Crop growth simulation models

Water and Energy Security in T he Arena of Climate Change

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Crop growth simulation models (InfoCrop v.2.1, DSSATv4.5, WOFOSTv1.5 and Cropsytv 4.19) software

S.B. Yadav1 , A.K. Misra1 , S.K. Mishra2 and Vyas Pandey1 1 Department of Agricultural Meteorology, B.A. College of Agriculture, Anand Agricultural University, Anand-388 110 2

Regional Research Station, Punjab Agricultural University, Faridkot-151203

Crop Growth Simulation Modeling The crop growth models are helpful to assess the impact of climate change on the stability of crop production under different management options (Hoogenboomet al., 1995). Crop growth simulation models provide means to quantify the effect of climate on soil, crop growth, productivity and sustainability of agriculture production. These tools can reduce the need for expensive and time consuming field experimentation and can be used to analyze yield gaps in various crops including wheat. Crop simulation model is quite useful as it forms a bridge between crop process analysis and performance assessment in which process operation are in their natural context. The last two decades have witnessed the development of numerous crop-growth and yield simulation models describing the dynamics of the soil-water-plant-atmosphere system. Models are now available for all the major crops such as wheat, rice, maize, cotton, sorghum, groundnut, soybean, chickpea, potato, millet and sunflower as well as for some plantation and horticultural crops. Current literature reviewed has revealed that there are at least 100 different crop simulation models of varying complexity that presently exist. Here we are giving information about only four dynamic Crop Growth Simulation Models these are 1. InfoCrop model (Information on Crop) 2. DSSAT model (Decision Support System for Agrotechnology) 3. WOFOST model (WOrldFOodSTudy) 4. CropSyst model (Cropping Systems Simulation Model) The detailed description of these models are provided belowInfoCrop model The InfoCrop model is written in FORTRAN SIMULATION TRANSLATOR (FST) language (Van Kraalingen 1995). The time step of the model is one day. InfoCrop is a dynamic crop-yield simulation model. This model was developed by Aggarwal and his coworkers from the Center for Application of Systems Simulation, IARI, New Delhi(Aggarwal et.al 2006). It is a mechanistic and dynamic crop simulation model, which can deal with the interaction among weather, crop/variety, soils and management besides major pest. It has capacity to evaluate the production of major annual crops viz, rice, wheat, sorghum, millet, sugarcane, chickpea, pigeonpea, cotton, mustard, groundnut, potato and of course maize and has an inbuilt data base of Indian soils. It simulates daily dry matter production as a function of irradiance, maximum and minimum temperatures, water, nitrogen and biotic stress (pests). The crop growth processes that can be simulated are: phenology, photosynthesis, respiration, leaf area growth, assimilates partitioning, source-sink balance, nutrient uptake partitioning and transpiration. These processes are

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arranged in sub models. The key component and modules of InfoCrop are illustrated in Fig.1 Potential yield estimation

Weather: Rainfall, Temperature, Solar radiation

Yield gap estimation Pests: Type, Population Yield forecasting Crop/ variety: Physiology, Phenology Morphology

InfoCrop

Agronomic inputs: seeds, FYM, irrigation, fertilizer, bio pesticides

Optimizing management practices

Impact assessment of climatic variability and climate change

Soil: Texture, salinity, sodicity, fertility

Plant type design and evaluation

Fig.1 Context diagram of InfoCrop depicting the input requirement on the left hand side and its possible application on the right 3.1.7.2 Leaf area development Phenological developments are mainly depending on temperature, photoperiod, water stress and nutrient stress besides varietal characters. Crop development in the model is considered in terms of thermal time requirement for three phases; sowing to germination, germination to flowering and flowering to physiological maturity. Thermal time is calculated as the sum of mean daily temperature over the base temperatures at which development stops. The assumptions underlying the calculation of thermal time requirement are: a) The temperature response is linear over the expected for growth b) Daily temperature does not fall below the base temperature of crop for a significant part of the day c) The other factors that affect the rate of development are photoperiod, water and nutrient stresses. Water and nutrient stresses accelerates rate of development and their effects are small as compared to that of temperature. Leaf area development is described as a function of leaf weight and specific leaf area (source limited). But, at early development stages, leaf area growth is accounted as sink limited and leaf area growth is coupled to temperature through its effect on cell division and expansion. Specific leaf area is calculated as area leaves per unit dry weight of leaves at different stages. Area of stems as well as that of spikes is considered along with the area of the leaves and also the senescence, while calculating the net photosynthesis area. While - 457 -


simulating senescence, aging N, water, temperature stress and mobilization of reserves from leaves are considered as contributing factors. Soil water

