Breeding Trials Research Articles

Prenatal exposure to a mixture of phthalates accelerates the age-related decline in reproductive capacity but may not affect direct biomarkers of ovarian aging in the F1 generation of female mice.

Phthalates are used in many consumer products, leading to daily human exposure. Although many studies focus on single phthalates, humans are exposed to mixtures of phthalates. Our laboratory created a phthalate mixture consisting of six different phthalates and found that it negatively affected female reproduction and accelerated some biomarkers of reproductive aging. However, it was unknown if prenatal exposure to the mixture accelerates the natural decline in reproductive capacity and ovarian aging in mice. Therefore, we tested the hypothesis that prenatal exposure to a phthalate mixture accelerates the age-related decline in reproductive capacity and biomarkers of ovarian aging in the F1 generation of mice. Pregnant CD-1 dams were orally dosed with control or phthalate mixture (20 µg/kg/day–200 mg/kg/day) daily from gestational day 10—birth. The F1 female pups were aged to 11–13 months, and then estrous cyclicity and breeding trials were conducted at 11 and 13 months. Ovaries were collected from the F1 females at 13 months to examine biomarkers of ovarian aging. Prenatal exposure to the phthalate mixture decreased the time the F1 females spent in proestrus and the ability of the F1 females to give birth at 11 and 13 months of age compared to control. In contrast, prenatal exposure to the mixture did not affect biomarkers of direct aging of the ovary in the F1 generation. Collectively, our data show that prenatal phthalate mixture exposure accelerates the natural age-related decline in reproductive capacity but may not affect some biomarkers of ovarian aging in the F1 generation.

Increased Predictive Accuracy of Multi-Environment Genomic Prediction Model for Yield and Related Traits in Spring Wheat (Triticum aestivum L.).

Genomic selection (GS) has the potential to improve the selection gain for complex traits in crop breeding programs from resource-poor countries. The GS model performance in multi-environment (ME) trials was assessed for 141 advanced breeding lines under four field environments via cross-predictions. We compared prediction accuracy (PA) of two GS models with or without accounting for the environmental variation on four quantitative traits of significant importance, i.e., grain yield (GRYLD), thousand-grain weight, days to heading, and days to maturity, under North and Central Indian conditions. For each trait, we generated PA using the following two different ME cross-validation (CV) schemes representing actual breeding scenarios: (1) predicting untested lines in tested environments through the ME model (ME_CV1) and (2) predicting tested lines in untested environments through the ME model (ME_CV2). The ME predictions were compared with the baseline single-environment (SE) GS model (SE_CV1) representing a breeding scenario, where relationships and interactions are not leveraged across environments. Our results suggested that the ME models provide a clear advantage over SE models in terms of robust trait predictions. Both ME models provided 2–3 times higher prediction accuracies for all four traits across the four tested environments, highlighting the importance of accounting environmental variance in GS models. While the improvement in PA from SE to ME models was significant, the CV1 and CV2 schemes did not show any clear differences within ME, indicating the ME model was able to predict the untested environments and lines equally well. Overall, our results provide an important insight into the impact of environmental variation on GS in smaller breeding programs where these programs can potentially increase the rate of genetic gain by leveraging the ME wheat breeding trials.

Improving Wheat Yield Prediction Using Secondary Traits and High-Density Phenotyping Under Heat-Stressed Environments.

A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from −0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.

