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| - | Chapter 2. Introduction into the principles of crop modelling (including basic crop model overview) and their implementation in AgroMetShell and the CMBox. The principle of indicators like ETa and the WSI index. | + | <blockquote> |
| + | __NOTOC__ | ||
| + | ==1.3. The principles of crop modelling and their implementation in the CMBox. == | ||
| + | ------------------------------------ | ||
| - | Factors determining crop yield | + | {| style="background-color:#F5F5F5; border-collapse:collapse" cellspacing="7" border="1" bordercolorlight="#0000FF" bordercolordark="#0000FF"> |
| + | |style="border-style: solid; border-width: 1px"|''Clarence Sakamoto, René Gommes, Peter Hoefsloot'' | ||
| + | |- | ||
| + | |} | ||
| - | Compared to a theoretical maximum yield for a crop under perfect environmental circumstances, practical crop yield is usually reduced due to: | + | ==Introduction== |
| - | 1. Poor seeds or planting materials | + | |
| - | 2. Pest and diseases | + | |
| - | 3. Mismanagement of the crop | + | |
| - | 4. Lack of nutrient availability, especially nitrogen, sometimes phosphate. | + | |
| - | 5. Less favourable soil structure. | + | |
| - | 6. Natural disasters like flooding, hurricanes etc. | + | |
| - | 7. Adverse weather conditions | + | |
| - | Factors 1-5 are known to be fairly stable from year to year, in other words, the influence on crop yield is usually more or less constant (except perhaps for pests and diseases in some years), making monitoring during the course of the season less important . Moreover, these factors are taken into account when reference yields in the field. | + | The ultimate goal of crop modelling for forecasting purposes is predicting yield at the end of the growing cycle. This yield will then be aggregated to regional values for reporting to relevant authorities. |
| - | Large natural disasters (6) and their influence on crop yields are very difficult to model and almost impossible to predict. | ||
| - | In principle factors can be modelled. However to successfully do so, models need: | + | ===Factors determining crop yield=== |
| - | • Calculation algorithms that model the factors | + | Compared to a theoretical maximum yield for a crop under perfect environmental circumstances, crop yield is usually reduced due to: |
| - | • Values for algorithm parameters through which the model is localized to a specific situation. | + | # Poor seeds or planting materials |
| - | • Input data | + | # Pest and diseases |
| - | The availability of input data is crucial and in the practical circumstances of crop forecasting the most restricting of the three prerequisites. | + | # Mismanagement of the crop |
| + | # Lack of nutrient availability, especially nitrogen, sometimes phosphate. | ||
| + | # Less favourable soil structure. | ||
| + | # Natural disasters like flooding, hurricanes etc. | ||
| + | # Adverse weather conditions | ||
| - | The factor caters for a large part of yield variation from year to year is weather conditions (7). The two main adverse weather conditions affecting crop yields are: | + | Factors 1-5 are known to be fairly stable from year to year, in other words, the influence on crop yield is usually more or less constant (except perhaps for pests and diseases in some years), making monitoring during the course of the season less important. Moreover, these factors are taken into account when reference yields in the field. |
| - | Radiation and temperature are limited | + | Large natural disasters (6) and their influence on crop yields are very difficult to model and almost impossible to predict. |
| - | This can be approached in practice with very intensively managed irrigated crops. The model is one of photosynthesis, partition of carbohydrate, and physiological growth stages (e.g., flowering, senescence). The only inputs to the model are temperature and | + | |
| - | radiation (perhaps inferred from cloudiness). There is no need to simulate soil processes at this level; only above-ground physiology is considered. | + | |
| - | Water is limited | + | The factor that caters for a large part of yield variation from year to year is weather conditions (7). |
| - | Growth is limited by water shortage at least part of the time, but when sufficient water is | + | |
| - | available, the growth rate increases up to the maximum rate set by the weather. This can be approached in practice by intensively managed rainfed crops. The model must determine water stress (so, needs to model soil water, the plant root system, and plant transpiration) and its effect crop growth processes. Another input to the model is precipitation, and the soil profile must be modelled at least for the water balance. | + | |
| - | Introduction to crop models | + | In principle all factors can be modelled. However to successfully do so, models need: |
| + | * Calculation algorithms that model the factors | ||
| + | * Values for algorithm parameters through which the model is localized to a specific situation. | ||
