Growth Simulation Model Research Articles

The time and location dependent prediction of crystal caking by a modified crystal bridge growth model and DEM simulation considering particle size and shape

Unequal crystal size and anisotropic shape lead to non-homogeneous caking which fails to be investigated by conventional caking testers. The simulation method in this work considers crystal size and shape and achieves the prediction of non-homogeneous caking from single caking bridge at each contact point to the entire caking bridge network. It develops a modified model for predicting single bridge growth with higher precision by considering the mass transfer of bridge dissolution and recrystallization. The non-homogeneous bridge networks are then constructed based on DEM (Discrete Element Method) simulation with crystal shapes represented by the multiple spheres method. The outputs include the time and location dependent mechanical, morphological and statistical indices. Furthermore, a full-scale anti-caking design system based on the simulation method is discussed. Parameters including particle size, particle shape, anti-caking agent, impurity, polymorph, temperature, humidity, packing, and breakage operation can be fully designed achieving anti-caking by the system.

Recrystallization and Grain Growth Simulations for Multiple-Pass Rolling and Annealing of U-10Mo

A simulation model of recrystallization and grain growth has been developed to investigate grain structure evolution during deformation and heat treatment in polycrystalline U-10 wt pct Mo (U-10Mo) fuel. Experimentally obtained U-10Mo post-homogenization microstructures were used as input for closed-loop simulations of multiple rolling passes, intermediate heating, and final annealing. Finite element model calculations of deformation and Potts model simulations of recrystallization and grain growth were used to iteratively inform each subsequent stage of simulation. The model was then applied to predict the grain structure evolution during multiple-pass hot rolling and annealing of U-10Mo and benchmarked against experimentally observed U-10Mo recrystallization behavior. The results showed that our model was able to capture the coupling between deformation and recrystallization as a function of microstructure, including particle stimulated nucleation and recrystallization nucleation on grain boundaries. Additionally, we have achieved reasonable quantitative agreement with U-10Mo recrystallization and grain growth behavior.

Application of the material inhomogeneity effect for the improvement of fracture toughness of a brittle polymer

In a multilayered structure with a crack, a spatial change in the mechanical properties of the material strongly influences the crack driving force. This material inhomogeneity effect can be utilized to improve the fracture toughness of a given structure by inserting thin, soft interlayers into the material. The effectiveness of this procedure has been demonstrated on high-strength materials, such as metallic alloys and ceramics. It is shown in this article that the material inhomogeneity effect can be also successfully applied to polymers and that it is possible to predict the improvement in fracture toughness by a numerical analysis. First, a numerical case study based on the configurational force concept is performed on a brittle polymer matrix with interlayers made of materials with different strength and Young’s modulus. After selecting the most appropriate interlayer material, a composite is fabricated, which contains a single interlayer. Fracture toughness experiments show approximately 7 times higher fracture toughness for the composite in comparison to the homogeneous matrix material. Numerical fracture mechanics tests are performed on homogeneous and composite material using the cohesive zone model for crack growth simulation. A procedure to calibrate the cohesive zone parameters is worked out, which is relatively easy for the homogeneous material, but more sophisticated for the composite material. The numerical analysis provides a tool for predicting the fracture toughness of multilayered polymer composites.

Assessment of water productivity and supply:demand ratio index in sugar beet (Beta vulgaris L.) in Khorasan Razavi province, Iran using growth simulation models

Assessment of water productivity and supply:demand ratio index in sugar beet (Beta vulgaris L.) in Khorasan Razavi province, Iran using growth simulation models

A narrow size diameter class model for tree growth and yield simulation in a mahoe (Talipariti elatum (SW.) Fryxell, Malvaceae) plantation in Puerto Rico

Forest management planning requires forest growth models that provide a reliable way to forecast growth, calculate yield, and examine the effects of silviculture. Here we used a size stem frequency approach based on differential equations to developed a growth simulator system to guide the management of mahoe ( Talipariti elatum (SW.) Fryxell) in a small plantation in Puerto Rico. We estimated the optimal harvest age using the Faustmann model, and evaluated alternative harvesting schemes using sensitivity analyses. The growth simulator system predicted that tree volume at first rises quickly, reaches a maximum value, and then decreases because of a lack of natural regeneration in the plantation. Thus, harvesting must be followed by replanting and large and healthy trees must be maintained as seed sources. The optimal harvest age was 43 years for a discount rate of 2.5% and 19 years for 5% interest. Analysis for alternative management schemes based on selective harvesting of the largest tress showed that 5%, 15%, or 30% of the trees ≥54.5 cm dbh could be harvested in cycles of 1, 5 or 10 years respectively, without drastically decreasing the basal area. The sustainability of these management schemes will depend on the costs of management, as well as the responses of mahoe to selective harvesting.

