Estimation of rice protein content before harvest using ground-based hyperspectral imaging and region of interest analysis

Protein content, which represents rice taste quality, must be estimated in order to create a harvesting plan as well as next year’s basal dressing fertilizer application plan. Ground-based hyperspectral imaging with high resolution (1 × 1 mm per pixel) was used for estimating the protein content of brown rice before harvest. This paper compares the estimation accuracy of rice protein content estimation models generated from the mean reflectances of five regions of interest (ROIs): the overall target area, dark area (less illuminated parts of the rice plants), canopy area (leaves, yellow leaves, and ears), leaf area, and ear and yellow leaf area. The size of the target sampling area was 0.85 × 0.85 m. An R + G + B histogram and a GNDVI–NDVI image were used to separate the target area into the individual ROIs. The values of the coefficient of determination R ² and the root mean square error of prediction (RMSE) were similar for each model: R ² ranged from 0.83 to 0.86 and RMSE ranged from 0.27 to 0.30% for all models except for the dark area model, where R ² = 0.76 and RMSE = 0.35%. There were no significant differences in the magnitude of the estimation error among all models. This result indicates that it is not necessary to obtain an image with a ground resolution that is greater than 0.85 × 0.85 m per pixel to estimate rice protein content before harvest. This result should provide useful information when deciding the altitude of platforms for imaging rice fields.     

Estimation of the rootzone depth above a gravel layer (in wild blueberry fields) using electromagnetic induction method

Wild blueberry (Vaccinium angustifolium Ait.) fields in the north east Canada are naturally grown in a course textured thin layer of soil and below this layer is a soilless layer of gravel. The root zone depth of this crop varies from 10 to 15 cm. Investigating the depth to the gravel layer below the course textured soil is advantageous, as it affects the water holding capacity of the root zone. Water and nutrient management are the two primary determinants of crop yield and the amount of leaching. The objective of this study was to estimate the depth to the gravel layer using DualEM-2 instrument. A C++ program written in Visual Studio 2010 was used to develop mathematical models for estimating the depth to the gravel layer from the outputs of DualEM-2 sensor. Two wild blueberry fields were selected in central Nova Scotia, Canada to evaluate the performance of DualEM-2 instrument in estimating the rootzone depth above the gravel layer. The mid points of squares created by grid lines were used as the sampling points at each experimental site. The actual depth to the interface was measured manually at selected grid points (n = 50). The apparent ground conductivity (EC_a) values of DualEM-2 were recorded and the depth to the interface was estimated for the same sampling points within the selected fields. The fruit yield samples were also collected from the same grid points to identify the impact of the depth to the gravel layer on crop yield. After calibrations, comprehensive surveys were conducted and the actual and estimated depths to the interface were established. The interpolated maps of fruit yield, and the actual (z_in) and estimated (\( {\text{z}}_{\text{in}}^{*} \)) depths to the interface were created in ArcGIS 10 software. Results indicated that the z_in was significantly correlated with \( {\text{z}}_{\text{in}}^{*} \) for the North River (R ² = 0.73; RMSE = 0.27 m) and the Carmel (R ² = 0.45; RMSE = 0.20 m) sites. Results revealed that the areas with shallow depth to the gravel layer were low yielding, indicating that the variation in the depth to the gravel layer can have an impact on crop productivity. Non-destructive estimations of the depth to the gravel layer can be used to develop erosion control strategies, which will result in an increased crop production.     