| + | * Input data | ||
| + | The availability of input data is crucial and in the practical circumstances of crop forecasting the most restricting of the three prerequisites. | ||
| - | There are several brands of crop models described in literature, implemented in computer code and applied to various climates. | + | The main adverse weather conditions (apart from disasters) affecting crop yields are temperature, radiation and rainfall. The following sections will look at these factors. |
| - | Mechanistic models. | + | ===Conditions where radiation and temperature are limited=== |
| - | These models explain crop growth on the basis of the underlying processes, such as photosynthesis and respiration, and how these processes are affected by environmental conditions. Examples of models that follow this approach are : | + | In practice this occurs with very intensively managed irrigated crops. Modeling these conditions is done through examining photosynthesis, partition of carbohydrate, and physiological growth stages (e.g., flowering, senescence). The inputs to the models are temperature and radiation (inferred from cloudiness). As water is not limited, there is no need to simulate soil processes at this level; only above-ground physiology will have to be considered. The CM box software AgrometShell contains a number of functions that help modeling these conditions. |
| - | 1. WOFOST. This model describes crop growth as biomass accumulation in combination with phenological development. It simulates the crop life cycle from sowing or emergence to maturity. Meteorological data (rain, temperature, windspeed, global radiation, air humidity) are needed as input. Other input data include volumetric soil moisture content at various suction levels, and other data on saturated and unsaturated water flow. Also data on site specific soil and crop management are requested. | + | ===Conditions where water is limited=== |
| + | Growth can limited by water shortage at least part of the time. When sufficient water is | ||
| + | available, the growth rate increases up to the maximum rate set by the weather. The model must determine water stress (so, needs to model soil water, the plant root system, and plant transpiration) and its effect crop growth processes. Another input to the model is precipitation, and the soil profile must be modelled at least for the water balance. | ||
| - | 2. SUCROS (Simple and Universal CROp growth Simulator) is a mechanistic model that explains crop growth on the basis of the underlying processes, such as CO2 assimilation and respiration, as influenced by environmental conditions. SUCROS simulates potential growth of a crop, i.e. its dry matter accumulation under ample supply of water and nutrients in a pest-, disease- and weed-free environment under the prevailing weather conditions. The basis for the calculation is the rate of CO2 assimilation (photosynthesis) of the canopy. | + | ==Modelling limiting conditions== |
| + | There are a large number of crop models described in literature, implemented in computer code and applied to various climates. Two basic groups exist: | ||
| + | * Mechanistic models | ||
| + | * Water Balance Models | ||
| - | 3. CropSyst simulates the soil water and nitrogen budgets, crop growth and development, crop yield, residue production and decomposition, soil erosion by water, and salinity. The development of CropSyst started in the early 1990s, evolving to a suite of programs including a cropping systems simulator (CropSyst), a weather generator(ClimGen), GIS-CropSyst cooperator program (ArcCS), a watershed model (CropSyst Watershed), and several miscellaneous utility programs. CropSyst and associated programs can be downloaded free of charge over the Internet. | + | ===Mechanistic models=== |
| + | These models explain crop growth on the basis of the underlying processes, such as photosynthesis and respiration, and how these processes are affected by environmental conditions. Examples of models that follow this approach are : | ||
| - | 4. APSIM is a model that simulates agricultural production systems. It has the ability to integrate models derived in fragmented research efforts. This enables research from one discipline or domain to be transported to the benefit of some other discipline or domain. It also facilitates comparison of models or sub-models on a common platform. This functionality has been achieved via the implementation of a "plug-in-pull-out" approach to APSIM design. APSIM has been developed in a way that allows the user to configure a model by choosing a set of sub-models from a suite of crop, soil and utility modules. Any logical combination of modules can be simply specified by the user "plugging-in" required modules and "pulling out" any modules no longer required. | + | * WOFOST. This model describes crop growth as biomass accumulation in combination with phenological development. It simulates the crop life cycle from sowing or emergence to maturity. Meteorological data (rain, temperature, windspeed, global radiation, air humidity) are needed as input. Other input data include volumetric soil moisture content at various suction levels, and other data on saturated and unsaturated water flow. Also data on site specific soil and crop management are requested. |