Effects of shift in growing season due to climate change on rice yield and crop water requirements

According to recent climate projections for South Korea, increases in temperature and precipitation will affect water use and crop production associated with paddy rice cultivation. Impacts are expected on crop growing season, crop varieties, and farming methods, all of which are highly vulnerable to disruption in weather conditions. Previous studies have estimated the effects on agricultural water resources according to climate change scenario, but most do not consider the growing season. In this study, changes in paddy rice yield and agricultural water requirement are estimated based on the impact of shift in growing season using a crop growth model. Future weather trends for four selected study areas in central and southern regions of South Korea were constructed using the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios developed by the UN Intergovernmental Panel on Climate Change, and the paddy rice yields were simulated using the Crop Environment Resource Synthesis–Rice growth simulation model. Weather, soil, crop data, cultivars, and experimental data were used as model inputs. The results show a delay in the optimal transplanting date in 2100, with the date in the RCP 8.5 scenario delayed rapidly compared with the delay in RCP 4.5. In addition, paddy rice yield and growing period decrease. Consumptive water use and irrigation requirements decrease in the RCP 4.5 scenario due to increased rainfall. However, in RCP 8.5, less precipitation falls during the growing period, leading to an increase in irrigation requirements in some regions. These results are expected to be useful for paddy water management and growing season adjustment and to help establish long-term food security policies.

Response of Four Tree Species to Changing Climate in a Moisture-Limited Area of South Siberia

The response of vegetation to climate change is of special interest in regions where rapid warming is coupled with moisture deficit. This raises the question of the limits in plants’ acclimation ability and the consequent shifts of the vegetation cover. Radial growth dynamics and climatic response were studied in Scots pine (Pinus sylvestris L.), Siberian larch (Larix sibirica Ledeb.), and silver birch (Betula pendula Roth.) in the forest-steppe, and for Siberian elm (Ulmus pumila L.) in the steppe of South Siberia, as indicators of vegetation state and dynamics. Climate–growth relationships were analyzed by the following two approaches: (1) correlations between tree-ring width chronologies and short-term moving climatic series, and (2) optimization of the parameters of the Vaganov–Shashkin tree growth simulation model to assess the ecophysiological characteristics of species. Regional warming was accompanied by a slower increase of the average moisture deficit, but not in the severity of droughts. In the forest-steppe, the trees demonstrated stable growth and responded to the May–July climate. In the steppe, elm was limited by moisture deficit in May–beginning of June, during the peak water deficit. The forest-steppe stands were apparently acclimated successfully to the current climatic trends. It seems that elm was able to counter the water deficit, likely through its capacity to regulate transpiration by the stomatal morphology and xylem structure, using most of the stem as a water reservoir; earlier onset; and high growth rate, and these physiological traits may provide advantages to this species, leading to its expansion in steppes.

Morphology Evolution during Lithium-Based Vertically Aligned Nanocomposite Growth.

Ceramic-based nanocomposites are a rapidly evolving research area as they are currently being used in a wide range of applications. Epitaxial vertically aligned nanocomposites (VANs) offer promising advantages over conventional planar multilayers as key functionalities are tailored by the strong coupling at their vertical interfaces. However, limited knowledge exists of which material systems are compatible in composite films and which types of structures are optimal for a given functionality. No lithium-based VANs have yet been explored for energy storage, while 3D solid-state batteries offer great promise for enhanced energy and power densities. Although solid-on-solid kinetic Monte Carlo simulation (KMCS) models of VAN growth have previously been developed, phase separation was forced into the systems by limiting hopping directions and/or tuning the activation energies for hopping. Here, we study the influence of the temperature and deposition rate on the morphology evolution of lithium-based VANs, consisting of a promising LiMn2O4 cathode and a Li0.5La0.5TiO3 electrolyte, by applying a KMCS model with activation energies for hopping obtained experimentally and with minimum restrictions for hopping directions. Although the model considers only the kinetic processes away from thermodynamic equilibrium, which would determine the final shape of the pillars within the matrix, the trends in pillar size and distribution within the simulated VANs are in good agreement with experiments. This provides an elegant tool to predict the growth of VAN materials as the experimental activation energies and higher degrees of freedom for hopping result in a more realistic and low computational cost model to obtain accurate simulations of VAN materials.

