Artificial intelligence in plant breeding.

Harnessing cutting-edge technologies to enhance crop productivity is a pivotal goal in modern plant breeding. Artificial intelligence (AI) is renowned for its prowess in big data analysis and pattern recognition, and is revolutionizing numerous scientific domains including plant breeding. We explore the wider potential of AI tools in various facets of breeding, including data collection, unlocking genetic diversity within genebanks, and bridging the genotype-phenotype gap to facilitate crop breeding. This will enable the development of crop cultivars tailored to the projected future environments. Moreover, AI tools also hold promise for refining crop traits by improving the precision of gene-editing systems and predicting the potential effects of gene variants on plant phenotypes. Leveraging AI-enabled precision breeding can augment the efficiency of breeding programs and holds promise for optimizing cropping systems at the grassroots level. This entails identifying optimal inter-cropping and crop-rotation models to enhance agricultural sustainability and productivity in the field.

Effect of Far-Red Light on Biomass Accumulation, Plant Morphology, and Phytonutrient Composition of Ruby Streaks Mustard at Microgreen, Baby Leaf, and Flowering Stages.

Far-red (FR) light influences plant development significantly through shade avoidance response and photosynthetic modulation, but there is limited knowledge on how FR treatments influence the growth and nutrition of vegetables at different maturity stages in controlled environment agriculture (CEA). Here, we comprehensively investigated the impacts of FR on the yield, morphology, and phytonutrients of ruby streaks mustard (RS) at microgreen, baby leaf, and flowering stages. Treatments including white control, white with supplementary FR, white followed by singularly applied FR, and enhanced white (WE) matching the extended daily light integral (eDLI) of FR were designed for separating the effects of light intensity and quality. Results showed that singular and supplemental FR affected plant development and nutrition similarly throughout the growth cycle, with light intensity and quality playing varying roles at different stages. Specifically, FR did not affect the fresh and dry weight of microgreens but increased those values for baby leaves, although not as effectively as WE. Meanwhile, FR caused significant morphological change and accelerated the development of leaves, flowers, and seedpods more dramatically than WE. With regard to phytonutrients, light treatments affected the metabolomic profiles for baby leaves more dramatically than microgreens and flowers. FR decreased the glucosinolate and anthocyanin contents in microgreens and baby leaves, while WE increased the contents of those compounds in baby leaves. This study illustrates the complex impacts of FR on RS and provides valuable information for selecting optimal lighting conditions in CEA.

UAV-based multi-sensor data fusion and machine learning algorithm for yield prediction in wheat.

Early prediction of grain yield helps scientists to make better breeding decisions for wheat. Use of machine learning (ML) methods for fusion of unmanned aerial vehicle (UAV)-based multi-sensor data can improve the prediction accuracy of crop yield. For this, five ML algorithms including Cubist, support vector machine (SVM), deep neural network (DNN), ridge regression (RR) and random forest (RF) were used for multi-sensor data fusion and ensemble learning for grain yield prediction in wheat. A set of thirty wheat cultivars and breeding lines were grown under three irrigation treatments i.e., light, moderate and high irrigation treatments to evaluate the yield prediction capabilities of a low-cost multi-sensor (RGB, multi-spectral and thermal infrared) UAV platform. Multi-sensor data fusion-based yield prediction showed higher accuracy compared to individual-sensor data in each ML model. The coefficient of determination (R 2) values for Cubist, SVM, DNN and RR models regarding grain yield prediction were observed from 0.527 to 0.670. Moreover, the results of ensemble learning through integrating the above models illustrated further increase in accuracy. The predictions of ensemble learning showed high R 2 values up to 0.692, which was higher as compared to individual ML models across the multi-sensor data. Root mean square error (RMSE), residual prediction deviation (RPD) and ratio of prediction performance to inter-quartile range (RPIQ) were calculated to be 0.916 t ha-1, 1.771 and 2.602, respectively. The results proved that low altitude UAV-based multi-sensor data can be used for early grain yield prediction using data fusion and an ensemble learning framework with high accuracy. This high-throughput phenotyping approach is valuable for improving the efficiency of selection in large breeding activities. Supplementary Information: The online version contains supplementary material available at 10.1007/s11119-022-09938-8.

Root system architecture of historical spring wheat cultivars is associated with alleles and transcripts of major functional genes.