Weather

Pests

Crop growth and yield

Damage mechanism

Water stress in crops

N stress in crops

Soil N balance

Fig.2 Key inputs for InfoCrop. The arrow labels symbolize the actual processes carried out at the zero levels. The eclipses symbolize main systems at zero level and processes at the rest of the levels. The rectangles are the main entities of the system Table 1 :The inputs required for InfoCrop models Site Data

Latitude, longitude, altitude

Daily weather data

Solar Radiation, maximum and minimum temperature, vapor pressure, wind speed, rainfall, relative humidity morning and evening

Soil data

Thickness, Bulk Density, pH, Organic Carbon, Sand Content, Initial Volumetric Water Content, Field Capacity, Wilting Point

Crop Data

Management Sowing Date, Seed Amount,Urea Applied

Rate,

Sowing Depth,

Surface Irrigation

Phenology Parameters for Deriving Genetic Coefficient Base temperature

Sowing to germination, Germination to 50% flowering, 50% flowering to physiological maturity

Thermal time

Sowing to germination, Germination to flowering, Flowering to Physiological Maturity, Optimum Temperature, Maximum Temperature, Sensitivity to photoperiod

Growth parameters

Relative growth rate of leaf area (°C/d), Specific leaf area (dm2 /mg), Index of greenness of leaves (scale 0.8 to 1.2), Extinction coefficient of leaves at flowering (ha soil/ha leaf fraction, Radiation use efficiency (g/mj/day), Root growth rate (mm/d), Sensitivity of crop to flooding scale (scale 1.0 to 1.2), Index of nitrogen fixation (scale 0.7 to 1.0)

Source-Sink

Slope of storage organ no./m2 to dry matter during storage organ - 458 -


balance

formation (storage organ/kg/day), Potential storage organ weight (mg/grain), Nitrogen content of storage organ (fraction), Sensitivity of storage organ setting to low and high temperature (scale 0.0 to 1.5)

1. DSSAT model The Decision Support System for Agro-technology Transfer (DSSAT) (Version 4.5) is an application software program that includes crop simulation models for more than 25 crops to make more reliable predictions (Jame and Cutforth, 1996). The crop simulation models simulate growth, development and yield, the soil and plant water, nitrogen and carbon balances. DSSAT and its crop simulation models have been used for a wide range of applications, including on-farm and precision management to regional assessments of the impact due to climate change. The Decision Support System for Agrotechnology Transfer (DSSAT) has been in use for more than 15 years by researchers in over 100 countries worldwide. DSSAT is a microcomputer software program combining crop soil and weather data bases and programs to manage them, with crop models and application programs, to simulate multiyear outcomes of crop management strategies. As a software package integrating the effects of soil, crop phenotype, weather and management options, DSSAT allows users to ask "what if" questions and simulate results by conducting, in minutes on a desktop computer, experiments which would consume a significant part of an agronomist's career. DSSAT also provides for validation of crop model outputs; thus allowing users to compare simulated outcomes with observed results. Crop model validation is accomplished by inputting the user's minimum data, running the model, and comparing outputs. By simulating probable outcomes of crop management strategies, DSSAT offers users information with which to rapidly appraise new crops, products, and practices for adoption. The release of DSSAT Version 4.5 incorporates changes to both the structure of the crop models and the interface to the models and associated analysis and utility programs. The DSSAT package incorporates models of 27 different crops with new tools that facilitate the creation and management of experimental, soil, and weather data files. DSSAT v4 includes improved application programs for seasonal and sequence analyses that assess the economic risks and environmental impacts associated with irrigation, fertilizer and nutrient management, climate change, soil carbon sequestration, climate variability and precision management. DSSAT was developed through collaboration between scientists at the University of Florida, the University of Georgia, University of Guelph, University of Hawaii, the International Center for Soil Fertility and Agricultural Development, Iowa State University and other scientists associated with ICASA. Table 2 : Minimum data requirement for DSSAT Site Data

Latitude, Longitude, Altitude

Daily weather data

Solar radiation (MJ/m²-day), Maximum temperature (ºC) and Rainfall (mm).

