Estimating the Impact of Climate Change on Maize Yield in the La Plata Region, Brazil, and Identification of Adaptation Strategies with the Help of Stakeholder and Situation Analysis

Contents

List of Figures and Tables

List of Abbreviations

1 Introduction

2 Goal, Scope, and Organization of the Paper

3 Methodology
3.1 Quantitative - MoNiCA
3.2 Qualitative - Expert Interviews

4 Climate Change in Brazil
4.1 Climatic Scenarios in Brazil
4.2 Extreme Climate Events in Santa Catarina State
4.3 Ecological, Economical and Social Implications of Climate Change in Santa Catarina

5 Yield Development of Corn until 2039 in the La Plata Region - Result of the Quantitative Research
5.0.1 Adaption to Santa Catarina, Brazil
5.1 Soils
5.2 Results

6 Situation and Stakeholder Analysis - Results of the Qualitative Research
6.1 Stakeholder Analysis
6.1.1 Legislation and Policy Environment
6.1.2 Stakeholder’s Perception on Climate Change
6.1.3 Requirements and Recommendations for Adaptation Strategies
6.1.4 Challenges for the Implementation of Adaptation Strategies
6.2 Existing Adaptation Strategies and their Implementation
6.2.1 Payment for Environmental Services
6.2.2 Agricultural Zoning and SEAF
6.2.3 ABC Plan
6.2.4 Farmer’s Introduced Adaptation Strategies

7 Conclusion and Discussion

References

Appendix

List of Figures

1 Land Cover Assessment of the La Plata Basin

2 Biomes Covered by the La Plata Basin in Brazil

3 Overview of MoNiCA

4 Projection of Climate Changes by 2100 After Region

5 Specific Leaf Area of all four varieties and all planting dates

6 Yield planting date 27.10.2010

7 Yield planting date 16.11.2010

8 Yield planting date 06.12.2010

9 Maize with aborted or not filled grains

10 Distribution of Utisol Soil in Brazil

11 Distribution of Oxisol Soil in Brazil

12 Distribution of Entisol Soil in Brazil

13 Distribution of Cambisols Soil in Brazil

14 +2 Degrees: LAI and Yield

15 +2 Degrees: Aboveground dry matter, N-uptake, Evapotranspiration

16 +2 Degrees: Mean Yield

17 +2 Degrees: Heat Stress

18 +2 Degrees: Transpiration-deficit Soil 2

19 +4 Degrees: LAI and Yield

20 +4 Degrees: Yield

21 +4 Degrees: Mean Yield

22 +4 Degrees: Heat Stress

23 +4 Degrees: Transpiration-deficit Soil 2

24 Yield - Comparison

25 Comparison of Aboveground Dry Matter, N-uptake and Evapotransppiration

26 Climate Data Network in Brazil

39 +2 Degrees: Aboveground Dry Matter, N-uptake and Evapotranspiration for Soil 1

40 +2 Degrees Yield, LAI, Leaf and Steam weight for Soil 1

41 +2 Degrees: Aboveground Dry Matter, N-uptake and Evapotranspiration for Soil 2

42 +2 Degrees Yield, LAI, Leaf and Steam weight for Soil 2

43 +2 Degrees: Aboveground Dry Matter, N-uptake and Evapotranspiration for Soil 3

44 +2 Degrees Yield, LAI, Leaf and Steam weight for Soil 3

45 +2 Degrees: Aboveground Dry Matter, N-uptake and Evapotranspiration for Soil 4

46 +2 Degrees Yield, LAI, Leaf and Steam weight for Soil 4

47 +4 Degrees: Aboveground Dry Matter, N-uptake and Evapotranspiration for Soil 1

48 +4 Degrees Yield, LAI, Leaf and Steam weight for Soil 1

49 +4 Degrees: Aboveground Dry Matter, N-uptake and Evapotranspiration for Soil 2

50 +4 Degrees Yield, LAI, Leaf and Steam weight for Soil 2

51 +4 Degrees: Aboveground Dry Matter, N-uptake and Evapotranspiration for Soil 3

