Dynamic Crop Models Research Articles

Modelling the Growth and Yield of Tea (Camellia sinensis L.) in Sri Lanka

For modelling the growth and yield of tea (Camellia smensis L.) a dynamic crop model (MACROS). written in the CSMP (Continuous Simulation Modelling Program) language was adapted Being a general crop model for annual crops, it was adjusted to the tea crop and validated for a certain clone. For model input parameters, an intensive monitoring of the relevant meteorological parameters together with short term observations of critical plant parameters was done for 2 years The MACROS model is based on source limned crop growth, commonly used for annual crops Because of the special case of the tea production (regular) plucking of shoots and leaves) the growth of the tea plant is sink limited under more or less favourable conditions. Therefore further algorithms for the growth of tea leaves were added to the model concerns the flushing cycle of shoots and the strong temperature effect on shoot growth. Validation was done for a location in Sri Lanka including the effect of water stress. The results show the general ability of the adapted model named TEACROS to predict the actual yield. Nevertheless further investigations and adaptations are necessary for a better sensitivity of the simulation to fast changes of growth conditions and their impact on the flushing cycle of shoots as well as validation of the model for different climates and clones

Testing Denitrification Functions of Dynamic Crop Models

AbstractDenitrification (DN) has an important role in models simulating soil nitrate leaching, because denitrification losses influence the soil N budget, hence the amount of nitrate available for leaching. In spite of the importance of DN estimate, the behavior of submodels simulating DN losses is not well known. The aim of this study was to improve the knowledge of the behavior of submodels simulating DN losses in selected crop growth/N‐leaching models. To remove the interference of the other processes on the results, we isolated the algorithms predicting the denitrification rate (DNR) and applied them to three data sets selected from the available literature. Denitrification subroutines were taken from the models EPIC, CropSyst, CREAMS, GLEAMS, CERES‐N, and NLEAP. A preliminary sensitivity analysis emphasized how quite similar algorithms can give rise to considerably different predictions. The measured vs. predicted values comparison confirmed DNR to be remarkably affected by the soil water content (SW) relationship with soil water values at field capacity and/or at saturation. CERES‐N, CREAMS, GLEAMS, and EPIC submodels underestimated even up to 100% the number of occasions where DNR > 0, since they simulated the DN process only when SW values were higher than SW at field capacity, whereas in real systems DN occurs even at lower SW values. Results suggest that DN modeling should take into greater account the contribution to DN losses during aerobic conditions. The large natural variation of DNR measurements invalidates the use of statistical criteria for the comparison of measured vs. estimated DN values in deterministic models.

Impacts of global climate change on Mediterranean agrigulture: Current methodologies and future directions

Current trends in Mediterranean agriculture reveal differences between the Northern and Southern Mediterranean countries as related to population growth, land and water use, and food supply and demand. The changes in temperature and precipitation predicted by general circulation models for the Mediterranean region will affect water availability and resource management, critically shaping the patterns of future crop production. Three companion papers analyze in detail future impacts of predicted climate change on wheat (Triticum aestivum L.) and maize (Zea mays L.) production in Spain, Greece, and Egypt, and test farm- level adaptation strategies such as early planting and cultivar change with the aid of dynamic crop models. Strategies to improve the assessment of the potential effects of future climate change on agricultural production are discussed.

Impacts of Global Climate Change on Mediterranean Agrigulture: Current Methodologies and Future Directions. An Introductory Essay

Current trends in Mediterranean agriculture reveal differences between the Northern and Southern Mediterranean countries as related to population growth, land and water use, and food supply and demand. The changes in temperature and precipitation predicted by general circulation models for the Mediterranean region will affect water availability and resource management, critically shaping the patterns of future crop production. Three companion papers analyze in detail future impacts of predicted climate change on wheat (Triticum aestivum L.) and maize (Zea mays L.) production in Spain, Greece, and Egypt, and test farm- level adaptation strategies such as early planting and cultivar change with the aid of dynamic crop models. Strategies to improve the assessment of the potential effects of future climate change on agricultural production are discussed.

Impacts of Future Climate Change Effects on Spring Barley and Maize Yield in Slovenia

Slovenia is a mid-latitude country, where future climatic changes are expected to be significant. So, a dynamic crop model AGROS was used to examine combined effects of increasing CO2 and higher temperatures on spring barley and maize yields in Slovenia. Model simulates water-limited production, but assumes healthy plants and appropriate nutrition. Simulations were carried out for various combinations of increased temperature (by 1, 2, 3, 4°C) and CO2 concentrations (350, 450 and 700ppm). The model predicted shorter growing period due to warming for both crops. In spring barley the total dry matter and grain yield increase due to increasing CO2, but if temperature rise exceeds 2.7°C, total dry matter and yield decrease. In maize the total dry matter, grain yield and harvest index generally increase.