Cardinal temperatures and modeling of vegetative development in guava

ABSTRACT The aim of this study was to estimate cardinal temperatures (base, optimum, and maximum), and simulate the vegetative development of guava crop through phyllochron (linear) and Wang and Engel (nonlinear) models at the seedling phase. The experiment was conducted in Itajubá, MG, Brazil (22° 24’ 46.43” S 45° 26’ 48.94” W), for two years in a completely randomized design with twelve sowing dates and five experimental units per treatment, totaling sixty experimental units. The cardinal temperatures were estimated using twelve methods described in the literature and the values were: base, 10.9 °C, optimum, 17.3 °C and maximum, 51.2 °C. The phyllochron model was superior to Wang and Engel’s model for simulation of vegetative growth of guava, with root mean square error of 1.5 leaves.

A Predictive Risk Model Analysis of the Potato Cyst Nematode Globodera pallida in Idaho.

Globodera pallida is a major nematode pest of potato (Solanum tuberosum) and is of great economic importance for the potato industry. Assessing potato yield loss caused by the Idaho G. pallida population under field conditions was not performed due to its quarantine status in Idaho, where it is prohibited by regulatory statutes to grow potato in any infested fields. The experimental data came from three trials that were conducted under greenhouse conditions. A predictive risk model analysis was performed to: (i) determine the effect of the Idaho population of G. pallida on potato yield; (ii) estimate reproduction rate from different initial nematode densities; and (iii) simulate potato yield losses in Idaho field conditions by integrating the coefficients of potato yield into the SUBSTOR-DSSAT crop simulation model. Experiments were conducted under greenhouse conditions using five initial G. pallida soil infestation levels (0, 10, 20, 40, and 80 eggs/g soil). The coefficients of potato yield achieved under each initial nematode density were integrated into the SUBSTOR-DSSAT potato growth simulation model. The model showed that tuber weight reached a maximum yield of 96 ton/ha in noninfested soil. Based on the greenhouse trials, the model predicted a minimum yield of 12 and 58 ton/ha in trial 1 and trial 2/3 respectively, when initial nematode density was 80 eggs/g soil. In trial 1, tuber weight was significantly reduced by 44% at 40 eggs/g soil and by 87% at 80 eggs/g soil, and 20% at 40 eggs/g soil and by 39% at 80 eggs/g soil in trial 2/3. The outputs of this study should facilitate common understanding between regulators, policymakers, and potato growers on the challenges and opportunities for controlling this economically important pest in Idaho.

Monitoring of Urban Growth with Improved Model Accuracy by Statistical Methods

While the rural population is decreasing day by day, the urban population is increasing rapidly. Urban growth, which occurs as a result of this increase, is sprawling toward natural and environmental areas in urban fringes, and constitutes the main source of many environmental, physical, social, and economic problems. In order to overcome these problems, the direction and rate of urban growth should be determined with simulation models. In this context, many urban growth models have been developed since the 1990s; the SLEUTH urban growth model is one of the most popular among them and has been used in many projects around the world. The brute force calibration process in which the best fit values of growth coefficients are determined is the most important stage of simulation models. The coefficient ranges are initially defined as being between 0 and 100 and are then narrowed in this step according to 13 separate regression scores, which are used to specify the characterization of urban growth. Consensus has not yet been reached as to which metrics should be used for calculating the best fit values, but the Lee–Sallee and Optimum SLEUTH Metric (OSM) methods have been mostly used in past studies. However, in rapidly growing study areas, these methods cannot truly explain urban growth properties. The main purpose of this paper is to precisely calibrate urban growth simulation models. Therefore, Exploratory Factor Analysis (EFA) was used to calculate the growth coefficients, as a new statistical approach for calibration, in this study. The district of Sancaktepe, Istanbul, which experienced population growth of 80% between 2008 and 2018, was selected as the study area in order to test the achievement of the EFA method, and two urban growth simulation models were generated for the years 2030 and 2050. According to the results, despite the fact that there is little effect of urban growth in the short term, more than 70% of forests and agricultural lands are at risk of urbanization by 2050.