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and

minimum

air


Desired soil data includes soil classification (SCS), surface slope, color, permeability, and drainage class. Soil profile data by soil horizons include upper and lower horizon depths (cm), percentage sand, silt, and clay centent, 1/3 bar bulk density, organic carbon, pH in water, aluminum saturation, and root abundance information

Soil data

Crop Data

Management Planting date, planting density, row spacing, planting depth, crop variety, irrigation, and fertilizer practices. Characterisation of Genetic coefficients of CERES-wheat model

Phyllochron interval (PHINT)

It describes the thermal time required between emergence of two successive leaves and its value is taken 95 for spring cultivar.

Vernalisation coefficient (P1V)

It ranges from 0-9 and describes the relative amount of slowing down the development for each day of unfulfilled vernalisation assuming that 50 days of vernalisation are sufficient for all cultivars.

Photoperiodism coefficient (P1D)

The coefficient governs the relative amount that development is slowed when plants are grown in a photoperiod 1 hour shorter than the optimum (which is considered to be 16 hours, Chipanshiet al. 1997).

Grain filling duration coefficient (P5)

Its accounts for thermal time in degree days above a base of 1 0 C where each unit increase above zero adds 20degree days to the initial value of 430 degree days. Coefficients related to growth aspects

Kernel number coefficient (G1)

The coefficient controls the kernel number per unit weight of stem (less leaf blades and sheaths) plus spike at anthesis (g-1 ).

Kernel weight coefficient (G2)

It is related to kernel filling rate under optimum conditions (mg/day).

Tiller weight coefficient (G3)

It accounts for the non-stressed dry weight (g) of a single stem (excluding leaf blades and sheaths) and spike when elongation ceases.

Components The Cropping System Model (CSM) released with DSSAT Version 4.5 represents a major departure from previously released DSSAT crop models, not in function, but in design. The computer source code for the model has been restructured into a modular format in which components separate along scientific discipline lines and are structured to allow easy replacement or addition of modules. CSM now incorporates all crops as modules using a single soil model and a single weather module. The new cropping system model

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now contains models of 17 crops derived from the old DSSAT CROPGRO and CERES models. 2. WOFOST model WOFOST is a member of the family of crop growth models developed in Wageningen by the school of C.T. de Wit. Related models are SUCROS (Simple and Universal CRop growth Simulator), MACROS (Modules of Annual CROp Simulator) and ORYZA1. WOFOST simulates the daily growth of specific crops, using the selected weather and soil data. Each simulation is conducted for specific boundary conditions, which comprise of the crop calendar and water and nutrient status of the soil. WOFOST follows the hierarchical distinction between potential and limited production. Light interception and CO2 assimilation are the growth driving processes, and crop phenological development is the growth controlling process. WOFOST can be used to estimate crop production, indicate yield variability, evaluate the effects of climate changes or soil fertility changes, and determine limiting biophysical factors. The crop models are available in following crops: wheat, maize, barley, rice, sugar beet, potato, field bean, soybean, rape and sunflower. Origins and genesis of WOFOST crop growth simulation model WOFOST (WOrld FOod STudy) model is originated in the framework of an interdisciplinary study on the potential world food production by the Centre for World Food Studies (CWFS) in cooperation with the Wageningen Agricultural University, Department of Theoretical Production Ecology (WAU-TPE) and the DLO-Centre for Agrobiological Research (currently Plant Research International), Wageningen, the Netherlands. After cessation of the CWFS in 1988, model development has been carried out at the DLO-Winand Staring Centre (currently Alterra) in cooperation with PRI and WAU-TPE.WOFOST was originally developed as a crop growth simulation model for the assessment of the yield potential of various annual crops in tropical countries (Van Keulen and Wolf, 1986; Van Diepen et al., 1988; Van Keulen and Van Diepen, 1990). At first it was tried to restrict the need for input data as much as possible, by using average input values. However, it soon became clear that the variability in the environmental conditions determining crop growth, both in space and time, to be taken into account. Development of WOFOST has been driven by its applications in several studies. Although most studies were not intended to develop the model as such, efforts were made to maintain parts of the developed software as options in subsequent model versions. WOFOST simulates the daily growth of specific crops, using the selected weather and soil data. Each simulation is conducted for specific boundary conditions, which comprise of the crop calendar and the soil’s water and nutrient status. WOFOST follows the hierarchical distinction between potential and limited production. Light interception and CO2 assimilation are the growth driving processes, and crop phenological development is the growth controlling process. WOFOST can be used to estimate crop production, indicate yield variability, evaluate the effects of climate changes or soil fertility changes, and determine limiting biophysical factors. The WOFOST model is applicable for various crops viz. wheat, maize, barley, rice, sugar beet, potato, field bean, soybean, rape, sunflower etc. General structure and functionality of WOFOST model WOFOST is a mechanistic model that explains crop growth on the basis of the underlying processes, such as photosynthesis, respiration and how these processes are influenced by - 461 -