52 +4 Degrees Yield, LAI, Leaf and Steam weight for Soil 3

53 +4 Degrees: Aboveground Dry Matter, N-uptake and Evapotranspiration for Soil 4

54 +4 Degrees Yield, LAI, Leaf and Steam weight for Soil 4

List of Tables

1 Lists of Organizations Represented by the Interviewed Experts

2 Brazilian National Climate Change Strategy

3 Requirements and Recommendations for the Elaboration of Successful Adap- tation Strategies

4 Challenges Identified to Hinder Successful Implementation of Adaptation Strategies

List of Abbreviations

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1 Introduction

Brazil is confronted with a high risk of vulnerability to climate change, not least due to its fragile, biologically diverse ecosystems. Taking into account its size in geographic, de- mographic and economic dimensions, the adaptation to climate change problems becomes highly complex.

Changing rainfall patterns will mean poorer water resources and a reduced water supply. Agriculture will be affected, exacerbating the risk of famines. Less rain will also affect the hydropower supply which provides more than 80 per cent of the electricity Brazil generates. Floods, which are already a serious problem for various regions, may increase.

Given abundant natural resources, Brazil started early to establish its energy sector largely on renewables. Therefore, in terms of mitigation Brazil has been doing very well. According to a research taking out by the NGO GermanWatch, that analyzed the efforts made by 56 countries being responsible for over 90 percent of CO[2] emissions, Brazil is found at rank 8 (GermanWatch, 2008)? . But the country still needs to assess its vulnerability to climate change so it can prepare for the unavoidable effects of climate change and support policy decisions on how to adapt to the inevitable impacts. With a great share of the economy depending on natural resources and the importance of the agricultural sector, climate change adaptation will play a crucial role in promoting further economic growth and promote food security. Unfortunately, in this field, Brazil is late in taking action.

While the challenge of mitigation is global, adaptation must take place mainly at the local and regional level. As climate is a highly complex system with a lot of detailed factors interacting with and influencing both global and local outcomes, computer modeling has to work with smaller cells to analyze local structures that then can be used to identify specific adaptation strategies.

How climate change will affect agricultural productivity is not yet understood in detail. But possible effects on crops that are particularly important to the country’s economy, such as maize, soybean, wheat and coffee, are a great concern.

In order to get an idea, how much the climate will affect the yield of maize in the next few decades we used a plant modelling program called MoNiCA. The plant modelling program simulate the maize yield until 2039 based on several input variables and climate scenarios.

2 Goal, Scope, and Organization of the Paper

The La Plata Basin covers a wide geographical area (see Figure 1). It is characterizes by a variety of soils and vegetations, as a response to different climates. It inherits several biomes within the Brazilian land: Cerrado (savannah-like plateaus and grasslands) and Pampa (open fields) in the Northern and Southern part, Pantanal (tropical wetland) in the Northwest and Mata Atlântica (dry and humid forest) (see Figure 2). The Pantanal is the world’s largest continental wetland. Its ecosystem highly depends on the climatic conditions of the region, especially its hydrology. Precipitation patterns and its interflows sustain an humid soil, that is recognized as one of the most important ones in the world.

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Figure 1: Land Cover Assessment of the La Plata Basin. Brazil is indicated with red line. (source: Coutinho et al., 2007? ; compiled by author)

One of the preoccupations that justify research in this area was the detection of lack of information and acknowledgement of climate conditions in the La Plata Basin, affecting the efficiency of adaptation and prevention strategies to changing climate conditions, especially extreme events of drought and heavy rainfall.

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Figure 2: Biomes covered by the La Plata Basin in Brazil. Border of the La Plata Basin is indicated with red line. (Source: Coutinho et al., 2007? ; compiled by author)

The general objectives of this study are:

i) to use information from different policy levels and research institutions to give a multidisciplinary perspective on the vulnerabilities and climate hazards to climate change in the Brazilian La Plata Region;
ii) to identify main stakeholder and approaches in Brazil to govern the adaptation to climate change;
iii) to contribute to the debate about what actions Brazil should take to abate negative effects of climate change based on quantitative and qualitative analysis.

