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| - | ===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== |
| + | |||
| + | 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. | ||
| + | |||
| + | |||
| + | ===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 | # Poor seeds or planting materials | ||
| # Pest and diseases | # Pest and diseases | ||
| Line 15: | Line 24: | ||
| # Adverse weather conditions | # 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. | + | 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. | 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: | + | 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 | * Calculation algorithms that model the factors | ||
| * Values for algorithm parameters through which the model is localized to a specific situation. | * Values for algorithm parameters through which the model is localized to a specific situation. | ||
| Line 25: | Line 36: | ||
| The availability of input data is crucial and in the practical circumstances of crop forecasting the most restricting of the three prerequisites. | The availability of input data is crucial and in the practical circumstances of crop forecasting the most restricting of the three prerequisites. | ||
| - | 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: | + | The main adverse weather conditions (apart from disasters) affecting crop yields are temperature, radiation and rainfall. The following sections will look at these factors. |
| - | ===Radiation and temperature are limited=== | + | ===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. | ||
| - | 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 | + | ===Conditions where water is limited=== |
| - | radiation (perhaps inferred from cloudiness). There is no need to simulate soil processes at this level; only above-ground physiology is considered. | + | 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. | ||
| - | ===Water is limited=== | + | ==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: | |
| - | Growth is limited by water shortage at least part of the time, but when sufficient water is | + | * Mechanistic models |
| - | 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. | + | * Water Balance Models |
| - | + | ||
| - | There are several brands of crop models described in literature, implemented in computer code and applied to various climates. | + | |
| - | + | ||
| - | ==Models== | + | |
| - | + | ||
| - | 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]] | + | |
| ===Mechanistic models=== | ===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 : | 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. | + | * 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. | + | * 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. | + | * 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. | + | * 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 | ||
| Line 63: | Line 69: | ||
| 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]] | ||
| ------------------------------- | ------------------------------- | ||
| </BLOCKQUOTE> | </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]