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AGRI 321 Crop Simulation Models
Model : A model is a simplified representation of a system or a process. Modeling is based on the assumption that any given process can be expressed in a formal mathematical statement/ equqtion or set of statements/ equations. Simulation is the process of building models and analyzing the system. Crop Model : Simple representation of a crop / plant. Crop models are tools of systems research which help in solving problems related to crop production. Why we need Simulation Models? To assimilate knowledge gained from field experimentation To provide a structure that promotes interdisciplinary collaboration To promote the use of systems analysis for solving problems To offer dynamic, quantitative tools for analyzing the complexity of cropping systems Crop Models require certain input data which is used by the model to further generate the required output.
- Soil
- Climate
- Plant
- Management Model Crop Yield Input data requirement : Crop modeling requires data related to weather, crop, soil, management practices and insect-pests Weather data includes : Maximum and minimum temperature, rainfall, relative humidity, solar radiation and wind speed, weather data is required at daily time step to assess daily crop growth processes. Crop data includes : Crop name, variety name, crop phenology (days to anthesis, days to maturity etc.) leaf area Index, grain yield above ground biomass, 1000 grain weight.
Soil data includes : Thickness of soil layer, pH, EC, N, P, K, soil organic carbon, soil texture, sand and clay percent, soil moisture, saturation, field capacity and wilting point of soil, bulk density. Crop management data include : Date of sowing of crop is required to initiate the simulation process. Generally sowing date is taken as the start time for the simulation. In case of transplanted rice date of transplanting is used instead of sowing date. Seed rate and depth of seeding are also required. Use of inputs in the crop field namely, irrigation, fertilizer, manure, crop residue etc. needs to be mentioned. Amount of these inputs are specified along with their type, date of sowing application and depth of placement. If crop residues or organic nutrient sources are applied in the field then C:N ratio of those sources are quantified. Pest data includes : Name and type of the pest, their mode of attack, pest population at different crop growth stages. Data on insects or pests are included only in those models which contains the pest module. Steps in Modeling : Define goals : Agricultural system is complex comprising of various disciplines. In order to develop or understand a crop model one requires strong knowledge base of different disciplines is integrated to develop a crop model. Define system and its boundaries : In agriculture, crop field is chosen as a system. Define key variables in system : Variables include state, rate, driving and auxillary variables. State variables are those which can be measured or quantified, e.g. soil moisture content, crop yield etc. Rate variables are the rates of different processes operating in a system, e.g. Photosynthesis rate, transpiration rate, Driving variables are the variables which are not part of the system but the affect the system, e.g. sunshine, rainfall. Auxillary variables are the intermediated products, e.g. dry matter partitioning, water stress etc. These variables are identified in the crop field. After identification of these variables relationship among different variables is determined and a relational diagram is drawn (Fig. 1). This helps in better understanding of the whole process.
Use of models in decision support : Once developed, calibrated and validated any model can be used in any decision support system for forecasting or making suitable decisions regarding crop management. Possible Applications of Crop Model : Estimation of potential yields Estimation of yield gaps : Principal causes and their contribution Yield forecasting Impact assessment of climatic variability and climatic change Optimizing management – Dates of planting, variety, Irrigation and nitrogen fertilizer Environmental impact – Percolation, N losses, GHG emission, SOC dynamics Plant type design and evaluation Limitations of Modeling : Input Data : Models require large amount of input data, which may not be available with the user. Skilled manpower Knowledge of computers & computer language Limited awareness and acceptance towards modeling Multidisciplinary knowledge No model can take into account all the existing complexity of biology stems. Hence simulation results have errors. A model is a tool for improving critical thought, not a substitute for it Models can help formulate hypotheses and improve efficiency of field experiments, but they do not eliminate the need for continued experimentation. Models developed for a specific region cannot be used as such in another region. Proper parameterization and calibration is need for continued experimentation. Models developed for a specific region cannot be used as such in another region. Proper parameterization and calibration is needed before using a Model Models are holistic, knowledge-based global tools for global and local applications Help us in assimilating knowledge gained from experimentation Help understand/ predict behaviour of biological systems on the basis of underlying level of integration. Offer dynamic, quantitative tools for analyzing the complexity of agricultural systems Promote inter-disciplinary research Increase the efficiency of agricultural research and management Improve agronomic efficiency and environmental quality.
