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University of São Paulo “Luiz de Queiroz” College of Agriculture Agricultural resource efficiency and reduction of impacts under land- use and climate change scenarios in Brazil

Agricultural resource efficiency and reduction of impacts under land-use and climate change scenarios in Brazil Cerrado is the second largest Brazilian biome and originally corresponded to 24% of the national territory, and since the 1970 ́s has been under agriculture and cattle activities. Soybean and maize are two of the most important grain-crops found in this region, with an estimated production of approximately 223 millions of tons in the Brazilian 2016/17 harvest. Changes in soil physical properties due to soil management affect productivity. Possible changes in climatic variables may also affect agricultural productivity, either per unit area (land productivity) or per unit of water volume (water productivity). One option for studying the relation between land and water productivity and how they are affected by soil hydraulic properties and climatic factors is by using an agro-hydrological model. In this study, the aim was to quantify aspects of the soil water balance and to make estimates of land and water productivity for soybean in a clay soil and maize in a medium texture in the Cerrado region using SWAP simulations for different irrigation strategies. Effects on agricultural productivity of a climatic prevision with increasing the air temperature and rainfall reduction for the years 2016-2040 were also simulated. Results show that an increase of soil porosity, resulting from a conservation tillage management, leads to a higher infiltration capacity and is shown to increase land and water productivity, when associated to irrigation scenarios. Higher water productivities were observed with only supplementary irrigation. Predicted climate changes will lead to a decrease of approximately 20% by the end of the years 2016-2040 in land productivity, under rainfed conditions.


ACKNOWLEDGMENTS
To my parents Noeli and Jair, to my sister Fernanda, my fiancé Felipe, Zanatta and to the whole Schwantes family. Thank you for being present every time I needed support and guidance during this process. This work is also for you that believe in the Brazilian agriculture and know the importance of research on atmosphere, soil and water, but do not necessarily agree with my opinion in these topics! To the farmers that provided the datasets for this study for their huge collaboration and for receiving the study group at the sites for the data collection.

INTRODUCTION
World population growth coupled with lifestyle changes lead to an increased demand for agricultural food, feed and energy production. Brazil is one of the world's largest producers and distributors of agricultural products, with an estimated 60 million hectares of grain-sown area in the 2016/2017 harvest year (CONAB, 2017).
This large production results in intensified competition for natural resources. To enable sustainable growth of production, the available resources must be used efficiently, especially at a time when a global shortage crisis is imminent. The solution would be to expand production even more quickly, but this becomes difficult due to the price of arable land and the unavailability of new areas (Ficarelli, Ribeiro, 2009). Periods of rapid economic expansion in Brazil coincide with higher land prices due to increased demand for agricultural commodities (Rangel, 2000).
The economic importance of maize and soybean is characterized by their diverse forms of use, ranging from food and feed, to the high technology industry. They are staple foods for the population in many countries, used in animal feed because of the high nutritional and energy value, which is increasingly important in many countries for industrial and pharmaceutical applications, with maize being used for the production of starch, ethanol, plastic and the base for the production of antibiotics .
Cerrado is the second largest Brazilian biome and originally corresponded to 24% of the national territory, or approximately two million square kilometers, presenting plant formations that range from dense forests to open fields Ratter, 2002). It is one of the biomes with the most floristic diversity of the planet, and among the most threatened ones. Such diversity lies in the large number of potentially economic species ranging from food to those used in regional crafts (Felfili et al., 2004). Due to the good conditions of topography, type of terrain and ease of deforestation, the Cerrado represents the main grazing and beef producing region of Brazil .