Exploring process-level genotypic and environmental effects on sugarcane yield using an international experimental dataset

Crop improvement aims to produce high yielding genotypes for target environments. Crop models simulate yield formation as the outcome of a series of low-level processes, driven by environmental (E) variables and regulated by genetic (G) traits. There is potential for crop models to aid sugarcane breeding, by identifying desirable genetic traits for target environments. The objective of this study was to evaluate existing concepts of G and E control of plant processes for explaining crop development, growth and yield, using an international growth analysis dataset.Crop development, growth and yield were monitored in the plant and 1st ratoon crops for seven cultivars (N41, R570, CP88-1762, HoCP96-540, Q183, ZN7 and NCo376) grown under well-watered conditions at La Mare (Reunion Island, France), Pongola (South Africa (RSA), Chiredzi (Zimbabwe), and Belle Glade (Florida, USA). Weather data were collected and environmental conditions characterized for each experiment. Derived process-level phenotypic parameters, based on concepts from four sugarcane growth simulation models (DSSAT-Canegro, Mosicas, APSIM-Sugar and Canesim), were calculated from observations and used to (1) evaluate current understanding of E drivers of sugarcane growth and development processes, and (2) identify and quantify G control at a process level.Final yields showed significant E and GxE variation; dry above-ground biomass and stalk yields were highest in La Mare and lowest in Pongola. Cultivar rankings in stalk dry mass for the common cultivars (N41, R570, CP88-1762) varied significantly between Es. Significant E variation in phenotypic parameters describing germination, tillering and timing of the onset of stalk growth (OSG) revealed shortcomings in the underlying simulation concepts. Significant G variation was found for germination rate, leaf appearance rate and canopy development rate per unit thermal time (TT), and maximum radiation use efficiency, indicating strong G control of the associated underlying processes.Solar radiation was found to influence tillering rate per unit TT, and TT to OSG, challenging the current theory of TT as the sole driver of these processes.By explaining more of the E variation, more stable and accurate G-specific model parameters can be defined and evaluated. This is anticipated to lead to less GxE confounding of modelled processes, and hence crop models that are better-equipped for supporting sugarcane crop improvement.

Simulation model for cucumber growth and development in sunlight greenhouse

The dynamic simulation of cucumber growth and development in sunlight greenhouse can provide technical support for the intelligent management of cucumber production. According to the cucumber response characteristics to light and temperature, the cucumber development module based on the algorithm of clock model was established by using data from four-stage experiment with 'Jinyou 35' as experiment variety in two years. Based on the relationship between the leaf growth and key meteorological factors (temperature and radiation), leaf area index (LAI) module was established with the accumulated product of thermal effectiveness and photosynthetically active radiation (TEP) as independent variables. The simulation module of cucumber dry matter production was established by taking into consideration the double integral of LAI and daily length in photosynthesis per unit leaf area as well as the respiratory expenditure of different organs. Combined with water content of organs, fresh weight simulation module of cucumber organs was constructed. The whole cucumber development and growth simulation model in greenhouse was built based on each sub-module. The model parameters were calibrated and determined. The results showed that root mean square error (RMSE) of simulated values and observed values of four deve-lopment stages (from transplanting date to stretch tendril, to initial flowering, to early harvested and to uprooting), was 3.9-10.5 d. The normalized root mean square error (nRMSE) was 6.5%-28.6%. The coincidence index (D) was 0.79-0.97. The relationship between LAI and TEP was the regression of 'S' type curve. The RMSE of simulated and observed LAI values was 0.19. The nRMSE was 17.2%. The D value was 0.90. The RMSE of dry weight of root, stem, leaf, flower and fruit of the simulated values and observed values were 0.39-8.94 g·m-2. The nRMSE were 10.9%-17.7%. The D values were all above 0.98. The growth and development model of cucumber could accurately simulate the key development period of cucumber, leaf area and the dry and fresh weight of various organs and quantify the growth and development of cucumber in sunlight greenhouse.

Growth, phenology and yield modeling for wheat-fallow cropping system in Gujarat

CropSyst is a daily time step crop growth simulation model that can be used to study the effect of cropping systems management on crop productivity and environment. A total five years experimental field observations (2009-10 to 2013-14) comprising four sowing dates, viz. November 1, 15, 30 and Dec 15 for wheat-fallow crop rotations were used to simulate the growth, phenology and yield for wheat cultivar GW 322 at Anand (Gujarat). Among the five years observations, initial two years observations (2009-10 and 2010-11) were used for model parameterization and remaining year’s observations (2011-12 to 2013-14) were used for model performance evaluation. The results indicated that the model was able to predict the wheat phenology precisely in terms of mean bias error (MBE) and root mean square error (RMSE) which was less than 6 days for all the phenological stages except maturity date on Dec 15 sowing. Nov 15 sowing has resulted in maximum grain and biological yield and found to be optimum date for sowing followed by Nov 30 for this region while Dec 15 resulted in highest yield reduction. It was also observed that CropSystmodel was efficient in simulation of yield and biomass of wheat crop during various years of observations. The model has efficiently predicted grain and biological yield with ±10% of model error for wheat crop. However, it failed to predict the leaf area index (LAI) precisely. It may also be concluded that the model error were less for early and normal sown crops but increased with the delay in sowing.

