Universidade Federal de Santa Maria

Ci. e Nat., Santa Maria v.42, e67, 2020

DOI:10.5902/2179460X35060

ISSN 2179-460X

Received 05/10/18 Accepted: 13/01/20 Published:09/09/20

Environment

Water footprint as a sustainable water use indicator in spring area of Pantanal biome, Brazil

Eliane Aparecida Antunes Fagundes^I

José Dantas Neto^II

Vicente Paulo Rodrigues Silva^III

Domingos Sávio Barbosa ^IV

Vera Lúcia Antunes Lima^V

^I Universidade Federal de Campina Grande, MT- Brasil - elifagundes_@hotmail.com

^II Universidade Federal de Campina Grande, MT- Brasil - zedantas1955@gmail.com

^IIIUniversidade Federal de Campina Grande, MT- Brasil - _vicente@dca.ufcg.edu.br

^IVUniversidade Federal de Mato Grosso, MT- Brasil - domingosbar@gmail.com

^VUniversidade Federal de Campina Grande, MT- Brasil - antuneslima@gmail.com

ABSTRACT

Water footprint (WF) is an indicator of fresh water consumption that considers in its calculation the used water volume during the production process. The research objective was to evaluatecotton, corn and soybean crops WF at the São Lourenço-MT sub-basin area. The water consumption was quantified in Green Water Footprint (WF_Green) and Gray Water Footprint (WF_Gray). The WF_Green of each crop was calculated by the evapotranspiration value throughout the crop growing period. The WF_Gray was counted separately for a group of nine agrochemicals. In the current scenario there is sustainability in the sub-basin of the São Lourenço river, but with the agricultural current area expansion rate, in 2025 there will be no such sustainability.

Keywords: water resources; gray footprint; agrochemicals; water consumption; sustainability

1 INTRODUCTION

Ensuring current and future generations the necessary water availability, in quality standards appropriate to their uses, should be the main objective of any public water management policy on the planet (Pizella e Souza 2007; Braga et al., 2008; et al., 2010). The planet water volume has not changed over time, but the delay between the use of water, which is often a polluting processes subject, and its purification in the atmosphere, is interpreted as if drinking water is running out, when in fact, there is a reduction in available quantities (Gleick 1998, Aldaya, Allan and Hoekstra 2010, FAO 2013).

The desired balance search, between the demanded and the offered water resources quantities in nature, requires strategies that can be used to reduce the risk factors (Vörösmarty et al., 2010), highlighting an increased efficiency of water use processes as well as reducing the negative impact of activities on water quality in a region (Empinotti and Jacobi 2013). In order to quantify the multiple sustainable water manners, several indicators have emerged (Gleick 1998), which provide a numerical result, a metric dimension for specific information evaluation about the economic, environmental and social dimensions, contributing to the decision-making process. These information is obtained through the use of management tools (Böhringer and Jochem 2007, Borowski and Hare 2007).

Brazil is one of the largest soy, corn and cotton exporters in the current commodity situation. In environmental terms, inside grains, large quantities of virtual water leave the country. Virtual water refers to the indirect water trade that is product embedded, from their production site to the final destination. As there is an unequal water availability in various regions of the planet, it is necessary to use specific indicators to analyze the water resources consumption. Hoekstra and Hung (2002) and Allan (2011) have shown that by quantifying the incorporated water in the products, that the global fresh water character can be understoodand used to quantifythe consumption and trade effects of water resources. This understanding could serve as a basis for improving and adapting the management of planet freshwater resources.

The concept of Water Footprint (WF) has been used as afreshwater consumption indicator for people and products in various parts of the world (Van Oel, Krol and Hoekstra, 2009). It was created by Hoekstra (2003) and aims to show the consumption patterns and global dimensions of water use (Hoekstra et al., 2011; Vanham, Hoekstra and Bidoglio, 2013). The calculation of the total Water Footprint considers three types of Water Footprint in reference to the types of water considered by the method: Blue Water Footprint (WF_blue), Green Water Footprint (WF_green) and Gray Water Footprint (Hockstra et al., 2011; Wang et al., 2014, Marano and Fillipi 2015).

