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Bing-Rui Ni and Kent J. Bradford

Cell growth models were applied to characterize the response of seed germination, based upon the timing of radicle emergence, to y and ABA. Using probit analysis, three basic parameters can be derived to describe the population characteristics of seed lots. In the response of seed germination to osmotic stress, these three parameters are the “hydrotime constant” (q H), the mean base water potential (y b), and the standard deviation (s b) population. In the response to ABA, they are the “ABA-time constant” (q ABA), the mean base ABA concentration (ABAb), and the standard deviation (s ABAb) of the seed population. Using only these three parameters, germination time courses can be predicted at any corresponding medium y or ABA concentration. In the presence of both ABA and osmotic stress, the same parameters can be used to predict seed germination time courses with any combination of y and ABA concentration. The water relations model and the ABA model were additive and it appeared that the two factors slowed down germination independently. Effects of osmotic stress and ABA on the parameters in Lockhart equation are also discussed.

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Michael C. Shannon

The lack of improvement for salt tolerance has been attributed to insufficient genetic variation, a need for rapid and reliable genetic markers for screening, and the complexities of salinity × environment interactions. Salt tolerance is a quantitative character that has been defined in a multitude of ways subject to changes with plant development and differentiation; thus, assessing salt tolerance among genotypes that differ in growth or development rate is difficult. Salt tolerance also varies based upon concentrations of both major and minor nutrients in the root zone. Plant growth models may provide a method to integrate the complexities of plant responses to salinity stress with-the relevant environmental variables that interact with the measurement of tolerance. Mechanistic models have been developed over the last few years that are responsive to nitrogen or drought stress but not to salinity stress. Models responsive to salinity stress would provide insights for breeders and aid in the development of more practical research on the physiological mechanisms of plant salt tolerance.

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Douglas A. Hopper and Kevin Cifelli

An interactive simulation model of plant growth must be flexible to accept specific crop equations from the user. ROSESIM functions as a dynamic plant growth model based on `Royalty' rose (Rosa hybrida L) response to 15 unique treatment combinations of photosynthetic photon flux (PPF), day temperature (DT), and night temperature (NT) under constant growth chamber conditions. Environmental factors are assumed constant over an entire day. Coefficients are read from an external ASCII file, thus permitting coefficients from any linear, quadratic, or interaction terms to be input into ROSESIM up to a full quadratic model form. Nonsignificant terms are given a coefficient of zero. ROSESIM has been restructured into Borland C++ object oriented program (OOP) code to execute in the Microsoft Windows 3.1 operating environment. This enables ease of operation in the user friendly graphical user interface (GUI) provided with most IBM personal computers (PC). The user chooses a set of environmental conditions which can be altered after any selected number of days, allowing conditions to be changed and modeled daily for interactive comparison studies.

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Douglas A. Hopper and Kevin T. Cifelli

Growth predictions derived from data collected in controlled-environment chambers would be expected to differ from growth responses observed in variable greenhouse conditions. ROSESIM was developed as a dynamic plant growth model based on `Royalty' rose (Rosa hybrida L.) response to 15 unique treatment combinations of photosynthetic photon flux (PPF), day temperature (DT), and night temperature (NT) under constant growth chamber conditions. Regression coefficients for growth equations are read from an external ASCII file, thus permitting coefficients up to a full quadratic model form. Calibration coefficients (CC) were added to ROSESIM to enable predictions to be altered proportionally to permit improved prediction of specific growth characteristics. Numerator and denominator values for CC are adjustable for the first 10 days (initial) growth equations, subsequent growth until anthesis equations, and for the prediction of anthesis. Validation studies were used to set CC values; this enables the model based on growth in controlled environment chambers to be systematically calibrated on site to fit actual growth measured at a specific greenhouse location.