SPATIALISATION OF RICE GROWTH AND YIELD MODEL USING OPTICAL AND SAR DERIVED BIOPHYSICAL PARAMETERS

Abstract. Integration of remote sensing derived biophysical parameters with process based crop growth simulation model is an emerging technology with diversified application for crop insurance as well as precision farming. Basically the crop growth simulation models are point based which simulate crop growth and yield as a function of soil, weather and crop management factors at a daily time scale. The temporal dimension of the crop growth model is supplemented by the spatial information on crop coverage and condition generated from remote sensing satellite derived biophysical parameters. In the present study an attempt is made to simulate pixel wise rice crop growth, condition and yield using Sentinel1 SAR, AWiFS/Landsat8 OLI and MODIS data with CERES rice growth simulation model on DSSAT platform. Temporal SAR data provides pixel wise start of season for rice crop at a spatial resolution of 10 m. Spectral indices derived from Landsat8/AWiFS and MODIS optical images are used for characterisation of rice growth environment and at the same time these indices are used to generate crop management information like irrigation and sowing dates. Bhadrak district of Odisha in the eastern coast of India is selected as the study area based on the prevalence of diversified rice growing environments. Sufficient in season field data are collected for checking the accuracy of rice map as well as for calibration and validation of the crop growth model. In season rice growth is initialised using SAR derived staggered sowing dates along with daily weather data and soil inputs. As CERES crop growth models are running well on DSSAT environment, rice crop growth simulation is carried out using multi date SAR images on DSSAT platform. The output of this study is maps depicting the spatial variability in rice area, staggered sowing dates and irrigation in the study region. These in season information are crucial for decision making particularly for crop insurance related activities.

AUTOMATION OF THE DSSAT CROP GROWTH SIMULATION MODEL

Abstract. Crop Simulation Models (CSM) simulate the growth, development, and yield of crops using various inputs such as soil water, carbon and nitrogen processes, and management practices. DSSAT (Decision Support System for Agrotechnology Transfer) is a software program that comprises dynamic crop growth simulation models for over 42 crops. It incorporates modules for crop, soil, and weather to simulate long-term outcomes of crop management strategies. DSSAT-CSM requires various data for model operation. This includes data on the site where the model is to be operated, on the daily weather during the growth cycle, on the characteristics of the soil at the beginning of the growing cycle or crop sequence, and on the management of the crop. Acquisition of the data and providing the data to the DSSAT model is tedious and time-consuming as each individual value has to be manually entered. Additionally, crop simulation models can only be run for specific points and not for entire locations. Sometimes site-specific data especially weather data cannot be obtained. The output thus produced is difficult to analyze spatially at a large scale. The main purpose of this paper is to take the required dataset directly from spatial data. This is done by dividing locations into grids and taking the data from each grid. Python scripts are then used to convert this data into crop model format which is then run through DSSAT on an individual basis. The output thus obtained is be entered back into their respective grids as spatial data.

INTEGRATING TIME-SERIES SAR DATA AND ORYZA CROP GROWTH MODEL IN RICE AREA MAPPING AND YIELD ESTIMATION FOR CROP INSURANCES