The more specific objectives of this study are:

i) to build a future scenario for yield development of maize in Santa Catarina State;
ii) to reveal the possible economical, ecological and social impacts of climate change in Santa Catarina State;
iii) to identify adaptation strategies in the agricultural sector and reveal its costs and benefits.

As little is know about adaptation strategies in Brazil and local data for future climate change impacts are not comprehensive, this paper intends to be explorative.

Our study project is embedded in the European Union project CLARIS LPB, which pre- dicting the regional climate change impacts on La Plata Basis in South America. The network of international research institutions, NGO’s, consulting companies, national and local governments tries to design adaptation strategies for land-use, agriculture, rural de- velopment, river transportation, water resources and ecological systems. The German research institute ZALF participate in the project to assess the impact of climate change on land use, cover of land and agriculture. ZALF focus on developing adaptation strategies together with stakeholders.

We participated in the activities of ZALF and estimate the impact of climate change on maize yield in South Brazil. Two different climate change scenarios were selected to simu- late from 2013 to 2039. Furthermore we carried out a situation analysis which include the design of a semi-structured questionnaire in order to ask stakeholders if and what kind of adaptation strategies they use to prevent declining crop yield in Santa Catarina, Brazil.

3 Methodology

3.1 Quantitative - MoNiCA

MoNiCA - Model for Nitrogen and Carbon in Agroecosystems - is a plant yield modelling program developed by the research institute Leibniz Centre for Agricultural Landscape Research (ZALF) in Muencheberg, Germany. The program based on the earlier developed plant yield modelling program HERMES by Dr. Kersebaum back in 1989. HERMES was the first modelling program to simulate the nitrogen cycle of different crops within a whole year. The plant modelling program HERMES can be used with slightly input variables but because of the German configurations like the evaporation formula by Dr. Haude, the modelling program cannot be used internationally. Therefore new features like additional evaporation formulas and parametrization of different soils were implemented to apply the modelling program outside of Germany as well. The upgrading program MoNiCA devel- oped by Dr. Nendel consider beside the nitrogen cycle also the carbon cycle. In addition the impact of carbon dioxide on photosynthesis and stomata resistance can be analysed. Irrigation and fertilization can be triggered and allow calculation of future scenarios. MoN- iCA was further developed to support the impact assessment of climate change related to agriculture production.

Until now MoNiCA has no user interface and can only be operate by command (ZALF, Nendel et al., 2011)?? .

MoNiCA need a database of crop parameters and input files to start a simulation of one plant. The Figure 3 gives an overview of the working processes.

Start

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Figure 3: Overview of MoNiCA (Source: Nendel et al., 2011 ) ?

The first database consist of the configuration and input files, which defines the soil, crop rotation, the used fertilizer, irrigation and the climate (see appendix 27 : III, 28 : IV). The second database provide the crop parameters and its already set up with the most common crops in Central Europe by ZALF. Furthermore the database allocate details of different types of organic fertilizers and soil parameters ^*.

The database provide information about the development stages of crop plants. The developing stages of grain maize are :

1. Stage) Sowing to Emergence
2. Stage) Emergence to Shooting
3. Stage) Shooting to Tasseling
4. Stage) Tasseling to Flowering
5. Stage) Flowering to Corn Filling
6. Stage) Corn Filling
7. Stage) Senescence

Within each developing stage the below information must be in place.

a) Stage Temperature Sum
b) Base Temperature
c) Vernilization Requirement
d) Day Length Requirement
e) Base Day Length
f) Drought Stress Threshold
g) Critical Oxygen Content
h) Specific Leaf Area
i) Stage Maximum Root N Content
j) Stage KC Factor

MoNiCA has not been used to simulate crop yields in South America. Most of the experi- ments to test the new implemented features has been done in Germany. The first step was to adapt the phenological stages of grain maize to the climate conditions in Brazil. Marcos Lana, doctorand at ZALF established a experimental field in Guaraciaba, Santa Catarina, Brazil. Four different varieties of maize were sowed on 27.10.2010, 16.11.2010 and 06.12.2010. The used varieties MPA01, IVANIR, FORTUNA (Epargi) and AS1548 (Agroeste) cover the wide range of maize grown in Brazil. Follow few informations of each genotype is given to introduced the used seeds.