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Elizabeth A. Wahle and John B. Masiunas

Greenhouse hydroponics and field experiments were conducted to determine how nitrogen (N) fertilizer treatments affect tomato (Lycopersicon esculentum Mill.) growth, yield, and partitioning of N in an effort to develop more sustainable fertilization strategies. In a hydroponics study, after 4 weeks in nitrate treatments, shoot dry weight was five times greater at 10.0 than at 0.2 mm nitrate. An exponential growth model was strongly correlated with tomato root growth at all but 0.2 mm nitrate and shoot growth in 10 mm nitrate. Root dry weight was only 15% of shoot biomass. In field studies with different population densities and N rates, height in the 4.2 plants/m2 was similar, but shoot weight was less than in the 3.2 plants/m2. At 12 weeks after planting, shoot fresh weight averaged 3.59 and 2.67 kg/plant in treatments with 3.2 and 4.2 plants/m2, respectively. In 1998, final tomato yield did not respond to N rate. In 1999, there was a substantial increase in fruit yield when plants were fertilized with 168 kg·ha-1 N but little change in yield with additional N. Nitrogen content of the leaves and the portion of N from applied fertilizer decreased as the plants grew, and as N was remobilized for fruit production. Both studies indicate that decreasing N as a way to reduce N loss to the environment would also reduce tomato growth.

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D. Scott NeSmith

Different planting dates were used to study the influence of thermal time on leaf appearance rate of four summer squash (Cucurbita pepo L.) cultivars. During the first year (1991), thermal time or growing degree days (GDD) were calculated using a base temperature of 8C and a ceiling temperature of 32C for several planting dates. Leaf numbers per plant were determined every 2 to 3 days. Leaves that were beginning to unfold with a width of 2 cm or greater were included in the counts. The relationship between leaf number and GDD was established from the initial data set, and data from subsequent years were used for model validation. Results indicated that single equation could be used to predict leaf appearance of all four cultivars in response to thermal time. The response of leaf appearance to GDD was curvilinear, with a lag over the first five leaves. After five leaves, the increase in leaf number per plant was linear with increased GDD. Segmented regression with two linear functions also fit the data well. With this approach, leaf 5 was the node, and a separate linear function was used to predict the leaf number below five leaves and above five leaves. The results of this model should prove to be useful in developing a model of leaf area development, and eventually a crop growth model, for summer squash.

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Catherine M. Grieve, Stacy A. Bonos and James A. Poss

Six selections of Kentucky bluegrass (Poa pratensis L.) cultivars, selected based on their drought tolerance under field and growth chamber conditions in New Brunswick, N.J., were evaluated for salt tolerance based on yield and growth rates at eight soil water salinities [2 (control), 6, 8, 10, 12, 14, 18, and 22 dSm-1] from Apr. to Sept. 2005 in Riverside, Calif. Cultivars Baron and Brilliant were selected as drought sensitive and `Cabernet', `Eagleton', and `Midnight' were selected as drought tolerant. A Texas × Kentucky bluegrass (Poa arachnifera × Poa pratensis) hybrid selection (identified as A01-856) developed for improved drought and heat tolerance was also included. Vegetative clones were established in a randomized complete-block design with three replications, each containing 11 clones. Cumulative biomass and clone diameters were measured over time to evaluate relative yields and growth rates for the six cultivar selections. Based upon maximum absolute biomass production as a function of increasing EC, the order of production was `Baron' > `Brilliant' > `Eagleton' > `Cabernet' ≥ `Midnight' > A01-856. Yield relative to the non-saline control (2 dSm-1) for each cultivar was similar, except that the differences between cultivars were less pronounced, and `Baron' slightly outperformed `Brilliant'. Clone area expansion rates were analyzed with a phasic growth model and beta, the intrinsic growth rate of the exponential phase parameter, significantly varied with salinity. Ranking of cultivars, based on expansion rates, was similar to that based on cumulative biomass. Salinity tolerance in this experiment did not appear to be related to the observed ranking for drought tolerance.