In its calculation, the method considers not only the consumed water volume from various sources, such as surface and groundwater and stored rainwater in the soil, but also the polluted water amount during the production process in a given location and period (Zeng et al., 2012). According to Chapagain et al. (2006), the total volume of fresh water used to produce goods, services or products, provides a basis for assessing impacts on freshwater systems and formulating strategies to reduce these impacts. The Water Footprint of a region is influenced by the economic development model, which is often deeplypracticed and based on the generation of wealth, which neglects the natural support life systems (Silva et al., 2013).

The world literature about people and products WF has increased very swiftly, however there are still few focused WF studies on specific river basins (UNEP, 2012, Zeng et al., 2012), especially for those carried on preserved biomes such as the Pantanal. Located in South America, the Pantanal is the largest floodplain in the world, with an area of about 250,000 square kilometers, of which, 62% is on Brazilian soil, in the states of Mato Grosso and Mato Grosso do Sul. The Pantanal extents still in the territories of Bolivia and Paraguay. It is also the second largest South American biome, surpassed only by the Amazon Bioma(Silva and Bates, 2002).

The WF assessment at the river basin level is an important step towards understanding how anthropic activities, agriculture, livestock and industry, force the natural water cycles and is presented as an integrated management tool of water resources and sustainable water uses (Zeng et al., 2012). The Pantanal is becoming increasingly threatened by major development programs. The expansion of agriculture and its agroindustry, as well as the construction of reservoirs for hydroelectric power generation in the hydrographic basins, modify the discharge pattern and sediment load of the region's rivers (Junk and Cunha 2005).

The objectives of the study were: (1) to evaluate cotton, corn and soybeans crops WF planted in the São Lourenço river basin area; (2) evaluate WF as a sustainabilityindicator of agricultural activities in the basin over an annual time interval.

The sub-basin chosen was from the São Lourenço river, in the state of Mato Grosso, Brazil, a Pantanal biome spring area. The average regional annual precipitation is 1700mm (INMET 2015) and all agriculture is rainfed. Thus the components were computed: green and gray water footprint.

The regionalpesticides overuse has worried the scientific community. The Ministry of Agrarian Development (MDA) and the National Agency of Sanitary Surveillance (ANVISA) affirm that Brazil is the world largest pesticides consumer and producer since 2009 and that these products marketis already four times higher than the world average (ANVISA 2012, Dellamatrice and Monteiro 2014). In the states of Mato Grosso and Mato Grosso do Sul, in the Pantanal region, Miranda et al. (2008) evaluated in their study the pesticides sediments contamination in seventeen rivers; from the 23 monitored pesticideswere detectedresidues of pyrethroids (permethrin, lambda-cyhalothrin and deltamethrin) and chlorinated (DDT). The WF assessment is an important indicator of basin sustainability as it can provide local environmental management authorities and producers the needed information to allocate monitoring and management efforts to the achieve the established objectives in the Brazilian water resources legislation.

2 MATERIAL AND METHODS

The study was carried out in the upper São Lourenço river region, southeast of Mato Grosso, Brazil, with a total area of 21,105 km², with 2,934 km² being occupied with agriculture. The annual average flow of the São Lourenço is about 317m³ / s (ANA 2004).

The WF was calculated following the proposed methodology in the Water Footprint Assessment Manual (Hoekstra et al., 2011) being divided into Green Water Footprint and Gray Water Footprint, as the area's agriculture is dryland.

2.1 Green Water Footprint calculation

The Green Water Footprint calculation of basin planted crops (cotton, corn and soybean) was carried out from Equation 1 and involves the Crop Green Water (c_green) consumption and the crop productivity in the studied region.

= (1)

Where: WF_green = Green Water Footprint (m³ / t); c_green, = Green Water consumption (m³ / ha); P = Productivity (t / ha);

The green water crop consumption was calculated through the daily evapotranspiration values of green water (ET_green), during the crop growing period (Equation 2), so that the consumption of green water represents the total rainwater evaporated by the crop during the growing period. In this study the cotton, corn and soybean ET₀reference was analyzed.