INVESTIGATING THE IMPACT OF mTOR INHIBITOR TREATMENT ON TRANSGENERATIONAL OFFSPRING HEALTH AND FERTILITY

We previously showed that mTOR inhibitors are effective fertoprotectants during gonadotoxic chemotherapy and prolong reproductive lifespan in a murine model. We investigated the impact of mTOR inhibitor treatment on transgenerational offspring health and fertility. Female mice (C57BL/6, 8 weeks, n=6-7/group, F0) were treated for 4 weeks (oral gavage) with Everolimus (mTORC1 inhibitor, RAD001), DMSO (RAD Vehicle), Sapanisertib (mTORC1/2 inhibitor, INK128), and/or PVP/NMP (INK Vehicle). Estrous cyclicity was evaluated by vaginal lavage. Following treatment, 13 mice were sacrificed for organ harvest; remaining mice (n=12) were bred at 20 weeks with proven breeders to generate two litters. F1 pups were maintained until 12 weeks: male offspring underwent semen analysis and F1 females were sacrificed for ovarian harvest or bred to produce one litter of F2 pups. F2 pups were sacrificed on day 3 and weight, crown-rump length, and survival (%) were recorded. The following data were recorded for each birth: pup number, weight, anomalies, survival, sex, and days from male interaction to birth. Data are presented as mean +/- SEM; significance P < 0.05. Data analyzed using GraphPad Prism Software (8.0, La Jolla, California). There were no differences in estrous cyclicity between groups (P > 0.05). Mice treated with RAD001 and INK128 trended toward higher primordial follicle counts (NS, P > 0.05). Days from male interaction to birth did not differ between groups for both the first and second litter of F1 pups (P > 0.05). Notably, significantly more cumulative F1 pups were noted in INK-treated mice compared to all other groups (P < 0.05). During the first breeding trial, INK128 pups had significantly higher survival rates than INK Vehicle ( P < 0.005) and RAD001 had significantly lower survival rates than RAD Vehicle (P < 0.001). Pup survival rates normalized in the second litter (P > 0.05). F1 pups showed appropriate weekly weight gain from 2 weeks until 12 weeks of age. F1 males demonstrated normal sperm counts across treatment groups (P > 0.05). In F1 female breeding trials, there was no difference in time from breeding to birth, F2 pups per litter, or F2 pup survival (P > 0.05). At the time of sacrifice, F2 pups showed similar weights across groups. Of note, F2 male pups from RAD-treated F0 mice demonstrated significantly smaller crown-rump lengths compared to RAD vehicle (** P < .005) and INK-treated groups (* P < 0.05). Sex ratios were similar across all treatment groups and generations. Female mice treated with mTORC1/2 inhibitors had significantly more pups over the breeding period and higher pup survival rates, consistent with our previous findings. Treatment with mTOR inhibitors resulted in reassuring offspring health and fertility, but isolated findings among offspring of RAD-treated mice warrant further investigation.

Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning

High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was to examine the potential of unmanned aerial vehicle (UAV)-based structural and spectral features and their combination in herbage yield predictions across diploid and tetraploid varieties and breeding populations of perennial ryegrass (Lolium perenne L.). Canopy structural (i.e., canopy height) and spectral (i.e., vegetation indices) information were derived from data gathered with two sensors: a consumer-grade RGB and a 10-band multispectral (MS) camera system, which were compared in the analysis. A total of 468 field plots comprising 115 diploid and 112 tetraploid varieties and populations were considered in this study. A modelling framework established to predict dry matter yield (DMY), was used to test three machine learning algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machines (SVM). The results of the nested cross-validation revealed: (a) the fusion of structural and spectral features achieved better DMY estimates as compared to models fitted with structural or spectral data only, irrespective of the sensor, ploidy level or machine learning algorithm applied; (b) models built with MS-based predictor variables, despite their lower spatial resolution, slightly outperformed the RGB-based models, as lower mean relative root mean square error (rRMSE) values were delivered; and (c) on average, the RF technique reported the best model performances among tested algorithms, regardless of the dataset used. The approach introduced in this study can provide accurate yield estimates (up to an RMSE = 308 kg ha−1) and useful information for breeders and practical farm-scale applications.