The occupation of the Cerrado region in the last decades was the greatest Green Revolution in the history of mankind, according to Norman Borlaug. During the last five decades the Cerrado has played a major role in the growth of Brazilian agriculture, and it is expected to continue doing so. Agriculture in this region was a development tool and an important tool for ensuring the Brazil's food security Martha, 2008).
Land use in the Cerrado is relevant due to diverse social, economic and environmental issues, as well as the reallocation of agricultural activities in consolidated regions of the Cerrado. Different segments of society have been monitoring the expansion of production and its possible impacts on the resources and quality of soil, water, air, vegetation and biodiversity, and as a result the adoption of practices with a lower environmental impact is necessary (Mueller; Martha, 2008).
One of the most important examples of land use change in the Cerrado biome has been the conversion of native vegetation into pasture and cropping areas (Carvalho et al., 2014). The advancement of the agricultural frontier has increased the pressure on natural resources, and led to a change of the natural vegetation. Myers et al. (2000) estimate that around 80% of the Cerrado's original area has already undergone some anthropic interference.
One of the challenges is to combine the sustainability of this biome with the productive system (Lopes;Daher, 2008). The technological research, the improvement of productivity and the substitution of activities in areas of Cerrado already exploited by agriculture, such as maize intercropping with forage species (Oliveira et al., 2011), are fundamental to reduce the pressure on the remaining area.
Within this context, system modeling emerges as an important tool to evaluate possible scenarios of climate change and land use. Researchers in the field of agronomy develop their studies in many areas of knowledge, where the amount of information on diverse subjects is still possible to be expanded (Martin, 2007). For research results to become reliable there is a need for agricultural experiments that increasingly have a high cost of installation, maintenance and data collection. In addition, the time used for conducting such experiments is not always available. In other cases, there is no possibility of installing experiments because of the complexity of the problem, requiring that decision-making be rapid. To overcome this situation, a simplified representation of the problem can be proposed, but it is necessary to know in depth the basic concepts of system operation (Martin, 2007). In this way, it is possible to elaborate mathematical models that represent the problem more efficiently, making the monitoring of the dynamics of this system possible through the simulation of alternative scenarios. From climate data, relief and vegetation, i.e., air temperature, rainfall, solar radiation, land slope and soil type, it is possible to estimate crop yield at a given location (Martin, 2007).
In agriculture, simulation is important to understand the interactions in the soil-plantatmosphere system, as well as to predict results of an environmental system or condition. In addition, analyses of plant growth responses as a function of certain factors improve the efficiency of crop production and provide directions for research . The simulation allows to create scenarios that are not yet known, alternative scenarios not explored in real experiments, ideal scenarios, very difficult to reproduce in the field reality . Using data collected from agricultural producers, a detailed analysis of productivity per unit of water and land consumption is possible, as well as the behavior of the water balance components. The database developed for the scenarios for land use change in the Cerrado can increase the vision on these processes and serve as a guide to make an efficient use of resources in a scenario of adaptation of agricultural practices to climate change. One way to accomplish this task is to use the concept of water productivity and water footprint (Hoekstra et al., 2011).
Regarding climate change, studies for the Brazilian scenario show a possible increase in temperatures and a decrease in the annual accumulated rainfall (Marengo et al., 2012;Pinheiro et al., 2017). Soil water content is expected to decrease, and, consequently, plants on those soils will be affected, according to the soil hydraulic properties.
Within this context, the objectives of the present work are to perform a detailed analysis of the land and water productivity of the maize and soybean crops under the current