| + | * SUCROS (Simple and Universal CROp growth Simulator) is a mechanistic model that explains crop growth on the basis of the underlying processes, such as CO2 assimilation and respiration, as influenced by environmental conditions. SUCROS simulates potential growth of a crop, i.e. its dry matter accumulation under ample supply of water and nutrients in a pest-, disease- and weed-free environment under the prevailing weather conditions. The basis for the calculation is the rate of CO2 assimilation (photosynthesis) of the canopy. | ||
| + | * CropSyst simulates the soil water and nitrogen budgets, crop growth and development, crop yield, residue production and decomposition, soil erosion by water, and salinity. The development of CropSyst started in the early 1990s, evolving to a suite of programs including a cropping systems simulator (CropSyst), a weather generator(ClimGen), GIS-CropSyst cooperator program (ArcCS), a watershed model (CropSyst Watershed), and several miscellaneous utility programs. CropSyst and associated programs can be downloaded free of charge over the Internet. | ||
| + | * APSIM is a model that simulates agricultural production systems. It has the ability to integrate models derived in fragmented research efforts. This enables research from one discipline or domain to be transported to the benefit of some other discipline or domain. It also facilitates comparison of models or sub-models on a common platform. This functionality has been achieved via the implementation of a "plug-in-pull-out" approach to APSIM design. APSIM has been developed in a way that allows the user to configure a model by choosing a set of sub-models from a suite of crop, soil and utility modules. Any logical combination of modules can be simply specified by the user "plugging-in" required modules and "pulling out" any modules no longer required. | ||
| Many derivates and flavours exist of these models are around | Many derivates and flavours exist of these models are around | ||
| - | Water balance models | + | ===Water balance models=== |
| - | + | ||
| - | 1. The ACRU model has its hydrological origins in a distributed catchment evapotranspiration. Multi-layer soil water budgeting is accomplished by partitioning and redistribution of soil water. | + | |
| - | + | ||
| - | 2. SWAP (Soil, Water, Atmosphere and Plant) simulates vertical transport of water, solutes and heat in unsaturated/saturated soils. The model is designed to simulate the transport processes at field scale level and during entire growing seasons. Basic, daily meteorological data are used to calculate daily, potential evaporation according to Penman-Monteith. | + | |
| - | + | ||
| - | 3. FAO water balance model. This model is implemented by | + | |
| - | + | ||
| - | + | ||
| - | CROPWAT | + | |
| - | CROPWAT is a practical tool to help agro-meteorologists, agronomists and irrigation engineers to carry out standard calculations for evapotranspiration and crop water use studies, and more specifically the design and management of irrigation schemes. It allows the development of recommendations for improved irrigation practices, the planning of irrigation schedules under varying water supply conditions, and the assessment of production under rainfed conditions or deficit irrigation. Typical applications of the water balance include the development of irrigation schedules for various crops and various irrigation methods, the evaluation of irrigation practices, as well as rainfed production and drought effects. Calculations of crop water requirements and irrigation requirements are carried out with inputs of climatic and crop data. | + | |
| - | + | ||
| - | + | ||
| + | * The ACRU model has its hydrological origins in a distributed catchment evapotranspiration. Multi-layer soil water budgeting is accomplished by partitioning and redistribution of soil water. | ||
| + | * SWAP (Soil, Water, Atmosphere and Plant) simulates vertical transport of water, solutes and heat in unsaturated/saturated soils. The model is designed to simulate the transport processes at field scale level and during entire growing seasons. Basic, daily meteorological data are used to calculate daily, potential evaporation according to Penman-Monteith. | ||
| + | * FAO water balance model. | ||
| In areas where water is a limiting factor for plant growth and subsequently yield, a relatively simple water balance calculation can reveal whether a crop experiences water stress (water deficit or water surplus). FAO has developed such a water balance model. This model has been described by Frere and Popov in their standard textbook “Agrometeorological crop monitoring and forecasting”. | In areas where water is a limiting factor for plant growth and subsequently yield, a relatively simple water balance calculation can reveal whether a crop experiences water stress (water deficit or water surplus). FAO has developed such a water balance model. This model has been described by Frere and Popov in their standard textbook “Agrometeorological crop monitoring and forecasting”. | ||