We evaluated root system architecture (RSA) of a set of 58 historical spring wheat cultivars from Pakistan representing 105 years of selection breeding. The evaluations were carried out under control and water-limited conditions using a high-throughput phenotyping system coupled with RhizoVision Explorer software. The cultivars were classified into three groups based on release year as cultivars released pre-1965, released between 1965 and 2000, and cultivars released post-2000. Under water-limited conditions a decline in 20 out of 25 RSA component traits was observed in pre-1965 cultivars group. Whereas cultivars released after the 1965, so-called green revolution period, showed a decline in 17 traits with significant increments in root length, depth, and steep angle frequency which are important root traits for resource-uptake under water-limited conditions. Similarly, cultivars released after 2000 indicated an increase in the number of roots, depth, diameter, surface area, and steep angle frequency. The coefficient of correlation analysis showed a positive correlation between root depth and yield-related traits under water-limited conditions. We also investigated the effects of green-revolution genes (Rht1) and some phenology-related genes such as DRO1, TaMOR, TaLTPs, TaSus-2B on RSA and identified significant associations of these genes with important root traits. There was strong selection pressure on DRO1 gene in cultivated wheat indicating the allele fixed in modern wheat cultivars is different from landraces. The expression of DRO1, and TaMOR were retrieved from an RNAseq experiment, and results were validated using qRT-PCR. The highest expression of DRO1 and TaMOR was found in Chakwal-50, a rainfed cultivar released in 2008, and MaxiPak-65 released in 1965. We conclude that there is a positive historic change in RSA after 1965 that might be attributed to genetic factors associated with favored RSA traits. Furthermore, we suggest root depth and steep angle as promising traits to withstand water-limited environments and may have implications in selection for breeding.

Potential Mechanisms of Transmission of Tick-Borne Viruses at the Virus-Tick Interface.

Ticks (Acari; Ixodidae) are the second most important vector for transmission of pathogens to humans, livestock, and wildlife. Ticks as vectors for viruses have been reported many times over the last 100 years. Tick-borne viruses (TBVs) belong to two orders (Bunyavirales and Mononegavirales) containing nine families (Bunyaviridae, Rhabdoviridae, Asfarviridae, Orthomyxovirida, Reoviridae, Flaviviridae, Phenuviridae, Nyamiviridae, and Nairoviridae). Among these TBVs, some are very pathogenic, causing huge mortality, and hence, deserve to be covered under the umbrella of one health. About 38 viral species are being transmitted by <10% of the tick species of the families Ixodidae and Argasidae. All TBVs are RNA viruses except for the African swine fever virus from the family Asfarviridae. Tick-borne viral diseases have also been classified as an emerging threat to public health and animals, especially in resource-poor communities of the developing world. Tick-host interaction plays an important role in the successful transmission of pathogens. The ticks' salivary glands are the main cellular machinery involved in the uptake, settlement, and multiplication of viruses, which are required for successful transmission into the final host. Furthermore, tick saliva also participates as an augmenting tool during the physiological process of transmission. Tick saliva is an important key element in the successful transmission of pathogens and contains different antimicrobial proteins, e.g., defensin, serine, proteases, and cement protein, which are key players in tick-virus interaction. While tick-virus interaction is a crucial factor in the propagation of tick-borne viral diseases, other factors (physiological, immunological, and gut flora) are also involved. Some immunological factors, e.g., toll-like receptors, scavenger receptors, Janus-kinase (JAK-STAT) pathway, and immunodeficiency (IMD) pathway are involved in tick-virus interaction by helping in virus assembly and acting to increase transmission. Ticks also harbor some endogenous viruses as internal microbial faunas, which also play a significant role in tick-virus interaction. Studies focusing on tick saliva and its role in pathogen transmission, tick feeding, and control of ticks using functional genomics all point toward solutions to this emerging threat. Information regarding tick-virus interaction is somewhat lacking; however, this information is necessary for a complete understanding of transmission TBVs and their persistence in nature. This review encompasses insight into the ecology and vectorial capacity of tick vectors, as well as our current understanding of the predisposing, enabling, precipitating, and reinforcing factors that influence TBV epidemics. The review explores the cellular, biochemical, and immunological tools which ensure and augment successful evading of the ticks' defense systems and transmission of the viruses to the final hosts at the virus-vector interface. The role of functional genomics, proteomics, and metabolomics in profiling tick-virus interaction is also discussed. This review is an initial attempt to comprehensively elaborate on the epidemiological determinants of TBVs with a focus on intra-vector physiological processes involved in the successful execution of the docking, uptake, settlement, replication, and transmission processes of arboviruses. This adds valuable data to the existing bank of knowledge for global stakeholders, policymakers, and the scientific community working to devise appropriate strategies to control ticks and TBVs.