Abstract
Changes in soil physical properties due to soil management affect the water productivity, and irrigation strategies can optimize this component. An option for studying the relation between land and water productivity and how they are affected by soil hydraulic properties is by using an agro-hydrological model. In this study, we aimed to quantify aspects of the soil water balance and to make estimates of land and water productivity for soybean and maize cultivation in the Brazilian Cerrado region using SWAP simulations for different irrigation strategies. Our results show that an increase of soil porosity, resulting from a conservation tillage management, leads to a higher infiltration capacity and is shown to increase land and water productivity in medium texture soils, when associated to irrigation scenarios. Higher water productivities were observed with only supplementary irrigation.
Modification of soil porosity, as to be expected from agricultural management, will bring about relevant alterations in the water balance components such as infiltrated water and bottom flux. Keywords: SWAP model; Brazilian savanna; Soil porosity

Introduction
Limited crop water availability is one of the main stress factors reducing agricultural crop production and is an important issue related to food security in many developing countries. Drought stress affects crop development and light use efficiency. Crop yield may be significantly reduced or even completely lost due to insufficient water availability at different plant stages, depending on duration and severity (Boyer et al., 2004;Boyer et al., 2013;Golbashy et al., 2010;Moradi et al., 2012;Steduto, et al., 2012). Despite the yield loss under water-limited conditions, moderate drought stress is often associated to an increase in water use efficiency (Blum, 2009;Esmaeili, 2011;Kulathunga, 2013).
Demand for food, feed and energy together with water scarcity requires an increase in water use efficiency of agricultural production, especially in drought-prone agricultural areas.
An example of such an area is the Brazilian savanna, also known by the local denomination Cerrado. The Cerrado region comprises a large area of 2 million km 2 ranging through Brazil's northeastern, central and southeastern regions partly in transition to intensive and irrigated agriculture. Until a few decades ago, the Cerrado biome was predominantly under natural pastures developed under low technology cattle production. At present, the region undergoes a rapid process of land use change from natural pasture and original vegetation to soybeans, maize, sugarcane, medium and high technology pastures and integrated systems (croplivestock or crop-livestock-forest), transforming Brazil into a main grain and cattle provider due to its favourable topographical and climatic conditions .
The introduced agricultural production systems are supposed to improve production from an economic point of view, while environmentally sustainable. It has been estimated that half of the Cerrado region is arable and has potential to be used for agriculture or livestock production (Goedert, 1989;Lopes, 1996;Oliveira and Ratter, 2002).
Climate in the Cerrado region is characterized by a dry winter, typically between May and September, and a rainy summer between October and April. The Köppen climate type is tropical, Aw in the major part, though some parts to the south are subtropical (Köppen Cw).
Average precipitation is around 1500 mm y -1 , slightly lower than the potential evapotranspiration. Although the dry winter period limits water availability, irrigation makes agriculture feasible during this season and is used on large scale in the region, mainly using centre pivot irrigation systems. A comprehensive study on water limitations for soybean production in the Cerrado region has been reported by Sentelhas et al. (2015).
Being part of Brazil's Precambrian surface, soils in the Cerrado region are highly weathered and deep, mostly well drained with kaolinite clay and high contents of sesquioxides. Classified as Ferralsols in the World Reference Base classification system (FAO, 2006) or as Oxisols according to USDA Soil Taxonomy (Soil Survey Staff, 1999) these soils are characterized by a low fertility and pH, leading to a high free aluminum content which chemically impedes root growth (Batlle-Bayer et al., 2010;Walter, 2006). These chemical drawbacks are, however, very well manageable. Physically, Ferralsols are among the best soils in the world with a very stable microstructure, soil water availability being the main limitation.
Many studies have been performed to investigate the effect of agricultural management practices on soil physical properties, especially regarding the change from a conventional tillage system to no-till management. Macroporosity, hydraulic conductivity, and temporal variation of soil water content are usually shown to be affected. Generally, it is known that organic matter content increases under no-till (Batlle-Bayer et al., 2010;Corbeels et al., 2006;Ferreira et al., 2016). In many cases, a decrease in macroporosity and hydraulic conductivity is observed (Azooz and Arshad, 1996;Osunbitana et al., 2005;Moret and Arrúe, 2007), but on the long term this tendency has been reported to invert (Green et al., 2003), possibly due to the increasing organic matter content and biological activity associated to notill.
Irrigation management together with tillage practices put a challenge to the planning of water use. One of the questions to be addressed is how changes in soil physical properties due to soil management affect the water use efficiency, and how irrigation strategies can optimize this water use efficiency.
An option for studying the relation between land and water productivity and how they are affected by soil hydraulic properties is by using an agro-hydrological model. One such model is SWAP , which combines a robust, Richards equation based hydrological module to the process based crop-growth model WOFOST (Van Diepen et al., 1989), including the transpiration reduction function proposed by . This combination of modules allows SWAP to reliably predict soil water balance components, land and water productivity as affected by soil hydraulic properties and meteorological boundary conditions.
In this study, we aimed to quantify aspects of the soil water balance and to make estimates of land and water productivity for soybean and maize cultivation in the Brazilian Cerrado region using SWAP simulations for different irrigation strategies. We test the sensitivity of water balance components and productivities to changes in soil hydraulic properties to be expected from occurring agricultural management changes.

Hydrological and crop growth modeling
The SWAP hydrological model , which includes the WOFOST crop growth model (Van Diepen et al., 1989)  Potential evapotranspiration was calculated from daily weather data using the Penman-Monteith equation . At the bottom of the soil profile, unit-gradient free drainage was used as boundary condition. SWAP simulates 1-D vertical soil water flow using the Richards equation and an implicit finite difference scheme (Van Dam and Feddes, 2000). Relations between soil water content, pressure head and hydraulic conductivity are described by the Van Genuchten (1980) equations:  are water content, residual water content and saturated water content (m 3 m -3 ), respectively, h is the pressure head (m), K and K sat (m d -1 ) are hydraulic conductivity and saturated hydraulic conductivity, respectively, and α (m -1 ), n, and λ are empirical parameters. Relative transpiration T r , the ratio between actual (T a , m d -1 ) and potential (T p , m d -1 ) transpiration is calculated using an adapted version of the reduction function proposed by : where p is the soil layer number, n is the total number of soil layers, w p (m) is the thickness of layer p, R p is the relative root length density in layer p and α p is the empirical reduction factor for layer p, defined by four typical soil water pressure heads h 1 , h 2 , h 3 , and h 4 , as follows: no uptake (α = 0) occurs in the very wet (h p > h 1 ) and very dry (h p < h 4 ) range. Uptake is maximum (α = 1) in the constant rate phase, for h p between h 3 and h 2 . When h p is between h 3 and h 4 , or between h 2 and h 1 , the reduction factor T r is interpolated linearly. The relative reduction in biomass accumulation is considered equal to the relative transpiration T r as calculated by equation [2.3].

