Models were developed to estimate nondestructively chlorophyll (Chl) content per unit of leaf area (Chlarea) and nitrogen content per unit of leaf area (Narea) using readings of two optical meters for five warm-temperate, evergreen, broadleaved tree species (Castanopsis sieboldii, Cinnamomum tenuifolium, Eurya japonica, Machilus thunbergii, and Neolitsea sericea). It was determined whether models should be adjusted seasonally. Readings (were obtained six times during a year period and Chlarea and Narea were determined using destructive methods. Bayesian inference was used to estimate parameters of models that related optical meter readings to Chlarea or Narea for each species. Deviance information criterion values were used to select the best among models, including the models with seasonal adjustment. The selected models were species-specific and predicted Chlarea accurately (R2 = 0.93-0.96). The best model included parameters with seasonal adjustments for one out of five species. Model-based estimates of Narea were not as accurate as those for Chlarea, but they were still adequate (R2 = 0.64-0.82). For all species studied, the best models did not include parameters with seasonal adjustments. The estimation methods used in this study were rapid and nondestructive; thus, they could be used to assess a function of many leaves and/or repeatedly on individual leaves in the field. and D. Mizusaki, K. Umeki, T. Honjo.
The sustainable production of upland cotton, an economically important fiber crop, is threatened by changing environmental factors including high temperatures and low-soil water content. Both high heat and low-soil water can reduce net photosynthesis resulting in low fiber yields or poor fiber quality. Leaf chlorophyll content has a direct relationship with photosynthetic rate. Understanding how high heat and low-soil water affect chlorophyll content can identify opportunities for breeding improvement that will lead to sustainable fiber yields. A two-year field trial located in Maricopa Arizona measured leaf chlorophyll content, available soil water, ambient air temperatures, and cotton growth measurements collected by a high-clearance tractor equipped with proximal sensors. The results showed that low-soil water significantly increased leaf chlorophyll content, while high temperatures significantly reduced content. Structured equation modeling revealed that cotton may divert available resources to leaf area and chlorophyll content for the production of photosynthates during periods of high temperatures.