Crop is defined as an “Aggregation of individual plant species grown in a unit area for economic purpose”. Growth is defined as an “Irreversible increase in size and volume and is the consequence of differentiation and distribution occurring in the plant”. Simulation is defined as “Reproducing the essence of a system without reproducing the system itself ”. In simulation the essential characteristics of the system are reproduced in a model, which is then studied in an abbreviated time scale. A model is a schematic representation of the conception of a system or an act of mimicry or a set of equations, which represents the behaviour of a system. Also, a model is “A representation of an object, system or idea in some form other than that of the entity itself”. Its purpose is usually to aid in explaining, understanding or improving performance of a system. A model is, by definition “A simplified version of a part of reality, not a one to one copy”. This simplification makes models useful because it offers a comprehensive description of a problem situation. However, the simplification is, at the same time, the greatest drawback of the process. It is a difficult task to produce a comprehensible, operational representation of a part of reality, which grasps the essential elements and mechanisms of that real world system and even more demanding, when the complex systems encountered in environmental management. The Earth’s land resources are finite, whereas the number of people that the land must support continues to grow rapidly. This creates a major problem for agriculture. The production (productivity) must be increased to meet rapidly growing demands while natural resources must be protected. New agricultural research is needed to supply information to farmers, policy makers and other decision makers on how to accomplish sustainable agriculture over the wide variations in climate around the world. In this direction explanation and prediction of growth of managed and natural ecosystems in response to climate and soil-related factors are increasingly important as objectives of science. Quantitative prediction of complex systems, however, depends on integrating information through levels of organization, and the principal approach for that is through the construction of statistical and simulation models. Simulation of system’s use and balance of carbon, beginning with the input of carbon from canopy assimilation forms the essential core of most simulations that deal with the growth of vegetation. Systems are webs or cycles of interacting components. Change in one component of a system produces changes in other components because of the interactions. For example, a change in weather to warm and humid may lead to the more rapid development of a plant disease, a loss in yield of a crop, and consequent financial adversity for individual farmers and so for the people of a region. Most natural systems are complex. Many do not have boundaries. The bio-system is comprised of a complex interaction among the soil, the atmosphere, and the plants that live in it. A chance alteration of one element may yield both desirable and undesirable consequences. Minimizing the undesirable, while reaching the desired end result is the principle aim of the agrometerologist. In any engineering work related to
Modelling in Agricultural Systems Complexity of Agricultural Systems Agricultural systems are characterized by having many organizational levels. From the individual components within a single plant , through constituent plants, to farms or a whole agricultural region or nation, lies a whole range of agricultural systems. Since the core of agriculture is concerned with plants, the level that is of main interest to the agricultural modeller is the plant. Reactions and interactions at the level of tissues and organs are combined to form a picture of the plant that is then extrapolated to the crop and their output. Models in Agriculture Agricultural models are mathematical equations that represent the reactions that occur within the plant and the interactions between the plant and its environment. Owing to the complexity of the system and the incomplete status of present knowledge, it becomes impossible to completely represent the system in mathematical terms and hence, agricultural models images of the reality. Unlike in the fields of physics and engineering, universal models do not exist within the agricultural sector. Models are built for specific purposes and the level of complexity is accordingly adopted. Inevitably, different models are built for different subsystems and several models may be built to simulate a particular crop or a particular aspect of the production system. Features of Crop Models The main aim of constructing crop models is to obtain an estimate of the harvestable (economic) yield. According to the amount of data and knowledge that is available within a particular field, models with different levels of complexity are developed. The most pertinent aspects of crop models are described below. Empirical Model Empirical models are direct descriptions of observed data and are generally expressed as regression equations (with one or a few factors) and are used to estimate the final yield. Examples of such models include the response of crop yield to fertiliser application, the relationship between leaf area and leaf size in a given plant species. the limitation of this model site specific, it cannot use universally. Mechanistic Model A mechanistic model is one that describes the behaviour of the system in terms of lower- level attributes. Hence, there is some mechanism, understanding or explanation at the lower levels. These models have the ability to mimic relevant physical, chemical or biological processes and to describe how and why a particular response results. Static and Dynamic Models A static model is one that does not contain time as a variable even if the end-products of cropping systems are accumulated over time, e.g., the empirical models. In contrast dynamic models explicitly incorporate time as a variable and most dynamic models are first expressed as differential equations:
Deterministic and Stochastic Models A deterministic model is one that makes definite predictions for quantities (e.g., animal live weight, crop yield or rainfall) without any associated probability distribution, variance, or random element. However, variations due to inaccuracies in recorded data and to heterogeneity in the material being dealt with are inherent to biological and agricultural systems. In certain cases, deterministic models may be adequate despite these inherent variations but in others they might prove to be unsatisfactory e.g. in rainfall prediction. The greater the uncertainty in the system, the more inadequate deterministic models becomes and in contrast to this stochastic models appears. Simulation and Optimizing Models Simulation models form a group of models that is designed for the purpose of imitating the behaviour of a system. They are mechanistic and in the majority of cases they are deterministic. Since they are designed to mimic the system at short time intervals (daily time-step), the aspect of variability related to daily change in weather and soil conditions is integrated. The short simulation time-step demands that a large amount of input data (climate parameters, soil characteristics and crop parameters) be available for the model to run. These models usually offer the possibility of specifying management options and they can be used to investigate a wide range of management strategies at low costs. Most crop models that are used to estimate crop yield fall within this category. Optimizing models have the specific objective of devising the best option in terms of management inputs for practical operation of the system. For deriving solutions, they use decision rules that are consistent with some optimising algorithm. This forces some rigidity into their structure resulting in restrictions in representing stochastic and dynamic aspects of agricultural systems. Linear and non-linear programming were used initially at farm level for enterprise selection and resource allocation. Later, applications to assess long-term adjustments in agriculture, regional competition, transportation studies, integrated production and distribution systems as well as policy issues in the adoption of technology, industry re-structuring and natural resources have been developed. Optimising models do not allow the incorporation of many biological details and may be poor representations of reality. Using the simulation approach to identify a restricted set of management options that are then evaluated with the optimising models has been reported as a useful option. Some Crop Models reported in recent literature Software Details SLAM II Forage harvesting operation SPICE Whole plant water flow REALSOY Soyabean MODVEX Model development and validation system IRRIGATE Irrigation scheduling model COTTAM Cotton APSIM Modelling framework for a range of crops GWM General weed model in row crops
Model Validation The model validation stage involves the confirmation that the calibrated model closely represents the real situation. The procedure consists of a comparison of simulated output and observed data that have not been previously used in the calibration stage. Ideally, all mechanistic models should be validated both at the level of overall system output and at the level of internal components and processes. The latter is an important aspect because due to the occurrence of feedback loops in biological systems, good prediction of system's overall output could be attributed to compensating internal errors. However, validation of all the components is not possible due to lack of detailed datasets and the option of validating only the determinant ones is adopted. For example, in a soil-water-crop model, it is important to validate the extractable water and leaf area components since biomass accumulated is heavily dependent on these. The methodology of model validation is still rudimentary. The main reason is that, unlike the case of disciplinary experiments, a large set of hypotheses is being tested simultaneously in a model. Furthermore, biological and agricultural models are reflections of systems for which the behavior of some components is not fully understood and differences between model output and real systems cannot be fully accounted for. The validation of system simulation models at present is further complicated by the fact that field data are rarely so definite that validation can be conclusive. This results from the fact that model parameters and driving variables are derived from site-specific situations that ideally should be measurable and available. However, in practice, plant, soil and meteorological data are rarely precise and may come from nearby sites. At times, parameters that were not routinely measured may turn out to be important and they are then arbitrarily estimated. Measured parameters also vary due to inherent soil heterogeneity over relatively small distances and to variations arising from the effects of husbandry practices on soil properties. Crop data reflect soil heterogeneity as well as variation in environmental factors over the growing period. Finally, sampling errors also contribute to inaccuracies in the observed data. Validation procedures involve both qualitative and quantitative comparisons. Before starting the quantitative tests, it is advisable to qualitatively assess time-trends of simulated and observed data for both internal variables and systems outputs. Inadequate predictions of model outputs may require "re-fitting" of the regression curves or fine-tuning of one or more internal variables. This exercise should be undertaken with care because arbitrary changes may lead to changes in model structure that may limit the use of the model as a predictive tool. In some cases, it is best to seek more reliable data through further experimentation than embarking on extensive modification of model parameters to achieve an acceptable fit to doubtful data. This decision relies on the modeller's expertise and rigour as well as on human resources and time available to invest in fine-tuning model predictions. Model Uses and Limitation Models are developed by agricultural scientists but the user-group includes the latter as well as breeders, agronomists, extension workers, policy-makers and farmers. As different