Estimation of bioenergy crop yield and N status by hyperspectral canopy reflectance and partial least square regression

The objective of this study was to compare performance of partial least square regression (PLSR) and best narrowband normalize nitrogen vegetation index (NNVI) linear regression models for predicting N concentration and best narrowband normalize different vegetation index (NDVI) for end of season biomass yield in bioenergy crop production systems. Canopy hyperspectral data was collected using an ASD FieldSpec FR spectroradiometer (350–2500 nm) at monthly intervals in 2012 and 2013. The cropping systems evaluated in the study were perennial grass {mixed grass [50 % switchgrass (Panicum virgatum L.), 25 % Indian grass “Cheyenne” (Sorghastrum nutans (L.) Nash) and 25 % big bluestem “Kaw” (Andropogon gerardii Vitman)] and switchgrass “Alamo”} and high biomass sorghum “Blade 5200” (Sorghum bicolor (L.) Moench) grown under variable N applications rates to estimate biomass yield and quality. The NNVI was computed with the wavebands pair of 400 and 510 nm for the high biomass sorghum and 1500 and 2260 nm for the perennial grass that were strongly correlated to N concentration for both years. Wavebands used in computing best narrowband NDVI were highly variable, but the wavebands from the red edge region (710–740 nm) provided the best correlation. Narrowband NDVI was weakly correlated with final biomass yield of perennial grass (r² = 0.30 and RMSE = 1.6 Mg ha^?1 in 2012 and r² = 0.37 and RMSE = 4.0 Mg ha^?1, but was strongly correlated for the high biomass sorghum in 2013 (r² = 0.72 and RMSE = 4.6 Mg ha^?1). Compared to the best narrowband VI, the RMSE of the PLSR model was 19–41 % lower for estimating N concentration and 4.2–100 % lower for final biomass. These results indicates that PLSR might be best for predicting the final biomass yield using spectral sample obtained in June to July, but narrowband NNVI was more robust and useful in predicting N concentration.     

RGB and multispectral UAV image fusion for Gramineae weed detection in rice fields

In this paper, a new method to fuse low resolution multispectral and high resolution RGB images is introduced, in order to detect Gramineae weed in rice fields with plants at 50 days after emergence (DAE).The images are taken from a fixed-wing unmanned aerial vehicle (UAV) at 60 and 70 m altitude. The proposed method combines the texture information given by a high resolution red–green–blue (RGB) image and the reflectance information given by a low resolution multispectral (MS) image, to obtain a fused RGB-MS image with better weed discrimination features. After analyzing the normalized difference vegetation index (NDVI) and normalized green red difference index (NGRDI) for weed detection, it was found that NGRDI presents better features. The fusion method consists of decomposing the RGB image using the intensity, hue and saturation (IHS) transformation, then, a second order Haar wavelet transformation is applied to the intensity layer (I) and the NGRDI image. From this transformation, the low–low (LL) coefficients of the NGRDI image are replaced by the LL coefficients of the I layer. Finally, the fused image is obtained by transforming the new wavelet coefficients to RGB space. To test the method, a one hectare experimental plot with rice plants at 50 DAE with Gramineae weeds was selected. Additionally, to compare the performance of the method, two indices were used, specifically, the M/M_GT index which is the percentage of detected weed area, and the MP index which indicates the precision of weed detection. These indices were evaluated in four validation zones using three Neural Networks (NN) detection systems based on three types of images; namely, RGB, RGB + NGRDI, and fused RGB-NGRDI. The best weed detection performance was obtained by the NN with the fused image, with M/M_GT index between 80 and 108% and MP between 70 and 85%.     

Sampling of subterranean termites <Emphasis Type="Italic">Syntermes</Emphasis> spp. (Isoptera: Termitidae) in a eucalyptus plantation using point process and geostatistics

The Syntermes genus, the most significant termite pest in eucalyptus cultivation, damages roots and debarks plant rings. This can kill the seedlings of this plant, and thus require replanting. Integrated management, based on sampling plans can reduce damage to eucalyptus seedlings and allows the rational use of chemical control. The objectives were to model the spatial distribution of the Syntermes spp. foraging holes using the Matérn-cluster point process in the Cerrado region (Brazilian savannah), simulate a sampling plan for termite hole density, produce maps of foraging hole densities using geostatistics and validate the simulated sampling plan in the field. The distribution of the Syntermes spp. foraging holes was spatially non-homogeneous and it adjusted to the Matérn-cluster point process model in the Minas Gerais Cerrado areas. The best Syntermes sampling plan simulation in the area of Cerrado is to launch a circular 5 m radius parcel every 100 m (sampling error <5 %) in a systematic manner. The approach of point processes, combined with geostatistics, is adequate to produce maps for the termite Syntermes spp. infestation in the eucalyptus plantation.     