Effect of the Solar Zenith Angles at Different Latitudes on Estimated Crop Vegetation Indices

Normalization of anisotropic solar reflectance is an essential factor that needs to be considered for field-based phenotyping applications to ensure reliability, consistency, and interpretability of time-series multispectral data acquired using an unmanned aerial vehicle (UAV). Different models have been developed to characterize the bidirectional reflectance distribution function. However, the substantial variation in crop breeding trials, in terms of vegetation structure configuration, creates challenges to such modeling approaches. This study evaluated the variation in standard vegetation indices and its relationship with ground-reference data (measured crop traits such as seed/grain yield) in multiple crop breeding trials as a function of solar zenith angles (SZA). UAV-based multispectral images were acquired and utilized to extract vegetation indices at SZA across two different latitudes. The pea and chickpea breeding materials were evaluated in a high latitude (46°36′39.92″ N) zone, whereas the rice lines were assessed in a low latitude (3°29′42.43″ N) zone. In general, several of the vegetation index data were affected by SZA (e.g., normalized difference vegetation index, green normalized difference vegetation index, normalized difference red-edge index, etc.) in both latitudes. Nevertheless, the simple ratio index (SR) showed less variability across SZA in both latitude zones amongst these indices. In addition, it was interesting to note that the correlation between vegetation indices and ground-reference data remained stable across SZA in both latitude zones. In summary, SR was found to have a minimum anisotropic reflectance effect in both zones, and the other vegetation indices can be utilized to evaluate relative differences in crop performances, although the absolute data would be affected by SZA.

Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat.

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

Supplementing Chitosan Oligosaccharide Positively Affects Hybrid Grouper (Epinephelus fuscoguttatus ♀ × E. lanceolatus ♂) Fed Dietary Fish Meal Replacement With Cottonseed Protein Concentrate: Effects on Growth, Gut Microbiota, Antioxidant Function and Immune Response

Oligosaccharides have recently received much attention from researchers owing to their multiple biological activities. This study was conducted to investigate the effects of a diet with reduced fish meal and chitosan oligosaccharide (COS) supplementation on a hybrid grouper (Epinephelus fuscoguttatus ♀ × E. lanceolatus ♂). Seven isonitrogenous and isolipidic types of diet were formulated to feed the groupers for 56 days. To prepare the feed, a reference diet (FM group) containing 55% fish meal was used. Concentrated cottonseed protein (CPC) was used to replace 45% of the fish meal protein, and different COS supplementation levels (0, 0.2%, 0.4, 0.6, 0.8, and 1%) were added. After an 8-week breeding trial, Vibrio harveyi bacteria were injected into the groupers for a 7-day challenge test. The results showed that the FM and COS0.4 groups showed the best growth performance among the groups (p &amp;lt; 0.05); however, there was no significant difference in the survival rate (p &amp;gt; 0.05). Unlike in the FM group, adhesion and breakage of the intestinal plica occurred in the COS0 group. The height and width of the gut fold reached maximum values in the COS0.4 group (p &amp;lt; 0.05). Microbiome sequencing suggested that there was a stable microbiota core in the gut of the groupers. With increasing COS levels, the abundance of both beneficial bacteria and conditional pathogens increased; the activities of serum glucose oxidase, catalase, and total superoxide dismutase also increased (p &amp;lt; 0.05). In the gut tissue, the activities of glutathione peroxidase, glutathione reductase, and glutamine increased first but then decreased (p &amp;lt; 0.05); the contents of lysozyme, acid phosphatase, complements C3 and C4, and IgM showed upward trends (p &amp;lt; 0.05). Compared with that in the FM group, the expression of IL-1β and TNF-α in the COS0 group was upregulated. Gene expression levels of TLR22, TGF-β, and Nrf2 increased first but then decreased with COS supplementation levels (p &amp;lt; 0.05). COS supplementation reduced the cumulative mortality of the groupers in the challenge test (p &amp;lt; 0.05). In general, the results of this study demonstrated that dietary COS supplementation enhanced growth performance, intestinal health, and antioxidant and immune responses of groupers fed with a low-fish meal diet. The optimal and acceptable levels of COS supplement were 0.45 and 0.4–0.6%, respectively; these values can provide a reference for developing aquatic prebiotics.