MPA01 (1. Variety): The open-pollinated seed is for small farmers with limited external inputs. The maintaining of the local maize MPA01 is done by small farmers in Anchieta, Santa Catarina, Brazil. Many of the local varieties have the base population MPA01. The features are high grain yield, resistance to pests and diseases, high nutritive value and less vulnerable to stress. Locals improved MPA01 by intercrossing and cross-breeding. The used varieties are well adapted to tropical regions ^*. The yield is about 7.450 kg/ha (Kist et al, 2010)? .

IVANIR (2. Variety): The same as MPA01, but derived by farmers using massal selcetion.

FORTUNA (3. Variety): Fortuna is more resistant to droughts. The variety is bet- ter adapted to soil and climate conditions in Santa Catarina, Brazil (Zoomnews)? . The yield was about 8.000 kg/ha in 2007 and 2008. The high yield, the early bloom- ing and the low price of the yellow-orange seeds is recommended to small farmers (Fetaesc)? . The open-pollinated seed is a cross-breeding of six hybrid cultivars. These cultivars are wide adapted to different climate conditions in Brazil. The fea- tures are good coverage of the soil, wide period of harvesting, maintaining of the high quality and resistance to diseases (Correio Lageano)? .

AS1548 (4. Variety): The hybrid seed developed by Agroeste has a good rooting system, high resistance to local diseases, droughts and water stress (Kesoja Agri- cola)? . The red seed is used as cover crop and its adapted to the conditions in Rio Grande do Sul, São Paulo and Mato Grosso. The yield in 2007 and 2008 was about 7.800 kg/ha (Embrapa)? .

Different informations within the stages VT, R1 and R4 were measured ^*. Therefore data of all four varieties within the three planting dates were available. Three planting dates were choose and therefore three simulations for each planting date were necessary. The three simulations used one crop database. The goal is to adapt the database in a way, that all three simulations of each planting date match the data of the field experiment.

3.2 Qualitative - Expert Interviews

We carried out 9 semi-structured interviews with experts between December 2011 and March 2012. The experts came from universities and other public research institutes, non-governmental organizations, and governmental and administrative organizations. Fur- thermore, data from interviews with farmers collected by Michelle Bonatti in 2009 were used to have representatives of three different groups: scientists, policy makers, and prac- titioners.

Within research methodology an expert is a person that is responsible for development, implementation or control of solutions, strategies or policies. Experts usually have a privileged access to information about groups of persons or decision processes and have a high level of aggregated and specific knowledge that is otherwise difficult to access (Meuser and Nagel, 1919)? . Expert interviews were used to explore and to understand the main problems in adaptation research and implementation of strategies.

The interviews sought to examine the experts’ experience relating to adaptation, iden- tifying innovative and the most promising adaptation options as well as barriers to and opportunities for their implementation. Each interview took about 60min and was recorded and transcribed. The list of organizations from which we interviewed experts is presented in table 1.

4 Climate Change in Brazil

4.1 Climatic Scenarios in Brazil

In recent years Brazil was affected severely by climate variability and extreme weather events. Groisman et al. (2005)? and Marengo et al. (2009)? found a systematic increase of very heavy precipitation occurring since the 1950 in the subtropical regions of Brazil as well as an increase in the frequency of extreme rainfall events in the South Eastern part of the country. For instance showedCarvalho et al. (2004)? that extreme rainfall events in the state of São Paulo exhibit an inter-annual variability linked to El Niño and La Niña, as well as intra-seasonal variations associated with the activity of the South Atlantic Convergence Zone (SACZ) and the South American Low Level Jet (SALLJ).

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Table 1: Lists of organizations represented by the interviewed experts

The task to create a future climate scenario for Brazil was undertaken in 2007 by the National Institute for Space Research (INPE). The goal was to describe the physical process resulting from global warming and to design future climate scenarios. The general question was what are the variations and expected changes in temperature and rainfall pattern in the diverse regions of Brazil. The results of their research are demonstrated by Figure 4. It shows the projection of climatic changes for the different geographical regions in Brazil for the year 2100 in a A2 scenario ^* in comparison to the average climatic situation during the years 1961 to 1990.