Genetic differentiation of the Novosibirsk population of Primorsky honey bee

Seven microsatellite loci were evaluated to compare the allele pool of Primorsky honeybee population (n = 90), which was introduced into the Novosibirsk region (south-western Siberia), with the populations of Middle Russian (n = 191, A.m. mellifera), Mountain Grey Caucasian (n =113, A.m. caucasica), Carniolan (n = 61, A.m. carnica) and Carpathian (n = 184, A.m. carpatica) races. The degree of genetic differentiation in Novosibirsk population using a variety of criteria (Fst, Rst (AMOVA), Nei genetic distances) was evaluated.     

Hyperspectral discrimination of foliar biotic damages in rice using principal component analysis and probabilistic neural network

Assessment of crop health status in real time could provide reliable and useful information for making effective and efficient management decisions regarding the appropriate time and method to control crop diseases and insect damage. In this study, hyperspectral reflectance of symptomatic and asymptomatic rice leaves infected by Pyricularia grisea Sacc, Bipolaris oryzae Shoem, Aphelenchoides besseyi Christie and Cnaphalocrocis medinalis Guen was measured in a laboratory within the 350–2?500 nm spectral region. Principal component analysis was performed to obtain the principal component spectra (PCs) of different transformations of the original spectra, including original (R), common logarithm of reciprocal (lg (1/R)), and the first derivative of original and common logarithm of reciprocal spectra (R′ and (lg (1/R))′). A probabilistic neural network classifier was applied to discriminate the symptomatic rice leaves from asymptomatic ones with the front PCs. For identifying symptomatic and asymptomatic rice leaves, the mean overall discrimination accuracies for R, lg (1/R), R′ and (lg (1/R))′ were 91.3, 93.1, 92.3 and 92%, and the mean Kappa coefficients were 0.771, 0.835, 0.829 and 0.82, respectively. To discriminate between disease and insect damage, the overall accuracies for R, lg (1/R), R′ and (lg (1/R))′ were 97.7, 98.1, 100 and 100%, and the Kappa coefficients were 0.962, 0.97, 1 and 1, respectively. These results demonstrated that hyperspectral remote sensing can discriminate between multiple diseases and the insect damage of rice leaves under laboratory conditions.     

Evaluating the performance of xanthophyll,chlorophyll and structure-sensitive spectral indices to detect water stress in five fruit tree species

This study assessed the capability of several xanthophyll, chlorophyll and structure-sensitive spectral indices to detect water stress in a commercial farm consisting of five fruit tree crop species with contrasting phenology and canopy architecture. Plots irrigated and non-irrigated for eight days of each species were used to promote a range of plant water status. Multi-spectral and thermal images were acquired from an unmanned aerial system while concomitant measurements of stomatal conductance (g_s), stem water potential (Ψ_s) and photosynthesis were taken. The Normalized Difference Vegetation Index (NDVI), red-edge ratio (R₇₀₀/R₆₇₀), Transformed Chlorophyll Absorption in Reflectance Index normalized by the Optimized Soil Adjusted Vegetation Index (TCARI/OSAVI), the Photochemical Reflectance Index using reflectance at 530 (PRI) and 515 nm [PRI_(570–515)] and the normalized PRI (PRI_norm) were obtained from the narrow-band multi-spectral images and the relationship with the in-field measurements explored. Results showed that within the Prunus species, Ψ_s yielded the best correlations with PRI and PRI_(570–515) (r² = 0.53) in almond trees, with TCARI/OSAVI (r² = 0.88) in apricot trees and with PRI_norm, R₇₀₀/R₆₇₀ and NDVI (r² from 0.72 to 0.88) in peach trees. Weak or no correlations were found for the Citrus species due to the low level of water stress reached by the trees. Results from the sensitivity analysis pointed out the canopy temperature (T_c) and PRI_(570–515) as the first and second most sensitive indicators to the imposed water conditions in all the crops with the exception of apricot trees, in which Ψ_s was the most sensitive indicator at midday. PRI_norm was the least sensitive index among all the water stress indicators studied. When all the crops were analyzed together, PRI_(570–515) and NDVI were the indices that better correlations yielded with Crop Water Stress Index, g_s and, particularly, Ψ_s (r² = 0.61 and 0.65, respectively). This work demonstrated the feasibility of using narrow-band multispectral-derived indices to retrieve water status for a variety of crop species with contrasting phenology and canopy architecture.     