Root system architecture in cereals: progress, challenges and perspective.

Roots are essential multifunctional plant organs involved in water and nutrient uptake, metabolite storage, anchorage, mechanical support, and interaction with the soil environment. Understanding of this 'hidden half' provides potential for manipulation of root system architecture (RSA) traits to optimize resource use efficiency and grain yield in cereal crops. Unfortunately, root traits are highly neglected in breeding due to the challenges of phenotyping, but could have large rewards if the variability in RSA traits can be fully exploited. Until now, a plethora of genes have been characterized in detail for their potential role in improving RSA. The use of forward genetics approaches to find sequence variations in genes underpinning desirable RSA would be highly beneficial. Advances in computer vision applications have allowed image-based approaches for high-throughput phenotyping of RSA traits that can be used by any laboratory worldwide to make progress in understanding root function and dissection of the genetics. At the same time, the frontiers of root measurement include non-invasive methods like X-ray computer tomography and magnetic resonance imaging that facilitate new types of temporal studies. Root physiology and ecology are further supported by spatiotemporal root simulation modeling. The discovery of component traits providing improved resilience and yield advantage in target environments is a key necessity for mainstreaming root-based cereal breeding. The integrated use of pan-genome resources, now available in most cereals, coupled with new in-field phenotyping platforms has the potential for precise selection of superior genotypes with improved RSA.

Rht24b, an ancient variation of TaGA2ox-A9, reduces plant height without yield penalty in wheat.

Rht-B1b and Rht-D1b, the 'Green Revolution' (GR) genes, greatly improved yield potential of wheat under nitrogen fertilizer application, but reduced coleoptile length, seedling vigor and grain weight. Thus, mining alternative reduced plant height genes without adverse effects is urgently needed. We isolated the causal gene of Rht24 through map-based cloning and characterized its function using transgenic, physiobiochemical and transcriptome assays. We confirmed genetic effects of the dwarfing allele Rht24b with an association analysis and also traced its origin and distribution. Rht24 encodes a gibberellin (GA) 2-oxidase, TaGA2ox-A9. Rht24b conferred higher expression of TaGA2ox-A9 in stems, leading to a reduction of bioactive GA in stems but an elevation in leaves at the jointing stage. Strikingly, Rht24b reduced plant height, but had no yield penalty; it significantly increased nitrogen use efficiency, photosynthetic rate and the expression of related genes. Evolutionary analysis demonstrated that Rht24b first appeared in wild emmer and was detected in more than half of wild emmer and wheat accessions, suggesting that it underwent both natural and artificial selection. These findings uncover an important genetic resource for wheat breeding and also provide clues for dissecting the regulatory mechanisms underlying GA-mediated morphogenesis and yield formation.

Quantifying senescence in bread wheat using multispectral imaging from an unmanned aerial vehicle and QTL mapping.

Environmental stresses from climate change can alter source-sink relations during plant maturation, leading to premature senescence and decreased yields. Elucidating the genetic control of natural variations for senescence in wheat (Triticum aestivum) can be accelerated using recent developments in unmanned aerial vehicle (UAV)-based imaging techniques. Here, we describe the use of UAVs to quantify senescence in wheat using vegetative indices (VIs) derived from multispectral images. We detected senescence with high heritability, as well as its impact on grain yield (GY), in a doubled-haploid population and parent cultivars at various growth time points (TPs) after anthesis in the field. Selecting for slow senescence using a combination of different UAV-based VIs was more effective than using a single ground-based vegetation index. We identified 28 quantitative trait loci (QTL) for vegetative growth, senescence, and GY using a 660K single-nucleotide polymorphism array. Seventeen of these new QTL for VIs from UAV-based multispectral imaging were mapped on chromosomes 2B, 3A, 3D, 5A, 5D, 5B, and 6D; these QTL have not been reported previously using conventional phenotyping methods. This integrated approach allowed us to identify an important, previously unreported, senescence-related locus on chromosome 5D that showed high phenotypic variation (up to 18.1%) for all UAV-based VIs at all TPs during grain filling. This QTL was validated for slow senescence by developing kompetitive allele-specific PCR markers in a natural population. Our results suggest that UAV-based high-throughput phenotyping is advantageous for temporal assessment of the genetics underlying for senescence in wheat.

QTL mapping of root traits in wheat under different phosphorus levels using hydroponic culture.

