Progress in the development of "Citros", a dynamic model of citrus productivity | [Progrès dans le développement de "Citros", modèle dynamique de la productivité des agrumes]
1999
Bustan, A. ((Hebrew University of Jerusalem (Israel). Faculty of Agriculture Food and Environmental Quality, Kennedy-Leigh Centre for Horticultural Research)) | Goldschmidt, E.E. | Erner, Y.
The foundations of the Citrus productivity model "CITROS" were laid by Goldschmidt and Monselise (1977). The model is based on the assumption that where all other needs (water, fertilizer, etc.) are managed at, or close to the optimum, dry matter production and allocation become the fundamental processes that limit tree productivity. The purpose of the model is to identify and quantify productivity problems, predict yields and devise means for optimization of Citrus crop production. Data from the literature supplemented by new field photosynthesis measurements were used to obtain reasonable estimates of daily and annual photoassimilate production by a Citrus tree canopy. The potential relative growth rate (RGR) of reproductive organs was followed through the growing season and the cost of fruit production was calculated. A model for an effective heat unit (EHU) was constructed to improve and refine the expression of temperature effects on fruit growth. Respiratory losses, including temperature effects, were also evaluated. This quantitative information was incorporated into a series of mathematical functions that provide estimates of carbohydrates might already occur during bloom. While early fruit development appears to be sink-limited, at least in certain cultivars, all subsequent stages of fruit growth and maturation are source-limited. Yield predictions by the model fall within a reasonable range, but the problem of trade-off between fruit number and fruit size (during the fruit set and abscission period) requires further investigation. Actual differences in the performance of grapefruit (Citrus paradisi Macf., cv "Marsh seedless") orchards from different climate regions were successfully predicted by the model. The model, which is already a powerful diagnostic and predictive tool, may gain further strength by incorporation of the effects of management practices (e.g., drip irrigation) and frequently encountered environmental stresses
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