Experimental sites
To calibrate the SWAP model for maize and soybean grown under local Cerrado conditions, data from three experimental sites were collected from the agricultural years 2006/07 to 2014/15. Maize growing was evaluated in Correntina (13°20'S, 44°38'W, 560 m asl), and soybean production was studied for the Ipiranga do Norte (12°14'S, 56°09'W, 470 m asl) and Primavera do Leste (15°33'S, 54°17'W, 630 m asl) sites. Soils of the experimental sites are sandy to medium textured in Correntina (0.23 kg kg -1 clay content) and clayey in Ipiranga (0.45 kg kg -1 ) and Primavera (almost 0.60 kg kg -1 ) ( Table 2.2). It is important to mention that the clay of these soils is of the 1:1 kaolinite type resulting in hydraulic properties very distinct from those of 2:1 clays commonly found in soils developed in temperate regions. Soils have been under no-tillage management for many years, incorporating phosphate fertilizer during sowing (Ipiranga and Primavera) or broadcast at the entire area (Correntina). No irrigation is regularly performed at any of the three study sites.
The available soil database did not contain specific data for coarse and fine sand as needed in equation [2.4], therefore, in agreement with reports for similar soils (Rosa et al., 2003;Resende and Rosolem, 2013) we assumed equal values for both fractions. Saturated hydraulic conductivity K sat for the Correntina sandy soil was 2.66 m d -1 , and for the clayey soils we used 0.216 m d -1 as determined by  for a similar soil. For the λ parameter in equation 2.2, the value of 0.5 was used according to .

Model calibration and evaluation
For calibration and validation of soybean crop parameters, data from the Ipiranga and Primavera sites were used, comprising 19 water-limited yields obtained between 2007 and 2015. Data refer to soybean production during the main cropping season, sowing dates going from around the second half of September to harvest around the beginning of March. To reserve independent data for model validation, only a randomly selected half of the available data was used in the parameter calibration procedure, whereas the other half was used for validation. A semi-automated assisted procedure was developed for both steps of the procedure.
Parameter calibration of WOFOST model parameters was performed following the protocol described by de Wit (2010). The calibration comprised the following parameters: temperature sum from emergence to anthesis (TSUMEA , °C) and between anthesis and maturity (TSUMAM, °C), crop base temperature (TBASE, °C), leaf lifespan (SPAN, d), development stage at the onset of maximum leaf assimilation rate reduction (DVS amax ), maximum leaf assimilation rate (AMAX, kg ha -1 h -1 ) and permanent wilting pressure head (h 4 , m). Further crop parameters were assumed as listed in  for maize (their appendix B1) and for soybean (their appendix H1). Critical pressure heads for the onset of the falling transpiration rate phase (h 3 from  were modified from the Taylor and Ashcroft (1972) critical pressure heads for grain crops root water extraction (h 3 = -3 m at high potential transpiration, 5 mm d -1 , and h 3 = -6 m at low potential transpiration rate, 1 mm d -1 ).
First, TSUMEA, TSUMAM and TBASE were adjusted using literature information on soybean (Correa, 2008) and maize (Muchow and Carberry, 1989). Simulated yields for specific scenarios were then confronted to observed yields, finding the optimum combination of crop parameters SPAN, DVS and AMAX and hydrological parameters by minimizing the Root Mean Square Error (RMSE) between observed and simulated yields.

Irrigation and land use change scenarios
Yield and crop water use data were simulated for several scenarios of irrigation management. For this purpose, field capacity was defined as corresponding to the hydraulic conditions at a bottom flux of 1 mm d -1 at the 0.6 m depth. This field capacity conditions were determined in a separate simulation of internal redistribution, starting at saturation, without evapotranspiration, as described in de Jong van Lier and Wendroth (2016).
Irrigation was supposed to be of the sprinkler type, and vegetation water retention of 2.5 mm was used. Four irrigation strategies were simulated: full irrigation (I 100 ), two supplementary irrigation scenarios (I 66 and I 33 ) and a baseline rainfed scenario (I 0 ). Full irrigation implied in replenishing the soil water content to field capacity, whereas supplementary scenarios I 66 and I 33 replenish to 2/3 or 1/3, respectively, of field capacity.
Corresponding pressure heads and water contents are listed in Table 2.3, as well as the critical pressure head to trigger irrigation in each scenario. At the occurrence of drought stress, the amount of irrigation (L F , mm) was calculated as: where z i (mm) is the rooted soil depth,  FC (m 3 m -3 ) is the water content at field capacity,  PWP (m 3 m -3 ) is the water content at permanent wilting point,  a (m 3 m -3 ) is the actual water content, and F (%) is the fraction of available water to be replenished (F = 100, 66 or 33%).
In the case of multiple layers, equation [2.5] was applied per layer. Land use change scenarios were simulated considering that soil management implies in a change in total porosity. This may be a reduction due to compaction, normally caused by agricultural machinery, but porosity may also increase due to conservation management resulting in increased organic matter contents in favor of a better soil structure. Changes in porosity affect soil water retention parameters and soil hydraulic conductivity. Five porosity scenarios were tested in this study: the current scenario (S 0 ), an increase in total porosity by 0.02 m 3 m -3 (S 2 ) or by 0.04 m 3 m -3 (S 4 ) and a decrease in total porosity by 0.02 m 3 m -3 (S -2 ) or by 0.04 m 3 m -3 (S -4 ). For each scenario, the corresponding increase or decrease in saturated hydraulic conductivity was estimated using a Childs and Collins-George (1950)-like approach. Other soil hydraulic parameters (, n, ) were supposed to be unaffected. Irrigation strategies and porosity scenarios were then combined to generate the respective soil water balance and land and water productivities.