environmental conditions. The predictive ability of mechanistic models does not always live up to its expectation. It is realized that each parameter estimation and process formulation has its own inaccuracy and these errors accumulate in the prediction of final yield. A schematic outline of the WOFOST crop growth simulation model as adapted and integrated in the Agricultural Information System for crop yield forecasting is presented in Fig.3. WOFOST calculates first the instantaneous photosynthesis at three depths in canopy, which is subsequently integrated over the depth of the canopy and over the light period, to arrive daily total canopy photosynthesis. After subtracting maintenance respiration, assimilates are partitioned amongst roots, leaves, stems and storage organs, using partitioning factors that are a function of the phenological development stage of the crop (Spitters et al., 1989). Fraction portioned to the leaves, determines leaf area development and hence dynamics of light interception. Dry weights of plant organs are obtained by integrating their growth rates over time. Leaf mass is subdivided into age classes. During crop development a part of living biomass dies due to senescence. Some simulated crop growth processes are influenced by temperature, viz. maximum photosynthesis and maintenance respiration rate. Other processes such as partitioning of assimilate or decay of crop tissues are steered by the phenological stages. Phenological development stage is calculated as a function of ambient temperature and possibly modified by the day length.

Fig. 3: Schematic outline of the WOFOST crop growth simulation model

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The WOFOST model describes phenological development, growth and yield formation of a crop from emergence till maturity on the basis of crop genetic properties and environmental conditions. It simulates dry matter accumulation of a crop as a function of irradiation, temperature and crop characteristics in time steps of one day. Basis for calculating dry matter production is the gross CO 2 canopy assimilation rate, which depends on the absorbed radiation energy and is a function of incoming radiation and crop leaf area. From the absorbed radiation and the photosynthetic characteristics of single leaves, the daily CO 2 crop assimilation rate is calculated. Part of the carbohydrates produced (CH2 O) are used to provide energy for maintenance of existing live biomass (maintenance respiration). Remaining carbohydrates are converted into structural matter. During this process some weight is lost due to growth respiration. The simulated processes include phenological development rate, CO 2 assimilation, maintenance respiration, dry matter partitioning resulting in biomass accumulation, growth and senescence of leaves, transpiration and extension of roots. Table 3 a : Minimum input data required for WOFOST Site Data

Latitude, Longitude, Altitude , Angstrom constant values a and b

Daily weather data

Minimum and Maximum temperature, sunshine duration, global radiation, wind speed, rainfall, vapour pressure

Soil data

Soil texture, Organic carbon, Field capacity , Permanent wilting point, Saturation moisture content and Bulk density

Crop Data

Phenological, assimilation and respiration characteristics, and Management partitioning of assimilates to plant organs (Boogaardet al., 1998).

Table 3 b : Genetic parameters of WOFOST model S.N.

