Invited review: Mid-infrared spectroscopy as phenotyping tool for milk traits

Interest in methods that routinely and accurately measure and predict animal characteristics is growing in importance, both for quality characterization of livestock products and for genetic purposes. Mid-infrared spectroscopy (MIRS) is a rapid and cost-effective tool for recording phenotypes at the population level. Mid-infrared spectroscopy is based on crossing matter by electromagnetic radiation and on the subsequent measure of energy absorption, and it is commonly used to determine traditional milk quality traits in official milk laboratories. The aim of this review was to focus on the use of MIRS to predict new milk phenotypes of economic relevance such as fatty acid and protein composition, coagulation properties, acidity, mineral composition, ketone bodies, body energy status, and methane emissions. Analysis of the literature demonstrated the feasibility of MIRS to predict these traits, with different accuracies and with margins of improvement of prediction equations. In general, the reviewed papers underlined the influence of data variability, reference method, and unit of measurement on the development of robust models. A crucial point in favor of the application of MIRS is to stimulate the exchange of data among countries to develop equations that take into account the biological variability of the studied traits under different conditions. Due to the large variability of reference methods used for MIRS calibration, it is essential to standardize the methods used within and across countries.     

Field‐based robotic phenotyping of sorghum plant architecture using stereo vision

Sorghum (Sorghum bicolor) is known as a major feedstock for biofuel production. To improve its biomass yield through genetic research, manually measuring yield component traits (e.g. plant height, stem diameter, leaf angle, leaf area, leaf number, and panicle size) in the field is the current best practice. However, such laborious and time‐consuming tasks have become a bottleneck limiting experiment scale and data acquisition frequency. This paper presents a high‐throughput field‐based robotic phenotyping system which performed side‐view stereo imaging for dense sorghum plants with a wide range of plant heights throughout the growing season. Our study demonstrated the suitability of stereo vision for field‐based three‐dimensional plant phenotyping when recent advances in stereo matching algorithms were incorporated. A robust data processing pipeline was developed to quantify the variations or morphological traits in plant architecture, which included plot‐based plant height, plot‐based plant width, convex hull volume, plant surface area, and stem diameter (semiautomated). These image‐derived measurements were highly repeatable and showed high correlations with the in‐field manual measurements. Meanwhile, manually collecting the same traits required a large amount of manpower and time compared to the robotic system. The results demonstrated that the proposed system could be a promising tool for large‐scale field‐based high‐throughput plant phenotyping of bioenergy crops.     

Correlation of cooking time with water absorption and changes in relative density during boiling of cassava roots

Consumers prefer cassava roots that cook quickly during boiling. Current methods to evaluate cooking time (CT) are slow and labour-intensive. This article describes improved protocols for assessing CT in roots. We evaluated CT in 36 genotypes monthly at 8–11 months after planting. CT showed differences for plant age at harvest and among genotypes. During boiling, roots absorbed water (WAB) and thus reduced their relative density (DEN). We classified three groups of genotypes with increasing CT (≤25 min, 25–40 min and >40 min), associated with decreasing WAB, respectively, 15.3 ± 3.1, 10.7 ± 1.7 and 4.9 ± 3.8% of initial root weight. A similar trend was observed for changes in DEN (46.3 ± 9.8, 54.5 ± 11.1 and 75.9 ± 6.9% of initial DEN, respectively). The highest correlations between WAB and DEN with CT (r² > 0.6) were found at 30-min boiling. These alternative protocols facilitate screening large numbers of cassava genotypes for CT.     

Application of genomics to grapevine improvement

Imagine a breeder browsing a grape chromosome nucleotide-by-nucleotide around a trait locus, scrolling down the list of catalogued genes along a genetic interval, resequencing for a few thousand dollars a potential parent or a selected breeding line. In the past couple of years, this vision has become a reality. The availability of the reference genome sequence has provided significant assistance in the saturation of loci with targeted genetic markers. Grape breeders are now offered unprecedented possibilities for selecting plants using deoxyribonucleic acid (DNA) sequences within or near the gene that controls a desirable trait rather than handling their phenotypes. Genomics-assisted selection offers unique advantages in the correct choice of elite genotypes, in order to improve traits for which limitations of phenotyping technologies or low hereditability adversely affect the efficiency of phenotypic selection. DNA technologies enable the application of marker-assisted selection to thousands of grape seedlings every year, which was previously feasible only for cereals and annuals, enhancing the possibilities of finding an ideal recombinant in populations bred from highly heterozygous parents. The expected outcome is a renewal of the varietal choices available to viticulturists, with novel genotypes that meet the demand for disease-free vines and flavourful grapes. The depth of exploration and characterisation of the existing germplasm is crucial for translating natural diversity into new varieties that could perform beyond the fence of the experimental vineyards and gain substantial market share. We review here how current achievements in genomics and genome sequencing are expected to increase the efficiency of grapevine breeding programs.     

Milk losses and dynamics during perturbations in dairy cows differ with parity and lactation stage

Milk yield dynamics during perturbations reflect how cows respond to challenges. This study investigated the characteristics of 62,406 perturbations from 16,604 lactation curves of dairy cows milked with an automated milking system at 50 Belgian, Dutch, and English farms. The unperturbed lactation curve representing the theoretical milk yield dynamics was estimated with an iterative procedure fitting a model on the daily milk yield data that was not part of a perturbation. Perturbations were defined as periods of at least 5 d of negative residuals having at least 1 day that the total daily milk production was below 80% of the estimated unperturbed lactation curve. Every perturbation was characterized and split in a development and a recovery phase. Based hereon, we calculated both the characteristics of the perturbation as a whole, and the duration, slopes, and milk losses in the phases separately. A 2-way ANOVA followed by a pairwise comparison of group means was carried out to detect differences between these characteristics in different lactation stages (early, mid-early, mid-late, and late) and parities (first, second, and third or higher). On average, 3.8 ± 1.9 (mean ± standard deviation) perturbations were detected per lactation in the first 305 d after calving, corresponding to an estimated 92.1 ± 135.8 kg of milk loss. Only 1% of the lactations had no perturbations. On average, 2.3 kg of milk was lost per day in the development phase, while the recovery phase corresponded to an average increase in milk production of 1.5 kg/d, and these phases lasted an average of 10.1 and 11.6 d, respectively. Perturbation characteristics were significantly different across parity and lactation stage groups, and early and mid-early perturbations in higher parities were found to be more severe with faster development rates, slower recovery rates, and higher milk losses. The method to characterize perturbations can be used for precision phenotyping purposes that look into the response of cows to challenges or that monitor applications (e.g., to evaluate the development and recovery of diseases and how these are affected by preventive actions or treatments).