users possess varying degrees of expertise in the modelling field, misuse of models may occur. Since crop models are not universal, the user has to choose the most appropriate model according to his objectives. Even when a judicious choice is made, it is important that aspects of model limitations be borne in mind such that modelling studies are put in the proper perspective and successful applications are achieved. Misperceptions and Limitations of Models Agricultural systems are characterised by high levels of interaction between the components that are not completely understood. Models are, therefore, crude representations of reality. Wherever knowledge is lacking, the modeller usually adopts a simplified equation to describe an extensive subsystem. Simplifications are adopted according to the model purpose and / or the developer's views, and therefore constitute some degree of subjectivity. Models that do not result from strong interdisciplinary collaboration are often good in the area of the developer's expertise but are weak in other areas. Model quality is related to the quality of scientific data used in model development, calibration and validation. When a model is applied in a new situation (e.g., switching a new variety ), the calibration and validation steps are crucial for correct simulations. The need for model verification arises because all processes are not fully understood and even the best mechanistic model still contains some empirism making parameter adjustments vital in a new situation. Model performance is limited to the quality of input data. It is common in cropping systems to have large volumes of data relating to the above-ground crop growth and development, but data relating to root growth and soil characteristics are generally not as extensive. Using approximations may lead to erroneous results. Most simulation models require that meteorological data be reliable and complete. Meteorological sites may not fully represent the weather at a chosen location. In some cases, data may be available for only one (usually rainfall) or a few (rainfall and temperature) parameters but data for solar radiation, which is important in the estimation of photosynthesis and biomass accumulation, may not be available. In such cases, the user would rely on generated data. At times, records may be incomplete and gaps have to be filled. Using approximations would have an impact on model performance. Model users need to understand the structure of the chosen model, its assumptions, its limitations and its requirements before any application is initiated, e.g, using a model like QCANE, developed for cane growth under non-limiting conditions, would lead to erroneous output and analysis if it is used to simulate under water or nitrogen stress conditions. At times, model developers may raise the expectations of model users beyond model capabilities. Users, therefore, need to judiciously assess model capabilities and limitations before it is adopted for application and decision-making purposes. Generally, crop models are developed by crop scientists and if interdisciplinary collaboration is not strong, the coding may not be well-structured and model documentation may be poor. This makes alteration and adaptation to simulate new situations difficult, specially for users with limited expertise. Finally, using a model for an objective for which it had not been designed or
Yield Analysis : When a model with a sound physiological background is adopted, it is possible to extrapolate to other environments. The use of several simulation models to assess climatically-determined yield in various crops. The CANEGRO model has been used along the same lines in the South African sugar industry. Through the modelling approach, quantification of yield reductions caused by non-climatic causes (e.g., delayed sowing, soil fertility, pests and diseases) becomes possible. Almost all simulation models have been used for such purposes. Simulation models have also been reported as useful in separating yield gain into components due to changing weather trends, genetic improvements and improved technology. As Crop System Management Tools Cultural and Input Management : Management decisions regarding cultural practices and inputs have a major impact on yield. Simulation models, that allow the specification of management options, offer a relatively inexpensive means of evaluating a large number of strategies that would rapidly become too expensive if the traditional experimentation approach were to be adopted. Many publications are available describing the use of simulation models with respect to cultural management (planting and harvest date, irrigation, spacing, selection of variety type) and input application (water and fertiliser). Risks Assessment and Investment Support : Using a combination of simulated yields and gross margins, economic risks and weather- related variability can be assessed. These data can then be used as an investment decision support tool. Site-specific Farming : Profit maximisation may be achieved by managing farms as sets of sub-units and providing the required inputs at the optimum level to match variation in soil properties across the farm. Such an endeavour is attainable by coupling simulation models with geographic information systems (GIS) to produce maps of predicted yield over the farm. But, one of the prerequisites is a systematic characterisation of units that may prove costly. As Policy Analysis Tools Best Management Practices : Models having chemical leaching or erosion components can be used to determine the best practices over the long-term. The EPIC model has been used to evaluate erosion risks due to cropping practices and tillage. Yield Forecasting : Yield forecasting for industries over large areas is important to the producer (harvesting and transport), the processing agent (milling period) as well as the marketing agency. The technique uses weather records together with forecast data to estimate yield across the industry.