Monitoring nitrogen,phosphorus and sulphur in hybrid rice (<Emphasis Type="Italic">Oryza sativa</Emphasis> L.) using hyperspectral remote sensing

Till date, the remote sensing research on crop nutrient monitoring has focused mainly on biomass and nitrogen (N) estimation and only a few attempts had been made to characterize and monitor macronutrients other than N. Field experiments were undertaken to study the remote detection of macronutrient status of rice using hyperspectral remote sensing. The variability in soil available N, phosphorus (P) and sulphur (S) and their content in plants were created using artificial fertility gradient design. The leaf and canopy hyperspectral reflectance was captured from variable macronutrient status vegetation. Linear correlation analysis between the spectral reflectance and plant nutrient status revealed significantly (p < 0.05) higher correlation coefficient at 670, 700, 730, 1090, 1260, 1460 nm for the nutrient under study. Published and proposed vegetation indices (VIs) were tested for canopy N, P and S prediction. The results of the investigation revealed that, published VIs (NDVI hyper and NDVI broadbands) could retrieve canopy N with higher accuracy, but not P and S. The predictability of the visible and short wave infrared based VI NRI₁₅₁₀ ((R₁₅₁₀ ? R₆₆₀)/(R₁₅₁₀ + R₆₆₀)) was the highest (r = 0.81, p < 0.01) for predicting N. Based on the outcomes of linear correlation analysis new VIs were proposed for remote detection of P and S. Proposed VI P_670_1260 ((R₁₂₆₀ ? R₆₇₀)/(R₁₂₆₀ + R₆₇₀)) retrieved canopy P status with higher prediction accuracy (r = 0.67, p < 0.01), whereas significantly higher canopy S prediction (r = 0.58, p < 0.01) was obtained using VI S_670_1090 ((R₁₀₉₀ ? R₆₇₀)/(R₁₀₉₀ + R₆₇₀)). The proposed spectral algorithms could be used for real time and site-specific N, P and S management in rice. Nutrient specific wavelengths, identified in the present investigation, could be used for developing relatively low-cost sensors of hand-held instruments to monitor N, P and S status of rice plant.     

Intervalent Crosses: the Main Way of Creating Triploid Apple Cultivars

This paper presents history and ways of creating triploid apple cultivars differing from common diploid ones by less pronounced fruiting periodicity by years, higher marketability and weight of fruits, and self-fertility. In the search for an effective method of mass production of triploid seedlings, crosses of the type 4x × 4x, 4x × 3x, 4x × 2x, 3x × 4x, 3x × 3x, 3x × 2x, 2x × 4x, and 2x × 3x were studied. The most effective way of mass production of triploid seedlings was the 2x × 4x cross (diploid × tetraploid). In the group of 2x × 4x crosses, more than 9000 seedlings from different families were subjected to cytological analysis. The ratio of hybrid progeny in terms of ploidy levels was as follows: 30.3% diploids, 69.5% triploids, 0.2% tetraploids, and 0.04% aneuploids. It was established that triploids can also be obtained in 4x × 2x crosses; however, it is necessary to castrate the flowers in the maternal parent, since all apple tetraploids have high self-fertility. For the first time in the world, a series of triploid cultivars (approximately 20) was obtained from intervalent crosses of the diploid × tetraploid type in the All-Russia Research Institute of Fruit Crop Selection. Particularly interesting are triploid cultivars that have immunity to scab, which include nine cultivars, of which Aleksandr Boiko, Vavilovskoe, Maslovskoe, and Yablochnyi Spas are included in the State Register of breeding achievements allowed for use.     