= β . . (2)

where: c_Green, = Green water consumption (m³ / ha); β = 10 (mm rainfall conversion factor for m³.ha-¹); dpc = duration of the growth period in days (d); EVT_green = Daily water green evapotranspiration (mm / day);

The evapotranspiration sum considers the included values between the seeding and the harvest day. Each crop evapotranspiration was obtained by multiplying ET₀, based on the Pennman-Monteith equation (Allen et al., 1998) and crop K_c, according to Doorenbos and Pruitt (1976). The daily meteorological variables averages needed to estimate ET₀by Penman-Monteith method were obtained from the automatic region station.

2.2 Gray Water footprint calculation

The study of analyzed pollutants was defined according to the survey questionnaire applied on agronomists working in the region plus the Mato Grosso Institute of Agricultural Economics (IMEA) data. The most commonly used herbicides, fungicides and insecticides for the cotton, corn and soybean crops of the region were selected. The maximum permissible concentrations (C_max) of selected pollutants were obtained through legislation that deals with natural state water quality standards. In the case of Brazil, CONAMA Resolution N^o 357/2005 (CONAMA 2005) was used for Class II freshwater. European Union (UE, 2013) and INERIS (2013) legislation were also used because, according to Franke, Boyacioglu and Hoekstra (2013), these standards are current and scientifically reliable. The application rates of the main agrochemicals used, per crop area, for the 2014/2015 harvest are listed in Table 1.

Table 1 - Main agrochemicals application rate used for cotton, corn and soybean crops in the Upper São Lourenço river, MT

Culture (1)	Agrotoxic (2)	Class (3)		TAPC (L.ha^-1) (4)		CS (g.L^-1) (5)		Nº Application(6)		TAPS (t.ha^-1) 7=(4x5x6)
Cotton	Glufosinate Ammonium	Herbicide		2,750		200		3,0		1,65x10^-3
	Difenoconazole	Fungicide		0,300		250		3,0		2,25x10^-4
	Zeta-Cypermethrin	Inseticide		0,112		350		4,0		1,57x10^-4
Corn	Atrazine	Herbicide		4,750		500		1,0		2,38x10^-3
	Azoxystrobin	Fungicide		0,300		200		1,0		6,00x10^-5
	Methomyl	Inseticide		0,600		216		1,5		1,94x10^-4
Soy	Glyphosate	Herbicide		1,500		480		2,0		1,44x10^-3
	Trifloxystrobin	Fungicide		0,350		150		2,0		1,05x10^-4
	Flubendiamide		Inseticide		0,045		480		1,5		3,24x10^-5

TAPC = Commercial product application rate; CS = Substance concentration; TAPS = Substance application rate.

The total application rate (T_tapl) is the amount of chemical applied substance per year. Considering the area of 1 hectare for each crop it is shown that the pesticides listed T_tapl in Table 1 is equal to column 7 (Rate of application of substance t.ha^-1) times 1 ha.

The Gray Water Footprint calculation of each crop (WF_gray, m³.t) was performed according to Equation 3:

= (3)

being: PH_Gray = Gray water footprint (m³.t^-1); α = Leaching fraction; TQ = Chemical substances application rate per hectare (kg.ha^-1); C_max = Maximum permissible pollutant concentration in the receiving aquatic environment (kg.m^-3); C_nat, = Natural pollutant concentration in the receiving aquatic environment (kg.m^-3); P = yield of the crop (t.ha^-1).

The average cotton, corn and soybean crop in the studied region in the 2014/2015 harvest was 4,044 t.ha^-1, 6,095 t.ha^-1, 3,000 t.ha^-1 respectively. The WF_Gray study was based on the area of one (01) cotton, corn and soybean hectare according to the cultural practices used in local farms.

The dimensionless α factor represents the fraction of leaching or flow, defined as the fraction of applied chemicals that reach the bodies of water and can be calculated using Equation 4:

α = +

(4)

being: α _max = maximum leaching fraction; α_min = fraction of leaching-minimum flow; Si = potential leaching-outflow; W_i = factor weight.

The minimum and maximum chemicals leaching-flow fractions in study were: minimum (α_min = 0.0001) and maximum (α_max = 0.1), according to Franke, Boyacioglu and Hoekstra (2013). By factor, the scoring for leaching potential (si) is multiplied by the weight (Wi) factor. With the help of frame 1, the scores for potential leaching flow per factor were found for the studied pesticides, which are seen in Table 2.

