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Summary: Leaf economic spectrum (LES) relationships have been studied across many different plant lineages and at different organizational scales. However, the temporal stability of the LES relationships is largely unknown. We used the wild blueberry system with high genotypic diversity to test whether trait–trait relationships across genotypes demonstrate the same LES relationships found in the global database (GLOPNET) and whether they are stable across years.We studied leaf structure, photosynthesis, and leaf nutrients for 16 genotypes of two wild blueberry species semi‐naturally grown in a common farm in Maine, USA, across 4 yr.We found substantial variation in leaf structure, physiology, and nutrient traits within and among genotypes, as well as across years in wild blueberries. The LES trait–trait relationships (covariance structure) across genotypes were not always found in all years. The trait syndrome of wild blueberries was shifted by changing environmental conditions over the years. Additionally, traits in 1 yr cannot be used to predict those of another year.Our findings show that LES generally holds among genotypes but is temporally unstable, stressing the significant influence of trait plasticity in response to fluctuating environmental conditions across years, and the importance of temporal dimensions in shaping functional traits and species coexistence. [ABSTRACT FROM AUTHOR]

Introduction: Mummy berry is a serious disease that may result in up to 70 percent of yield loss for lowbush blueberries. Practical mummy berry disease detection, stage classification and severity estimation remain great challenges for computer vision-based approaches because images taken in lowbush blueberry fields are usually a mixture of different plant parts (leaves, bud, flowers and fruits) with a very complex background. Specifically, typical problems hindering this effort included data scarcity due to high manual labelling cost, tiny and low contrast disease features interfered and occluded by healthy plant parts, and over-complicated deep neural networks which made deployment of a predictive system difficult. Methods: Using real and raw blueberry field images, this research proposed a deep multi-task learning (MTL) approach to simultaneously accomplish three disease detection tasks: identification of infection sites, classification of disease stage, and severity estimation. By further incorporating novel superimposed attention mechanism modules and grouped convolutions to the deep neural network, enabled disease feature extraction from both channel and spatial perspectives, achieving better detection performance in open and complex environments, while having lower computational cost and faster convergence rate. Results: Experimental results demonstrated that our approach achieved higher detection efficiency compared with the state-of-the-art deep learning models in terms of detection accuracy, while having three main advantages: 1) field images mixed with various types of lowbush blueberry plant organs under a complex background can be used for disease detection; 2) parameter sharing among different tasks greatly reduced the size of training samples and saved 60% training time than when the three tasks (data preparation, model development and exploration) were trained separately; and 3) only one-sixth of the network parameter size (23.98M vs. 138.36M) and one-fifteenth of the computational cost (1.13G vs. 15.48G FLOPs) were used when compared with the most popular Convolutional Neural Network VGG16. Discussion: These features make our solution very promising for future mobile deployment such as a drone carried task unit for real-time field surveillance. As an automatic approach to fast disease diagnosis, it can be a useful technical tool to provide growers real time disease information that can prevent further disease transmission and more severe effects on yield due to fruit mummification. [ABSTRACT FROM AUTHOR]

Early detection and accurately rating the level of plant diseases plays an important role in protecting crop quality and yield. The traditional method of mummy berry disease (causal agent: Monilinia vaccinii-corymbosi) identification is mainly based on field surveys by crop protection experts and experienced blueberry growers. Deep learning models could be a more effective approach, but their performance is highly dependent on the volume and quality of labeled data used for training so that the variance in visual symptoms can be incorporated into a model. However, the available dataset for mummy berry disease detection does not contain enough images collected and labeled from a real-field environment essential for making highly accurate models. Complex visual characteristics of lesions due to overlapping and occlusion of plant parts also pose a big challenge to the accurate estimation of disease severity. This may become a bigger issue when spatial variation is introduced by using sampling images derived from different angles and distances. In this paper, we first present the "cut-and-paste" method for synthetically augmenting the available dataset by generating additional annotated training images. Then, a deep learning-based object recognition model Yolov5s-CA was used, which integrates the Coordinated Attention (CA) module on the Yolov5s backbone to effectively discriminate useful features by capturing channel and location information. Finally, the loss function GIoU_loss was replaced by CIoU_loss to improve the bounding box regression and localization performance of the network model. The original Yolov5s and the improved Yolov5s-CA network models were trained on real, synthetic, and combined mixed datasets. The experimental results not only showed that the performance of Yolov5s-CA network model trained on a mixed dataset outperforms the baseline model trained with only real field images, but also demonstrated that the improved model can solve the practical problem of diseased plant part detection in various spatial scales with possible overlapping and occlusion by an overall precision of 96.30%. Therefore, our model is a useful tool for the estimation of mummy berry disease severity in a real field environment. [ABSTRACT FROM AUTHOR]