BACKGROUND: Phosphorus (P) is an important in ensuring plant morphogenesis and grain quality, therefore an efficient root system is crucial for P-uptake. Identification of useful loci for root morphological and P uptake related traits at seedling stage is important for wheat breeding. The aims of this study were to evaluate phenotypic diversity of Yangmai 16/Zhongmai 895 derived doubled haploid (DH) population for root system architecture (RSA) and biomass related traits (BRT) in different P treatments at seedling stage using hydroponic culture, and to identify QTL using 660 K SNP array based high-density genetic map. RESULTS: All traits showed significant variations among the DH lines with high heritabilities (0.76 to 0.91) and high correlations (r = 0.59 to 0.98) among all traits. Inclusive composite interval mapping (ICIM) identified 34 QTL with 4.64-20.41% of the phenotypic variances individually, and the log of odds (LOD) values ranging from 2.59 to 10.43. Seven QTL clusters (C1 to C7) were mapped on chromosomes 3DL, 4BS, 4DS, 6BL, 7AS, 7AL and 7BL, cluster C5 on chromosome 7AS (AX-109955164 - AX-109445593) with pleiotropic effect played key role in modulating root length (RL), root tips number (RTN) and root surface area (ROSA) under low P condition, with the favorable allele from Zhongmai 895. CONCLUSIONS: This study carried out an imaging pipeline-based rapid phenotyping of RSA and BRT traits in hydroponic culture. It is an efficient approach for screening of large populations under different nutrient conditions. Four QTL on chromosomes 6BL (2) and 7AL (2) identified in low P treatment showed positive additive effects contributed by Zhongmai 895, indicating that Zhongmai 895 could be used as parent for P-deficient breeding. The most stable QTL QRRS.caas-4DS for ratio of root to shoot dry weight (RRS) harbored the stable genetic region with high phenotypic effect, and QTL clusters on 7A might be used for speedy selection of genotypes for P-uptake. SNPs closely linked to QTLs and clusters could be used to improve nutrient-use efficiency.

Entropy Weight Ensemble Framework for Yield Prediction of Winter Wheat Under Different Water Stress Treatments Using Unmanned Aerial Vehicle-Based Multispectral and Thermal Data.

Crop breeding programs generally perform early field assessments of candidate selection based on primary traits such as grain yield (GY). The traditional methods of yield assessment are costly, inefficient, and considered a bottleneck in modern precision agriculture. Recent advances in an unmanned aerial vehicle (UAV) and development of sensors have opened a new avenue for data acquisition cost-effectively and rapidly. We evaluated UAV-based multispectral and thermal images for in-season GY prediction using 30 winter wheat genotypes under 3 water treatments. For this, multispectral vegetation indices (VIs) and normalized relative canopy temperature (NRCT) were calculated and selected by the gray relational analysis (GRA) at each growth stage, i.e., jointing, booting, heading, flowering, grain filling, and maturity to reduce the data dimension. The elastic net regression (ENR) was developed by using selected features as input variables for yield prediction, whereas the entropy weight fusion (EWF) method was used to combine the predicted GY values from multiple growth stages. In our results, the fusion of dual-sensor data showed high yield prediction accuracy [coefficient of determination (R 2) = 0.527-0.667] compared to using a single multispectral sensor (R 2 = 0.130-0.461). Results showed that the grain filling stage was the optimal stage to predict GY with R 2 = 0.667, root mean square error (RMSE) = 0.881 t ha-1, relative root-mean-square error (RRMSE) = 15.2%, and mean absolute error (MAE) = 0.721 t ha-1. The EWF model outperformed at all the individual growth stages with R 2 varying from 0.677 to 0.729. The best prediction result (R 2 = 0.729, RMSE = 0.831 t ha-1, RRMSE = 14.3%, and MAE = 0.684 t ha-1) was achieved through combining the predicted values of all growth stages. This study suggests that the fusion of UAV-based multispectral and thermal IR data within an ENR-EWF framework can provide a precise and robust prediction of wheat yield.

Assessment of Water and Nitrogen Use Efficiencies Through UAV-Based Multispectral Phenotyping in Winter Wheat.