Innovative UAV LiDAR Generated Point-Cloud Processing Algorithm in Python for Unsupervised Detection and Analysis of Agricultural Field-Plots

The estimation of plant growth is a challenging but key issue that may help us to understand crop vs. environment interactions. To perform precise and high-throughput analysis of plant growth in field conditions, remote sensing using LiDAR and unmanned aerial vehicles (UAV) has been developed, in addition to other approaches. Although there are software tools for the processing of LiDAR data in general, there are no specialized tools for the automatic extraction of experimental field blocks with crops that represent specific “points of interest”. Our tool aims to detect precisely individual field plots, small experimental plots (in our case 10 m2) which in agricultural research represent the treatment of a single plant or one genotype in a breeding trial. Cutting out points belonging to the specific field plots allows the user to measure automatically their growth characteristics, such as plant height or plot biomass. For this purpose, new method of edge detection was combined with Fourier transformation to find individual field plots. In our case study with winter wheat, two UAV flight levels (20 and 40 m above ground) and two canopy surface modelling methods (raw points and B-spline) were tested. At a flight level of 20 m, our algorithm reached a 0.78 to 0.79 correlation with LiDAR measurement with manual validation (RMSE = 0.19) for both methods. The algorithm, in the Python 3 programming language, is designed as open-source and is freely available publicly, including the latest updates.

Multi-trait genomic-enabled prediction enhances accuracy in multi-year wheat breeding trials.

Implementing genomic-based prediction models in genomic selection requires an understanding of the measures for evaluating prediction accuracy from different models and methods using multi-trait data. In this study, we compared prediction accuracy using six large multi-trait wheat data sets (quality and grain yield). The data were used to predict 1 year (testing) from the previous year (training) to assess prediction accuracy using four different prediction models. The results indicated that the conventional Pearson’s correlation between observed and predicted values underestimated the true correlation value, whereas the corrected Pearson’s correlation calculated by fitting a bivariate model was higher than the division of the Pearson’s correlation by the squared root of the heritability across traits, by 2.53–11.46%. Across the datasets, the corrected Pearson’s correlation was higher than the uncorrected by 5.80–14.01%. Overall, we found that for grain yield the prediction performance was highest using a multi-trait compared to a single-trait model. The higher the absolute genetic correlation between traits the greater the benefits of multi-trait models for increasing the genomic-enabled prediction accuracy of traits.

Joint Modeling of Genetics and Field Variation in Plant Breeding Trials Using Relationship and Different Spatial Methods: A Simulation Study of Accuracy and Bias

Modelling field spatial patterns is standard practice for the analysis of plant breeding. Jointly fitting the genetic relationship among individuals and spatial information enables better separability between the variance due to genetics and field variation. This study aims to quantify the accuracy and bias of estimative parameters using different approaches. We contrasted three settings for the genetic term: no relationship (I), pedigree relationship (A), and genomic relationship (G); and a set of approaches for the spatial variation: no-spatial (NS), moving average covariate (MA), row-column adjustment (RC), autoregressive AR1 × AR1 (AR), spatial stochastic partial differential equations, or SPDE (SD), nearest neighbor graph (NG), and Gaussian kernel (GK). Simulations were set to represent soybean field trials at F2:4 generation. Heritability was sampled from a uniform distribution U(0,1). The simulated residual-to-spatial ratio between residual variance and spatial variance (Ve:Vs) ranged from 9:1 to 1:9. Experimental settings were conducted under an augmented block design with the systematic distribution of checks accounting for 10% of the plots. Relationship information had a substantial impact on the accuracy of the genetic values (G &gt; A &gt; I) and contributed to the accuracy of spatial effects (30.63–42.27% improvement). Spatial models were ranked based on an improvement to the accuracy of estimative of genetic effects as SD ≥ GK ≥ AR ≥ NG ≥ MA &gt; RC ≥ NS, and to the accuracy of estimative of spatial effects as GK ≥ SD ≥ NG &gt; AR ≥ MA &gt; RC. Estimates of genetic and spatial variance were generally biased downwards, whereas residual variances were biased upwards. The advent of relationship information reduced the bias of all variance components. Spatial methods SD, AR, and GK provided the least biased estimates of spatial and residual variance.