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K.T. Morgan, J.M.S. Scholberg, T.A. Obreza and T.A. Wheaton

Growth and nitrogen (N) accumulation relationships based on tree size, rather than age, may provide more generic information that could be used to improve sweet orange [Citrus sinensis (L.) Osbeck] N management. The objectives of this study were to determine how orange trees accumulate and distribute biomass and N as they grow, investigate yearly biomass and N changes in mature orange trees, determine rootstock effect on biomass and N distribution, and to develop simple mathematical models describing these relationships. Eighteen orange trees with canopy volumes ranging between 2 and 43 m3 were dissected into leaf, twig, branch, and root components, and the dry weight and N concentration of each were measured. The N content of each tree part was calculated, and biomass and N distribution throughout each tree were determined. The total dry biomass of large (mature) trees averaged 94 kg and contained 0.79 kg N. Biomass allocation was 13% in leaves, 7% in twigs, 50% in branches/trunk, and 30% in roots. N allocation was 38% in leaves, 8% in twigs, 27% in branches/trunk, and 27% in roots. For the smallest tree, above-/below-ground distribution ratios for biomass and N were 60/40 and 75/25, respectively. All tree components accumulated biomass and N linearly as tree size increased, with the above-ground portion accumulating biomass about 2.5 times faster than the below-ground portion due mostly to branch growth. The growth models developed are currently being integrated in a decision support system for improving fertilizer use efficiency for orange trees, which will provide growers with a management tool to improve long-term N use efficiency in orange orchards.

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Beth Ann A. Workmaster and Jiwan P. Palta

Little is known about the growth and development of the cranberry plant (Vaccinium macrocarpon Ait.) in response to air and soil temperatures in the spring. During this period, marked changes in cranberry bud hardiness are known to occur (from –20 to 0 °C), with the greatest changes occuring before bud elongation. The ability to predict changes in bud phenology and hardiness in relation to thermal time would be useful to growers in making frost management decisions. To establish a working growth model, canopy air and soil temperatures were continuously recorded in 1996, 1997, and 1998 in a cranberry bed (cv. Stevens) in central Wisconsin. In spring, samples of uprights were randomly collected from several locations within the bed and sorted according to a nine stage bud classification from tight bud to bloom. Controlled freezing tests were performed on uprights from the most advanced stages present that constituted 10% or more of a sample on a given date. Heat units were calculated from hourly canopy air temperatures. Despite the varied weather conditions over the3 years, a distinct relationship existed between the accumulation of heat units and the advancement of the crop. Spring 1998 was very early and resulted in the accumulation of more heat units before initial and advanced bud swell was observed compared to the other 2 years. Initial evaluation suggests that soil temperatures between 5 to 10 °C and photoperiod may play a role in modulating the effect of air temperatures. Further refinement of this model and the predictive value for frost hardiness changes will be discussed.

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De-Xing Chen and J. Heinrich Lieth

A two-dimensional mathematical model was developed to describe the time course of root growth and its spatial distribution for container-grown plants, using chrysanthemum [Dendranthema ×grandiflorum (Ramat.) Kitamura] as the model system. Potential root growth was considered as consisting of several concurrent processes, including branching, extension, and death. Branching rate was assumed to be related sigmoidally to existing root weight density. Root growth extension rate was assumed to be proportional to the existing root weight density above some threshold root weight density in adjacent cells. The senescence rate of root weight density was assumed to be proportional to existing root mass. The effects of soil matric potential and temperature on root growth were quantified with an exponential function and the modified Arrhenius equation, respectively. The actual root growth rate was limited by the amount of carbohydrate supplied by the canopy to roots. Parameters in the model were estimated by fitting the model to experimental data using nonlinear regression. Required inputs into the model included initial root dry weight density distribution, soil temperature, and soil water potential data. Being a submodel of the whole-plant growth model, the supply of carbohydrates from canopy to roots was required; the total root weight incremental rate was used to represent this factor. Rather than linking to a complex whole-plant C balance model, the total root weight growth over time was described by a logistic equation. The model was validated by comparing the predicted results with independently measured data. The model described root growth dynamics and its spatial distribution well. A sensitivity analysis of modeled root weight density to the estimated parameters indicated that the model was more sensitive to carbohydrate supply parameters than to root growth distribution parameters.