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Luciano Alves de Oliveira [email protected]

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"Luiz de Queiroz” College of Agriculture (ESALQ/USP)

Av. Pádua dias n.11 LEB/ESALQ/USP

Zip Code: 13.418-900 Piracicaba, SP

Brazil

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Jarbas Honorio Miranda ASABE Member

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2015 ASABE Annual International Meeting Paper Page 1

Paper Number; pages 1-____

15_______


An ASABE Meeting Presentation

Paper Number: 2189697

Water management for sugarcane and maize under future climate scenarios in Brazil

Oliveira, L. A.(1); Miranda, J.H.(2); Cooke, R.(3)

(1) PhD Student, Graduate Program in Agricultural Systems Engineering, "Luiz de Queiroz” College of Agriculture (ESALQ/USP), Piracicaba, SP, Brazil, [email protected] (2) Associate Professor #2, Department of Biosystems Engineering, "Luiz de Queiroz” College of Agriculture (ESALQ/USP), Piracicaba, SP, Brazil, [email protected] (3) Full Professor, University of Illinois, Department of Agricultural and Biological Engineering, UIUC, [email protected]

Written for presentation at the

2015 ASABE Annual International Meeting

Sponsored by ASABE

New Orleans, Louisiana

July 26 – 29, 2015

Abstract. Over the last several decades, there have been concerns worldwide about coping with increasing energy demand. Several alternatives to oil have emerged, among them the use of plant biomass for fuel. Sugarcane and maize have exhibited excellent potential in this regard. These crops are highly efficient in producing carbohydrates which can easily be fermented to produce ethanol. Proper irrigation practices, providing the ideal amount of water that the plant needs to reach its full potential yield, are needed to maximize income from these crops. Sugarcane and maize are of great economic importance to the state of São Paulo, where it is common to irrigate these crops. This research aims to determine conditions for increased productivity and water availability for these crops in the region of Piracicaba, SP, under future climate scenarios. To achieve this goal, the DSSAT/CANEGRO and CERES-MAIZE crop growth models were coupled with the MarkSim model for estimating data for future climate scenarios. Information from the Intergovernmental Panel on Climate Change (IPCC) was used to generate data for current conditions, as well as for the A1B, A2 and B1 future scenarios. Based on these results, for future scenarios, sugar cane productivity will be reduced by approximately 40%, and there will not be enough water to mitigate such an effect. To maintain the same levels of productivity in corn, will be necessary to increase irrigation water by 81%.

Keywords. irrigation requirements, modeling, climate change

Introduction

The culture of sugarcane (Saccharum officinarum) is of great importance for the Brazilian economy to contribute extensively to generate foreign exchange. This is achieved mainly by the extent of its use, which can be employed so fresh, as fodder in animal feed, sugar, ethanol, energy, brandy, brown sugar and molasses.


Currently in Brazil, the culture of cane sugar occupies an area of approximately 8.6 million hectares, with average yield of 70 t ha-1 getting a full crop of about 596 million tons year-1, with the State São Paulo, the largest producer, with an area of 4.5 million hectares (COMPANHIA NACIONAL DE ABASTECIMENTO - CONAB, 2013). In Brazil highlights are the states of São Paulo, Goiás, Minas Gerais, Paraná, Mato Grosso do Sul, Alagoas and Pernambuco. On average, 50% of Brazilian cane sugar production are converted into alcohol and 40% sugar (CONAB, 2013).

Maize (Zea mays) is one of the main cereals grown more around the world, providing widely used products for human and animal consumption and raw materials for industry, mainly due to the amount and nature of the accumulated reserves in grains. Because of its multitude of applications, the corn takes important role in terms of socio-economic aspects in addition to be in essential raw material for various agro-industrial complex (FANCELLI; DOURADO NETO, 2000). Currently, maize occupies an area of 14.7 million hectares, with an average yield of 4.9 t ha-1 resulting in a total of slightly more than 72 million tonnes in production (CONAB, 2013). Paraná is the largest producing state of Brazil with a production of nearly 17 million tons. The main maize producing countries are: China, USA and Brazil and in Brazil the states that stand out are: Paraná, Mato Grosso, Goiás, Minas Gerais, Mato Grosso do Sul, Sao Paulo and Rio Grande do Sul (CONAB , 2013).

