Is Drought Increasing in Maine and Hurting Wild Blueberry Production?

A few severe drought events occurred in the Northeast (NE) USA in recent decades and caused significant economic losses, but the temporal pattern of drought incidents and their impacts on agricultural systems have not been well assessed. Here, we analyzed historical changes and patterns of drought using a drought index (standardized precipitation-evapotranspiration index (SPEI)), and assessed drought impacts on remotely sensed vegetation indices (enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI)) and production (yield) of the wild blueberry fields in Maine, USA. We also analyzed the impact of short- and long-term water conditions of the growing season on the wild blueberry vegetation condition and production. No significant changes in the SPEI were found in the past 71 years, despite a significant warming pattern. There was also a significant relationship between the relatively long-term SPEI and the vegetation indices (EVI and NDVI), but not the short-term SPEI (one year). This suggests that the crop vigor of wild blueberries is probably determined by water conditions over a relatively long term. There were also significant relationships between 1-year water conditions (SPEI) and yield for a non-irrigated field, and between 4-year-average SPEI and the yield of all fields in Maine. The vegetation indices (EVI and NDVI) are not good predictors of wild blueberry yield, possibly because wild blueberry yield does not only depend on crop vigor, but also on other important variables such as pollination. We also compared an irrigated and a non-irrigated wild blueberry field at the same location (Deblois, Maine) where we found that irrigation decoupled the relationship between the SPEI and NDVI or EVI.

Elucidation of the molecular responses during the primary infection of wild blueberry phenotypes with Monilinia vaccinii-corymbosi under field conditions

Background Monilinia blight caused by Monilinia vaccinii-corymbosi (Reade) Honey (M.vc) is a major disease of wild blueberry that can result in severe crop losses in the absence of an integrated disease management programme. The fungus causes blight in the emerging floral and vegetative buds, but the degree of susceptibility varies among the different wild blueberry phenotypes, ranging from the highly susceptible V. a. f. nigrum to the moderately susceptible V. angustifolium and the least susceptible V. myrtilloides. Results The present study evaluated the defense responses of these major phenotypes during their primary infection (floral buds) with M.vc. The temporal expression profiles of PR genes (PR3 and PR4) and the flavonoid pathway structural genes (CHS, ANS, ANR, DFR and FLS) were analysed. The PR3 and PR4 gene expression profiles revealed that V. myrtilloides responded to M.vc infection by activating the expression of both PR genes. V. a. f. nigrum, on the other hand, failed to activate these genes, while V. angustifolium, exhibited an intermediate response. Our study with the flavonoid pathway genes indicated variability in activation of the genes during post-infection time points with ANS and ANR in V. myrtilloides, FLS in V. angustifolium and no response observed in V. a. f. nigrum. Conclusions Altogether, this study highlights that the degree of phenotype susceptibility is associated with the timely activation of host defense responsive genes. Data obtained in this study provided a starting point for a better understanding of the wild blueberry- M. vaccinii-corymbosi pathosystem.

Field Capacity and Harvest Efficiency Evaluation of Traditional Small Box and Semi-Automated Bin Handling Systems for Wild Blueberries

Mechanical harvesters with small box and semi-automated bin handling systems are increasingly being used for harvesting wild blueberries in Eastern Canada, and Northeastern, USA. However, their field capacity and performance have not been quantified and compared. Important measures of field capacity and efficiency for a traditional mechanical harvester were compared with a novel semi-automatic bin handling harvester. Data were obtained from on-farm field trials conducted at four sites in Nova Scotia, Canada in 2017 and 2018. Both harvesters had double head configurations, along with other similar engineering configurations: (i) 0.66 m picking reels; (ii) 16 picker bars per head and 65 teeth per bar; (iii) 1.72 m picking width; (iv) 21 rpm head speed; and (v) 0.31 ms−1 ground speed. Each harvester was operated for 120 min and data such as berry harvesting time and box handling time were recorded, with six replications during each year. Statistical methods were used to compare the harvest efficiency of the two mechanical harvesters. Harvest time efficiency was significantly higher for the semi-automatic bin handling technology than for the small box handling technology both in 2017 (p < 0.001), and 2018 (p < 0.001). Weed coverage did not have a significant effect of harvest time in either 2017 (p = 0.694) or 2018 (p = 0.765), though it did significantly affect yield in both 2017 (p = 0.011) and 2018 (p = 0.045). The findings provide useful insights for decision-makers contemplating the choice of harvesting technology to sustain profits from wild blueberry production.

Rooting of Wild Blueberry (Vaccinium spp.) Cuttings From the Peruvian Northeast

The objective of this study was to evaluate the effect of indole-3-butyric acid (IBA) on the rooting of five wild accessions of blueberry (HCHA-262, HCHA-271, HCHA-283, HCHA-286, and HCHA-290) obtained from natural populations in the province of Chachapoyas (northern Peru). Buds were collected from the middle third of the plant and treated with IBA at concentrations of 0, 1,000, 2,000, and 3,000 mg L −1 . The results indicated that of the IBA doses, the 2,000-mg L −1 dose achieved the best effects on the rooting percentage and the lengths and numbers of roots and shoots of each accession studied. Accessions HCHA-262 and HCHA-283 showed greater regeneration of new seedlings than the other accessions after 45 days. The rhizogenic capacity of the cuttings varied because of the influences of both the IBA concentration and the genotype of the mother plant.