Unmanned aerial vehicle (UAV) based remote sensing is a promising approach for non-destructive and high-throughput assessment of crop water and nitrogen (N) efficiencies. In this study, UAV was used to evaluate two field trials using four water (T0 = 0 mm, T1 = 80 mm, T2 = 120 mm, and T3 = 160 mm), and four N (T0 = 0, T1 = 120 kg ha-1, T2 = 180 kg ha-1, and T3 = 240 kg ha-1) treatments, respectively, conducted on three wheat genotypes at two locations. Ground-based destructive data of water and N indictors such as biomass and N contents were also measured to validate the aerial surveillance results. Multispectral traits including red normalized difference vegetation index (RNDVI), green normalized difference vegetation index (GNDVI), normalized difference red-edge index (NDRE), red-edge chlorophyll index (RECI) and normalized green red difference index (NGRDI) were recorded using UAV as reliable replacement of destructive measurements by showing high r values up to 0.90. NGRDI was identified as the most efficient non-destructive indicator through strong prediction values ranged from R 2 = 0.69 to 0.89 for water use efficiencies (WUE) calculated from biomass (WUE.BM), and R 2 = 0.80 to 0.86 from grain yield (WUE.GY). RNDVI was better in predicting the phenotypic variations for N use efficiency calculated from nitrogen contents of plant samples (NUE.NC) with high R 2 values ranging from 0.72 to 0.94, while NDRE was consistent in predicting both NUE.NC and NUE.GY by 0.73 to 0.84 with low root mean square errors. UAV-based remote sensing demonstrates that treatment T2 in both water 120 mm and N 180 kg ha-1 supply trials was most appropriate dosages for optimum uptake of water and N with high GY. Among three cultivars, Zhongmai 895 was highly efficient in WUE and NUE across the water and N treatments. Conclusively, UAV can be used to predict time-series WUE and NUE across the season for selection of elite genotypes, and to monitor crop efficiency under varying N and water dosages.

Molecular detection of small ruminant piroplasmosis and first report of Theileria luwenshuni (Apicomplexa: Theileridae) in small ruminants of Pakistan.

Theileriosis is a widespread and economically important disease of small ruminants in Pakistan. Ruminants are the intermediate hosts in the lifecycle of Theileria spp., with ticks of the family Ixodidae being the definitive hosts. To better understand the distribution and prevalence of theileriosis in Pakistan, a molecular survey was performed in small ruminants from the Lower Dir district of the Khyber Pakhtunkhwa province. A total of 200 healthy sheep and goats were screened from Maidan, Samar Bagh and Munda districts of district Dir Lower, Pakistan during December (2017) to April (2018). DNA samples were screened through nested PCR using universal primers. The amplified 492-498 bp amplicon was subjected to RLB analysis which was based on the hypervariable of the 18S rRNA gene to test for the presence of genotypes of Theileria in blood samples. A phylogeny was constructed to determine the species of Theileria genotypes. Nested PCR results indicated 53.5% prevalence of one or more Theileria genotypes in the blood of the host animal. From RLB assay, 27 animals (13.5%) showed infection with only a single species of Theileria while 80 animals (40%) showed coinfection by multiple Theileria spp. Based on the 18S rRNA phylogeny, the unknown genotype is of the species Theileria luwenshuni and is closely related to Chinese isolates. The present finding is the first report on molecular diagnosis of Theileria luwenshuni in small ruminants in Pakistan.

Appraisal of wheat genomics for gene discovery and breeding applications: a special emphasis on advances in Asia.

KEY MESSAGE: We discussed the most recent efforts in wheat functional genomics to discover new genes and their deployment in breeding with special emphasis on advances in Asian countries. Wheat research community is making significant progress to bridge genotype-to-phenotype gap and then applying this knowledge in genetic improvement. The advances in genomics and phenomics have intrigued wheat researchers in Asia to make best use of this knowledge in gene and trait discovery. These advancements include, but not limited to, map-based gene cloning, translational genomics, gene mapping, association genetics, gene editing and genomic selection. We reviewed more than 57 homeologous genes discovered underpinning important traits and multiple strategies used for their discovery. Further, the complementary advancements in wheat phenomics and analytical approaches to understand the genetics of wheat adaptability, resilience to climate extremes and resistance to pest and diseases were discussed. The challenge to build a gold standard reference genome sequence of bread wheat is now achieved and several de novo reference sequences from the cultivars representing different gene pools will be available soon. New pan-genome sequencing resources of wheat will strengthen the foundation required for accelerated gene discovery and provide more opportunities to practice the knowledge-based breeding.

In vitro influence of Theileria annulata on the functions of bovine dendritic cells for stimulation of T lymphocyte proliferation.