The Amazon region and the North Eastern part of Brazil are considered as most vulnerable to climate change. Average temperature rise in the North East might go up to 5 ◦ C. The Amazon might even face a gradual temperature rise up to 7 to 8 ◦ C. Rainfall tends to reduce during the 21th century, with the most severe decline in the North East (2 to 2.5mm/d) and the Amazon (1 to 1.5mm/d). All of Brazil will face an increase in mean temperature as well as more frequent heat waves. Furthermore, an increase of the minimum temperature will reduce frequencies of freeze. Especially affected by this are the South Eastern, Southern and Central Western parts of Brazil.

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Figure 4: Projection of climate changes by 2100 after region. (Source: Margulis and Burle Schmidt Dubeux (2010)? ; compiled by author)

4.2 Extreme Climate Events in Santa Catarina State

Meteorological data show that mean temperatures in Santa Catarina are rising since the beginning of the 90s. Guimarães Camargo Campos, Braga and Alves (2006)? point out that research about temperature changes have to consider inter-annual vari- abilities in extreme temperature. Most studies just work with mean temperatures, thus ignoring important observations about climate change. They show that minimum temper- atures in Santa Catarina are increasing but maximum temperature are decreasing. In the long term mean temperature rise but thermal amplitude diminishes. Those tendencies are accelerated by land-use change. The researchers assume that former vegetation areas being asphalted or being used for new constructions lead to a higher absorption capacity of heat during the day, thus lowering maximum temperatures. This heat is then released during the night, with rising minimum temperatures being the result. Within agricultural pro- duction a similar assumption was made. Irrigation could enhance the soil’s heat capacity, contributing to higher minimum temperatures.

Santa Catarina faces also changes in precipitation patterns. Total annual precipitation is since the beginning of the 90s increasing. At the same time the number of consecutive dry days without rain increases. This means that extreme rain events with rainfall of more than 100mm occur more frequent with less time between two such extreme events (ibid.).

In the first two weeks of January 2012 already 73 municipalities of Santa Catarina declared a state of emergency due to persisting drought, affecting 73.000 people. According to newspaper articles ^* production losses amount for USD 71 m with a decline in maize yields of 8.5 percent. But this is just another event in a series of drought events that affects the La Plata Region in Brazil and Argentina since 2008 causing tremendous decreases in production of soybeans and grains.

From November 22 to 24 in 2008 Santa Catarina was hit by heavy rainfalls, causing severe flooding and deadly mudslides. 1.5 million people were affected, 120 people died and 69,000 people were left homeless. The storm also destroyed a stretch of pipeline that provides Southern Brazil with natural gas from Bolivia with the result of the suspension of fuel supply to part of Santa Catarina and the entire neighboring state Rio Grande do Sul. Economic losses were estimated to USD 350 million. According to Marengo (2011)? this event has been described as the region’s worst weather tragedy in its history.

In 2004 a hurricane at the coastal region caused losses of up to USD 1 billion (Pezza et al., 2009)? . It was reported as the first hurricane in the previously thought to be hurricane-free South Atlantic Ocean.

Three years ahead, in 2001, Southeast Brazil was struggling with a significant reduction in river flows due to severe rainfall deficits, which led to a tremendous energy bottleneck, as hydroelectric power is the main source of energy production in Brazil. Cavalcanti and Kousky (2004)? report that the government was forced to impose energy conservation measures in order to avoid blackouts.

4.3 Ecological, Economical and Social Implications of Climate Change in Santa Catarina

Besides the negative short-term effects of extreme climate events as mentioned in Section 4.2 Santa Catarina also faces ecological, economical and social problems on the long run. Santa Catarina consists of rather small cells of farm activities. Consequently, land in Santa Catarina is pricy compared to other regions in Brazil. What could be observed in the last years, was a migration of Santa Catarina farmers towards the Amazon region. The Amazon region, however, is historically populated by indigenous people. This leads to severe conflicts over natural resources as farmers seek to buy big areas of land. The conflict is even aggravated by the often very unclear land titles.