Land and water productivity
Land and water productivity for soybean and maize were determined from the output of SWAP simulations, with and without irrigation. Land productivity Y (kg ha -1 ) was considered as the final dry grain mass per hectare and is a SWAP output variable. Water productivity  (kg m -3 ) was calculated from land productivity and accumulated actual evapotranspiration ET a (m) during the crop development as: where the factor 10 4 [m 2 ha -1 ] stands for the conversion between square meters and hectares.
The water productivity increase obtained by irrigation unit ( I , kg m -3 ) was estimated by the difference between the respective land productivity in an irrigation scenario Y I (kg ha -1 ) and in the rainfed scenario Y 0 (kg ha -1 ), divided by the amount of applied irrigation water (I, m):

Calibration, sensitivity analysis and model evaluation
Rainfall was within the range defined by the thirty-year observations standard deviation for all observed years in Correntina. In Ipiranga, the first year (2007/08) showed an above average rainfall amount, whereas for the Primavera location two out of the three observed years were above average with respect to rainfall (    The 2011-2012 season shows pressure head within the optimum range during the entire cycle. A small reduction of productivity due to drought stress can be observed at the end of the cycle, in the full grain stage at onset of senescence. Some small reductions were simulated earlier during the cycle, due to water shortage in a layer that is not shown in the graph. In the 2012-2013 cropping season, some drought stress was simulated at the onset of the season, making the relative dry mass to reduce to around 80%. Although the pressure head was outside the optimum range only in the surface layer, at this stage of crop development (around one week after emergence) the root system is still very shallow and surface water depletion can lead to severe biomass reduction, as simulated. Later in the season, by the end of December and the beginning of January, a more severe drought stress was simulated. This period coincides with a development stage with high leaf CO 2 assimilation, therefore, consequent relative dry mass reduction was large. Under a potential evapotranspiration condition during this DVS, the crop was simulated to accumulate 4882 kg ha -1 of dry grain, whereas the amount including drought stress was 1252 kg ha -1 (26%).  For soybean, an Ω of 0.685 kg m -3 and an average Y of 2920 kg ha -1 were simulated (Table 2.6). According to Sadras et al. (2010), Ω ranges from 0.6 to 1.0 kg m -3 for soybean in rainfed conditions. Y was 7756 kg ha -1 (Table 2.5) for maize, with an Ω of 1.476 kg m -3 . In the review on measured crop water productivity for some crops by Zwart and Bastiaanssen (2004), Ω was ranging from 1.1 to 2.7 kg m -3 . The large simulated difference between Ω for the two soil textures can be ascribed to climate and soil management, and irrigation when it is the case.

S im u la te d ( t h a
In the medium texture soil with maize cultivation, 59.7 (±8.3) cm were lost by bottom flux, and 115.6 (±6.8) cm were infiltrated water. Maize ET a ranges from 48 to 80 cm ; 52.6 (±4.5) cm was simulated in scenario S 0 I 0 ( Table 2.5). In this case, when irrigation became available, differences in the water balance components were observed, as well in the land productivity, and these results will be discussed in the next item.

Sensitivity of land and water productivity to land use change and irrigation scenarios
According to Hoekstra et al. (2011), the pressure on water resources of a certain location is reduced when it is possible to increase . If a supplementary irrigation management is chosen, the goal should be to reach the maximum  instead of the maximum Y. It is possible to observe that Y increases along increased irrigation scenarios, described in Table 2.3, and reaches the maximum Y for scenario I 100 . In the I 100 scenario, the pressure head remains always within the range of maximum soil water uptake by the plant, the no-stress range between -57.6 and -500 cm (Table 2.3).
The same increasing tendency is observed in the average results for all plots ( Regarding water use, in places where restrictions about irrigation exist, this result indicates that the aim of agriculture should consider not only high Y, but also the efficiency of the use of each unit of evapotranspired water. For the soybean scenarios, no irrigation was simulated, independent of the porosity scenario (Table 2.6). All of these simulation scenarios refer to the rainy cropping season.
These locations show high rainfall amounts (Table 2.6) which, linked to the clayey soils, resulted in no need for irrigation. No drought stress was observed in the different locations, years and scenarios. Y tends to increase according to the porosity increase, as well as the infiltrated water (around 5%), and, consequently, the bottom flux (7%). Results show that the adoption of good agricultural practices, here represented by an increase in soil porosity, is important to increase soil water infiltration and land productivity.