Introduction of a New Crop : Agricultural research is linked to the prevailing cropping system in a particular region. Hence, data concerning the growth and development of a new crop in that region would be lacking. Developing a simulation model based on scientific data collected elsewhere and a few datasets collected in the new environment helps in the assessment of temporal variability in yield using long-term climatic data. Running the simulations with meteorological data in a balanced network of locations also helps in locating the industry. Global Climate Change and Crop Production : Increased levels of CO 2 and other greenhouse gases are contributing to global warming with associated changes in rainfall pattern. Assessing the effects of these changes on crop yield is important at the producer as well as at the government level for planning purposes. Crop/ Soil Simulation Models basically applied in three sections (1) Tools for research (2) Tools for decision-making and (3) Tools for education, training and technology-transfer The greatest use of crop/ soil models so far has been by the research community, as models are primarily tools for organizing knowledge gained in experimentation. However, there is an urgent need to make the use of models in research more relevant to problems in the real world, and find effective means of dissemination of results from work using models to potential beneficiaries. Nevertheless, crop models can be used for a wide range of applications. As research tools, model development and application can contribute to identify gaps in our knowledge, thus enabling more efficient and targeted research planning. Models that are based on sound physiological data are capable of supporting extrapolation to alternative cropping cycles and locations, thus permitting the quantification of temporal and spatial variability. Over a relatively short time span and at comparatively low costs, the modeller can investigate a large number of management strategies that would not be possible using traditional methodologies. Despite some limitations, the modelling approach remains the best means of assessing the effects of future global climate change, thus helping in the formulation of national policies for mitigation purposes. Other policy issues, like yield forecasting, industry planning, operations management, consequences of management decisions on environmental issues, are also well supported by modelling.
Sowing of seeds: Seeds used for sowing should be of good quality, healthy, viable and free of infections. Seeds are sown manually by broadcasting or by seed drills. Broadcasting is the scattering of seeds over the soil surface by hand. Addition of manure and fertilizers: Plants require nutrients for growth. They get these nutrients from the soil. This can be done either by natural methods or by adding manures and fertilizers to the soil. Natural methods: Field fallow: The method of leaving the field without cultivating any crops to replenish nutrients in the soil. Crop rotation: It involves growing two or more crops alternatively on the same land in the same growing season so that the soil is not depleted of any particular nutrients. Differences between manures and fertilizers: Manures Fertilizers These are natural organic substances that are derived from animal wastes and plant residues. These are inorganic salts made by humans. These are rich in humus but not in inorganic nutrients. These are rich in inorganic nutrients but do not contain humus. They are quite bulky and difficult to transfer. They are less bulky and easy to handle. Irrigation: Irrigation is the artificial supply of water to farms when needed. Some of the modern irrigation methods are as follows: Sprinkler system Drip irrigation Protection from weed and pests: Weeding: Weeds are unwanted plants that grow along with the crops. They compete with the crops for water, minerals and sunlight and, therefore reduce crop yield. Amaranthus is very common weed which grow with almost every crop. Weeding can be done manully using a trowel or a harrow or by using a seed drill using certain chemicals called weedicides for example- 2,4-D. some common weedicides are Dalapon, Siniazine and Picloram. Pests: Insects that attack crops and damage them are called pests. Pests can be controlled by pesticides which are poisonous chemicals. Pesticides kill pests as well as their eggs and larvae but do not affect the plants.