According Doorenbos and Kassam (1979), the water requirement of cane sugar is 1500-2500 mm per season and irrigation management should be done in accordance with the water tension in the soil, recommended for each period of the cycle phenological.

Souza et al. (1999), using sprinkler irrigation system type cannon found for RB72-454 varieties, RB76-418 and SP70-1011, maximum yield in stalks of 155.8, 126.9 and 141.9 t ha-1, with water depths of 1,568, 1,424 and 1,589 mm, respectively, at 13 months of cultivation.

The daily water consumption by cane sugar, the main producing areas of the country, depends on the variety, the stage of development of culture and evapotranspirométrica demand depending on the month and region (temporal and spatial variation) in general and has varied from 2 to 6 mm day-1. The culture requires 250 g of water to form 1 g dry matter (Bernard, 2005).

According Kassan and Doorenbos (1994), the maize average cycle requires 500 to 800 mm of water, depending on climate, for good production. Shaw (1977) analyzing the results obtained by different authors found that the consumption of water from the corn ranged 410-610 mm. Fancelli (1991) reports a minimum requirement 300-350 mm of water for a satisfactory production without irrigation use, however, this amount should be well distributed during the crop cycle.

The average cycle maize grown for the production of dried grains, consuming on average from 400 to 700 mm of water during the entire cycle, depending on weather conditions (Resende et al., 2003). As for water, in the early vegetative stages of corn plant in mild climate water consumption does not exceed 2.5 mm day -1, reaching 6.5 to 7.5 mm day -1 during the flowering and fruiting.

Computer simulations allow us to evaluate various conditions of agricultural production considering several possible environmental conditions. Simulations of production, considering various environmental conditions, allow the producer to determine the best crops / varieties and forms of management to reduce losses due to adverse growing conditions. In addition, the agricultural models for determining the risks for crops in each agricultural region assisting the planning of public policies (Godoy, 2007).

The Decision Support System and Transfer Agrotecnologia - DSSAT (Decision Suport System Agrotechnology Transfer) simulates the growth and development of cultures over time, taking into account climatic, edaphic, management and genetic aspects of culture. It also assists in climate data organization, soil, field observations, experimental conditions and genotypic information (Jones et al., 2003). For cane sugar production system, the DSSAT uses DSSAT / CANEGRO model (INMAN-BAMBER, 1991; singels et al., 2008). Being based on CERES-MAIZE model (Jones; KINIRY, 1986) and developed in South Africa in order to model the most relevant physiological processes of the South African sugar industry (INMAN-BAMBER, 1991).

The CERES-MAIZE model according to Jones and Kiniry (1986), is one of the most detailed to predict phenological stages and number of corn leaves. Sensitivity to photoperiod and high temperature effects on the final number of leaves are considered. Furthermore, the three components of vegetative growth can be independently tested: leaf initiation and leaf primordia appearance of initiation of flowering male (KINIRY, 1991).

The CERES-MAIZE model is grouped in a Decision Support System for Technology Transfer (DSSAT) and has been widely used around the world. He considers the process water balance in the soil, radiation interception by the canopy, the dry matter production and biomass partition (KINIRY et al., 1997). His version 3.1 entails the user to obtain the spatial performance of simulations and analysis of the results (THORNTON et al., 1997).


Climate change may have a significant impact on the world economy in the twenty-first century (Houghton et al., 1996), affecting all economic sectors to a certain degree. Agricultural production, however, is the most sensitive and vulnerable sector, since the weather is a determining factor in crop productivity (Watson et al., 1996).

Marin et al. (2009) reported that the simulation of future scenarios is useful to quantify the vulnerability of the productive sectors and to plan actions according to these new conditions. Still using the CANEGRO model, observed a trend of increase in productivity for the state of São Paulo in regions located south of the state.

In addition to these aspects there is a model that has been used along with the DSSAT to simulate future scenarios, called MarkSim. Its ease is to provide the weather data in the formats used by DSSAT.

MarkSim® is a third-order Markov precipitation generator (Jones and Thornton, 1993, 1997, 1999., 2000; Jones et al, 2002), which was developed over 20 years. The basic algorithm is a MarkSim daily rain simulator that uses a Markov chain with a third-order process to predict the occurrence of a rainy day.

