The effects of temperature, photosynthetic photon flux density (PPFD) and photoperiod on vegetative growth and flowering of the raspberry (Rubus idaeus L.) `Autumn Bliss' were investigated. Increased temperature resulted in an increased rate of vegetative growth and a greater rate of progress to flowering. Optimum temperatures lay in the low to mid 20°C range. Above this the rate of plant development declined. Increased PPFD also advanced flowering. While photoperiod did not significantly affect the rate of vegetative growth, flowering occurred earliest at intermediate photoperiods and was delayed by extreme photoperiods. These responses suggest that there is potential for adjusting cropping times of raspberry grown under protection by manipulating the environment, especially temperature.
J.G. Carew, K. Mahmood, J. Darby, P. Hadley and N.H. Battey
Catherine M. Grieve, James A. Poss, Peter J. Shouse and Christy T. Carter
had reached marketable stage with ≈50% of the florets on the inflorescence open ( Armitage, 1993 ; Healy, 1998 ). To test the phasic growth model described by Lieth et al. (1995) ( Fig. 1 ), however, plants were grown to full bloom. The following
Xiaofeng Yang, Gang Li, Weihong Luo, Lili Chen, Shaopeng Li, Ming Cao and Xuebin Zhang
concentration in cotton J. Plant Nutr. Soil Sci. 170 811 817 Yin, X. Schapendonk, A.H.C.M. Kropff, M.J. van Oijen, M. Bindraban, P.S. 2000 A generic equation for nitrogen-limited leaf area index and its application in crop growth models for predicting leaf
Maynard E. Bates
A simple plant growth model has been developed based on the analysis of growth curves of lettuce and spinach in numerous controlled environment experiments. The model incorporates elements for genetic potential, plant spacing, photosynthetic photon flux, photoperiod, environment, and morphology. Predicted parameters are relative growth rate, mean plant weight, and plant growth efficiency. Prediction may be on an hourly or daily basis. Examples drawn from data on various species and cultivars will be presented.
Alan N. Lakso
Fruits of different species grow in different patterns (such as the “double sigmoid” of stone fruits and grapes or the apparent single sigmoid of apples), and each has periods of cell division followed by periods of only cell expansion. It should not be expected that one mathematical growth description would hold for all species, or even at all times of the season for one species. Perhaps hybrid growth models need to be developed, although specific questions asked about fruit growth may be satisfactorily answered with models of only parts of the fruit growth period of interest.
J.M.S. Scholberg, B.L. McNeal, J.W. Jones, S.J. Locascio, S.R. Olsen and C.D. Stanley
Modeling the growth of field-grown tomato (Lycopersicon esculentum Mill.) should assist researchers and commercial growers to outline optimal crop management strategies for specific locations and production systems. A generic crop-growth model (CROPGRO) was previously adapted to simulate the growth of fresh-market tomato under field conditions. Plant growth and development of field-grown tomato, and fruit yields, will be outlined and compared to model predictions for a number of locations in Florida, nitrogen fertilizer rates, and irrigation management practices. Possible application of the model to quantify effects of crop management on crop production will be discussed using simulated yield values for a wide range of environmental conditions.
The modified Mitscherlich plant growth model was used to quantify the threshold leaf Zn level influencing nut yield and vegetative growth, on an orchard basis, for pecan [Carya illinoinensis (Wangenh.) C. Koch]. Four indices of tree performance, including percentage of trees without deficiency symptoms, vegetative growth, nut yield, and trees without deficiency symptoms plus nut yield, were analyzed with regard to leaf Zn concentration. Data available from published and unpublished sources on any single performance index were combined for mathematical modeling. The threshold value for leaf Zn was determined to be ≈50 μg·g-1 for these tree performance indices. Thus, nut yield and vegetative growth in an orchard will be reduced with a leaf Zn concentration below ≈50 μg·g-1, but will not be affected above this value.
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 characteristic that has been defined in many 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 on concentrations of 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 developing more practical research on the physiological mechanisms of plant salt tolerance.
Jonathan M. Frantz and Bruce Bugbee
Cloudy days cause an abrupt reduction in daily photosynthetic photon flux (PPF), but we have a poor understanding of how plants acclimate to this change. We used a unique 10-chamber, steady-state, gas-exchange system to continuously measure daily photosynthesis and night respiration of populations of a starch accumulator [tomato (Lycopersicon esculentum Mill. cv. Micro-Tina)] and a sucrose accumulator [lettuce (Lactuca sativa L. cv. Grand Rapids)] over 42 days. All measurements were done at elevated CO2 (1200 μmol·mol-1) to avoid any CO2 limitations and included both shoots and roots. We integrated photosynthesis and respiration measurements separately to determine daily net carbon gain and carbon use efficiency (CUE) as the ratio of daily net C gain to total day-time C fixed over the 42-day period. After 16 to 20 days of growth in constant PPF, plants in some chambers were subjected to an abrupt PPF reduction to simulate shade or a series of cloudy days. The immediate effect and the long term acclimation rate were assessed from canopy quantum yield and carbon use efficiency. The effect of shade on carbon use efficiency and acclimation was much slower than predicted by widely used growth models. It took 12 days for tomato populations to recover their original CUE and lettuce CUE never completely acclimated. Tomatoes, the starch accumulator, acclimated to low light more rapidly than lettuce, the sucrose accumulator. Plant growth models should be modified to include the photosynthesis/respiration imbalance and resulting inefficiency of carbon gain associated with changing PPF conditions on cloudy days.
Royal D. Heins and Paul Fisher
Height control is a major challenge in the production of high quality poinsettia crops. Graphical tracking is a technique where growers make height control decisions by comparing actual measured plant height with a desired height. A computer decision support tool, the Poinsettia Care System, is being developed to combine graphical display of plant height with an expert system to provide height control advice. A simulation model is used to predict future growth of the crop based on greenhouse temperature, growth retardant applications, plant spacing, plant maturity, and light quality. Growth retardant and temperature recommendations are made based on a crop's deviation from the target height, expected future growth rate, and crop maturity. The program was beta tested by 8 Michigan growers over the 1991 poinsettia season. The test growers reacted positively to the program in a follow-up survey. Perceived benefits included improved height control, consistent crop recording, and a `second opinion' when making height control decisions. Improvements were suggested to combine the advice of different crops within the same greenhouse zone, to improve the predictive growth model, and to streamline data entry and output.