Frame 1 - Influencing factors the leaching and potential flow of pesticides. The state of the factor that determines the leaching and flow potential is expressed as an (S) value between 0 and 1.

Agrotoxics
Category		Factor	Potencial de leaching and flow potential	Very Low	Low	High	Very High
			Value (S)→	0,00	0,33	0,67	1,00
			Weight (w)↓
Chemical properties		K_oc (L.kg^-1)	20	>1000	1000-200	200-50	<50
		PL (D_T50 Days)	15	<10	10-30	30-100	>100
		PE (D_T50 Days)	10	<10	10-30	30-100	>100
Enviromental factors	Soil	TSL	15	Clay	Silt	Loam	Sand
		TSE	10	Sand	Loam	Silt	Clay
		MO* content (dag. Kg^-1)	10	Excelent >7,00	Nice 4,01-7,00	Medium 2,01-4,00	Low <2,00
	Weather	IP (mm)	5	Low	Moderate	High	Very High
	Weather	P (mm)	5	>600	600-1200	1200-1800	>1800
Cultural practices		PM	10	Excelent	Nice	Bad	Worst

(Adapted from Franke Boyacioglu and Hoekstra, 2013). Koc = Partition coefficient of organic carbon of the soil-water complex; PL = Persistence relevant to leaching (50% of the duration time); PE = Persistence relevant to the flow (50% of the duration time); IP = Precipitation intensity; TSL = soil texture relevant to leaching; TSE = soil texture relevant to runoff; P = Precipitation; PM = Management practices relevant to the outflow; Weight (w) of the factor. * SOURCE: Ribeiro, Guimarães and Alvarez (1999).

Table 2 - Values and weights, by pesticide, factors related to chemical properties, environmental factors and cultural practices, which influence leaching and flow in the study area.

Agrotoxic

K_oc(L. kg^-1)20*

PL (days) 15*

PE (days) 10*

TSL 15*

TSE 10*

MO (dag.Kg^-1) 10*

IP 5*

P (mm) 5*

PM 10*

Glufosinate Ammonium

600

(0,33)

7,0

(0)

7,0

(0)

(0,67)

(0,33)

3,2

(0,67)

1700

(0,67)

(0,33)

Difenoconazole

>1000

(0)

130

(1)

(0)

(0,67)

(0,33)

3,2

(0,67)

1700

(0,67)

(0,33)

Zeta-Cypermethrin

>1000

(0)

(0,67)

(0)

(0,67)

(0,33)

3,2

(0,67)

1700

(0,67)

(0,33)

Atrazine

100

(0,67)

(0)

(0,67)

(0,33)

3,2

(0,67)

1700

(0,67)

(0,33)

Azoxystrobin

589

(0,33)

(0,67)

(0)

(0,67)

(0,33)

3,2

(0,67)

1700

(0,67)

(0,33)

Methomyl

660

(0,33)

1,4

(0)

8,1

(0)

(0,67)

(0,33)

3,2

(0,67)

1700

(0,67)

(0,33)

Glyphosate

>1000

(0)

(0,33)

9,0

(0)

(0,67)

(0,33)

3,2

(0,67)

1700

(0,67)

(0,33)

Trifloxystrobin

>1000

(0)

7,0

(0)

1,0

(0)

(0,67)

(0,33)

3,2

(0,67)

1700

(0,67)

(0,33)

Flubendiamide

>1000

(0)

500

(1)

6,9

(0)

(0,67)

(0,33)

3,2

(0,67)

1700

(0,67)

(0,33)

* Factor weight; () = Value for potential leaching and flow; F = Regarding the texture of the sandy loam soil; A = referring to the high precipitation intensity of the region; B = referring to good cultural practices relevant to the outflow

2.3 Green Water Footprint sustentability

The Green Water Footprint environmental sustainability of the studied area in the upper São Lourenço was related to the amount of available green water (Hoekstra et al., 2011). The availability of green water (DA_green) in a xbasin at a tgiven period is defined as total evapotranspiration of rainwater (ET_green) minus the sum of evapotranspiration reserved for natural vegetation (ET_amb) and evapotranspiration of non-productive areas, according to equation 5:

[volume/time] (5)

being: (DA_green) - availability of green water in anx basin; t - a certain period; (ETgreen) - total rainwater evapotranspiration; (ET_{environmental}) - the sum of evapotranspiration reserved for natural vegetation; (ET_unproductive) - evapotranspiration of non-productive areas.

