3. METHODOLOGY
3.7 The Model: CropSyst
The synthetic climate data (generated from the CLIMGEN weather generator) was then inputted into the CropSyst model. With all other input data complete (soils and all needed maize parameters), the CropSyst model was run with the measured data as well as the synthetic data to determine yields. This was represented graphically and the differences were noted. The synthetic climate data was then subjected to changes according to the stipulated Scenarios from section 3.9 and then was inputted into the CropSyst.
45 3.7.1 Model description
The CropSyst (Cropping Systems Simulation Model) is a multi-year, multi-crop, daily time- step crop growth simulation model designed to serve as an analytical tool to study the effect of cropping systems management on productivity and the environment (Stöckle et al., 2003).
CropSyst attempts to reproduce soil plant biophysical processes based on known physical and biological laws or empirical relationships based on climatic and crop management practices (Stockle and Nelson, 2000; Stöckle et al., 2003; Yadav, 2005). The model simulates the soil water budget, soil-plant nitrogen budget, crop canopy and root growth, crop phenology, dry matter production, crop yield, residue production and decomposition and erosion. This is affected by the input files: daily weather data (rainfall, maximum and minimum air temperature, solar irradiance, relative humidity and wind speed), location, soil chemical and physical characteristics and management practices (Stöckle et al., 2003).
The water budget sub-model of CropSyst includes rainfall, irrigation, runoff, interception, infiltration, redistributions within soil profiles as well as evapotranspiration. The model uses a simple cascading approach or the Richard’s soil flow equation (Garofalo et al., 2009). Grass reference evapotranspiration (ETo) is estimated by the Penman-Monteith or Priestley-Taylor methods within the model. An option of a simpler Priestley-Taylor method that only requires air temperature is also available. Crop evapotranspiration (ET) is determined from a crop coefficient at full canopy and ground coverage determined by canopy leaf area index (Stöckle et al., 2003). The soil nitrogen (N) budget includes transformations (mineralization, nitrification, denitrification, and volatilization), ammonium sorption, symbiotic N fixation, and crop N demand and uptake. Interaction between the water and N budget produce the simulation of N transport within soil profiles (Bellocchi et al., 2002).
In the model, crop development is simulated based on thermal time accumulated to reach specific plant growth stages. Thermal time is the required daily accumulation of average air temperature above a base temperature and below the optimum temperature to reach specific growth stages (considering photoperiod and vernalization requirements) (Yadav, 2005). Daily crop growth is a function of biomass increase per unit ground area factoring in four limiting factors to crop growth: water, nitrogen, light and temperature (Stöckle et al., 2003). Figure 3.8 shows the flow chart describing the approach used in the model to calculate biomass accumulation. The core of the calculations is based on potential biomass growth based on
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crop potential transpiration and crop intercepted photosynthetic active radiation (PAR) (Stöckle et al., 2003).
Figure 3.8 Flow chart of biomass growth calculations in CropSyst (Stöckle et al., 2003).
The daily above ground biomass accumulation is then calculated using a relationship between crop transpiration and biomass production. The relationship is shown in Equation 2.6 (Stöckle et al., 2003; Yadav, 2005).
BT = KBTT / VPD (2.6)
where BT is the transpiration-dependent biomass production (kg m-2 day-1), T is actual transpiration (kg m-2 day-1), and VPD is the mean daily vapour pressure deficit of the air (kPa).
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However, at low VPD, this relationship becomes unstable and can estimate infinite growth at near zero VPD. Therefore, the model provides another method of calculating biomass production:
BL = e IPAR (2.7)
where BL is the light-dependent biomass production (kg m-2 day-1), e is the light-use efficiency (kg MJ-1) and IPAR is the daily amount of crop-intercepted photosynthetically active radiation (MJ-1 m-2 day-1) (Stöckle et al., 2003; Yadav, 2005).
Each simulation day, the minimum of BT and BL is taken as the biomass production for the day. The e variable in Equation 2.7 accounts for temperature limitations in biomass accumulation. To account for nitrogen limitations, the minimum of BT and BL is used as a base to determine the nitrogen-dependent biomass production (BN):
BN = Min (BT , BL) [1 - (Npcrit - Np) / (Npcrit - Npmin)] (2.8)
where BN is in kg m-2 day-1, Np is plant nitrogen concentration in (kg kg-1), Npcrit is the critical plant nitrogen concentration (kg kg-1) below which growth is limited, and Npmin is the minimum plant nitrogen concentration (kg kg-1) at which growth stops (Stöckle et al., 2003;
Yadav, 2005).
Increase in leaf area index (LAI) during the vegetative period of plant growth is expressed as leaf area per unit soil area. It is calculated as a function of biomass accumulation, specific leaf area and a partitioning coefficient and is expressed as follows:
LAI = SLAB/1 + pB (2.9)
where LAI is in m2 m-2, is the above-ground biomass (kg m-2), SLA is the specific leaf area (m2 kg-1), and p is a partitioning coefficient (m2 kg-1) controlling the fraction of biomass apportioned to leaves (Stöckle et al., 2003; Yadav, 2005).
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Based on Equation 2.9, biomass change can be estimated which allows for the determination of the new LAI amount produced in each simulation day as a function of biomass production on that day (Yadav, 2005). LAI in the model is vital since it affects canopy senescence. Root growth is synchronized with canopy growth, and root density by soil layer is a function of root depth penetration (Stöckle et al., 2003).
Yield is determined according to the harvest index and a total biomass accumulated at physiological maturity (Stöckle et al., 2003; Garofalo et al., 2009). The relationship is expressed as:
Y = BPM HI (2.10)
where Y is yield in kg m-2, BPM is the total biomass accumulated at physiological maturity (kg m-2) and HI is the harvest index (grain yield/above ground biomass) (Stöckle et al., 2003;
Yadav, 2005).
The CropSyst model requires four input data files: Location, Soil, Crop, and Management files. These are described in more detail in Section 3.8.
3.7.2 Calibration and validation
Model calibration is a requirement for every model prior to use. With the CropSyst model, calibration of the model must be done sequentially (Donatelli et al., 1997):
Crop phenology (thermal time at emergence, flowering and physiological maturity),
Crop morphology (maximum root depth and PAR extinction coefficient),
Crop physiological parameters (specific leaf area, stem/leaf partitioning coefficient, leaf duration, optimum temperature for growth and the duration of the effect).
Comparing the model outputs and the experimental observations validates the model. This is important in ensuring that the model simulates outcomes that represent adequately the natural system being modeled well. Use of the model for areas in the Highveld region posed a major challenge in the study. Lack of maize phenological and adequate soils information at study
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sites limited chances to calibrate and validate the model at specific sites. However, since the model has been calibrated and validated for South African conditions, the model was applied at all sites.
Abraha and Savage (2008) calibrated and validated the CropSyst prior to investigating impacts of climate change on the maize crop in Cedara, KwaZulu-Natal. They calibrated and validated various parameters in the model but in this study, concern was given to aspects within the model involved in simulating maize crop yield, i.e. crop phenology (thermal time, photoperiod) and crop growth (water and radiation dependent growth). Their findings showed that the model performed well in simulating fallow and cropped plots (maize). They found that the soil water was slightly under-estimated in maize-planted plots and advised that soil water parameters should be updated using field observations especially following high rainfall events. They also found that the models ability to model maize phenological stages was good(Abraha and Savage, 2008). In this study, version 4.14.04 (19 January 2013) of the CropSyst model was used.
3.8 Model input data requirements