Conclusions
1. Irrigation was predicted to increase maize productivity on medium textured soils by about 11%, while increasing water productivity by 7%. Maximum land productivity was achieved with a full irrigation scenario, but higher water productivities were observed with only supplementary irrigation.
2. Modification of soil porosity, as to be expected from agricultural management, will bring about relevant alterations in some water balance components, as infiltrated water and bottom flux, and is shown to increase land and water productivity in medium texture soils, when associated to irrigation scenarios.
3. There is no demand for irrigation in soybean cropping on clayey soils during the rainy season in the studied Cerrado region. Soybean land productivity increased 1.5% when soil porosity increased by 0.04 m 3 m -3 , while water productivity increased around 1%.

Abstract
Modified climate parameters have been carried out in Brazil and a warming trend in the oncoming years is believed to occur by the vast majority of researchers. Concerns about the vulnerability of agricultural production to modified climate parameters are increasing and their impacts were widely predicted by using crop models together with climate change scenarios. In this study, we generated crop and climate data from two representative soils cultivated with maize and soybean in Brazil, for the period 2016-2040, to investigate whether a predicted climate change would have a significant impact on water balance components, soil water content and land and water productivity. Our results show that: the rainfall amount reduction over time provides a soil drier condition, and soil water flow to the roots is expected to be reduced due to frequently lower unsaturated hydraulic conductivity of coarse textured soils when compared to those of fine texture. As a consequence, transpiration decreased 21% in the medium texture soil; temperature increase has shown to increase transpiration in 10% during the rainy season in a clay soil; maize and soybean land productivity decreased approximately 20% in a medium texture soil and in a clay soil. Water productivity decreased 42%. Keywords: SWAP model; Brazilian savannah; Temperature increase; Drought

Introduction
The cerrado is the second largest biome in Brazil, its original size corresponds to 24% of the Brazilian territory, presenting plant formations that range from dense forests to open fields . This vegetation has been replaced by agricultural fields and cattle ranching since the 1970´s, and nowadays is considered as the most important agricultural frontier in Brazil.
Climate in the Cerrado region is characterized by a dry winter, typically between May and September, and a rainy summer between October and April. The Köppen climate type is tropical, Aw in the major part, though some parts to the south are subtropical (Cw). Average precipitation is around 1500 mm y -1 . Regarding climate change, regional scenarios applied in the Brazilian territory have shown a very likely increase in air temperatures and a decrease in the annual accumulated rainfall, reducing water availability both at watershed and soil profile scales (Marengo et al., 2012;Pinheiro et al., 2017). Under such scenarios and depending on the soil hydraulic properties, soil water fluxes are expected to decrease, and, consequently, crops grown on those soils may be affected. Concerns about the vulnerability of agricultural production to climate change are increasing and the impact of these scenarios on crop yield has been widely predicted by crop models.
From climate data, terrain and vegetation, such as air temperature, rainfall, solar radiation, land slope and soil type, it is possible to estimate crop yield at a given location (Martin, 2007). In agriculture, yield simulation is important to understand the interactions in the soil-plant-atmosphere system, as well as to predict impacts of changes in environmental driving forces or soil properties on crop yield. In addition, analyses of plant growth responses as a function of certain factors improve the efficiency of crop production and provide directions for research . Such simulations allow to investigate likely scenarios and alternative scenarios not explored in real experiments due to unfeasible aspects to be reproduced in the field reality .
Maize is a staple food and energy provider for the population in many countries  Within this context, our objective was to study the effects of modified climate variables based on regional scenarios of climate change on the soil-water balance components and land and water productivity of two representative Cerrado sites. Agro-hydrological simulations were performed focusing on the agricultural years 2016-2040.