The ET_amb variable is the 'green water environmental demand' and refers to the amount of green water used by natural preserved vegetation basin areas, aiming at maintaining biodiversity and sustaining communities that depend on natural ecosystems. The natural preservation area was stipulated by Law N^o. 12,651 of May 25, 2012, Forest Code, to preserve 35% of the Cerrado biome (Brazil 2012).

The green water scarcity level in a xbasin in tperiod is defined as the ratio of the total green water footprint in the basin to the availability of green water, equation 6:

(6)

2.4 Gray Water Footprint sustentability

The Gray Water Footprint environmental sustainability of the upper São Lourençostudied area was estimated as the consumed fraction of the effluents assimilation capacity and calculated by the ratio between the total gray water footprints (ΣWF_gray) and the actual flow of the basin (Q_real) as equation 7. According to Hoekstra et al. (2011), this is a relevant local impact indicator for calculating the level of water pollution (NPA) in a basin.

(7)

3 RESULTS

3.1 Green Water Footprint (WF_Green)

The largest Green Water Footprint (WF_Green) was soybean with 1673 m³.t^-1, followed by cotton with 864 m³.t^-1 and corn with 464 m³.t^-1. Precipitation and climate determine evapotranspiration and therefore, influence the crops Water Footprint. According to COSTA, D.C. et al., (2018), the temporal dynamics of the water footprint indicate that cultivars with high yield potential are efficient in using the rain supply thus reducing soybean water footprint. The studied area ET₀in the 2013/2014-year crop was 3.5 mm.day^-1, considered satisfactory to soybean, cotton and corn crops cultivation under rainfed conditions.

The WF_Green soybean results were consistent with values found in Indonesia, which is 1644 m³.t^-1 (Bulsink, Hoekstra and Booij, 2010), but lower than those found by Ercin, Aldaya and Hoekstra ( 2012) in non-irrigated farms in Canada and France, whose WF_Green were 2069 m³.t^-1 and 2048 m³.t^-1 respectively. The global meandescribed in the Mekonnen and Hoekstra (2011) paper is 2037m³.t^-1, which shows that the found soybean WF_Green in the studied region (1673 m³.t^-1) is lower due to the favorable crop situations such as climatic conditions and no-till farming practices. In this system, the soil is always covered by straw, which contributes to an evaporation and soil temperature reduction and at the same time increase the organic stuff, favoring the water soil stowing (Figueiredo, Ramos and Tostes, 2008). It is noticed that the sum of these factors contributes to the fact that the regional cultivated soybeans WF_Green is smaller than the global average.

Cotton has the second highest WF_Green of this study with 864 m³.t^-1 whose value is similar to the Australia, which is 870 m³.t ^-1 (Chapagain et al., 2006) and below countries like India (6490 m³ / t), USA (2114 m³ / t) and China (1440 m³ / t), according to Franke and Mathews (2013). The average for the global cotton water footprint is 755 m³ / t (Mekkonnen and Hoekstra, 2010).

Corn presents the lowest WF_Green, 464 m³.t^-1, and this result is due to the lower life cycle of the previous variety planted in the region. The presented corn WF_Green value in this study is lower than those found in some literatures such as: Carvalho and Menezes (2014), who found corn WF_Green of 955 m³.t^-1, to that found by Mekkonnen and Hoekstra (2010); which was 947 m³.t^-1 and that of Soares e Campos (2013), whose result found in the semi-arid region was 709 m³.t^-1.