The present study was performed on antigen-presenting cells (APCs) of Theileria annulata transformed dendritic cells (TaDCs) and monocyte-derived dendritic cells (MoDCs) to compare differences in antigen presentation and stimulation of T lymphocyte proliferation. Antigen presentation for T lymphocyte proliferation was analysed by flow cytometry. Additionally, the level of mRNA transcription of small GTPases of the Rab family expressed in the TaDC cell line was analysed by quantitative real-time polymerase chain reaction (Q-RT-PCR). The endocytosis rate of TaDCs was significantly (P < 0.01) lower than in MoDCs. In contrast, when T lymphocytes were co-cultured with TaDC-APCs T cell proliferation was similar, while co-culture with MoDC-APC stimulated proliferation of CD4+ cells to a greater degree than CD8+ cells. However, the efficacy of TaDC-APCs to stimulate T lymphocytes dropped as the number of passages of TaDC-APC increased. Likewise, the transcription level of Rab family genes also significantly (P > 0.001) declined with progressive passages (>50) of the TaDC cell line. We conclude that initially the TaDC cell line efficiently presents antigen to stimulate T lymphocyte proliferation to produce a cellular immune response against the presented antigen.

Establishment and Expression of Cytokines in a Theileria annulata-Infected Bovine B Cell Line.

This study aimed to establish a pure single-cell Theileria annulata-infected B cell line for the assessment of cytokine production in transformed and lipopolysaccharide (LPS)-stimulated cells. Several studies have aimed to identify cell surface markers in T. annulata-transformed cells; however, no information on cytokine production in these cells is available. To investigate the potential of the transformed cells to produce cytokines and their potential responses to antigen-stimulation, we purified mature B cells (CD21) from the whole blood of cattle experimentally infected with the T. annulata Kashi strain by magnetic separation. The purity and specificity of the established cell line was assessed by the identification of specific cell surface markers (CD21, IgM, and WC4) by flow cytometry analysis. The transcript levels of the cytokines IL1A, IL1B, IL2, IL4, IL6, IL8, IL10, IL16, LTA, TGFB1, TNFA, IFNA, and IFNB in transformed, buparvaquone (BW720c)-treated cells, and antigen-stimulated cells were analyzed by quantitative polymerase chain reaction (qPCR) using cDNA from these cells. A T. annulata-infected bovine B cell line was successfully established with a purity of ~98.8% (CD21). IL4 and IL12A were significantly (p < 0.01) upregulated in the transformed cells. In BW720c-treated transformed cells, IL12B, TGFB1, and IFNB were significantly (p < 0.01) upregulated. Notably, no significant (p > 0.05) upregulation of cytokines was observed in LPS-stimulated transformed cells. Moreover, IL1A, IL1B, IL8, and IL16 were significantly (p < 0.01) upregulated in LPS-stimulated B cells. Our data signify the potential use of this cell line for cytokine production, observance of immunoglobulins, and production of an attenuated vaccine against tropical theileriosis.

Accuracy assessment of plant height using an unmanned aerial vehicle for quantitative genomic analysis in bread wheat.

BACKGROUND: Plant height is an important selection target since it is associated with yield potential, stability and particularly with lodging resistance in various environments. Rapid and cost-effective estimation of plant height from airborne devices using a digital surface model can be integrated with academic research and practical wheat breeding programs. A bi-parental wheat population consisting of 198 doubled haploid lines was used for time-series assessments of progress in reaching final plant height and its accuracy was assessed by quantitative genomic analysis. UAV-based data were collected at the booting and mid-grain fill stages from two experimental sites and compared with conventional measurements to identify quantitative trait loci (QTL) underlying plant height. RESULTS: A significantly high correlation of R 2 = 0.96 with a 5.75 cm root mean square error was obtained between UAV-based plant height estimates and ground truth observations at mid-grain fill across both sites. Correlations for UAV and ground-based plant height data were also very high (R 2 = 0.84-0.85, and 0.80-0.83) between plant height at the booting and mid-grain fill stages, respectively. Broad sense heritabilities were 0.92 at booting and 0.90-0.91 at mid-grain fill across sites for both data sets. Two major QTL corresponding to Rht-B1 on chromosome 4B and Rht-D1 on chromosome 4D explained 61.3% and 64.5% of the total phenotypic variations for UAV and ground truth data, respectively. Two new and stable QTL on chromosome 6D seemingly associated with accelerated plant growth was identified at the booting stage using UAV-based data. Genomic prediction accuracy for UAV and ground-based data sets was significantly high, ranging from r = 0.47-0.55 using genome-wide and QTL markers for plant height. However, prediction accuracy declined to r = 0.20-0.31 after excluding markers linked to plant height QTL. CONCLUSION: This study provides a fast way to obtain time-series estimates of plant height in understanding growth dynamics in bread wheat. UAV-enabled phenotyping is an effective, high-throughput and cost-effective approach to understand the genetic basis of plant height in genetic studies and practical breeding.