| - | + | ===Some further reading=== | |
| - | + | An in-depth study of agrometeorological crop yield modeling is written by R. Gommes (Agrometeorological Crop Yield Modelling, 1998). This publication (in PDF) can be found [[http://www.hoefsloot.com/downloads/agrometeorological%20crop%20yield%20modelling.pdf here]] | |
| - | + | ||
| - | + | ||
| - | + | ||
| - | Soil moisture contents at sowing and fruiting times are closely related to emergence, growth and productivity of plants. Also, in order to use irrigation efficiently as per needs of crop growth stages soil moisture estimation for the root zone depth is essential to know the actual amount of water required to make up the depleted portion of the soil moisture. Hence techniques have been developed for forecast – or assessment – of available moisture in a 1 m layer of soil at the beginning of the growing period which is of great assistance to farm operators and agricultural planning agencies as a forecasting variable. Such a forecast is often based on climatological water-balance methods or empirical regression-type equation.An assessment of moisture conditions is based on past and present climatologicaldata (e.g. precipitation, radiation, temperature, wind) with or without the use of soilmoisture measurements. An extrapolation of this current estimate into the near future ispossible through the use of long-term averages or other statistical values of the abovemeteorological data in the water balance equation. On the other hand, a soil moistureforecast equation is based on a statistical analysis of recorded soil moisture data related toone or several other agrometeorological variables. This approach uses, sometimes on aprobability basis, the occurrence of events in the past for extrapolation in the near future.Water balance methods use the following basic equation : | + | |
| - | + | ||
| - | P – Q – U- E – ΔW = 0 | + | |
| - | where P is the precipitation or irrigation water, Q is run off, U is deep drainagepassing beyond the root soil, E is evapotranspiration and ΔW is change in soil-waterstorage.Each of the terms in this equation has special problems associated with its terms, such as Q or U, are negligible. Another assumption is that ΔW, at least over large areas and extended periods of time, can be set equal to zero. For short-term or seasonalapplications an approximate value of ΔW, i.e. the soil-water storage at the beginning and end of the period under consideration, is required. Such a value can be obtained from soil moisture measurements (WMO Technical Note No. 97) but, more practically, from using climatic data in appropriate estimation techniques such as those by Thornthwaite, Penman, Fitzpatrick, Palmer, Baier-Robertson or Budyko (WMO Technical Note No. 138). | + | ------------------------------- |
| + | </BLOCKQUOTE> | ||
Current revision
[edit]1.3. The principles of crop modelling and their implementation in the CMBox.
Clarence Sakamoto, René Gommes, Peter Hoefsloot [edit]Introduction
The ultimate goal of crop modelling for forecasting purposes is predicting yield at the end of the growing cycle. This yield will then be aggregated to regional values for reporting to relevant authorities.
[edit]Factors determining crop yield
Compared to a theoretical maximum yield for a crop under perfect environmental circumstances, crop yield is usually reduced due to:
- Poor seeds or planting materials
- Pest and diseases
- Mismanagement of the crop
- Lack of nutrient availability, especially nitrogen, sometimes phosphate.
- Less favourable soil structure.
- Natural disasters like flooding, hurricanes etc.
- Adverse weather conditions
Factors 1-5 are known to be fairly stable from year to year, in other words, the influence on crop yield is usually more or less constant (except perhaps for pests and diseases in some years), making monitoring during the course of the season less important. Moreover, these factors are taken into account when reference yields in the field.
Large natural disasters (6) and their influence on crop yields are very difficult to model and almost impossible to predict.
The factor that caters for a large part of yield variation from year to year is weather conditions (7).
In principle all factors can be modelled. However to successfully do so, models need:
- Calculation algorithms that model the factors
- Values for algorithm parameters through which the model is localized to a specific situation.
- Input data
The availability of input data is crucial and in the practical circumstances of crop forecasting the most restricting of the three prerequisites.
The main adverse weather conditions (apart from disasters) affecting crop yields are temperature, radiation and rainfall. The following sections will look at these factors.
