The calibrated and validated APSIM model was used in long-term simulation of tillage system and residues on maize yield. Simulation focused on changes in maize yield with the decrease by 10% of daily rainfall. This was done in management modules, climate control simulation which is incorporated in APSIM model. The simulation was done for the period of 1962 to 2010 considering only long in season (September to January)
3.8.1 Choice of scenario
The choice of 10% reduction in rainfall scenario was based on IPCC climate projection described in (Cooper et al., 2009), This projection presented high variability in model prediction for Africa and Asia and hence difficulty to identify single climate scenario. Based on this, sensitivity based analysis was run where 10% reduction in rainfall was considered suggesting that there will be no considerable implication of rainfall increase in Bugesera district. Several modules were used and included management module, maze module, soil module, Fertilizer, Manure and Soil Organic Matter (Residue) Modules and climate control.
3.8.2 APSIM Model Parameterization
The key inputs are Soil properties, daily climate data, cultivar characteristics, and agronomic management. APSIM requires daily values of rainfall, maximum and minimum temperature and solar radiation. Climatic data for Bugesera were obtained from Rwanda national meteorological service while soil properties were extracted from soil map using ArcMap tool (attribute table) as it is shown in table 3. The soil dominant in Bugesera district is Xanthic Ferralsols according to FAO soil classification (Figure 2). The maize variety used was Katumani which is suited for Bugesera district. Agronomic management options were tillage systems, Residues application as mulch with or without inorganic fertilizer.
Table 3: An example of some soil properties used for specifying APSIM simulations
Soil layer 1 2 3 4 5 6 7 8
Soil water parameters
Bulk density(km_3) 1.52 1.54 1.51 1.44 1.35 1.35 1.35 1.35 Saturated water content(m3m_3) 38.2 38.9 39.9 42.6 45.9 45.9 45.9 45.9 Field capacity(m3m_3) 30.9 32.3 35.8 41.2 44.3 44.3 44.3 44.3 Wilting point(m3m_3) 19.9 22.2 24.9 29.4 32 32 32 32 SWCON 0.45 0.3 0.3 0.3 0.3 0.3 0.3 0.3 Soil N parameters Organic C (gkg_1) 0.89 0.825 0.57 0.47 0.31 0.31 0.31 0.31 F inertb 0.04 0.02 0.01 0.01 0.01 0.01 0.01 0.01 F biomc 0.4 0.6 0.9 0.99 0.99 0.99 0.99 0.99
Table 4: Cultivar parameters
Parameter or variables Acronym Value Units
Emergence - end juvenile tt_emerg_to_endjuv 150 0C days
Flag leaf - Flowering tt_flag_to_flower 10 0C days
Flowering - Start grain filling tt_flower_to_start_grain 120 0C days
Flowering - Maturity tt_flower_to_maturity 660 0C days
Maturity - Harvest-ripe tt_maturity_to_ripe 1 0C days
Genetic
Cultivar name name katumani
Potential grain number per head head_grain_no_max 450 ginsgin−1
Potential grain growth te gin_gth_te 10.5 mghead−1day−1
Table 5: Initial Soil Organic Matter Status
Layer Hum-C Hum-N Biom-C Biom-N FOM-C FOM-N
(kg/ha) (kg/ha) (kg/ha) (kg/ha) (kg/ha) (kg/ha)
1 19823.7 1982.4 468.3 58.5 138.9 3.5 2 18908 1890.8 149.5 18.7 108.1 2.7 3 25795.4 2579.5 25.6 3.2 65.6 1.6 4 20302 2030.2 2 0.3 39.8 1 5 12553.8 1255.4 1.2 0.2 24.1 0.6 6 12553.8 1255.4 1.2 0.2 14.6 0.4 7 12553.8 1255.4 1.2 0.2 8.9 0.2 Totals 122490.5 12249 649.1 81.1 400 10 Management module
The management module provides the capability to specify a set of rules using conditional logic during simulations to control the actions of modules within APSIM. It does this by using ―if‖ constructs created by the user. It also allows the user to create their own variables and define these as a function of other variables within APSIM. This module manages by issuing messages to modules in the system, many of which are conditional upon states or events within the modules during simulation.
Maize module
The maize module simulates the growth of a maize crop in a daily time-step (on an area basis not single plant). Maize growth in this model responds to climate (temperature, rainfall and radiation from the input module), soil water supply (from the soilwat module) and soil nitrogen (from the soil N module). The maize module returns information on its soil water and nitrogen uptake to the soilwat and soiln modules on a daily basis for the reset of these systems. Information on crop cover is also provided to the soilwat module for calculation of evaporation rates and runoff. Maize stover and root residues are ‗passed' from maize to the residue and soiln module respectively at harvest of the maize crop. A list of the module outputs is provided in the ‗Maize module outputs' section below, but basically the module will predict leaf area development, N% and biomass of stover; depth, N% and biomass of roots; grain N% and biomass; grain yield and N%, grain size and grain number all on a daily basis.
Soil-water module (Soilwat)
The SoilWater module is a cascading water balance model that owes much to its precursors in CERES (Jones et al., 1986) and PERFECT. The algorithms for redistribution of water throughout the soil profile have been inherited from the CERES family of models. The water characteristics of the soil are specified in terms of the lower limit (ll15), dined upper limit (dul) and saturated (sat) volumetric water contents. Water movement is described using separate algorithms for saturated or unsaturated flow. It is notable that redistribution of solutes, such as nitrate- and urea-N, is carried out in this module.
Modifications adopted from PERFECT include:
i. The effects of surface residues and crop cover on modifying runoff and reducing potential soil evaporation,
ii. Small rainfall events are lost as first stage evaporation rather than by the slower process of second stage evaporation, and
iii. Specification of the second stage evaporation coefficient (cona) as an input parameter, providing more flexibility for describing differences in long term soil drying due to soil texture and environmental effects.
The module is interfaced with the RESIDUE and crop modules so that simulation of the soil water balance responds to change in the status of surface residues and crop cover (via tillage, decomposition and crop growth).
Fertilizer, Manure and Soil Organic Matter (Residue) Modules
The three modules share structural frameworks in APSIM model. During this study, these modules were either applied on the surface (mulch) or were incorporated (Mineral fertilizer) into the soil through user defined tillage operations (30 cm deep). Fertilizer, manure and other organic material modules were set to decompose in order to provide fertility nutrients in APSIM. Decomposition of residues with a wide C/N ratio would trigger N immobilization demand, which would be buffered from mineral-N in the topmost soil layers. In extreme occasions, insufficient mineral-N in the soil would restrict decomposition of the organic materials.
3.8.3 APSIM Calibration and validation
APSIM calibration was based on parameters collected from field experiment in season A2014. For model validation, examination and comparison of measured versus simulated parameter values were based on all the 8 treatments as described in Table 1. Simulated crop parameters were grain yield and total above ground biomass as influenced tillage and residues application (Conventional tillage (CT) or no tillage (NT), and Residues applied (RA) with or without inorganic fertilizers (IF).