All-male production by marker-assisted selection for sex determining loci of admixed Oreochromis niloticus and Oreochromis aureus stocks.

Crossing Oreochromis niloticus (On) females with Oreochromis aureus (Oa) males results in all-male progeny that are essential for effective tilapia farming. However, a reproductive barrier between these species limits mating and mass-fry production. One approach to overcoming this barrier is to select parental stocks of mixed genetic backgrounds, which allow interspecific reproductive recognition, while closely maintaining the genetic profiles for sex-determination (SD) of the respective purebred species. Here, we test this approach in a data set of 160 On × Oa spawns of 109 male and 100 female parents randomly collected from admixed stocks, and genotyped for microsatellite markers representing the known SD loci on linkage groups (LGs) 1, 3, and 23. Following crossbreeding, the most significant paternal effects on male proportions in progeny were found for LG1-BYL018 (P < 2 × 10-32 ) and for LG3-UNH168 × LG23-UNH898 interaction (P < 1 × 10-17 ; R2  = 0.98). Furthermore, a maternal effect for LG3-UNH168 (P < 9 × 10-7 ) was associated with low female proportions in progeny (<7%), indicating a non-Mendelian effect on SD. Eighty-four males (77%) and 30 females (30%) were selected as parents, based on their genetic profiles for the SD loci that were associated with male production. Of these, 51 of 53 crosses produced all-male progeny, while two crosses had low female proportions in their progeny (<4%). This suggests that selection could be improved using the causative sequence variation underlying SD on LG3, since the large non-recombining block of the SD region in purebred Oa readily breaks down in hybrids. Nevertheless, marker-assisted selection for sex determining loci of admixed parental stocks may be used for all-male production.

A multifunctional matching algorithm for sample design in agricultural plots.

Collection of accurate and representative data from agricultural fields is required for efficient crop management. Since growers have limited available resources, there is a need for advanced methods to select representative points within a field in order to best satisfy sampling or sensing objectives. The main purpose of this work was to develop a data-driven method for selecting locations across an agricultural field given observations of some covariates at every point in the field. These chosen locations should be representative of the distribution of the covariates in the entire population and represent the spatial variability in the field. They can then be used to sample an unknown target feature whose sampling is expensive and cannot be realistically done at the population scale. An algorithm for determining these optimal sampling locations, namely the multifunctional matching (MFM) criterion, was based on matching of moments (functionals) between sample and population. The selected functionals in this study were standard deviation, mean, and Kendall's tau. An additional algorithm defined the minimal number of observations that could represent the population according to a desired level of accuracy. The MFM was applied to datasets from two agricultural plots: a vineyard and a peach orchard. The data from the plots included measured values of slope, topographic wetness index, normalized difference vegetation index, and apparent soil electrical conductivity. The MFM algorithm selected the number of sampling points according to a representation accuracy of 90% and determined the optimal location of these points. The algorithm was validated against values of vine or tree water status measured as crop water stress index (CWSI). Algorithm performance was then compared to two other sampling methods: the conditioned Latin hypercube sampling (cLHS) model and a uniform random sample with spatial constraints. Comparison among sampling methods was based on measures of similarity between the target variable population distribution and the distribution of the selected sample. MFM represented CWSI distribution better than the cLHS and the uniform random sampling, and the selected locations showed smaller deviations from the mean and standard deviation of the entire population. The MFM functioned better in the vineyard, where spatial variability was larger than in the orchard. In both plots, the spatial pattern of the selected samples captured the spatial variability of CWSI. MFM can be adjusted and applied using other moments/functionals and may be adopted by other disciplines, particularly in cases where small sample sizes are desired.

The genome of an Encephalitozoon cuniculi type III strain reveals insights into the genetic diversity and mode of reproduction of a ubiquitous vertebrate pathogen.