Study areas
The study was carried out for two experimental sites under Cerrado conditions, for the agricultural years 2016-2040. Table 3.1 shows the location of the selected soils. Soybean production was simulated for the municipality of Ipiranga do Norte (12°14'S, 56°09'W, 470 m asl, located in Mato Grosso State), with a clayey soil type. Maize production was simulated for the climate conditions of the Correntina municipality (13°20'S, 44°38'W, 560 m asl, located in Bahia state), in a sandy clay loam soil which corresponds to a medium texture. In the following we will refer to locations according to their soil texture, i.e., clay and medium texture (FAO-Unesco, 1974). For the studied sites, two emergence dates (ED) were considered, one at the beginning and the other approximately in the middle of the common sowing season, according to local information collected from farmers. For maize, ED1 was October 08 and ED2 was November 24, while for soybeans, September 29 and November 03.
It is important to mention that both soils are economically important in soybean and maize production, but simulations were organized according to the available data for the calibration of the SWAP model, described in chapter 2 of this thesis: maize on the medium texture soil and soybean on the clay soil. The clay soil contains 0.45 kg kg -1 of clay, while the medium texture soil contains about 0.23 kg kg -1 (Table 3.1). It is important to mention that the clay mineralogy of these soils is 1:1 kaolinitic, resulting in hydraulic properties very distinct from those of 2:1 clays commonly found in soils developed in temperate regions. Soils have been under no-tillage management for many years, with phosphate fertilizer incorporation during sowing (clay) or at the total area (medium). No irrigation is regularly performed at these two sites.
Meteorological data were collected from nearby weather stations that are part of the National Meteorology Institute network (INMET, 2015). Annual rainfall is lower (1429 mm y -1 ± 213) at the medium texture site than at the clay site (1669 mm y -1 ± 384), and a dry period occurs from May to September at both locations. The annual average air temperature is slightly lower (24.2 °C) at the first location in comparison to the second (25.4 °C).
where p is the soil layer number, n is the total number of soil layers, w p (m) is the thickness of layer p, R p is the relative root length density in layer p and α p is the empirical reduction factor for layer p, defined by four typical soil water pressure heads h 1 , h 2 , h 3 , and h 4 , as follows: no uptake (α = 0) occurs in the very wet (h p > h 1 ) and very dry (h p < h 4 ) range. Uptake is maximum (α = 1) in the constant rate phase, for h p between h 3 and h 2 . When h p is between h 3 and h 4 , or between h 2 and h 1 , the reduction factor T r is interpolated linearly. The relative reduction in biomass accumulation is considered equal to the relative transpiration T r as calculated by equation [3.3].
Results from the calibration and evaluation procedures of the SWAP model described in chapter 2 were used in this chapter. Some of the parameters are available in Table 2.4.
Further specific information for different crops can be found at  report.

Soil hydraulic properties
Values for parameters , n,  r and  s from equations [3.1] and [3.2] were estimated for each soil layer using the Tomasella et al. (2000) pedotransfer function developed based on data of more than 500 horizons in Brazilian soils: where X i is a parameter value, being X 1 = log (α), X 2 = n, X 3 = θ s and X 4 = θ r . S C , S F , S and C are the mass fractions (in %) of coarse sand (2-0.2 mm), fine sand (0.2-0.05 mm), silt (0.05-0.002 mm) and clay (<0.002 mm), respectively; is the soil bulk density (g cm -1 ); C O is the mass fraction (in %) of soil organic carbon. Coefficients a i,j (1≤i≤4; 1≤j≤14) are pedotransfer fitting coefficients described in Tomasella et al. (2000) their table 8.
The available soil database did not contain specific data for coarse and fine sand as needed in equation [3.4], therefore, in agreement with reports for similar soils (Rosa et al., 2003;Resende and Rosolem, 2013) we assumed equal values for both fractions. Saturated hydraulic conductivity K sat for the Correntina sandy soil was 2.66 m d -1 , and for the clayey soils we used 0.216 m d -1 as determined by  for a similar soil. For the λ parameter in equation [3.2], the value of 0.5 was used according to .

Scenario with modified climate variables
Statistical weather generator software uses data series and its statistical properties to produce a series of synthetic daily climatic data (Pinheiro et al., 2017). The Climatic data generator (ClimGen) described by Stöckle, Campbell and Nelson (1999)  Scenarios with modified climate variables were implemented according to the regional downscaling performed by Marengo et al. (2012) for the Brazilian territory. In the generated period of time, 9% of rainfall reduction and 1.9 ºC of air-temperature increase are projected for the Amazon basin, corresponding to our clay soil. While for the São Francisco basin (medium texture soil) is projected rainfall reduction up to 15% and air-temperature increase in the order of 1.5 °C.
Rainfall reduction and temperature increase were linearly incorporated into a stochastic weather generator model such that the maximum changes were reached by the year 2040. As the stochastic weather generator relies on random numbers, one hundred stochastic realizations were performed in order to obtain scenarios with more representativeness. The 100 generated weather datasets were integrated into the agro-hydrological model SWAP, resulting in 100 water balance simulations for each year of the period.