[edit]Conditions where radiation and temperature are limited
In practice this occurs with very intensively managed irrigated crops. Modeling these conditions is done through examining photosynthesis, partition of carbohydrate, and physiological growth stages (e.g., flowering, senescence). The inputs to the models are temperature and radiation (inferred from cloudiness). As water is not limited, there is no need to simulate soil processes at this level; only above-ground physiology will have to be considered. The CM box software AgrometShell contains a number of functions that help modeling these conditions.
[edit]Conditions where water is limited
Growth can limited by water shortage at least part of the time. When sufficient water is available, the growth rate increases up to the maximum rate set by the weather. The model must determine water stress (so, needs to model soil water, the plant root system, and plant transpiration) and its effect crop growth processes. Another input to the model is precipitation, and the soil profile must be modelled at least for the water balance.
[edit]Modelling limiting conditions
There are a large number of crop models described in literature, implemented in computer code and applied to various climates. Two basic groups exist:
- Mechanistic models
- Water Balance Models
[edit]Mechanistic models
These models explain crop growth on the basis of the underlying processes, such as photosynthesis and respiration, and how these processes are affected by environmental conditions. Examples of models that follow this approach are :
- WOFOST. This model describes crop growth as biomass accumulation in combination with phenological development. It simulates the crop life cycle from sowing or emergence to maturity. Meteorological data (rain, temperature, windspeed, global radiation, air humidity) are needed as input. Other input data include volumetric soil moisture content at various suction levels, and other data on saturated and unsaturated water flow. Also data on site specific soil and crop management are requested.
- SUCROS (Simple and Universal CROp growth Simulator) is a mechanistic model that explains crop growth on the basis of the underlying processes, such as CO2 assimilation and respiration, as influenced by environmental conditions. SUCROS simulates potential growth of a crop, i.e. its dry matter accumulation under ample supply of water and nutrients in a pest-, disease- and weed-free environment under the prevailing weather conditions. The basis for the calculation is the rate of CO2 assimilation (photosynthesis) of the canopy.
- CropSyst simulates the soil water and nitrogen budgets, crop growth and development, crop yield, residue production and decomposition, soil erosion by water, and salinity. The development of CropSyst started in the early 1990s, evolving to a suite of programs including a cropping systems simulator (CropSyst), a weather generator(ClimGen), GIS-CropSyst cooperator program (ArcCS), a watershed model (CropSyst Watershed), and several miscellaneous utility programs. CropSyst and associated programs can be downloaded free of charge over the Internet.
- APSIM is a model that simulates agricultural production systems. It has the ability to integrate models derived in fragmented research efforts. This enables research from one discipline or domain to be transported to the benefit of some other discipline or domain. It also facilitates comparison of models or sub-models on a common platform. This functionality has been achieved via the implementation of a "plug-in-pull-out" approach to APSIM design. APSIM has been developed in a way that allows the user to configure a model by choosing a set of sub-models from a suite of crop, soil and utility modules. Any logical combination of modules can be simply specified by the user "plugging-in" required modules and "pulling out" any modules no longer required.
Many derivates and flavours exist of these models are around
[edit]Water balance models
- The ACRU model has its hydrological origins in a distributed catchment evapotranspiration. Multi-layer soil water budgeting is accomplished by partitioning and redistribution of soil water.
- SWAP (Soil, Water, Atmosphere and Plant) simulates vertical transport of water, solutes and heat in unsaturated/saturated soils. The model is designed to simulate the transport processes at field scale level and during entire growing seasons. Basic, daily meteorological data are used to calculate daily, potential evaporation according to Penman-Monteith.
- FAO water balance model.
In areas where water is a limiting factor for plant growth and subsequently yield, a relatively simple water balance calculation can reveal whether a crop experiences water stress (water deficit or water surplus). FAO has developed such a water balance model. This model has been described by Frere and Popov in their standard textbook “Agrometeorological crop monitoring and forecasting”.
[edit]Some further reading
An in-depth study of agrometeorological crop yield modeling is written by R. Gommes (Agrometeorological Crop Yield Modelling, 1998). This publication (in PDF) can be found [here]