His sets are A1b, A2 and B1 and to get them is required only the geographical position of the town. From the geographical position of the town, the MarkSim model uses current weather data from the region and from the IPCC projections, generates weather data for future years. Currently, the maximum existing projection is for the year 2095, which will be used in this research.

Therefore, the main objectives of this study Were:

Determine and compare irrigation levels for crops of cane sugar and corn for the Piracicaba, SP, by applying the models DSSAT / CANEGRO and CERES-MAIZE, before future scenarios, which simulated by the model MarkSim .

Material and Methods

The research was conducted at the Department of Biosystems Engineering of the College of Agriculture "Luiz de Queiroz", ESALQ / USP, located in Piracicaba, SP, Cwa climate according to the Köppen classification, (22 ° 43 '33 "of south latitude and 47 ° 38 '00 "W, with 546 m altitude) and conducted in three distinct stages: 1) understanding of the models DSSAT / CANEGRO and CERES-MAIZE and use / development of a climate database, data edaphic to the city of Piracicaba, SP; 2) understanding of future scenarios and projections of the IPCC (Intergovernmental Panel Climate Changes) to simulate different scenarios of climate change, development and use of a climatological database by applying the MarkSim model and 3) statistical analysis for the two crops (cane sugar and corn) to obtain any differences between irrigation management adopted at the present time with respect to future scenarios simulated by models DSSAT / CANEGRO and CERES-MAIZE.

Climatic data were obtained from the conventional weather station database of the top College of Agriculture "Luiz de Queiroz", USP, in Piracicaba, SP, for the period 1982-2012, a total number of data 31 years. The data collected were: solar radiation MJ m-2 day-1, maximum temperature and minimum daily air (° C), precipitation (mm) which were used to estimate the potential evapotranspiration based on Penman-Monteith equation the climatic water balance in the water balance of culture and the potential and actual yields of cane sugar and corn.

The DSSAT / CANEGRO model should be calibrated in order to have a good performance. This calibration should be in accordance with the variety of cane sugar chosen and all are 74 parameters, as follows: 31 ecotypes parameters , 20 parameters cultivars and 23 species of parameters.

The variety used in this research was the RB86-7515, calibrated and parameterized by Nassif et al. (2012) and the parameters of farming can be given in Table 5. The plant life cycle averaged 530 days and therefore all considered "year of cane and a half."

The CERES-Maize model needs to have its genetic coefficients calibrated to predict the growth and development of corn. Genetic coefficients P1, P2 and P5 define the phenology of culture while the G2 and G3 are related to the definition of income in grain (Table 6).

The hybrid used in this study was calibrated by BR201 and Gedanken (1998), with 275.2 genetic coefficient


P1, which means that this requires 275.2 OCD to reach the end of the juvenile stage. The value of P2 was 0.75, but was not simulated response to photoperiod because of the length of the day during the time of "off-season" is less than the critical photoperiod (12.5 hours). The genetic coefficient P5 is related to the thermal time from the female flowering to physiological maturity, being 780oCd. The genetic factor G2 is related to the maximum possible number of seeds per plant, having a value of 902. For the G3 was found that the hybrid BR201 the value 5 mg d-1. The filocron (PHY) is the range in thermal time (degree days) between the appearance of subsequent sheets to the value of 43 OCD.

The three scenarios present in MarkSim model came from the IPCC report (Intergovernmental Panel Climate Changes) 2001 (Intergovernmental Panel CLIMATE CHANGES - IPCC, 2001a, 2001b, 2001c) are the A1B, A2 and B1. Each has their respective characteristics are as follows:

A1B: Economic Growth fast; global population peaks in mid-century and then starts to decline; globalization to reduce regional differences; reduction in regional differences in per capita incomes; balance in the use of sources of fossil and non-fossil fuels.

A2: heterogeneous world; maintenance of local identities; continued population growth; economic development oriented regional level; fragmented technological development.

B1: Local solutions to economic, social and environmental sustainability; consistently growing population, however at lower rates; economic and technological development accelerated and less diverse, oriented towards environmental protection and social equality.

Year obtained for the three scenarios were always the same: a series of 31 years from 2062 to 2092.

Results and Discussion

Sugar cane

One way to achieve compare the cane-sugar yields of the different scenarios was the application of the concept of relative productivity, ie what a particular scenario presents in terms of productivity when compared to the current condition.

