Modeling peanut leaf gas exchange for the calibration of crop models for different cultivars

Abstract

The leaf anatomy of two peanut cultivars, Florman INTA (Virginia type) and Manfredi 393 INTA (intermediate Virginia–Spanish type) was studied using digital images of abaxial/adaxial leaf surfaces and leaf cross-sections. Thirteen cross-section characteristics and five surface parameters were measured using image quantification software. Both cultivars had the same general anatomical structure described for other cultivars in earlier publications. Some characteristics, such as the length and width of stomata, or the area of epidermal cells or guard cell pairs, were not significantly different for the two cultivars; other characteristics, such as stomatal density, leaf thickness, relative volume of water storage cells, cell sizes, and number of mesophyll cells per unit leaf surface, varied significantly between them. Leaf gas exchange was simulated with the 2DLEAF model for both cultivars using schematizations derived from the observed leaf anatomy. Simulated photosynthesis rates of Florman leaves were higher than those of Manfredi 393 at all the studied combinations of light and temperature. The maximum difference, about 15%, was observed at the highest light intensity, 2000 μmol (photons) m −2 s −1 and at the optimal temperature of 35°C. Simulated transpiration rates were higher for Manfredi 393 leaves than for Florman at all stomatal apertures and temperature values. The maximum difference, approximately 50%, occurred at 5°C and at the maximal stomatal aperture, 10 μm. For both cultivars, the simulated optimal temperature for photosynthesis varied with light intensity, from 20°C at 400 μmol (photons) m −2 s −1 to 35°C at 2000 μmol (photons) m −2 s −1. The simulated results agree with observed crop growth and water consumption data from experimental plots in Córdoba, Argentina in the peanut growing season of 1997–1998. This suggests that two-dimensional leaf gas exchange modeling may be a valuable tool to help understand differences in biomass production and yield between different cultivars, and could be used for other applications such as the fine-tuning of a crop model to the individual responses of different cultivars to environmental conditions.