3.2 Gray Water Footprint (WF_Gray)

From the leaching-flow fraction (α); of the total rate of application of the substance (T_tapl); of the pollutant load of the substance (L) and the value of the maximum permissible concentration (C_max), the annual Gray Water Footprint (WF_Ca) of each pesticide used on the farm of one hectare of cotton, maize and soybean study (Table 3)

Table 3 - Annual Water footprint ash (WF_ca) of each pesticide used in one hectare of cotton, corn and soybean crops in the Upper São Lourenço river, MT

Culture (1)	Agrotoxic (2)	α (3)	T_tapl (t) (4)	L (t) (5=3x4)	C_max (t.m^-3) (6)	PH_Ca(m³) (7=5/6)
Cotton	Glufosinate Ammonium	0,0367	1,65x10^-3	6,00 x10^-5	1,0x10^-7	6,10x10²
	Difenoconazole	0,0694	2,25x10^-4	1,56 x10^-5	6,0×10^-10	2,60x10⁴
	Zeta-Cypermethrin	0,0402	1,57x10^-4	6,31 x10^-6	6,0×10^-4	0,01100
Corn	Atrazine	0,0569	2,38x10^-3	1,35x10^-4	2,0×10^-9	6,75x10⁴
	Azoxystrobin	0,0467	6,00x10^-5	2,81x10^-6	9,5×10^-9	296,000
	Methomyl	0,0367	1,94x10^-4	7,12 x10^-6	1,0×10^-10	7,10x10⁴
Soy	Glyphosate	0,0351	1,44x10^-3	5,05x10^-5	2,8×10^-7	180,000
	Trifloxystrobin	0,0301	1,05x10^-4	3,16 x10^-6	1,0×10^-10	3,16x10⁴
	Flubendiamide	0,0451	3,24x10^-5	1,46 x10^-6	1,0×10^-10	1,46x10⁴

(α) = Leaching flow fraction; T_tapl = Total application substance rate; L = Pollutant substante rate; C_max= Maximum permitted concetration rate.

According to Table 3, the agrochemical that presents the highest WF_Ca in the 1 ha cotton farm was the fungicide difenoconazole with a value of 26,000 m³. Considering that the average cotton yield in the 2013/2014 harvest was 4.044 t.ha^-1, the produced cotton Gray Water Footprint (WF_Gray) in the region was 6.43 x 103 m³.t^-1(26,000 m³ / 4.044 t). The agrochemical that presents the highest WF_Ca in the cultivation of 1 ha of corn was the methomilinsecticide with a value of 71,200 m³. Considering that the average maize productivity was 6,095 t / ha^-1, the gray water footprint (WF_Gray) of produced corn in the region was 11.68 x 103 m³.t^-1 (71,200 m³ / 6,095 t). Also, according to Table 4, the agrochemical that presents the highest WF_Gray in the agricultural holding of 1 ha of soybean was the fungicide trifloxystrobin with a value of 31,600 m³. Considering that the average soybean yield was 3,000 t.ha^-1, the produced soybean WF_Gray in the region was 10.53 x 103 m³.t_-1 (31,600 m³ / 3,000 t).

The pesticides ranking expresses the relative position for the total volume of WF_Gray and can be used to select pesticides that can minimize the volume of gray water in agricultural crops. According to the type of pesticide, in the herbicide line, the lowest WF_Ca was for glyphosate (180m³), in the fungicide line the lowest WFCa was Azoxystrobin (296m³) and in the insecticide line was Zeta-cypermethrin with WF_Ca of 0.011m³ .

Figure 1 shows the WF_Gray, WF_Green and Total Water Footprint (WFT) of each crop. Very close values are observed for soybean and corn crops. The lowest WFT is from the cotton crop with 7.29x103 m³.t^-1, although it is the crop that, in this study, presents the highest number of pesticides application. The corn crop presents the most polluting pesticide, therefore the highest WF_Gray, which contributes to the increase of WFT that was 1.21x104 m³.t^-1 and the soybean crop presented the highest WFT of the three cultures with 1, 22x104 m³.t^-1. The values between the gray and green water footprints of each crop are very different, with the WF_Gray value always higher than the WF_Green. Thus for cotton the WF_Gray was 7.4 times higher than WF_Green, for corn the WF_Gray was 25.2 times and for soy the WF_Gray was 6.3 times.

The found WFT values in this research are very above the world culture averages, according to Mekkonnen and Hoekstra (2010), which are: 4029 m³.t^-1, 2145 m³.t^-1, 1222 m³.t^-1 for cotton, corn and soybean respectively. These differences are due to the methodology used by Mekkonnen and Hoekstra (2010) to find the WF_Gray, which did not take into account the type of pesticides used, but the nitrogen leached fraction in the nitrate form. When the WF_Gray was calculated using the agrochemicals Franke, Boyacioglu and Hoekstra. (2013), found in India cotton farms WF_Gray of 38x106 m³.t^-1, well above that found in this research that was 6.43x103 m³.t^-1.