Parameters

1.

Parameter Reference Extinction coefficient for diffuse visible light

2.

Efficiency of conversion of assimilation into leaves, stems, roots and storage organs

3.

Maintenance respiration factors of leaves, stems, roots and storage organs

4.

Partitioning: fraction of total dry matter to roots (FR), fraction of above-ground dry matter to leaves (FL), stems (FS) and storage organs (FO) as function of DVS

5.

Leaf area index at emergence

6.

Daily increase in TSUM as function of average temperature - 463 -

Reference

Van Heemst, 1988

Boons-Prins et al., 1993


7.

Maximum leaf CO 2 assimilation as function of DVS

8.

Specific leaf area 3. CropSyst model

CropSyst(Cropping Systems Simulation Model) is a simulation model developed by Stöckle et al. (2003) at Washington State University, USA. It is a multi-year, multi-crop, daily time step crop growth simulation model, developed with emphasis on a friendly user interface, and with a link to GIS software and a weather generator. Link to economic and risk analysis models is under development. The model’s objective is to serve as an analytical tool to study the effect of cropping systems management on crop productivity and the environment. For this purpose, CropSyst simulates the soil water budget, soil-plant nitrogen budget, crop phenology, crop canopy and root growth, biomass production, crop yield, residue production and decomposition, soil erosion by water, and pesticide fate. These are affected by weather, soil characteristics, crop characteristics, and cropping system management options including crop rotation, cultivar selection, irrigation, nitrogen fertilization, pesticide applications, soil and irrigation water salinity, tillage operations, and residue management. The CropSyst model code is written in C++ and can be used on WINDOWS or UNIXbased platforms. An advanced user friendly interface allows users to easily manipulate input files, verify input parameters for range errors and cross-compatibility, create simulations, execute single and batch run simulations, customize outputs, produce text and graphical reports, and link to spreadsheet programs. Simulations can be customized to invoke only those modules of interest for a particular application (e.g., erosion and nitrogen simulation can be disabled if not desired), producing more efficient runs and simplifying model parameterization Crop development in CropSyst is based on thermal time accumulation. The thermal time accumulation is modulated by photoperiod, vernalization requirements, and water stress as explained below. The thermal time (sum of growing degree days) required for each developmental phase must be specified. Degree days are accumulated from planting. Depending on crop type, certain growth stages are relevant: emergence, tuber initiation, beginning of flowering, end of leaf expansion in determinate crops, end of flowering, beginning of grain filling, and physiological maturity. CropSyst represents a crop as “big leaf” and individual plants or plant cohorts are not simulated separately. Model Inputs There are four input data files which are required to run CropSyst viz., weather, soil, crop, and management files. A Simulation Control file combines the input files as desired to produce specific simulation runs. In addition, the Control file determines the start and ending day for the simulation, define the crop rotations to be simulated, and set the values of all parameters requiring initialization.

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Source: (Stöckle et al. 2003) Fig 4. Flowchart of biomass growth calculations in CropSyst. Table 4 : Input data required for CropSyst Model Site Data

Latitude, Longitude, Altitude

Daily weather data

Weather file code name and directories, rainfall intensity parameters (for erosion prediction), freezing climate parameters (for locations where soil might freeze), and local parameters to generate daily solar radiation and vapor pressure deficit values.

Soil data

Cation exchange capacity, pH, curve number, texture, layer wise thickness, field capacity, permanent wilting point, bulk density and bypass coefficient.

Crop Data

Phenology, Morphology (Maximum LAI, root depth, specific leaf area and other parameters defining canopy and root characteristics), Growth (transpiration-use efficiency normalized by VPD, light- use efficiency, stress response parameters, etc.), Residue - 465 -


(decomposition and shading parameters for crop residues), Nitrogen Parameters (defining crop N demand and root uptake), Harvest Index (unstressed harvest index and stress sensitivity parameters), and Salinity Tolerance.

Management Data

Management events can be scheduled using actual date, relative date, or using synchronization with phenological events (e.g., number of days after flowering). Scheduled events include irrigation (application date, amount, chemical or salinity content), nitrogen fertilization (application date, amount, source- organic and inorganic-, and application mode- broadcast, incorporated, injected), tillage operations (primary and secondary tillage operations, which are basically related to residue fate), and residue management (grazing, burning, chopping, etc.).

