Many tree crops experience sub-optimal yields and low fruit quality due to inadequate pollination, low fruit set, and poor crop nutrition. Boron (B) is a critical crop nutrient for fruit set because B levels affect pollen germination and pollen tube growth. However, the relationship between floral B concentration and fruit set is not well understood. The aim of this study was to determine the effect of B applications on the initial fruit set, yield, quality, and paternity of macadamia (Macadamia integrifolia). Cultivar ‘816’ trees received one of three treatments: (a) 0 g, (b) 15 g, or (c) 30 g B per tree prior to flowering. Boron application increased the B concentration of macadamia flowers. Application of 15 g B increased fruit set at 3 weeks after peak anthesis, but this higher initial fruit set was not translated into higher fruit set at 6 or 10 weeks after peak anthesis or higher yield. Boron application increased B concentrations in kernels but did not affect nut-in-shell (NIS) mass, kernel mass, kernel recovery, kernel oil concentration or incidence of whole kernels. Cultivar ‘816’ was highly outcrossing, with 97–98% cross-paternity among kernels from all treatments. Our results indicate that higher B concentration in macadamia flowers can be associated with an increased initial fruit set. However, high B levels did not affect yield, nut quality, or the proportion of self-pollinated fruit at maturity. The heavy dependence on outcrossing highlights the importance of inter-planting different cultivars and managing bee hives to sustain the productivity of macadamia orchards.

Tree crop yield is highly dependent on fertiliser inputs, which are often guided by the assessment of foliar nutrient levels. Traditional methods for nutrient analysis are time-consuming but hyperspectral imaging has potential for rapid nutrient assessment. Hyperspectral imaging has generally been performed using the adaxial surface of leaves although the predictive performance of spectral data has rarely been compared between adaxial and abaxial surfaces of tree leaves. We aimed to evaluate the capacity of laboratory-based hyperspectral imaging (400–1000 nm wavelengths) to predict the nutrient concentrations in macadamia leaves. We also aimed to compare the prediction accuracy from adaxial and abaxial leaf surfaces. We sampled leaves from 30 macadamia trees at 0, 6, 10 and 26 weeks after flowering and captured hyperspectral images of their adaxial and abaxial surfaces. Partial least squares regression (PLSR) models were developed to predict foliar nutrient concentrations. Coefficients of determination (R2P) and ratios of prediction to deviation (RPDs) were used to evaluate prediction accuracy. The models reliably predicted foliar nitrogen (N), phosphorus (P), potassium (K), calcium (Ca), copper (Cu), manganese (Mn), sulphur (S) and zinc (Zn) concentrations. The best-fit models generally predicted nutrient concentrations from spectral data of the adaxial surface (e.g., N: R2P = 0.55, RPD = 1.52; P: R2P = 0.77, RPD = 2.11; K: R2P = 0.77, RPD = 2.12; Ca: R2P = 0.75, RPD = 2.04). Hyperspectral imaging showed great potential for predicting nutrient status. Rapid nutrient assessment through hyperspectral imaging could aid growers to increase orchard productivity by managing fertiliser inputs in a more-timely fashion.