Figure 1 - Total Water Footprint (WFT), Ash (WF_gray) and Green (WF_green) of cotton, corn and soybean crops in the upper São Lourenço river, MT.

The reduction of the Water Footprint can be achieved through the efficient use of water in the agricultural sector, through the correct use of pesticides and better utilization of rainwater, avoiding mainly the pesticides transport to surface water. Thus, even considering the same number of applications of pesticides per crop, if glyphosate herbicide, fungicide Azoxystrobin and Zeta-Cypermethrin insecticide were used, the WF_Gray of the analyzed crops would decrease dramatically, being 219 m³.t^-1, 145 m³.t^-1, 197 m³.t^-1, for cotton, corn and soybean respectively.

3.3 Water footprint sustentability on upper São Lourenço river

The WF_Green sustainability calculations were estimated according to Hoekstra et al. (2011) methodology. The total upper São Lourenço River area is 21,105 km². The annual evapotranspiration for the crop year 2013/2014 was 1294.32 mm and the period from May to July was 274.43 mm, according to data collected on the INMET website (2015). The natural reserve for the area is 35% for the preservation of biodiversity and the unproductive area represents 1% (INPE, 2016) and refers to the urban area, occupation mosaic of the studied area and water reservoirs. The calculations showed that the ET_green total of the area was of 27,30x109 m₃, ET_amb was of 9,56x109 m³ and ET_improd was of 0.27x109 m³. The green water availability (DA_green) of the studied area was: 27,30x109 m³ - 9,56x109 m³ - 0,27x109 m³ = 17,47x109 m³.

According to the IBGE (2015) in the 2013/2014 harvest, 202,000ha of soybeans, 61,000ha of cotton and 87,030ha of Corn were planted in the field, totaling a total area of 350,030ha, or 3500,03 Km². Considering the crop yields in the area as well as WF_Green of each crop, theneeded water volume to supply the entire WF_Green in the São Lourenço Alto basin is obtained according to Table 4.

Table 4. Volume of water needed to supply PHV in the high São Lourenço-MT river, in the 2013/2014 harvest.

CULTURE	AP (ha) (1)	PT. (t/ha) (2)	PD. (t) (3)=(1x2)	PHV (m³/t) (4)	V (m³) (5)=(3x4)
Cotton	61.000	4,044	246.684,0	864	213.134.976
Corn	87.030	6,095	530.447,9	464	246.127.872
Soy	202.000	3,000	606.000,0	1675	1.019.056.000
∑ Green Water Footprint volume in watershed					1,47x10⁹

AP = Planted area; PT = Productivity; PD = Production; WFgreen = Green Water Footprint; V = water required to supply the WFgreen in the watershed.

The green water (EA_green) scarcity level in upper São Lourenço sub-basin is defined as the ratio between the total volume of water used in green water footprints in the microbasin and the availability of green water found was (1.47x109 m³ / 17.47x109 m³) x100 = 8.4%. This result indicates that there was still green water sustainability in the studied area.

The increase in the area for agricultural production in the upper São Lourenço sub-basin has grown over time and, if the national average is followed, the planted area will increase around 15% in the 2025year, with a planted area of 70150 ha of cotton, 100084 ha of corn and 232300 ha of soy respectively. In this scenario of increased planted area for the 2024/2025 harvest, the green water scarcity level (EA_green) in the upper São Lourenço sub-basin would be (1,88x109 m3 / 17,47x109 m3) x100 = 10,8 %. These data show that, even with all planted cropsarea increase, if the same planting system continues, the area will maintain green water sustainability.

In the months of May to July, the green water availability tends to decrease what could increase the green water shortage level in that period and the sustainability of the area may be compromised. In the spring area, this concern should be throughout the year, since the area is in a recovery phase and needs reserved land for biodiversity preservation, where ecosystems depend on the availability of green water for this preservation. A reforestation is being carried out in the spring area of the sub-basin of the São Lourenço river, but this area still presents great vulnerability, since the planting of crops still happens very close to the springs.