Encephalitozoon cuniculi is a model microsporidian species with a mononucleate nucleus and a genome that has been extensively studied. To date, analyses of genome diversity have revealed the existence of four genotypes in E. cuniculi (EcI, II, III and IV). Genome sequences are available for EcI, II and III, and are all very divergent, possibly diploid and genetically homogeneous. The mechanisms that cause low genetic diversity in E. cuniculi (for example, selfing, inbreeding or a combination of both), as well as the degree of genetic variation in their natural populations, have been hard to assess because genome data have been so far gathered from laboratory-propagated strains. In this study, we aim to tackle this issue by analyzing the complete genome sequence of a natural strain of E. cuniculi isolated in 2013 from a steppe lemming. The strain belongs to the EcIII genotype and has been designated EcIII-L. The EcIII-L genome sequence harbors genomic features intermediate to known genomes of II and III lab strains, and we provide primers that differentiate the three E. cuniculi genotypes using a single PCR. Surprisingly, the EcIII-L genome is also highly homogeneous, harbors signatures of heterozygosity and also one strain-specific single-nucleotide polymorphism (SNP) that introduces a stop codon in a key meiosis gene, Spo11. Functional analyses using a heterologous system demonstrate that this SNP leads to a deficient meiosis in a model fungus. This indicates that EcIII-L meiotic machinery may be presently broken. Overall, our findings reveal previously unsuspected genome diversity in E. cuniculi, some of which appears to affect genes of primary importance for the biology of this pathogen.

Responses of apple fruit size to tree water status and crop load.

The combined effects of irrigation rate and crop load on apple yield and fruit size were examined in two commercial apple orchards (cv. Golden Delicious) in a semi-arid zone. The irrigation rates applied were 1, 3 and 7 mm day(-1), and the two fruit thinning treatments involved adjusting crop load to 100 and 300 fruits per tree at Ortal and 50 and 150 fruits per tree at Matityahu. Unthinned trees served as the control. The fruit from each tree was picked separately, and fruit size distribution was determined with a commercial grading machine. Midday stem water potentials varied from -0.9 to -2.8 MPa, crop load varied from 80,000 to 1,900,000 fruit ha(-1) and crop yield varied from 10 to 144 Mg ha(-1). Midday stem water potential decreased with increasing crop load in all irrigation treatments at Matityahu, but only in the 1 mm day(-1) treatment at Ortal. The extent of the lowering of midday stem water potential by crop load decreased with increasing soil water availability. At both orchards, a similar response of total crop yield to crop load on a per hectare basis was observed. Mean fruit mass and relative yield of fruit > 70 mm in diameter increased with midday stem water potential, with the low crop loads having similar but steeper slopes than the high crop load. The responses of mean fruit mass and relative yield of fruit > 70 mm in diameter to midday stem water potential were similar at both orchards, perhaps indicating that thresholds for irrigation scheduling are transferable to other orchards within a region. Factors that may limit the transferability of these thresholds are discussed.

Use of thermal and visible imagery for estimating crop water status of irrigated grapevine.

Achieving high quality wine grapes depends on the ability to maintain mild to moderate levels of water stress in the crop during the growing season. This study investigates the use of thermal imaging for monitoring water stress. Experiments were conducted on a wine-grape (Vitis vinifera cv. Merlot) vineyard in northern Israel. Irrigation treatments included mild, moderate, and severe stress. Thermal and visible (RGB) images of the crop were taken on four days at midday with a FLIR thermal imaging system and a digital camera, respectively, both mounted on a truck-crane 15 m above the canopy. Aluminium crosses were used to match visible and thermal images in post-processing and an artificial wet surface was used to estimate the reference wet temperature (T(wet)). Monitored crop parameters included stem water potential (Psi(stem)), leaf conductance (g(L)), and leaf area index (LAI). Meteorological parameters were measured at 2 m height. CWSI was highly correlated with g(L) and moderately correlated with Psi(stem). The CWSI-g(L) relationship was very stable throughout the season, but for that of CWSI-Psi(stem) both intercept and slope varied considerably. The latter presumably reflects the non-direct nature of the physiological relationship between CWSI and Psi(stem). The highest R(2) for the CWSI to g(L) relationship, 0.91 (n=12), was obtained when CWSI was computed using temperatures from the centre of the canopy, T(wet) from the artificial wet surface, and reference dry temperature from air temperature plus 5 degrees C. Using T(wet) calculated from the inverted Penman-Monteith equation and estimated from an artificially wetted part of the canopy also yielded crop water-stress estimates highly correlated with g(L) (R(2)=0.89 and 0.82, respectively), while a crop water-stress index using 'theoretical' reference temperatures computed from climate data showed significant deviations in the late season. Parameter variability and robustness of the different CWSI estimates are discussed. Future research should aim at developing thermal imaging into an irrigation scheduling tool applicable to different crops.