Therefore, in Figure 1A, it can be seen that the relative yield of dry matter in the three irrigated environment future scenarios are around 65% of the yield obtained by the current situation (indicating reduced productivity of around 35%). It is noteworthy that the highest value was found for the A2 scenario, around 70%, and the lowest value for the B1 scenario, around 60%. In terms of the relative productivity of sucrose for rainfed environment was noted the same observed characteristics, revolving around 70% (Figure 1B). The highest value was also found for the A2 scenario, 75%, and the lowest value for the B1 scenario, slightly less than 65%.

As for irrigated environment, the relative productivity of dry matter, the values are around 65%, and in upland conditions, both the A2 scenario A1B scenario as the reach the value of 70%, while only the B1 scenario has a value below 60% (Figure 1C). But when it comes to the relative productivity of sucrose, the B1 scenario stands out, with values above 70%, along with A1B scenario. These differences are mainly due to the fact that there are changes in the availability of solar radiation to the plant, as well as rising temperatures, which help in the accumulation of carbohydrates to increase the plant dry matter production and sucrose.


From Table 11 may be given the amounts of water used for cultivation of sugarcane (mm day -1) and the mass of sucrose produced for each millimeter of water used by the crop both in rainfed conditions as irrigated and for different scenarios. It may be noted that in irrigation conditions, sugarcane culture also uses more water than in dry conditions. This happens because there is no water deficit, rising occurrence of maximum sweating culture. Along with this, it is observed that the irrigated environment is produced the same amount of sucrose for every millimeter of water used by the plant. This occurs, most likely due to the influence of climate parameters such as temperature, amount of CO2 and solar radiation.

Table 1 - Quantidade de água utilizada pela cultura da cana-de-açúcar e massa de sacarose por milímetro de água utilizada pela cultura da cana-de-açúcar nas condições de sequeiro e irrigado para os cenários atual e futuro, simulados pelo DSSAT/CANEGRO

Scenario Rainfall Irrigated kg mm-1 H2O

mm day-1 mm day-1 Rainfall Irrigated

Currently 3.13 3.96 71.7 65.6

A1B 3.16 3.63 46.2 47.1

A2 3.10 3.57 48.9 48.1

B1 3.38 3.78 43.0 44.4

(A)

(B)

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Figure 1 - Relative productivity of cane sugar considering future scenarios (A1B, A2 and B1) relative to the current scenario: Dry Matter

(Rainfed condition) (A), Sucrose (Rainfed condition) (B), dry matter (condition Irrigated ) (C), Sucrose (Irrigated condition)

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From Table 11 could be noted that the sugarcane crop in the current scenario is the one that stands out most in use of water as it produces nearly 70 kg of sucrose per milliliter of water available while in future scenarios productivity is in meda 47 kg of sucrose per ml of water available. Noting the sucrose mass produced for each mm of water used by the plant, it is concluded that irrigation does not seem to be an interesting practice, because in dry conditions sucrose mass produced is much higher than when used irrigation. In order to evaluate the productivity of cane sugar and verify that the irrigation management used today can be used in future scenarios, we tried to compare the amount of sucrose produced (kg ha-1) in the different scenarios and different growing conditions, as well as to compare the used irrigation levels (day -1 mm). It was observed that in rainfed crop condition, future scenarios have lower sucrose productivity that today, analyzing the level of 5% probability, with average yields of around 14,000 kg ha-1 while in the current scenario productivity reached the value of 20,146 kg ha-1. However, with the use of irrigation, such productivity increases getting an average value of 15,800 kg ha-1, however, can not reach the values of the current situation that has 23,600 kg ha-1, at 5% probability. Another point to be evaluated is the mass of sucrose produced for each mm of water applied. Note that the B1 scenario, the most optimistic of scenarios studied, is the one that will take the water applied. Made mean comparison Tukey analysis, at 5% probability, irrigation will have greater influence on the B1 scenario than for other scenarios (Table 12). Table 2 - Sucrose productivity of cane sugar per hectare per mm of applied water and applied water depth in mm day-1 in the culture of cane sugar, present and future scenarios simulated by DSSAT / CANEGRO