Inbred and hybrid sweet corn genotype performance in diverse organic environments

AbstractThe value of selection in conventional breeding trials of cultivars destined for organic systems depends on the correlation between systems and relative heritability of key traits. Genotype × environment interaction is a common phenomenon in plant breeding trials. Thus, multiple‐environment testing to identify stable genotypes is a high priority for organic systems. In addition, because organic systems have limited inputs to buffer the environment, they may have greater spatial heterogeneity, which may be better accounted for by additional spatial blocking terms beyond traditional randomized complete‐block design. Over 2 yr, we evaluated 100 hybrid and 40 inbred sweet corn (Zea mays L.) genotypes in 11 trials in organic systems across six locations and evaluated the addition of augmented incomplete block and row–column design to estimate the performance of sweet corn genotypes. Hybrids differed in their performance for all tested traits. Inbred parents differed in per se performance and general combining ability for all traits. For the hybrid entries, modeling spatial factors beyond the replicated complete blocks improved the model fit for days to anthesis, plant height, ear height, husk protection, ear width, and ear length. For inbred entries, modeling spatial factors beyond the replicated complete blocks improved ∖ model fit for plant height, ear height, tenderness, and ear width. Wricke's ecovalence (W2i) was a useful measure of stability, correlating reasonably well with two of the three stability statistics considered in this analysis. Based on Wricke's ecovalence, some inbred parents were more stable than others across tested environments in their combining ability for all traits.

Spatial Super Resolution of Real-World Aerial Images for Image-Based Plant Phenotyping

Unmanned aerial vehicle (UAV) imaging is a promising data acquisition technique for image-based plant phenotyping. However, UAV images have a lower spatial resolution than similarly equipped in field ground-based vehicle systems, such as carts, because of their distance from the crop canopy, which can be particularly problematic for measuring small-sized plant features. In this study, the performance of three deep learning-based super resolution models, employed as a pre-processing tool to enhance the spatial resolution of low resolution images of three different kinds of crops were evaluated. To train a super resolution model, aerial images employing two separate sensors co-mounted on a UAV flown over lentil, wheat and canola breeding trials were collected. A software workflow to pre-process and align real-world low resolution and high-resolution images and use them as inputs and targets for training super resolution models was created. To demonstrate the effectiveness of real-world images, three different experiments employing synthetic images, manually downsampled high resolution images, or real-world low resolution images as input to the models were conducted. The performance of the super resolution models demonstrates that the models trained with synthetic images cannot generalize to real-world images and fail to reproduce comparable images with the targets. However, the same models trained with real-world datasets can reconstruct higher-fidelity outputs, which are better suited for measuring plant phenotypes.

Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

Temporal Vegetation Indices and Plant Height from Remotely Sensed Imagery Can Predict Grain Yield and Flowering Time Breeding Value in Maize via Machine Learning Regression

Unoccupied aerial system (UAS; i.e., drone equipped with sensors) field-based high-throughput phenotyping (HTP) platforms are used to collect high quality images of plant nurseries to screen genetic materials (e.g., hybrids and inbreds) throughout plant growth at relatively low cost. In this study, a set of 100 advanced breeding maize (Zea mays L.) hybrids were planted at optimal (OHOT trial) and delayed planting dates (DHOT trial). Twelve UAS surveys were conducted over the trials throughout the growing season. Fifteen vegetative indices (VIs) and the 99th percentile canopy height measurement (CHMs) were extracted from processed UAS imagery (orthomosaics and point clouds) which were used to predict plot-level grain yield, days to anthesis (DTA), and silking (DTS). A novel statistical approach utilizing a nested design was fit to predict temporal best linear unbiased predictors (TBLUP) for the combined temporal UAS data. Our results demonstrated machine learning-based regressions (ridge, lasso, and elastic net) had from 4- to 9-fold increases in the prediction accuracies and from 13- to 73-fold reductions in root mean squared error (RMSE) compared to classical linear regression in prediction of grain yield or flowering time. Ridge regression performed best in predicting grain yield (prediction accuracy = ~0.6), while lasso and elastic net regressions performed best in predicting DTA and DTS (prediction accuracy = ~0.8) consistently in both trials. We demonstrated that predictor variable importance descended towards the terminal stages of growth, signifying the importance of phenotype collection beyond classical terminal growth stages. This study is among the first to demonstrate an ability to predict yield in elite hybrid maize breeding trials using temporal UAS image-based phenotypes and supports the potential benefit of phenomic selection approaches in estimating breeding values before harvest.