Dissecting the subtropical adaptation traits and cuticle synthesis pathways via the genome of the subtropical blueberry Vaccinium darrowii

Vaccinium darrowii is a subtropical wild blueberry species, which was used to breed economically important southern highbush cultivars. The adaptation traits of V. darrowii to subtropical climate would provide valuable information for breeding blueberry and perhaps other plants, especially against the background of global warming. Here, we assembled the V. darrowii genome into 12 pseudochoromosomes using Oxford Nanopore long reads complemented with Hi-C scaffolding technologies, and predicted 41 815 genes using RNAseq evidence. Syntenic analysis across three Vaccinium species revealed a highly conserved genome structure, with the highest collinearity between V. darrowii and V. corymbosum. This conserved genome structure may explain the high fertilization during crossbreeding between V. darrowii and other blueberry cultivars. Gene expansion and tandem duplication analysis indicated possible roles of defense and flowering associated genes in adaptation of V. darrowii to the subtropics. The possible SOC1 genes in V. darrowii were identified with phylogeny and expression analysis. Blueberries are covered in a thick cuticle layer and contain anthocyanins, which confer their powdery blue color. Using RNA-sequencing, the cuticle biosynthesis pathways of Vaccinium species were delineated here in V. darrowii. This result could serve as a reference for breeding berries with customer-desired colors. The V. darrowii reference genome, together with the unique traits of this species, including diploid genome, short vegetative phase, and high compatibility in hybridization with other blueberries, make V. darrowii a potential research model for blueberry species.

Parameterization and Calibration of Wild Blueberry Machine Learning Models to Predict Fruit-Set in the Northeast China Bog Blueberry Agroecosystem

Data deficiency prevents the development of reliable machine learning models for many agroecosystems, especially those characterized by a dearth of knowledge derived from field data. However, other similar agroecosystems with extensive data resources can be of use. We propose a new predictive modeling approach based upon the concept of transfer learning to solve the problem of data deficiency in predicting productivity of agroecosystems, where productivity is a nonlinear function of various interacting biotic and abiotic factors. We describe the process of building metamodels (machine learning models built and trained on simulation data) from simulations built for one agroecosystem (US wild blueberry) as the source domain, where the data resource is abundant. Metamodels are evaluated and the best metamodel representing the system dynamics is selected. The best metamodel is re-parameterized and calibrated to another agroecosystem (Northeast China bog blueberry) as the target domain where field collected data are lacking. Experimental results showed that our metamodel developed for wild blueberry achieved 78% accuracy in fruit-set prediction for bog blueberry. To demonstrate its usefulness, we applied this calibrated metamodel to investigate the response of bog blueberry to various weather conditions. We found that an 8% reduction in fruit-set of bog blueberry is likely to happen if weather becomes warmer and wetter as predicted by climate models. In addition, southern and eastern production regions will suffer more severe fruit-set decline than the other growing regions. Predictions also suggest that increasing commercially available honeybee densities to 18 bees/m2/min, or bumble bee densities to 0.6 bees/m2/min, is a viable way to compensate for the predicted 8% climate induced fruit-set decline in the future.

Are Wild Blueberries a Crop with Low Photosynthetic Capacity? Chamber-Size Effects in Measuring Photosynthesis

Wild lowbush blueberries, an important fruit crop native to North America, contribute significantly to the economy of Maine, USA, Atlantic Canada, and Quebec. However, its photosynthetic capacity has not been well-quantified, with only a few studies showing its low photosynthetic rates. Its small leaves make accurate leaf-level photosynthetic measurements difficult and introduce potential uncertainties in using large leaf chambers. Here, we determined the photosynthetic rate for five different wild blueberry genotypes using a big leaf chamber enclosing multiple leaves and a small leaf chamber with a single leaf to test whether using big leaf chambers (branch-level measurements) underestimates the photosynthetic capacity. Photosynthetic rates of wild blueberries were significantly (35–47%) lower when using the big leaf chamber, and they are not a crop with low photosynthetic capacity, which can be as high as 16 μmol m−2 s−1. Additionally, wild blueberry leaves enclosed in the big chamber at different positions of a branch did not differ in chlorophyll content and photosynthetic rate, suggesting that the difference was not caused by variation among leaves but probably due to leaf orientations and self-shading in the big chamber. A significant linear relationship between the photosynthetic rate measured by the small and big leaf chambers suggests that the underestimation in leaf photosynthetic capacity could be corrected. Therefore, chamber-size effects need to be considered in quantifying photosynthetic capacity for small-leaf crops, and our study provided important guidelines for future photosynthesis research. We also established the relationship between the Electron Transport Rate (ETR) and photosynthetic CO2 assimilation for wild blueberries. ETR provides an alternative to quantify photosynthesis, but the correlation coefficient of the relationship (R2 = 0.65) suggests that caution is needed in this case.