Abstract. Lowland rice in tropical and subtropical regions can be detected precisely and its crop growth can be tracked effectively through Synthetic Aperture Radar (SAR) imagery, especially where cloud cover restricts the use of optical imagery. Parameterised classification with multi-temporal features derived from regularly acquired, C-band, VV and VH polarized Sentinel-1A SAR imagery was used for mapping rice area. A fully automated processing chain in MAPscape-Rice software was used to convert the multi-temporal SAR data into terrain-geocoded σ0 values, which included strip mosaicking, co-registration of images acquired with the same observation geometry and mode, time-series speckle filtering, terrain geocoding, radiometric calibration and normalization. Further Anisotropic non-linear diffusion (ANLD) filtering was done to smoothen homogeneous targets, while enhancing the difference between neighbouring areas. Multi-Temporal Features viz., max, min, mean, max date, min date and span ratio were extracted from VV and VH polarizations to classify rice pixels. Rice detection was based on the analysis of temporal signature from SAR backscatter in relation to crop stages. About sixty images across four footprints covering 16 samba (Rabi) rice growing districts of Tamil Nadu, India were obtained between August 2017 and January 2018. In-season site visits were conducted across 280 monitoring locations in the footprints for classification purposes and more than 1665 field observations were made for accuracy assessment. A total rice area of 1.07 million ha was mapped with classification accuracy from 90.3 to 94.2 per cent with Kappa values ranging from 0.81 to 0.88. Using ORYZA2000, a weather driven process based crop growth simulation model developed by IRRI, yield estimates were made by integrating remote sensing products viz., seasonal rice area, start of season and backscatter time series. By generating average backscatter for each time series and dB stack for each SoS, LAI values were estimated. The model has generated rice yield estimate for each hectare which were aggregated at administrative boundary level and compared against CCE yield. Yield Simulation accuracy of more than 86–91% at district level and 82–97% at block level from the study indicates the suitability of these products for policy decisions. SAR products and yield information were used to meet the requirements of PMFBY crop insurance scheme in Tamil Nadu and helped in identifying or invoking prevented/failed sowing in 529 villages and total crop failure in 821 villages. In total 303703 farmers were benefitted by this technology in getting payouts of INR 9.94 billion through crop insurance. The satellite technology as an operational service has helped in getting quicker payouts.

Sorghum drought and heat stress patterns across the Argentinean temperate central region

Argentinean current sorghum management is similar across the entire temperate region, and no environmental characterization is assisting breeding and management decisions. Crop growth and development simulation models are a valuable tool for generating this characterization. Our study calibrated and validated APSIM-sorghum for our genetics and production environments, and used it to characterize the main water and heat stress patterns at our temperate central region. The calibration and validation provided accurate phenology, biomass, and yield estimations. Long-term weather records (44–61 years per site) and soil data were used to simulate the seasonal drought patterns at seven representative sites across the region. Clustering analysis identified three major drought environmental types (ENVT): (i) a pre-flowering drought stress, showing large occurrence frequency (39%), (ii) a low terminal drought stress, showing similar frequency (38%), and (iii) a grain-filling drought stress, showing lower frequency (23%). The most frequent ENVT at individual sites agrees with the spatial distribution of annual rainfall. However, most sites evidenced variable frequency of all ENVT. Flowering heat stress (>33 °C) showed an intermediate occurrence frequency (20–50%) only at lower latitudes, and was independent of drought ENVT. Defined ENVT helped explain observed genotype x environment (GxE) interactions for yield in an independent data set, showing they have practical implications for optimizing breeding and management strategies across the region of interest. Grouping sites of similar frequency can help to handle the spatial variability when defining these strategies, but dealing with seasonal variability will be challenging in the context of no predominant ENVT.

A sharp interface model for deterministic simulation of dendrite growth

A high order level set model is developed for deterministic simulation of dendritic growth in unstable solidifying systems. The model captures motion of the front implicitly on a structured finite difference grid, enables calculation of its geometric properties and also applies boundary conditions on the immersed interface. Interfacial capillary effect is incorporated in the model through the Gibbs-Thomson condition. Canonical problems for evaluating grid convergence of the numerical method and validation tests for stability of a growing nucleus in the presence of isotropic surface tension are presented. The growth morphology of solidifying nuclei in undercooled metallic melts is quantitatively analyzed. Effects of crystal anisotropy and melt undercooling on the front geometry, propagation speed and formation of branched dendritic structures are examined. The complex morphological changes, such as remelting of secondary perturbations under specific conditions of undercooling, are also captured during the quantitative analysis.

Using Crop Models and Climate Forecasts to Aid in Peanut Crop Insurance Decisions

Crop insurance is one of the strategies producers can use to reduce risk of income loss due to climate variability. The best approach for reducing risks involves a combination of crop insurance with a pre-harvest marketing plan that includes strategies like hedging and forward contracting. A producer's choice among strategies is often complicated when both price and yield risk are present. However, about 69% of crop failures in the U.S. are because of either drought or excessive moisture (Ibarra and Hewitt, 1999). Are there options for a farmer to reduce these weather and climate-based risks, and can a grower take advantage of climate forecast information to decide about insurance levels? This paper explores this idea for peanuts and provides examples of how to use crop growth simulation models in combination with climate forecasts to decide about coverage levels. This document is CIR 1468, one of a series of the Agricultural and Biological Engineering Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida. First published July 2005. Reviewed: September 2008.