36 Table 4.1 Air temperature trends in °C/decade for annual mean maximum (Tx), minimum (Tn) and daily air temperature range (DTR) based on daily minimum and maximum air temperatures for selected locations in the Highveld region of South Africa and Cedara. 54 Table 4.2 Air temperature trends in °C/decade for annual mean maximum (Tx), minimum (Tn) and daily air temperature range (DTR) based on daily minimum and maximum air temperatures for selected locations in the Highveld region of South Africa and Cedara.
INTRODUCTION
- Motivation
- Aims
- Objectives
- Thesis structure
Most studies on maize crops in South Africa have focused on the phenological and genetic aspects of the plant. It includes descriptions of the research sites and some of the modeling exercises carried out in the project.
LITERATURE REVIEW
Climate change
- The greenhouse effect
- Gases of concern
- Warming
- Adaptation and mitigation
The second field originates from the hot surface of the earth in the atmosphere in the infrared region (Parry et al., 2007; Le Treut et al., 2007). Larger amounts are released when burned, especially when fossilized earlier over time (Le Treut et al., 2007).
Potential impacts of climate change on crops
- Crop response to CO 2 enrichment and air temperature increase
- Economic impact
Concentrations can increase up to 3 to 6 times higher than concentrations in the atmosphere (Leakey et al., 2006). At the same time, photosynthesis increases, resulting in a positive response to increased CO2 concentration (Drake et al., 1996).
Important crops grown globally
- Maize
- Other major crops
Maize covers 58% of the total agricultural production land in the SADC region, with approximately 50% of production contributed by South Africa alone. In developed countries, wheat is mainly used for animal feed, which accounts for 19% of global wheat production.
Crop modelling
- Model types
- Crop simulation models
Parameter models are most commonly used in hydrological modeling because they are concerned with the development and analysis of relationships between hydrological components within a system (Savage, 2001). As a result, these types of models are used to represent the soil-plant-atmosphere continuum in order to assess and implement management strategies that would improve yields.
Maize crop modelling in climate studies
- Global examples
- South African examples
For example, Stockle et al., (1997) evaluated the benefits of irrigation in south-west France using the CropSyst model. Using the CERES-Maize model, Pfeifer et al. (2000) studied the impacts of future climate change and its variability on maize yields in the midwestern United States of America (USA).
METHODOLOGY
Overview
Sourcing climate data
The MWS data sets had a longer data record with more data errors than the AWS data sets. As a result, the data sets were characterized by shorter up-to-date data sets, mostly less than 10 years. Therefore, the AWS data were only used to augment the MWS datasets provided that the stations were at the same location or close enough to have the same climatic and geophysical characteristics.
These data were not replaced, but were used to validate the model used to estimate solar radiation (results for this are in Section 3.4).
Site description
This distinction in MAP between the west and east of the Highveld region is shown in Figure 3.2. Due to the longer sunshine duration, solar radiation is greater in the western than in the eastern part of the Highveld. From December to March, temperatures range from 28 to 30 oC in the west and from 26 to 30 oC in the east, while the average minimum air temperature in these months is between 12 and 16 oC throughout the region.
Depending on data quality and availability, representative stations (sites) representing the western and eastern parts of the Highveld Ecoregion were selected.
Patching missing climate data
- Rainfall and Air temperature
- Solar irradiance
- Relative humidity
The performance of the model was then graphed and an example is shown in Figure 3.3 which is representative of Marble Hall for the year 2006. The difference in measured and modeled solar radiation in Figure 3.3 can be easily noted in Figure 3.4. Despite the difference in solar irradiance, the Hargreaves and Samani (1982) model performed relatively well (Figure 3.5) and is confirmed by an r2 value of 0.72, indicating a relatively good correlation.
Difference between measured and modeled solar radiation (MJ -2). a) It was assumed that the minimum air temperature is equal to dew point temperature from which the water vapor pressure was calculated;.
Temperature trends analyses
Fortunately, the effect on the simulations is likely to be minimal, as most data sets had a complete set of relative humidity data. The study also required corn crop biophysical parameters and soil information to be used as inputs into the model. This was achieved by determining the slope of the relationship between the years and the corresponding minimum and maximum air temperatures for all years for the entire record lengths at all locations considered in this study.
The results were then compared between the study areas and with previous studies done in South Africa and Africa.
Modelling
- The CLIMGEN weather generator: a brief description
The generated maximum and minimum air temperatures are the result of a multivariable stochastic process where the daily means and standard deviations are determined by the dry or wet state of the data (Stöckle et al., 2001). Precipitation amount and wind speed are generated using the Weibull distribution, the latter being generated independently of other variables (Stöckle et al., 2001). To determine whether precipitation data generation was successful, comparisons of wet and dry day counts of the observed and generated climate data were made.