Scenario

Produtividade de sacarose Irrigation depth

Rainfall Irrigated Sucrose yield per mm of water applied

kg ha-1 kg ha-1 mm dia-1 kg mm-1

Currently 20,146.00 a 23,557.00 a 0.83 a 44.5 a

A1B 13,725.33 b 15,636.67 b 0.47 b 52.9 ab

A2 14,296.67 b 15,826.00 b 0.44 bc 46.6 ab

B1 13,987.00 b 16,194.00 b 0.39 c 58.00 b

* lowercase there is an indication of the result of the comparison of averages of columns made by the Tukey test at 5% probability Anyway, statistically analyzing the values of the applied water levels, it could be concluded that the management currently used is much higher, being practically twice that will be needed in the future scenarios. However, this should be a warning, because if the management currently used is used in any of the future scenarios, there will certainly be wasting water (Table 12). What is expected is just the opposite, water savings. So, will not help increase the supply, and that the limiting factor productivity is another. A suggestion that productivity reaches the same level as the current one, ie the greater the development of new varieties, more technology, so that making the most of available resources both nutritional as water. Maize Figure 2A shows the relative dry matter yield in rainfed environment. Note that the values are around 60% over the current scenario. Another interesting point is that the B1 scenario was one that showed the highest, reaching 70% of the current productivity, whereas the A2 scenario presented values below 60%. The same pattern can be found for grain yield (Figure 2B). But the average values were


approximately 50%, with the highest values provided by the B1 scenario, around 60%, and the lowest values presented by the A2 scenario, about 40%. In irrigated condition, presented a different situation from that observed to date. The relative productivity of dry matter in the future scenarios presented values similar to the current scenario, showing that the practice of irrigation can meet the environmental demands of the maize plant (Figure 2C). This happens probably by the plant has gone through a stress due solely to lack of water in the dry environment. But what drew the most attention was the grain yield. The three future scenarios exceeded productivity presented by the current scenario in about 15% on average. The B1 scenario is highlighted values were higher than 20% of productivity, above that presented by the current scenario. This means that the practice of irrigation will increase the productivity of maize in future scenarios (Figure 2C and 2D).

In the case of irrigation and future scenarios is important to pay attention to water management. As irrigation is one of the practices that employs more water, the study highlights the importance of studies on this issue. From Table 3 may be given the quantities of water used by corn and grain mass produced for each millimeter of water used by the crop both in rainfed conditions as irrigated and for different scenarios. It can be seen that the irrigation conditions, maize to use more water than in dry conditions mean of 3.8 mm to 1.7 day-1 day-1 mm. This happens because there is no water deficit, rising occurrence of maximum sweating culture. Along with this, it is observed that the irrigated environment takes place much more corn kernels for each millimeter of water used by the plant. This can be considered a hybrid productivity optimization.

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Figure 2 - Relative productivity of maize considering future scenarios (A1B, A2 and B1) relative to the current scenario: Dry Matter (Rainfed condition) (A), grains (Rainfed condition) (B), dry matter (Irrigated condition) (C) , Grains (Irrigated condition) (D)

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Table 3 - Amount of water used by corn and grain yield per millimeter of water used by the crop of corn in dryland and irrigated conditions for current and future scenarios

Scenario Rainfall Irrigated kg mm-1 H2O mm dia-1

mm dia-1 Rainfall

Irrigated

atual 2.91 3.98 10.8 11.9

A1B 1.38 3.66 10.7 15.7

A2 1.44 3.67 9.8 16.4

B1 1.70 3.65 10.8 16.8 Still analyzing Table 3, it appears that the best water use occurs in the B1 scenario, the projections of the IPCC, in which produces an average of about 11 kg of grain for each mm of water absorbed by the plant. In this scenario is when there is the increased use of water for the plant, while in the A2 scenario, the worst case is when the plant has the lower efficiency of use of water. In future scenarios, the grain yield will average in the order of 16 kg per millimeter of water available, while in the current scenario presents 12 kg per millimeter of water available in irrigated condition. As for rainfed condition, future scenarios present average of 10.4 kg per millimeter of water available while in the current scenario this value is 10.8 kg per millimeter of water available. In order to evaluate the corn yield and the irrigation management used today can be used in future scenarios, we tried to compare the amount of grain produced (kg ha-1) in the different scenarios and different growing conditions, as well as comparison of irrigation levels used (mm). It can be seen that in upland farming condition, future scenarios have smaller grains productivity showing the current values of 2.000 and 4405 kg ha-1, respectively, analyzing the 5% level of probability. However, with the use of irrigation, such productivities are statistically equal, at 5% probability. This shows the amount due to the practice of irrigation should continue to receive, and the rational management of water. Another point to be addressed is the grain mass produced for each mm of applied water. Note that the B1 scenario, the most optimistic of scenarios studied, was the one that will take the water applied presenting 21.7 kg of grain per millimeter of water available. Made mean comparison Tukey analysis, at 5% probability, irrigation will have greater influence on the B1 scenario than for other scenarios. In addition there is an increase of over 100% grain yield for each mm of water applied by irrigation if compared to the current situation.

