Dvoralai Wulfsohn

The management and marketing of fruit requires data on expected numbers, size, quality and timing. Current practice estimates orchard fruit load based on the qualitative assessment of fruit number per tree and historical orchard yield, or manually counting a subsample of trees. This review considers technological aids assisting these estimates, in terms of: (i) improving sampling strategies by the number of units to be counted and their selection; (ii) machine vision for the direct measurement of fruit number and size on the canopy; (iii) aerial or satellite imagery for the acquisition of information on tree structural parameters and spectral indices, with the indirect assessment of fruit load; (iv) models extrapolating historical yield data with knowledge of tree management and climate parameters, and (v) technologies relevant to the estimation of harvest timing such as heat units and the proximal sensing of fruit maturity attributes. Machine vision is currently dominating research outputs on fruit load estimation, while the improvement of sampling strategies has potential for a widespread impact. Techniques based on tree parameters and modeling offer scalability, but tree crops are complicated (perennialism). The use of machine vision for flowering estimates, fruit sizing, external quality evaluation is also considered. The potential synergies between technologies are highlighted.

Summary We describe a stereological procedure to estimate the total leaf surface area of a plant canopy in vivo, and address the problem of how to predict the variance of the corresponding estimator. The procedure involves three nested systematic uniform random sampling stages: (i) selection of plants from a canopy using the smooth fractionator, (ii) sampling of leaves from the selected plants using the fractionator, and (iii) area estimation of the sampled leaves using point counting. We apply this procedure to estimate the total area of a chrysanthemum (Chrysanthemum morifolium L.) canopy and evaluate both the time required and the precision of the estimator. Furthermore, we compare the precision of point counting for three different grid intensities with that of several standard leaf area measurement techniques. Results showed that the precision of the plant leaf area estimator based on point counting is high. Using a grid intensity of 1.76 cm²/point we estimated plant and canopy surface areas with accuracies similar to or better than those obtained using image analysis and a commercial leaf area meter. For canopy surface areas of approximately 1 m² (10 plants), the fractionator leaf approach with sampling fraction equal to 1/9 followed by point counting using a 4.3 cm²/point grid produced a coefficient of error of less than 7%. The smooth fractionator can be used to ensure that the additional contribution to the estimator variance due to between‐plant variability is small.

We present an unbiased estimator of the total number of alveolar structures distal to the transition from a bronchiole to an alveolar duct system ('ventilatory units', VUs). In species without respiratory bronchioles, including mice, the number of VUs is equivalent to the number of acini. The acinus is a functional unit of gas exchange, defined as a parenchymal unit distal to a terminal bronchiole in which all airways contain alveoli and thus participate in gas exchange. The estimator combines two different estimators of the number of VUs: (1) an estimator derived from the Euler number of all the openings of the bronchial tree and (2) an estimator derived from direct counts of topological changes occurring at bronchiole-alveolar duct junctions. Combining the two estimators eliminates the requirement to be able to identify even vanishingly small pieces of bronchial tissue in physical disectors. We implemented the fractionator estimator in five adult mice lungs using physical fractionators with varying but known sampling fractions (Horvitz-Thompson estimator). We obtained total values of about 4200 VUs (CV = 0.05) in 21-day-old and 4480 (CV = 0.06) in 69-day-old animals. Being fractionator estimates, these total numbers are independent of shrinkage. The densities of VUs per unit volume of tissue (values corrected for tissue shrinkage) were similar in left and right lungs.

To investigate if latent manganese (Mn) deficiency leads to increased transpiration, barley plants were grown for 10 weeks in hydroponics with daily additions of Mn in the low nM range. The Mn‐starved plants did not exhibit visual leaf symptoms of Mn deficiency, but Chl a fluorescence measurements revealed that the quantum yield efficiency of PSII (F_v/F_m) was reduced from 0.83 in Mn‐sufficient control plants to below 0.5 in Mn‐starved plants. Leaf Mn concentrations declined from 30 to 7 μg Mn g⁻¹ dry weight in control and Mn‐starved plants, respectively. Mn‐starved plants had up to four‐fold higher transpiration than control plants. Stomatal closure and opening upon light/dark transitions took place at the same rate in both Mn treatments, but the nocturnal leaf conductance for water vapour was still twice as high in Mn‐starved plants compared with the control. The observed increase in transpiration was substantiated by ¹³C‐isotope discrimination analysis and gravimetric measurement of the water consumption, showing significantly lower water use efficiency in Mn‐starved plants. The extractable wax content of leaves of Mn‐starved plants was approximately 40% lower than that in control plants, and it is concluded that the increased leaf conductance and higher transpirational water loss are correlated with a reduction in the epicuticular wax layer under Mn deficiency.