Furthermore, validation was also done using the CropSyst model by modeling maize yields using measured and the corresponding generated data.
The Model: CropSyst
- Model description
- Calibration and validation
It is influenced by the input files: daily weather data (rainfall, maximum and minimum air temperature, solar radiation, relative humidity and wind speed), location, soil chemical and physical properties and management practices (Stöckle et al., 2003). The model uses a simple cascade approach or the Richard's soil flow equation (Garofalo et al., 2009). Crop evapotranspiration (ET) is determined from a crop coefficient at full canopy and ground cover determined by canopy leaf area index (Stöckle et al., 2003).
Root growth is synchronous with canopy growth, and root density per soil layer is a function of root depth penetration (Stöckle et al., 2003).
Model input data requirements
- Location file
- Soil file
- Crop file
- Management file
- Simulation control file
However, because the model has been calibrated and validated for South African conditions, the model was applied to all locations. They calibrated and validated several parameters in the model, but this study focused on aspects within the model involved in simulating maize crop yield, i.e. their findings showed that the model performed well in simulating fallow and cultivated plots (corn).
Typical yield input parameters for maize are available in the model, but if field-measured data are available, appropriate adjustments must be made. This file is structured in four general sections: phenology (thermal time requirements to reach certain growth phases, modulated by photoperiod and vernalization requirements if necessary), morphology (maximum LAI, root depth, specific leaf area and other parameters that define characteristics crown and root), growth (transpiration use efficiency normalized by VPD, light use efficiency, stress response parameters, etc.) and harvest component.
Scenarios
Yield results from the CropSyst model were compared and statistical analysis using a t-test at a 5% level of significance was used to determine if there were differences between yields in the different scenarios. This was achieved by comparing yields simulated from different scenarios to yields simulated under baseline conditions. Scenarios F and G were used to determine the statistical difference when rainfall was adjusted by 10 and 20%, while scenarios D and E were representative of the effect of air temperature change on yield.
RESULTS AND DISCUSSION
Temperature trend analysis
- Discussion
- Conclusion
None of the areas studied in either the western (Bothaville and Lichtenburg) or the eastern (Bronkhorstspruit and Marble Hall) part of the Highveld showed simultaneous increases in both Tx and Tn. This implies that temperatures are increasing faster in the western half than the eastern part of the Hoëveld Eco-region. However, it is clear that temperatures are increasing, especially in the western part of the.
However, the Tn trends from the western part of the Highveld ecoregion should be of concern as they are larger than the global trends.
Maize yields changes
- Comparison of measured and generated data
- Yields undertaken for the different Scenarios
- Discussion
Table 4.9 (with reference to the baseline conditions) shows that there was a general increase in yield even after the increase in air temperature at different locations compared to the baseline conditions. Referring to Table 4.8, an increase in CO2 concentration generally decreased CV (see scenarios A and baseline) and an increase in air temperature increased CV (see scenarios A, D and E). This is consistent with the findings in this study where mean returns from measured data for Bronkhorstspruit and Cedara (Table 4.6) were higher.
In Table 4.10, a t-test at a 5% significance level showed a statistical difference in yields affected by the increase in rainfall at all locations except Marble Hall.
CONCLUSIONS AND RECOMMENDATIONS
On the other hand, an analysis of temperature trends in the eastern part showed a negative rate of change for Tn and positive Tx and DTR, while the western part showed the opposite. Also, as both yellow and white maize are grown in the Highveld, studying the impacts separately could yield more accurate results helping to find out which of the two is more affected by climate change. This study focused more on the impacts of seasonal rainfall amounts than intra-seasonal variation and thus, there is a need to expand the study to include this aspect with greater emphasis on the western part of the Highveld.
It must also be ensured that site-specific input data such as initial water content, plant density, soil physical and chemical properties are determined through fieldwork to improve the accuracy of the simulations.
Contribution to the Vulnerability & Adaptation project of the South African Country Study on Climate Change. Measuring the economic impact of climate change on major South African field crops: a Ricardian approach. The potential impacts of climate change on maize production in Africa and Latin America in 2055.
Eds.), Measuring the Impact of Climate Change on Indian Agriculture, World Bank Technical Paper No.
APPENDIX
Appendix A
Appendix B
Biomass-transpiration coefficient (kPa kg m-3) 10.0 Photosynthetically active radiation (PAR) (g MJ-1) 4.0 Average daily temperature limiting early growth (oC) 12.0.