Predicting cover crop biomass by lightweight UAS-based RGB and NIR photography: an applied photogrammetric approach

Easy-to-capture and robust plant status indicators are important factors when implementing precision agriculture techniques on fields. In this study, aerial red, green and blue color space (RGB) photography and near-infrared (NIR) photography was performed on an experimental field site with nine different cover crops. A lightweight unmanned aerial system (UAS) served as platform, consumer cameras as sensors. Photos were photogrammetrically processed to orthophotos and digital surface models (DSMs). In a first validation step, the spatial precision of RGB orthophotos (x and y, ± 0.1 m) and DSMs (z, ± 0.1 m) was determined. Then, canopy cover (CC), plant height (PH), normalized differenced vegetation index (NDVI), red edge inflection point (REIP), and green red vegetation index (GRVI) were extracted. In a second validation step, the PHs derived from the DSMs were compared with ground truth ruler measurements. A strong linear relationship was observed (R ² = 0.80?0.84). Finally, destructive biomass samples were taken and compared with the remotely-sensed characteristics. Biomass correlated best with plant height (PH), and good approximations with linear regressions were found (R ² = 0.74 for four selected species, R ² = 0.58 for all nine species). CC and the vegetation indices (VIs) showed less significant and less strong overall correlations, but performed well for certain species. It is therefore evident that the use of DSM-based PHs provides a feasible approach to a species-independent non-destructive biomass determination, where the performance of VIs is more species-dependent.     

Genetic shifts in selection of hybrid offspring soybeans to economic-valuable signs

The article presents the importance of plant genetic hybrid shift in the elements of productivity, allowing the prediction of the conduct in the selection of promising lines obtained by interspecific hybridization spontaneous soybeans for further study in the selection process. The maximum shift of genetic selection on the basis of elements of efficiency in terms of the number of marked beans in combinations Volzhan × KZ-671, KZ-Rosinka × 1236, and Luch nadezhdy × KZ-6332 in terms of the mass of 1000 seeds only in the combination Luch nadezhdy × KZ-6332.     

Mobile terrestrial laser scanner applications in precision fruticulture/horticulture and tools to extract information from canopy point clouds

LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r = 0.56 to r = 0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e., height, width and volume) and on canopy structure (i.e., light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.     

Use of interspecific hybridization for increasing buckwheat tolerance to inbreeding

The productivity of inbred buckwheat lines I₁–I₈ with the homostyly gene of Fagopyrum esculentum and Fagopyrum homotropicum is studied. The species F. homotropicum is recommended for use in buckwheat breeding as a donor of homostyly and tolerance to inbreeding depression.     

Productivity and parameters of the adaptive ability and stability of pea varieties

The complex assessment has been given to the pea varieties permitted for use by productivity and parameters of the adaptive ability based on the results of their test in three natural and climatic zones in Tyumen oblast. The best varieties by average productivity have been found—in conditions of subtaiga, Yamal’skii; in northern forest steppe, Yamal; and in southern forest steppe, Omskii 9. All varieties had strong variability of productivity. The highest level of realization of productivity potential of varieties was noticed in the conditions of subtaiga. The highest general adaptive ability was peculiar to the varieties Yamal’skii, Agrointel, and Yamal; and the largest values of a variance of the specific adaptive ability were detected in the varieties Omskii 9, Batrak, and Tyumenets. By plasticity, the varieties were divided into three groups: weakly responsive to the improvement of conditions (b _i < 1)—Omskii 9 and Batrak; intensive, with high response to the improvement of conditions (b _i > 1)—Yamal and Agrointel; and plastic, changing the productivity of which fully corresponds changing the growth environment (b _i = 1)—Tyumenets and Yamal.     