A demographic model of assimilation and allocation of carbon and nitrogen in grapevines

A dynamic crop model is presented for grapevine ( Vitis vinifera L.) dry matter and nitrogen (N) assimilation and allocation with distributed developmental times and age structures of the plant subunits. The model forms a basis for future analyses in the vineyard ecosystem. A flowchart of the daily computations is presented and the corresponding mathematical structure is described. In the model, the plant is divided into annual populations of fruit, leaves, shoots and roots which develop on a perennial frame. In general, these populations are age-structured and have the attributes of numbers, dry matter and N mass, and their dynamics are simulated as a time-invariant distributed delay process with attrition. Growth occurs per degree-day above the developmental threshold of 10°C. The seasonal N dynamics is the net result of the processes of new tissue formation with high N concentrations and the constant proportional export of N from ageing parts to reserves. The latter process commences immediately after tissue formation. The mean concentration of N in a population of plant subunits is determined by the ratio between young N-rich zones and ageing N-exporting tissues. Simulations were used to assess the patterns and magnitudes of photoassimilate allocation to the three sinks: maintenance respiration, reproductive growth and vegetative growth. In summer, roughly one-third of the assimilate available daily is allocated to each.

Phyllochron Change in Winter Wheat with Planting Date and Environmental Changes

AbstractThe phyllochron (degree‐days/leaf) of wheat (Triticum aestivum L.) differs with planting dates and latitudes. An understanding of the causes for these differences is needed to model the development of wheat crop canopies. Stephens and Yamhill winter wheat cultivars were planted on a range of dates in outdoor pots and in field plantings to study the relationship of phyllochron to the daylength and mean air temperature at seedling emergence. Both cultivars had a nearly constant phyllochron for a given planting in both pot and field plantings until 600 cumulated degree‐days (CDD), which was about the time of double ridge formation. Phyllochron values were different, however, for the different planting dates. At about 600 CDD the phyllochron increased in 18 of 20 cultivar‐planting date combinations. Fifty to 60% of the variation in phyllochron prior to double ridge formation could be normalized by either the thermo/photo ratio at seedling emergence (mean temperature to photoperiod) or by the rate of change of photoperiod. These two methods have been suggested previously to normalize phyllochron for differences due to different planting dates. After 600 CDD the phyllochron was not related to thermo/photo ratio, the rate of change of photoperiod, maximum or minimum temperatures, photoperiod or its inverse, or the intensity of solar radiation. Thus, use of a constant value for phyllochron in dynamic crop models of wheat leaf development is not appropriate for all circumstances.

SIMULATION FOR DETERMINING GREENHOUSE TEMPERATURE SETPOINTS

ABSTRACT Two separately developed simulation models were linked and used to evaluate different environmental control strategies in Florida tomato production greenhouses. POLY-2 is a model of a double poly, quonset-style greenhouse typical of Florida. It is a dynamic model that realistically simulates environmental control equipment actions. TOMGRO is a dynamic crop model that simulates 1) growth, 2) development, and 3) quantity and timing of yield of tomatoes. Both models are based on independent empirical data sets used for calibration and validation, respectively. The two models were linked by incorporating POLY-2 into TOMGRO as a sub-routine. Historical weather data for Tallahassee, Rorida and Raleigh, North Carolina are used by the POLY-2 subroutine to simulate greenhouse environmental conditions which are used in turn by TOMGRO to simulate development and growth of the tomato crop. During simulation runs POLY-2 keeps track of heating fuel requirements and TOMGRO keeps track of tomato yield. Simulations over a range of setpoints showed that the optimal setpoint depends directly on the price of fuel, the value of the tomatoes, and location.

A review of methods for evaluating the economic efficiency of irrigation

This paper reviews methods for evaluating irrigation efficiency. Static production function models which do not consider the timing of water applications are contrasted with dynamic crop models and methods for optimizing the timing of irrigation. Methods for considering irrigator risk preferences in evaluating irrigation strategies are covered. Different approaches to the problem of allocating a limited amount of water optimally among competing crops and/or competing units of the same crop are examined. Methods of determining the value of information as a way of improving irrigation efficiency are dealt with also. The paper points out advantages of the economic approach. These advantages include the fact that the methods deal with the dynamic, uncertain environment in which irrigation occurs. Also, the methods recognize the importance of managers' goals such as profit maximization and risk minimization as well as the impact of limited information on the attainment of these goals. The paper draws conclusions on ways in which the methods could be improved.