A rapid monitoring of NDVI across the wheat growth cycle for grain yield prediction using a multi-spectral UAV platform.

Wheat improvement programs require rapid assessment of large numbers of individual plots across multiple environments. Vegetation indices (VIs) that are mainly associated with yield and yield-related physiological traits, and rapid evaluation of canopy normalized difference vegetation index (NDVI) can assist in-season selection. Multi-spectral imagery using unmanned aerial vehicles (UAV) can readily assess the VIs traits at various crop growth stages. Thirty-two wheat cultivars and breeding lines grown in limited irrigation and full irrigation treatments were investigated to monitor NDVI across the growth cycle using a Sequoia sensor mounted on a UAV. Significant correlations ranging from R2 = 0.38 to 0.90 were observed between NDVI detected from UAV and Greenseeker (GS) during stem elongation (SE) to late grain gilling (LGF) across the treatments. UAV-NDVI also had high heritabilities at SE (h2 = 0.91), flowering (F)(h2 = 0.95), EGF (h2 = 0.79) and mid grain filling (MGF) (h2 = 0.71) under the full irrigation treatment, and at booting (B) (h2 = 0.89), EGF (h2 = 0.75) in the limited irrigation treatment. UAV-NDVI explained significant variation in grain yield (GY) at EGF (R2 = 0.86), MGF (R2 = 0.83) and LGF (R2 = 0.89) stages, and results were consistent with GS-NDVI. Higher correlations between UAV-NDVI and GY were observed under full irrigation at three different grain-filling stages (R2 = 0.40, 0.49 and 0.45) than the limited irrigation treatment (R2 = 0.08, 0.12 and 0.14) and GY was calculated to be 24.4% lower under limited irrigation conditions. Pearson correlations between UAV-NDVI and GY were also low ranging from r = 0.29 to 0.37 during grain-filling under limited irrigation but higher than GS-NDVI data. A similar pattern was observed for normalized difference red-edge (NDRE) and normalized green red difference index (NGRDI) when correlated with GY. Fresh biomass estimated at late flowering stage had significant correlations of r = 0.30 to 0.51 with UAV-NDVI at EGF. Some genotypes Nongda 211, Nongda 5181, Zhongmai 175 and Zhongmai 12 were identified as high yielding genotypes using NDVI during grain-filling. In conclusion, a multispectral sensor mounted on a UAV is a reliable high-throughput platform for NDVI measurement to predict biomass and GY and grain-filling stage seems the best period for selection.

Assessing the prevalence and economic significance of coccidiosis individually and in combination with concurrent infections in Pakistani commercial poultry farms.

Coccidiosis is one of the most economically important diseases of the poultry, around the globe. In order to assess seasonal and age-wise prevalence of coccidiosis individually and with concurrent infections, this study was conducted on commercial poultry farms in and around Multan division, province of Punjab, Pakistan. A total of 28,126 boilers, 4,052 layers and 7,699 golden bird samples, provided by regional farmers and consultants, were examined by microscopy for the diagnosis of coccidiosis. Based on postmortem lesions, several infections including coccidiosis, viral, and miscellaneous diseases were catalogued, whereas samples were cultured to identify concurrent bacterial diseases. Cumulative analysis of this large set of samples revealed a prevalence of 14.16, 11.01, and 19.57% in broiler, layer and golden birds, respectively. Ascaridia galli (A. galli) showed a higher prevalence in layer (2.47%) compared to golden (0.21%) birds (P < 0.01). Amongst all concurrent infections investigated, bacterial infections were identified in highest percentage of samples (59.24%; P < 0.05). The age-wise and season-wise prevalence of coccidiosis and A. galli was found to be significant (P < 0.05). During the study period, an estimated of 10.69 (coccidiosis) and 0.29 (A. galli) million poultry birds were treated or provided prophylaxis and supportive therapy at Pakistani commercial poultry farms. Depending upon the prevailing market conditions, the total economic losses (treatment, prophylaxis, and supportive therapy) from coccidiosis and A. galli were estimated to be US $45,405.00 and 2,638.50, respectively, while production (weight and eggs) losses for broiler (US$ 2,750,779.00), layer and golden, young (US$ 13,974.98 and 50,228.76) and adult (US$ 104.74 and 203.77) were estimated. Unit loss of coccidiosis with concurrent diseases and intestinal worm was estimated to be US$ 0.005 and 0.01, respectively. These results highlight the potential impact of coccidiosis individually and with concurrent infections on the poultry productivity and will inform farmers, policy makers, and other governmental and non-governmental stakeholders on the use of control and management measures in containing these infections.