Genetic Diversity of <Emphasis Type="Italic">E. coli O157:H7</Emphasis> Isolated from Some Leafy Greens,Irrigated by Aleppo River,Using Random Amplified Polymorphic DNA (RAPD) Marker

In recent years, fresh fruits and vegetables have been linked to numerous foodborne illness outbreaks in different regions of the world. In Syria, there is not a lot of research that study E. coli and its serotypes by PCR technology. In this study, we have fulfilled a total count of bacteria, the census total coliform group, and Escherichia coli, as well as the serotype E. coli O157:H7 on some leafy greens (Spinacia oleracea, Beta vulgaris) irrigated by Aleppo River. The molecular characterization was done for ten strains of E. coli isolated from collected samples. The samples were inoculated on blood agar and suspicious colonies, then transferred to EMB and MacConkey agar using a primer (COL 1) in RAPD technic. Molecular characterization also performed ten strains of serotype E. coli O157:H7 isolated in medium (Sorbitol-MacCONKEY Agar), then by primers (OPA-03, OPA-13 OPC-12, OPE-20) in RAPD technic. The results showed significant differences between collected samples. The total count of bacteria in the first site (Handarat) for spinach and chard were the lowest, but in the fourth site (Jezraya) it was the highest among all samples. The results showed also the existence of E. coli in all sites except the first one in riverbed Handarat, and E. coli O157:H7 was found only in Jezraya village. Extracted DNA from samples was amplified by RAPD. after electrophoresis in the gel agarose, eleven different bands were detected from isolated strains of E. coli. These results refer to the great genetic diversity of Escherichia coli. For serotype E. coli O157:H7 thirty-four different bands were detected. RAPD analysis had the high discriminatory capacity for typing E. coli isolates. Because of its simplicity and rapidity, RAPD analysis appears to be a highly valuable tool for studying E. coli molecular epidemiology.     

Breed characteristics associated with <Emphasis Type="Italic">LEP</Emphasis> gene polymorphisms in Holstein cattle

Genotyping of the leptin gene locus (LEP) (SNP: R25C, Y7F, and A80V) has been performed in the animals from two groups in the Holstein cattle herd. Groups 1 and 2 included the fourth to seventh lactation cows and the heifers, respectively. The objective of the surveys was to study the Holstein cattle polymorphisms of the leptin gene associated with the productive longevity according to the related literature data. It has been ascertained that the LEP gene (SNP: R25C, Y7F, and A80V) polymorphism in the Holstein animals determines the certain breed characteristics that can be used for introduction to marker assisted selection. (Particularly, a low (0.04–0.15) frequency of occurrence of the F allele is typical for the LEP-Y7F SNP; as for SNP LEP-A80V, a low frequency of occurrence (0–0.14) is typical for the homozygous animals with the VV genotype when compared to a rather high frequency of occurrence (0.36–0.45) in the heterozygous animals).     