Harvesting: Harvesting is the cutting and gathering of the mature crop from the fields. Threshing is the process of removal of the edible part of grain from the scaly, inedible chaff that surrounds it. Combine harvester is a farm machine which does both harvesting as well as threshing. Wind winnowing is a method of separating grain from chaff by throwing the mixture into the air with a winnowing fan. Storage: Large scale storage of grains is done in granaries or silos to protect them from pests like rodents, microbes or insects. Increasing crop produce: Crop produce can be increased by increasing the land under cultivation, by improvement in the methods of agriculture, and by developing better varieties of crops by plant breeding. Hybridization is a technique used for plant breeding in which new varieties with desired characteristics of high yield and resistance to disease, are developed. Nitrogen cycle: Air contains about 78% nitrogen. Nitrogen is used by life forms for the formation of protein, amino acids and nucleic acids. The cyclic process of nitrogen being fixed, used by plants and animals and later returned to the atmosphere is referred to as the nitrogen cycle. Nitrogen cycle involves the following steps: Nitrogen fixation: fixing free nitrogen gas of the atmosphere into inorganic compounds by organism such as Rhizobium. Nitrogen assimilation: converting inorganic nitrogen into usable organic compounds in organisms. Ammonification: Conversion of organic nitrogen into ammonia. Nitrification: Ammonia is converted into nitrates in the soil with the help of bacteria. Denitrification: Conversion of nitrates into nitrogen gas by denitrifying bacteria. Animal husbandry: The breeding, feeding and caring of domestic animals for food and other purposes is called animal husbandry. Meat or egg yielding animals such as goat, poultry animals (e.g. chicken, duck and trkey), fish, sheep. Milch or (milk yielding) animals such as cow, buffalo, goat and camel. Large scale rearing of fish for food is known as pisciculture. Large scale rearing of honeybee is known as apiculture.
- Dynamic model:These models predict changes in crop status with time.
- Deterministic model: In which a definite output is given e.g. NPK doses are applied and the definite yields are given out.
- Stochastic model: The models are based on the probability of occurrence of some event or external variable. Probabilities are given out. The statistical techniques used in designing the models are as follows:
- Simple regression analysis
- Simple correlation technique.
- Curvilinear correlations techniques
- Multiple regression analysis
- Stepwise regression analysis
- Fishers orthogonal polynomial techniques
- Mallow’s Cp techniques.
- Marko cham model. Basic types of crop weather models as follows:
- Crop growth simulation models
- Crop weather analysis models
- Empirical statistical model
Crop Simulation Model and Terms Photosynthesis: The process by which green plants and some other organisms use sunlight to synthesize nutrients from carbon dioxide and water. or The process by which a green plant turns water and carbon dioxide into food when the plant is exposed to light. Photosynthesis in plants generally involves the green pigment chlorophyll and generates oxygen as a by-product. Definition : Use by green plants of the energy in sunlight to carry out chemical reactions, such as the conversion of carbon dioxide into oxygen. Photosynthesis also produces the sugars that feed the plant. Note: Green plants depend on chlorophyll to carry out photosynthesis. Facts about Photosynthesis Photosynthesis uses sunlight, carbon dioxide and water to produce oxygen, glucose and water. The structure of the leaf allows for carbon dioxide and oxygen to enter and leave the leaf, which is where photosynthesis actually takes place. The water from the leaves evaporates through the stomata, and filling its place, entering the stomata from the air, is carbon dioxide. Plants need carbon dioxide to make food. The water given off cools a plant on a hot, sunny day, similar to the way human beings cool off when perspiring. A mature house plant can transpire its body weight daily. The roots of the plant provide the water that is required for the process to proceed and the chlorophyll in the cells of the leaf absorbs the necessary sunlight. Photosynthesis is not limited to green plants, it is also a process that occurs in certain algae, specifically blue-green algae and bacteria. An example of photosynthesis is how plants convert sugar and energy from water, air and sunlight into energy to grow.
- the biological synthesis of chemical compounds in the presence of light
- the production of organic substances, chiefly sugars, from carbon dioxide and water occurring in green plant cells supplied with enough light to allow chlorophyll to aid in the transformation of the radiant energy into a chemical form Photosynthesis A. sunlight is absorbed by chlorophyll in a leaf B. carbon dioxide is taken in from the air C. water is transported from the roots D. glucose is synthesized and distributed throughout the plant E. oxygen is released Respiration: Respiration is taking a breath or the act of breathing.
- act or process of respiring; breathing; inhaling and exhaling air