Table 4 - Productivity of maize grain per hectare per mm of water applied and applied water depth in mm day -1 in maize in the different scenarios

Scenario

Grain yield Irrigation depth

Rainfall Irrigated Grain yield per mm of water applied

kg ha-1 kg ha-1 mm dia-1 kg mm-1

currently 4,405.10 a 6,509.94 a 1.2 a 9.7 a

A1B 1,776.10 b 6,931.10 a 2.3 b 18.7 b

A2 1,698.65 b 7,296.13 a 2.2 b 20.7 bc

B1 2,358.75 b 7,901.77 a 2.0 b 21.7 c

* in lowercases there is an indication of the result of the comparison of averages of columns made by the Tukey test at 5% probability


Finally, comparison of applied water levels in corn in mm day-1 indicates that none of future scenarios used the blade can now be used. Knowing that the drought has always been non-existent in the simulations, any water depth less than that proposed by Table 18 indicate decline in productivity. Thus, the water requirement in the future will be much higher than today. Thus, it emphasizes the importance of rational water management. The B1 scenario, the most optimistic, is one that has a lower water demand compared to other future scenarios.

Conclusion

From the results obtained by the simulation models DSSAT / CANEGRO and CERES-MAIZE, taking as a basis the analysis of comparison tests Tukey's, at 5% probability, it can be concluded that:

1) In the case of cane sugar, water management, in future scenarios, should not follow the lines of the current scenario. If adopted the same management, there would be waste of water, something that is currently inconceivable, as it seeks to economy and the rational management of water. The productivity of cane sugar will fall, according to the responses of the models, that way, so that the water deficit was supplied, the blade would be needed on average 0.45 mm day -1;

2) Irrigation assist in stabilizing the productivity of cane sugar, but not the only practice that will help keep productivity. In addition to it, it is suggested that other techniques, such as genetic and technologies that can help tolerance to high temperatures, are searched.

3) As to corn, irrigation management used today will not be enough for future scenarios, as the desirable levels of productivity would not be maintained. For these levels are reached, a water will require increasing applied average 2.2 mm day-1.

4) Still on maize cultivation, irrigation is a practice of vital importance to maintaining productivity, always taking due care to the preservation and rational use of water.

5) The models DSSAT / CANEGRO and CERES-MAIZE are great tools for studying the performance of crops in future scenarios. However, we point out the importance and performance of MarkSim model that led to the provision of data of future scenarios indicated by the IPCC, which "fed" both models.

Acknowledgment

These writers are grateful to the and the following Brazilian Institutions for their financial support: National Scientific and Technological Development Council (CNPq), Sao Paulo State Scientific Foundation (FAPESP) and National Institute of Science and Technology in Irrigation Engineering (INCTEI) .

References

BERNARDO, S. Manual de irrigação. 7. ed. Viçosa: Impressa Universitária, 2005. 611 p.

COMPANHIA NACIONAL DE ABASTECIMENTO. Acompanhamento da safra brasileira: cana-

de-açúcar safra 2012/2013. Comparativo de área, produção e produtividade. Disponível em:

<http://www.conab.gov.br>. Acesso em: 11 jan. 2013.

______. Acompanhamento da safra brasileira: grãos safra 2012/2013. Comparativo de área,

produção e produtividade. Disponível em: <http://www.conab.gov.br>. Acesso em: 11 jan. 2013.

DOORENBOS, J.; KASSAM, A.H. Yield response to water. Rome: FAO, 1979. 193 p. (Irrigation

and Drainage Paper, 33).

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send your completed paper to sandy rutter at rutter@asabe ......found that the consumption of water...

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