Characterisation of Genetic coefficients for pear orchard Specific leaf area (m2 kg−1) Rooting depth (m) Thermal time to bud break (gdd °C) Begin flowering (gdd °C) Begin initial fruit growth (gdd °C) Begin rapid fruit growth (gdd °C) Physiological maturity (gdd °C) Leaf duration (gdd °C) Extinction coefficient for solar radiation Maximum expected LAI Genetic coefficients

Crop coefficient at full canopy Maximum water uptake_rapid fruit growth (mm day−1) Maximum water uptake_postharvest (mm day−1) Estimated from other experiments or literature Transpiration use efficiency when VPD = 1 kPa (g BM kg−1 H2O)a Scaling coefficient of transpiration use efficiencya Stem water potential at the onset of stomatal closure (J kg−1) Wilting stem water potential (J kg−1)b Stem water potential that begins reduction in canopy expansion (J kg−1) Stem water potential that stops canopy expansion (J kg−1)

Applications of crop growth simulation model Simulation models can be used to meaningfully reduce additional experimentation and shorten time taken in decision making to increase yield (Sharma and Kumar, 2006). - 466 -


The use of various crop simulation models has been classified (Booteet al., 1996) into three primary categories: (i) for research knowledge synthesis (ii) for crop system decision management and (iii) for policy analysis. Crop models have been used to assist in genetic improvement of crops by (i) determining optimal genetic traits of plants for specific environment (Whistler et al., 1986) and (ii) by predicting the performance of new cultivars for specific environments, thus reducing the number of locations/seasons of multi-location breeding trials (Hunt, 1993 and Palaniswamyet al., 1993). Models were used for estimating climatic yield potential of crop and to analyze yield gap due to weather and other factors (Aggarwal and Kalra, 1994). The models are effective tools for assessment of growth and yield of crop as well as to suggest optimal resource management options (Kalra and Aggarwal, 1994). Recent advances in crop modelling have made it possible to simulate the yield and growth of several crops under varied soil and weather conditions with different management practices. Using crop simulation models, the effects of climate change and climatic variability on crop growth and yield have also been predicted (Lalet al., 1997). Regional estimates of maturity and yield from the models are of great value to growers intending to store their crop for phased delivery to the transportation industry to move the produce and to the national agencies estimating the effect of production on future prices. Models have also been used in management decisions to reduce fertilizer and pesticides leaching and soil erosion (Willamset al., 1984). It is likely that with improved agronomicweather information and interpretation, farmers could reduce production risks and increase crop yields by tailoring management decisions to current and expected weather. These models integrate the effect of important factors on productivity and thus provide a unique opportunity to supplement results of field trials. They can also test numerous combinations of factors of production within a short period of time. Limitations of crop growth simulation model The major limitation in the use of simulation models is the absence of validation of such models in areas other than where they are developed. They are developed under a specific crop, soil and climatic set up. Before their application on a regional scale in different regions, they need standardization and calibration with locally measured soil, plant and weather parameters. Thus, standardization, calibration and performance evaluation are the basic requisites for application of simulation models at new locations. Reference Aggarwal PK, Kalra N, Chander S, Pathak H (2006) Infocrop: A dynamic simulation model for the assessment of crop yields, losses due to pests, and environmental impact of agro-ecosystems in tropical environments. I. Model description, Agric Systems 89: 1-25 Aggarwal, P.K. and Kalra, N. (1994). Analyzing the limitations set by climatic factors, genotype and water and nitrogen availability on productivity of wheat II. Climatically potential yields and management strategies. Field Crops Res. (38): 93103. Boogaard, H.L., Van Diepen, C.A., Rötter, R.P., Cabrera, J.M.C.A., Van Laar, H.H., (1998). WOFOST 7.1, User’s guide for the WOFOST 7.1 crop growth simulation model and WOFOST Control Center 1.5. DLO Winand Staring Centre, Wageningen, The Netherlands.

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