Another form of migration Santa Catarina has to deal with is the land-city migration. When moving to the cities, the land of the small-farmers is sold and becomes conventional agricultural land of big farmers, thus putting a higher risk on the environment due to conventional land practices like monocultures and the use of agro-toxics or GMOs. In the cities problems like unemployment and poverty rise concerns. What becomes clear is that adaptation to climate change can not be successful without development. Especially small farmer need to be integrated into the market. Without economic incentives the migration problem and thus environmental and social problems will rise.

If farmers are reluctant to adapt to changing climate conditions due to traditional believes, they put the environment at risk. For instance one of the organizations being interviewed is working on a project to reduce the cultivation of tobacco in Santa Catarina. Tobacco production involves a lot of chemicals and farmer use to burn wood to dry the leaves. Furthermore, it has social impacts as the rate of smokers in those areas are especially high. Again, to change behavior it lacks economic incentives. And without them it is very hard to change cultivars as farmers depend on income they get from tobacco.

Changing rainfall patterns and more frequent drought periods will most likely increase the need of watershed and irrigation programs. Strategies that seek to save water, which will be necessary in the Western part of Santa Catarina, might be an economic problem for small farmers that are not organized in cooperatives.

Another implication of warmer weather conditions is the risk of decreasing water resources and thus a decrease in hybrid-power production. As a consequence, crop production in Santa Catarina could switch from maize to the more profitable, sugarcane production. This might cause environmental problems. However, as reported by one of the interviewed researchers, it also creates business and resources and employs people. On the other hand there is also the potential that due to decreasing freeze, coffee might migrate to the South. It has a high market price and thus could help to increase the income of small farmers.

If heavy rainfall and precipitation will increase erosion is more likely to occur which has tremendous effects on agricultural farm land.

5 Yield Development of Corn until 2039 in the La Plata Region - Result of the Quantitative Research

The climate scenarios of IPCC has not been ready by March 2012. Therefore two common climate scenarios were developed. In consolidation with ZALF the most predicated climate changes are + 2 Degrees and + 4 Degrees. The climate station Chapeco provided climate data of the last 28 years. Caused by limited climate data 8 ^* simulations were done from 2012 to 2039. The used management system is described below:

i) sowing date: 27.10
ii) harvesting date: 13.03
iii) tilliage: 20.10 and 10 cm deep
iiii) fertilization: 9.000 kg/ha turkey manure on 27.10 and incorporate

5.0.1 Adaption to Santa Catarina, Brazil

Before the simulation can be done, MoNiCA needed to be adapted to the climate conditions in Brazil. Therefore at first the new analysed sum temperature ^* of the stages VT, R1 and R4 were implemented. Now the transition between the developing stages were similar to the field experiment. The next step was to adapt the specific leaf area by using the average of the collected data (see Figure 5).

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Figure 5: Specific Leaf Area of all four varieties and all planting dates (source: Lana, 2011)

Following the assimilation rate was changed. The yield also depends on the assimilation capacity of the plant. Now the simulated yield of each planting date was close to the measured yields and therefore the calibration of the database was finished. The leaf and steam weight, yield and LAI in stage VT, R1 and R4 were used to analyse the variances between the simulations and the actual values.

The yield until 2039 was simulated to work out the impact of climate changes and ris- ing temperature. Therefore the yield is an important indicator. In Figure 6 (see page 13) the first planting date (27.10.2010) was analysed. The results of the simulations are slightly above the actual yields, but just minor changes are identified. The yield simulations of the second planting (16.11.2010) were in line with the actual yields of variety 1 and 3.

Minor changes identified for variety 2 and 4 (see Figure 7 : 14). On planting date three (06.12.2010) an extremely drop of the actual yields for variety 1 and 3 were identified. The simulations for variety 2 and 4 were above the actual yield, but the variances can be considered as small (see Figure 8 : 14).

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Figure 6: Yield planting date 27.10.2010

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Figure 7: Yield planting date 16.11.2010

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Figure 8: Yield planting date 06.12.2010

About the fact that the yields drops in the third planting date is related to the weather. There was a dry spell during the grain filling stage between 17.02.2011 and 13.04.2011. The high temperatures also contributed to that event. The pictures of maize below show few or sometimes no grains in the ears. The number of grains were also drastically reduced and several with aborted or not filled grains. Events like that happened between the developing stages V4 or V6 probably related to a short but intense thermal stress, Lana explained.