Effects of Root-Zone Temperature on Growth,Chlorophyll Fluorescence Characteristics and Chlorophyll Content of Greenhouse Pepper Plants Grown under Cold Stress in Southern China

In Southern China, plants are usually exposed to cold stress during winter in an unheated greenhouse, but due to the high energy consumption and costs, most of the greenhouses remain unheated. In an attempt to find a simple and affordable solution to this problem, this study was undertaken. In this research, Capsicum frutescens L. plants were studied to investigate the effect of different root zone temperatures on its growth and chlorophyll fluorescence characteristics under cold stress. The plants were cultivated under cold stress conditions in a root zone temperature (RZT) control system where the roots were subjected to four different root-zone temperature treatments of 20°C-T20, 25°C-T25, 45°C-T45 and a control CK group. Growth characteristics studied included plant height, stem diameter, plant width, root length, biomass accumulation. Whilst fluorescence characteristics investigated were chlorophyll fluorescence ratio F_v/F_m, photochemical quenching (qL), efficiency of Photosystem II (Y[II]) and electron transport rate (ETR). Chlorophyll content in the leaves of the plants was also investigated. The findings demonstrated that plants in the CK group suffered a detrimental effect on the growth characteristics registering the lowest values in the measured variables. Conversely, the highest values were observed in T25 RZT treatment. In fluorescence characteristics, values of F_v/F_m were maintained at between 0.8 and 0.83 but also suffered a photo-inhibitory depression in CK and T45 RZT treatments to F_v/F_m values of <0.79. This depicted that root zone heating protected the PS II of these plants from photoinactivation induced by cold stress. Similar trends were seen in the qL, Y[II], ETR values with the T20 and T25 treatments registering the highest values. Chlorophyll content was significantly higher in the leaves of the plants in the T20 and T25 group. The lowest chlorophyll content was recorded in the CK group. Plants in all the treatments accumulated more biomass in the shoot than in the roots as depicted by a significantly lower shoot to root ratio values with the exception of those in the CK group. The findings of this study suggest that pepper plants can successfully be grown in an unheated greenhouse in the Yangtze River Delta area of Southern China during winter by heating the root zone of the plants to a RZT value of 25°C, thereby providing a simple, affordable and cost-effective technique.     

Genetic diversity of genus <Emphasis Type="Italic">Malus</Emphasis> Mill. for scab resistance genes

The results of molecular genetic analysis of wild species and varieties of genus Malus Mill. for loci of monogenic scab resistance are presented. Dominant allele of gene Rvi6 was identified in varieties of M. robusta 43199, M. hupehensis, and M. floribunda. Locus Rvi4 was detected in genotypes of M. robusta v. persicifolia, M. asiatica 2343, M. sieversii 13280, M. niedzwetzkyana 13279, M. purpurea 2392, M. orientalis 41623, M. orientalis 29476, M. orientalis 29 460, M. spectabilis v. albi plena, and M. spectabilis v. rubra plena. Gene Rvi5 in the test collection of wild species is absent.     

Effect of <Emphasis Type="Italic">GH</Emphasis> and <Emphasis Type="Italic">DGAT1</Emphasis> gene polymorphism on feeding qualities of bull calves

The aim of this paper is to study effects of GH and DGAT1 gene polymorphisms on feeding qualities of Hereford and Limousin bull calves bred in conditions of the Cis-Ural steppe zone. SNPs of genes GH and DGAT1 are investigated by polymerase chain reaction (PCR) and DNA restriction fragment length polymorphism (RFLP). The studied population of animals was assessed by defining the allele frequency and animal genotype occurrence for the studied gene SNPs, indicators of the actual and expected heterozygosity, and Pearson’s test. A study of polymorphism C214G of gene GH revealed that genotype LL prevails in Hereford and Limousin animals, 47.37 and 57.7%, respectively, while frequency of allele L is higher in Limousin bull calves (0.731). A study of polymorphism K232A of gene DGAT1 gene in both the populations showed absence of genotypes AA, which can be related to the low number of the studied animals. Expected heterozygosity indicators of gene GH are higher than the observed ones, and the observed heterozygosity is higher for DGAT1. The number of efficient alleles for the studied genes is higher for Hereford bull calves. In general, according to Pearson’s test, both the studied populations are in equilibrium. There is a significant effect of GH gene polymorphism on live weight gain rates at the end of sagination and total and average daily weight gains during animal raising.