Land and water productivity
Land and water productivity for soybean and maize were determined from the output of SWAP simulations. Land productivity Y (kg ha -1 ) was considered as the final dry grain mass per hectare and is a SWAP output variable. Water productivity  (kg m -3 ) was calculated from land productivity and accumulated evapotranspiration ET a (m) during the crop development as: a ET Y 4 10   [3.5] where the factor 10 4 [m 2 ha -1 ] stands for the conversion between square meters and hectares.
The water productivity increase obtained by irrigation unit ( I , kg m -3 ) was estimated by the difference between the respective land productivity in an irrigation scenario Y I (kg ha -1 ) and in the rainfed scenario Y 0 (kg ha -1 ), divided by the amount of applied irrigation water (I, m):

Genarated climatic data
The 100 stochastic daily rainfall series simulated by ClimGen for each year in the period 2016-2040 resulted in total annual rainfall ranging from 1283 to 1392 for areas with medium texture soil, while for clay soil areas it ranged from 1794 to 1903 mm ( Fig. 3.1  Year agricultural year, maize Y will reach 6161 and 6039 kg ha -1 at ED1 and ED2 the medium texture soil areas. The annual decline rate will be in the order of 51 and 46 kg ha -1 , higher in first ED probably due to the rainfall regime that is not very regular in the early cropping season. Ω will decrease linearly until an average of 1.0 kg m -3 in the agricultural year 2039-40.
Regarding the clay soil area and the current yield scenario, soybean Y decreases in the period 2016-2040, reaching 2227 and 2420 at ED1 and ED2, respectively. This represents a magnitude of around 20% less productivity than in the current scenario, and an annual decline rate of 11 and 17 kg ha -1 , while Ω decreased 42%.
For the soybean crop in 2039-40 in the clay soil areas, transpiration decreased 6% in the ED2 (Table 3.2) when compared to ED1 (Figure 3.3), while evaporation and interception increased 35% and 25%. When the average of the two soybean emergence dates is compared to the results from 2006-20014 for 19 plots (Table 3.3), transpiration increased 10% and evaporation, 26%. In the mean time, interception was 32% lower.  Evapotranspiration is not water-limited in places with high accumulated rainfall. At the medium texture soil area, 30 years average annual rainfall ranged from 1216 mm to 1642 mm, while at the clay area, ranged from 1285 mm to 2053 mm, concentrated in around half of the year from September to April. In the medium texture soil area, transpiration decreased in relation to the current scenario. As the rainfall amount was reduced over time, soil drier condition became prevalent, thus reduced water flow towards roots is expected due to frequently lower unsaturated hydraulic conductivities of coarse textured soils areas when compared to those of fine texture (Reichardt and Tim, 2004).
Regarding root water uptake, soil water availability is important in the surface layers in the Cerrado region because the annual crops usually have higher root density at a small soil depth. Daily average soil water pressure head over two depths at the beginning  and at the end of the simulated period show large variations during cropping seasons, especially for 2039-40 ( Fig. 3.5).
In the agricultural year 2039-40 year, drought stress was simulated close to the maize anthesis, from DVS 0.86 to 1.29 (November 16, 2039to December 8, 2039, with pressure head values reaching -2226 cm. Althought at this stage of crop development the root system is close to reach the maximum depth, the pressure head was below h 3 in both soil layers, and water depletion in the soil profile lead to an average relative biomass reduction of 0.6.   Under potential evapotranspiration conditions during this time of the crop cycle, the simulated crop could accumulate 8626 kg ha -1 of total plant mass. Later in the season, during the month of December and the beginning of January, a sequence of rain events was simulated, and soil water pressure head was between the limits of the high and the low transpiration rates, where it is possible to the crop to maintain an adequate root water uptake.
It is also important to mention that, in comparison to the agricultural year 2016-17, the temperature increase lead to a shortening of the maize cycle, as well as for the soybean cycle in the clay soil area. Temperature increase is also defining transpiration rates during the rainy season in these two situations. In regions whit high rainfall and consequently, elevated air humidity and a proper soil water content for the crop, transpiration rates increase.

Conclusions
1. Rainfall amount reduction over time provides a soil drier condition, and soil water flow to the roots is expected to be reduced due to frequently lower unsaturated hydraulic conductivity values in coarse textured soil area when compared to those of fine texture. As a consequence, transpiration decreased 21% in the medium texture soil area.
2. Temperature increase has shown to increase transpiration in 10% during the rainy season in clay soil area.
3. Maize and soybean land productivity decreased approximately 20% in the medium texture soil area and in the clay soil area, by the end of the predicted years. Water productivity decreases ranged from 28 to 47%.