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Figure 9: Maize with aborted or not filled grains (source: Lana, 2011)

The analysis of the dry matter leaf revealed minor changes. The maximum and minimum were barely above or below the simulation. One exception was planting date 16.11.2010, variety 4. In stage VT greater variances existed. Also great variances were identified between the simulations and actual values of the dry matter of the shoot for planting date 27.10.2010 and 16.11.2010. Interestingly the means were almost on the line of the simulations. The simulations of planting date 06.12.2010 shows little variances below the actual values. The simulations of the LAI showed minor changes in total. On planting date 27.10.2010 and 16.11.2010 the simulations were minimal greater than the actual LAI. On planting date 06.12.2010 the LAI were minimal below the actual values ^*. The minor changes are acceptable and the adaptation of MoNiCA was completed.

5.1 Soils

Four typical soils of Santa Catarina, Brazil are used for the simulations. The soil data were collected by Lana in 2010 and 2011. In the beginning the soils are introduced ^†.

1. Soil: Ultisol (Terra Roxa Estruturada)

The soil is known as red clay soil. The acidic soil is suitable for forestry and is found primary in humid and tropical regions. Ultisol ^‡ has low native fertility but can be used in agriculture with application of lime and fertilizers. The soil is wide spread in Santa Catarina as the Figure 10 (see page: 16) illustrates (Ministerio da Agricultura)? .

2. Soil: Oxisol (Latossolo Roxo Eutrofico)

Oxisol is found primary in topical rain forest regions closed to the equator and is used for tropical crops. The Figure 11 (page 17) shows Oxisol as one of the most distinctive and common soil in Brazil. The soil has low pH and contains high levels of aluminium and ion. The low amount of weatherable minerals and the low cation exchange capacity make permanent cropping difficult for small farmers with limited input. Fertilizers and lime supports the cultivation of crops (CIAT)? .

3. Soil: Entisol (Associacao Solos Litolicos)

Entisols are mostly young soils with the absence or near absence of horizons. Usually they have no genetic horizons except in the first horizon A. They are defined as flat and sandy. The soil is primary found in steep and rocky regions. In Santa Catarina the soil is mostly found in the north (see Figure 12 : 17) (University of Idaho)? .

4. Soil: Cambisols (Cambissolo)

Cambisols soils just begin soil formations and therefore a weak horizon differentiation is characteristic. The soil is suitable for agriculture because under well environmental conditions they are highly productive. They are found in regions with active erosions but less common in tropical regions. The Figure 13 (page 18) shows the primary location of Cambisols close to the coast because of active erosions (FAO)? .

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Figure 10: Distribution of Utisol in Brazil (Source: Universidade Federal de Lavras)

?

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Figure 11: Distribution of Oxisol Soil in Brazil (Source: Universidade Federal de Lavras)

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Figure 12: Distribution of Entisol Soil in Brazil (Source: Universidade Federal de Lavras)

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Figure 13: Distribution of Cambisols Soil in Brazil (Source: Universidade Federal de Lavras)

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[...]

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^* Extract of the second database: see appendix 29 : V

^* Tropical regions deal with droughts, low soil fertility, unpredictable rainfalls, diseases and insect inci- dences

^* The measured data are LAI, steam and leaf weight, and yield

^* The scenario is described in the Special Report on Emissions Scenarios that was commissioned by the IPCC; http://www.grida.no/publications/other/ipcc_sr/?src=/climate/ipcc/emission/

^* http://g1.globo.com/politica/noticia/2012/01/pr-reduz-safras-de-soja-e-milho-por-seca-sc-tambem- tem-quebra.html, accessed 07.01.2012

^* Four soils and two climate scenarios.

^* Sum temperature of the developing stages according to the crop database.

^* see appendix page 32, 31, 30, 35, 34, 33, 38, 37, 36

^† see appendix for the used soil profiles 5, 8, 6, 7

^‡ The soil class Ultisols contains Nitisol.