The WF_Gray was evaluated using the most commonly used pesticides of each crop studied (Table 5). Although the pesticide that presented the highest contamination rate was metomil, an insecticide used mainly in maize, the fungicide Trifloxystrobin, used in soybean, was responsible for the largest Gray Water Footprint in the microbasin as a whole, since soybeans had a cultivated area much larger than corn. This Gray Water Footprint of 6.38x109m³ encompassed all the gray water footprints of other pesticides, so this volume will be used to calculate the WF_Gray's sustainability in the microbasin.

Table 5. Gray water footprint of the most polluting pesticides used in cotton, corn and soybean crops in the Upper São Lourenço Sub-Basin, MT, in the 2013/2014 harvest.

CULTURE	AGROTÓXIC	WFgray (m³/ha) (1)	AP (ha) (2)	WFgray m (m³) (3)=(1x2)
Cotton	Difenoconazole	26.000	61.000	1,59x10⁹
Corn	Methomyl	71.200	87.030	6,20x10⁹
Soy	Trifloxystrobin	31.600	202.000	6,38x10⁹

WFgray = Footprint Gray of the pesticide; AP = Planted area; WFgray m = Gray Footprint of the agrotoxic in the microbasin;

The water pollution level (NPA) in the upper São Lourenço sub-basin, defined as the fraction of the effluent assimilation capacity, consumed and calculated by the largest ratio between the Gray Water Footprint and the actual flow of a basin (Q_real), which according to Gonçalves et al. (2011), is 317 m³ / s. Thus the NPA of the microbasin = (6.38x109m³ / 10.00 x109m³) x100 = 63.8%. Therefore, in the São Lourenço high basin 63.8% of the annual average volume is used to assimilate the effluents from agrochemicals. Even so, for the planted area level of the 2013/2014 harvest, there is still sustainability. In a scenario of increased planted area to the 2024/2025 harvest, the water pollution level (NPA) in the upper sub-basin of São Lourenço would be = (10.18x109 m³ / 10.00x109 m³) x100 = 101.8%. A water pollution level of 100% indicates that waste assimilation capacity has been fully utilized. When the level of pollution exceeds 100%, the water quality standards in their natural state are violated (Hoekstra et al., 2011).

Therefore, with the increase of the planted area of all crops, especially soybeans, and using the same pesticides, the annual volume of water in the sub-basin of São Lourenço will be used to assimilate pesticidesdilution, which characterizes an unsustainability the expansion of the agricultural area of the region in a 2024/2025 scenario.

The Water Footprint environmental sustainability is not only focused on numbers, but mainly on critical points that violate the qualitative and quantitative water resources patterns in the studied area and during a certain period. Soil and water quality plus the biodiversity downstream impact are also important concerns, as well as concerns about the health of agricultural workers and animals using water for consumption.

In addition to WF_Gray, special attention is given to pesticide containers handling and application equipment. The equipment washin rivers should be avoided, as it presents great toxicity to surface waters, directly affecting ecosystems biodiversity.

The great agricultural expansion in areas near the Mato Grosso wetland altered the soil cover, which may present alterations in the local hydrology, such as springs decreases mainly due to the high level of mechanization used in the area, making the soil more compacted. Proper land use management helps prevent negative consequences for the ecosystem such as soil erosion and degradation, thus avoiding the loss of biodiversity and the region's natural resources.

4 CONCLUSIONS

The Water Footprint values of cotton, maize and soybean crops were high when compared to other regions of the world, but they contributed to a regionalized assessment of water use by these crops and provided subsidies for the analysis of the use of water resources in the area studied from the upper São Lourenço, MT.

The knowledge of the Gray Water Footprint of the pesticides used in this study admits the choice of chemical products that minimize the volume of gray water. Thus, the Water Footprint presents itself as a useful source of management being a tool that can be used to minimize the risk of contamination of fresh water by agrochemicals used in agricultural crops.

The Green and Gray Water Footprint presented as an indicator that makes it possible to analyze the sustainability of the agriculture of the studied area. In the current scenario and even in a future scenario there is green water sustainability, however with the expansion of the agricultural area, at the current rate, in the year 2025, the entire annual volume of water in the upper São Lourenço sub-basin will be used to assimilate the dilution of agrochemicals, which characterizes the area's unsustainability.

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