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Blackberries (Rubus spp.) are the fourth most economically important berry crop worldwide. Genome assemblies and annotations have been developed for Rubus species in subgenus Idaeobatus, including black raspberry (R. occidentalis), red raspberry (R. idaeus), and R. chingii, but very few genomic resources exist for blackberries and their relatives in subgenus Rubus. Here we present a chromosomelength assembly and annotation of the diploid blackberry germplasm accession “Hillquist” (R. argutus). “Hillquist” is the only known source of primocane-fruiting (annual-fruiting) in tetraploid fresh-market blackberry breeding programs and is represented in the pedigree of many important cultivars worldwide. The “Hillquist” assembly, generated using Pacific Biosciences long reads scaffolded with high-throughput chromosome conformation capture sequencing, consisted of 298Mb, of which 270Mb (90%) was placed on 7 chromosome-length scaffolds with an average length of 38.6Mb. Approximately 52.8% of the genome was composed of repetitive elements. The genome sequence was highly collinear with a novel maternal haplotype-resolved linkage map of the tetraploid blackberry selection A-2551TN and genome assemblies of R. chingii and red raspberry. A total of 38,503 protein-coding genes were predicted, of which 72% were functionally annotated. Eighteen flowering gene homologs within a previously mapped locus aligning to an 11.2Mb region on chromosome Ra02 were identified as potential candidate genes for primocane-fruiting. The utility of the “Hillquist” genome has been demonstrated here by the development of the first genotyping-by-sequencing-based linkage map of tetraploid blackberry and the identification of possible candidate genes for primocane-fruiting. This chromosome-length assembly will facilitate future studies in Rubus biology, genetics, and genomics and strengthen applied breeding programs.


Parametric modeling of downwelling longwave irradiance under all-sky conditions (LW↓) typically involves “correcting” a clear- (or non-overcast) sky model estimate using solar-irradiance-based proxies of cloud cover in lieu of actual cloud cover given uncertainties and measurement challenges of the latter. While such approaches are deemed sound, their application in time and space is inherently limited. We report on a correction model free of solar irradiance-derived cloud proxies that is applicable at the true daily (24 hr) and global scales. The new “cloud-free” correction model demonstrates superior performance in a range of environments relative to existing cloud-free modeling approaches and to corrections based on solar-derived cloudiness proxies. Literature-based performance benchmarking indicates a performance that is often comparable to—and in some cases superior to—performances yielded by conventional parametric modeling approaches employing locally or regionally calibrated parameters, as well as to performances of satellite-based algorithms.


This study aims to estimate eco-efficiency scores and identify determinants of Norwegian dairy farms using a parametric approach that accounts for methane emissions. The study incorporates an environmental output measure and draws on 30 years of panel data from 692 specialist dairy farms (1991–2020). The findings indicate that Norwegian dairy farms are inefficient, with room for improvement in the dairy production system and the environment. According to the average eco-efficiency score, conventional dairy farms could cut input use and CH4 emissions by 5% while maintaining output. Furthermore, the study found that land tenure, experience, and government subsidies all positively impact eco-efficiency. Policymakers should encourage the best-performing dairy farms to share information on increasing productivity while considering environmental concerns to achieve better social and agricultural development. It should be noted that the study only looks at livestock methane emissions; future research may investigate other environmental factors.