using series that are composed of climatologic and observed precipitation – containing observed precipitation values from the first cycle day – on different periods (ends extended at each 1 day) that are completed by climatologic

iii. Productivity estimates considering precipitation series that are respectively composed by observation, climate forecast (3-month season) and climatology on different periods of the crop cycle that are extended at each 1 day in the same way as in the previous case. In this

In regard to climatology precipitation in the studied region is well known that the Rio Grande do Sul State presents a double peak in the wet season: from summer to spring and then to late winter, presenting a phase discontinuity (Grimm et al, 1998). In the location of Passo Fundo, at the northern part of the state of Rio Grande do Sul, the peak rainy season is the austral spring, with the largest volume in September (206 mm) and the lowest in April (118 mm). In general, the precipitation in Passo Fundo is well distributed throughout the year. There is a marked seasonal cycle of temperatures in Passo Fundo, with maximum temperatures around 28 ºC (17 °C) in January (July) and minimum temperatures near 17 ºC

Whereas, that the suitable productivity estimate is reached by using the data observed throughout the period, that is, simulating a yield forecast model with the data observed throughout the crop cycle period, this type of simulation was used as basis comparison. The estimated soybean productivity for Passo Fundo using observed meteorological data was of 2588 Kg/ha in 2005/2006, 2525 Kg/ha in 2006/2007 and 2652 Kg/ha in 2007/2008. The verified soybean productivity was of 2500 kg/ha, 3000 kg/ha and 2450 kg/ha for the harvest of 2005/2006, 2006/2007 and 2007/2008, respectively (IBGE, 2008). The estimate productivity using data observed was better adjusted to 2005/2006 and 2007/2008 harvest

It is important to estimate the productivity estimate gain at different forecast periods, using whatever precipitation forecasts are available in a real-time application. To develop realtime productivity estimates in different periods of the crop cycle, there are observed data until the day of estimative, 3-month season of climate forecast and climatology for the remainder. Thus, various soybean productivity estimates were made in Passo Fundo, assuming that such estimates had been made in different crop periods, considering that there were observed data up to the beginning of the process, completed by forecasts and climatology from the end of the precipitation forecast period until harvest. Results are

When comparing the results of the productivity estimated by the observed precipitation with that based on the precipitation climate forecasts throughout the crop cycle and with the climatological precipitation, it is verified that the estimate based on the precipitation forecasts is closer to the observed productivity than the estimate based on the climatological rainfall to

case, the values corresponding values of climatic forecasts were updated monthly. Thus, to accomplish the second and third types of simulation was necessary to process the crop forecast model 125 times, that corresponds to the cycle day number, since the observed

precipitation and temperature data (October to February);

values until the crop cycle ends;

data is updated daily.

(7 ºC) in January (July).

than 2006/2007 period.

presented in form of graphics in Figures 2 to 4.

**3. Results and discussion** 

2005/2006 and 2007/2008 harvest (Figure 2 and Figure 4), in the period of between 40th and 70th day of the cycle. This is a period that of plant is most affected by water necessity is higher, being the estimative of productivity sensitive to variations of precipitation. This demonstrates the importance of using precipitation climate forecasts accurate to attain the productivity estimates, main in this cycle periods. Cardoso et al. (2010) found similar results by use of up to 15 day wheather forecasts to improve the soybean productivity prediction.

For the cases of 2005/2006 harvest (neutral year) and 2007/2008 harvest (La Niña year) was verified gain when using precipitation climate forecasts, because the climate forecast hit the category of precipitation occurred between November and January, periods when the crop is more sensitive to water deficit. In these two years was verified below normal precipitation in November and December, persisting until February in case of La Niña year.

There were no differences between estimate productivity using forecasts precipitation and only climatology information to 2006/2007 harvest, because although it is an El Niño year the climate forecast indicated normal to all period, being observed above normal precipitation from November by the end of the period. Maybe the forecast climate wrong because it was a weak El Niño, making more difficult the estimation of their impacts. However the error of climate forecast no harmful the estimate crop, because was forecast normal precipitation, ie, climatology that is data used when there is not climate forecast.

Fig. 2. Values of the total estimated productivity of soybean in Passo Fundo, 2005/2006 harvest, from the data observed throughout the cycle period (black dotted line) and from the series composed respectively by observation-climatology (black line) and observation-forecastclimatology (circles). It is highlighted that these composed series contain observed precipitation values from the first cycle day in different periods (extended at each 1 day). This also includes the value of the verified productivity (black thick line) published by the IBGE.

Fig. 3. Values of the total estimated productivity of soybean in Passo Fundo, 2006/2007 harvest, from the data observed throughout the cycle period (black dotted line) and from the series composed respectively by observation-climatology (black line) and observationforecast-climatology (circles). It is highlighted that these composed series contain observed precipitation values from the first cycle day in different periods (extended at each 1 day). This also includes the value of the verified productivity (black thick line) published by the IBGE.