Our objective was to evaluate the effects of application rate (AR) of sodium lignosulfonate and propionic acid on high moisture alfalfa hay spoilage during storage. Treatments (TRT) were sodium lignosulfonate (NaL) and Propionic Acid (PRP), which were applied at four AR: 0 (CON), 0.25, 0.5 and 1% (w/w fresh basis) to 20% bloom alfalfa hay (second cut) at 68.5% DM and packed into mini bales (10.3x10.8x13cm; 346kg fresh/m3). The treated bales were stored for 33 days in opentop insulation boxes in a room kept at 22°C. Data were analyzed as a randomized complete block design (5 blocks) with a 2 (TRT) x 4 (AR) factorial arrangement, and differences were declared at P = 0.05. At day 0, no differences were observed for DM (68.5 ± 0.61 %), CP (23.2 ± 0.31% DM), and NDF (42.6 ± 0.66% DM), but NaL had more sugars than PRP (0.99 vs. 0.87 ± 0.66% DM) and PRP at 1% reduced mold counts vs. CON (4.71 vs. 4.97 ± 0.05 log cfu/fresh g). At day 33, DM losses were decreased by PRP at 0.5 and 1% (0.9), relative to CON (6.92) and to NaL (6.63 ± 1.13%). Both TRT decreased NDF at 0.25% (46.3) and furthermore at 1% (44.2) relative to CON (49.4), but overall PRP had lower NDF (45.2) than NaL (47.2± 0.81% DM). No differences were observed for sugars and CP. Visual moldiness (0-10) and mold counts were also decreased by PRP at 0.5% (2.4 and 5.30) and 1% (0 and 2.7) relative to CON (6.0 and 7.13) and NaL (5.85 ± 0.67 and 7.21 ± 0.31 log cfu/fresh g, respectively). In conclusion, NaL failed to prevent spoilage of high moisture hay while PRP was effective at doses >0.5%. [ABSTRACT FROM AUTHOR]

This study evaluated the effects of application rate (AR) of sodium lignosulfonate (MBL) and propionic acid (PRP) on high moisture alfalfa hay spoilage during storage and its nutritive value. Treatments (TRT; MBL and PRP) were applied at four AR: 0 (CON), 2.5, 5 and 10 g/kg (fresh basis) to alfalfa hay at 68.5 % dry matter (DM), packed into mini bales and stored for 33 d. Data were analyzed as a randomized complete block design (5 blocks) with a 2 TRT × 4 AR factorial arrangement. At d 33, DM losses were decreased by PRP at 5 and 10 g/kg ( x ¯ = 0.9), vs. CON (6.92) and MBL (6.63 ± 1.13 %). Visual moldiness (0–10 ranking) and mold counts were also decreased by PRP at 5 (2.4 and 5.30) and 10 g/kg (0 and 2.7) relative to CON (6.0 and 7.13) and MBL (5.85 ± 0.67 and 7.21 ± 0.31 log cfu/fresh g, respectively). Both TRT kept aNDF and ADF concentrations low at 2.5 g/kg AR (464 and 319) and to a greater extent at 10 g/kg (442 and 307), relative to CON (494 ± 8.1 and 333 ± 4.71 g/kg DM, respectively; P <0.001). Both TRT increased apparent in vitro DM digestibility at all AR relative to CON ( x ¯ = 543 vs. 501 ± 12.0 g/kg of DM, respectively), but, compared to MBL, PRP also increased the asymptotic maximal gas produced (188.4 vs. 179.7 ± 4.82 mL/g of incubated DM), the rate of gas production (9.46 vs. 8.72 ± 0.45 %/h), and total volatile fatty acids (117.5 vs. 114.8 m M , respectively; P <0.035), due to its greater concentration of nutrients. In conclusion, MBL failed to prevent spoilage of high moisture alfalfa hay while PRP was effective at doses >5 g/kg. However, MBL prevented the increase of aNDF and improved in vitro DM digestibility to the same extent as PRP, possibly due to its strong surfactant properties. • Sodium lignosulfonate (MBL) is a paper mill by-product with antimicrobial properties. • MBL failed to prevent spoilage of high moisture alfalfa hay mini bales. • In vitro dry matter digestibility was improved in hay treated with MBL. • The strong surfactant properties of MBL warrant further exploration as a preservative. [ABSTRACT FROM AUTHOR]

The fungus Monilinia vaccinii-corymbosi (Mvc) causes mummy berry disease in blueberries including lowbush blueberry, Vaccinium angustifolium , and is a significant pathogen of concern for Maine lowbush blueberry growers. This disease is typically managed with fungicides or by burning of plant debris containing overwintering pseudosclerotia. The population structure of Mvc in various fields in Maine was investigated using microsatellites and isolates collected from three stages in the Mvc lifecycle. The impacts of management strategies were also examined. A high level of genetic diversity was observed in Mvc from 12 lowbush blueberry fields with 199 unique multilocus haplotypes (MLHs) occurring in an original sample of 232 isolates. Twelve private alleles, including six private alleles with frequencies above 0.05, which indicated gene flow, were observed in six out of 12 fields. The population of Mvc in Maine as a whole is mostly a sexual, outcrossing population, as was seen in the diversity of MLHs and low amounts of linkage disequilibrium, although some apothecia appear to result from selfing. Three fields appear to have some clonal reproduction but were not strictly clonal, as multiple MLHs were noted in these fields. Management does not appear to affect population structure, and Mvc may be one large statewide population in Maine., Competing Interests: The author(s) declare no conflict of interest.