A systematic review on modelling approaches for economic losses studies caused by parasites and their associated diseases in cattle.

Parasites reside inside or outside their hosts and get host nutrition and blood. Here, we have emphasized economic losses in cattle caused by parasitic diseases due to ecto- and endo- parasites (flies, ticks, mites and helminths). We have outlined different methods/models including economic evaluation techniques and dynamic analysis as a major class, used for the calculation of economic losses caused by parasites in cattle. According to already conducted studies, a decrease in production is mentioned in quantity and percentage while financial losses are expressed in the form of account with respect to per head, herd or for the specific study area. The parasites cause the reduced production and financial losses due to control, treatment and mortality costs. We calculated the average decrease in milk production and organ condemnation as 1.16 L animal-1 day-1 and 12.95%, respectively, from overall cattle parasitic infections. Moreover, the average calculated financial and percentage losses were US$ 50.67 animal-1 year-1 and 17.94%, respectively. Economically important parasitic diseases mentioned here are caused by specific spp. of protozoans and helminths according to data collected from the literature. Protozoan diseases include tick-borne diseases, coccidiosis, neosporosis, trypanosomiasis and cryptosporidiosis. Losses due to tick-borne infections were encountered for decreased milk production, mortality, treatment and control. Losses from coccidiosis were due to decreased weight gain, treatment costs and mortality. While abortion losses were encountered in neosporosis. Trypanosomiasis caused losses due to a decrease in milk yield. Moreover, only diagnostic (conventional or molecular techniques) cost was taken into account for cryptosporidiosis. Economically important nematode parasites are Oesophagostomum spp., Cooperia spp., Trichostrongylus spp., Strongyloides spp., Ostertagia spp. and Haemonchus placei. Due to the zoonotic importance of echinococcosis, Echinococcus granulosus is the most economically important cestode parasite. Losses caused by echinococcosis were due to organ condemnation, carcass weight loss and decreases hide value, milk production and fecundity. While, fascioliasis is one of the most economically important trematodal disease, which causes cirrhosis of the liver due to parasite migration, and thus, the organ becomes inedible. So, it would be helpful for farmers and researchers to approach these methods/models for calculation of parasitic losses and should adopt suitable measures to avoid long-term economic losses.

Molecular survey of piroplasm species from selected areas of China and Pakistan.

BACKGROUND: Piroplasmosis is an important animal disease that is a major constraint to the development of the livestock industry, often resulting in significant economic losses. Therefore, there is an urgent need to further understand the etiology of this and other tick-borne infections. METHODS: Blood samples were collected from tick-infested animals from the Chakwal, Jhang, and Faisalabad districts of Punjab, Pakistan and from peri-urban areas around Hohhot, Inner Mongolia, China to investigate the presence of Babesia and Theileria species. In total, 450 blood samples were collected with FTA cards from cattle of the study areas of Pakistan; the genomic (g)DNA of one hundred and twenty samples from cattle in Inner Mongolia were provided by the Lanzhou Veterinary Research Institute, China. Following the extraction of gDNA, the 18S rRNA gene (V4 hypervariable region) of piroplasms was amplified in all samples using semi-nested PCR. Positively identified samples were sequenced for the identification of Theileria and Babesia species. The partial full-length sequence of 18S rDNA was amplified for species confirmation of Theileria-positive samples, whereas the RAP-1c gene was amplified for Babesia bigemina-positive samples. RESULTS: Semi-nested PCR results revealed that 144 (25.26%) samples were positive for piroplasms. Theileria annulata was the most prevalent species (115/144; 20.17%), followed by Theileria orientalis (16/144; 2.80%). Among Babesia, the only species recorded was Babesia bigemina (13/144; 2.28%). CONCLUSION: The present study reveals new data on the prevalence of piroplasm species in bovine populations of selected areas of China and Pakistan and their phylogenetic relationships. It is also the first detailed report of T. orientalis from native animals in Pakistan.