Effect of Oats and Wheat Genotype on In Vitro Gas Production Kinetics of Straw

Simple SummaryIncreases in cereal grain yields cause the accumulation of large amounts of straw on the soils after grain harvest. Straw is usually burned in the field to help soil preparation for the next crop, a practice resulting in local and global pollution, erosion, loss of soil carbon, and wildfires. An alternative is feeding straw to ruminants, but straw has poor nutritive value, making this option unattractive to Chilean farmers. Oats and wheat have been bred for greater grain yield and improved agronomic traits, but it is unknown whether the straw of different varieties and breeding lines differs in nutritive quality. To investigate this possibility, we incubated the straws from 49 different varieties and breeding lines of oats and 24 of wheat with rumen microorganisms, and studied gas production as an indication of the extent of straw digestion. We found moderate differences among varieties and breeding lines of oats and wheat in gas production, which were not detrimental to agronomic characteristics of importance. If these results can be confirmed in animal experiments, gas production of straw incubated in rumen microbial cultures may be used to identify cereal genotypes whose straw has a better nutritive quality for ruminants. Increases in cereals grain yield in the last decades have increased the accumulation of straw on the soil after harvest. Farmers typically open burn the straw to prepare the soil for the next crop, resulting in pollution, emission of greenhouse gases, erosion, loss of soil organic matter, and wildfires. An alternative is feeding straw to ruminants, but straw nutritive value is limited by its high content of lignocellulose and low content of protein. Cereal breeding programs have focused on improving grain yield and quality and agronomic traits, but little attention has been paid to straw nutritive value. We screened straw from 49 genotypes of oats and 24 genotypes of wheat from three cereal breeding trials conducted in Chile for in vitro gas production kinetics. We found moderate effects of the genotype on gas production at 8, 24, and 40 h of incubation, and on the maximum extent and rate of gas production. Gas production was negatively associated with lignin and cellulose contents and not negatively associated with grain yield and resistance to diseases and lodging. Effects observed in vitro need to be confirmed in animal experiments before gas production kinetics can be adopted to identify cereal genotypes with more digestible straw.

Establishing optimal broodstock sex ratios for the royal gramma (Gramma loreto) in small scale system aquaculture

The royal gramma (Gramma loreto) is vulnerable to anthropogenic impact in its coral reef habitats, and is deemed an “indicator” species of pollution. It is also heavily fished for the aquarium trade, being among the top 1% of imported marine aquarium fish species into the U.S. The objective of this study was to establish optimal sex ratios of broodstock groups for aquaculture as an alternative to wild capture, while balancing territorial aggression. Three two-month breeding trials were carried out in six 23 L aquaria stocked with one male and one, two, or three females. During the trials, each aquarium was filmed weekly for one hour at first light to record courtship, nest use, and aversive behaviors. Behaviors were quantified with JWatcher software and analyzed in repeated measures ANOVAs. Results suggest that in small aquaria, a sex ratio of one male to two females may be optimal for spawning because courtship and nest use behaviors are optimized while aversive behaviors are moderated. Past efforts to aquaculture the species have typically been conducted at broodstock sex ratios of 1:1. These results offer the potential to increase productivity of commercial aquaculture of the species.

Maize-IAS: a maize image analysis software using deep learning for high-throughput plant phenotyping

BackgroundMaize (Zea mays L.) is one of the most important food sources in the world and has been one of the main targets of plant genetics and phenotypic research for centuries. Observation and analysis of various morphological phenotypic traits during maize growth are essential for genetic and breeding study. The generally huge number of samples produce an enormous amount of high-resolution image data. While high throughput plant phenotyping platforms are increasingly used in maize breeding trials, there is a reasonable need for software tools that can automatically identify visual phenotypic features of maize plants and implement batch processing on image datasets.ResultsOn the boundary between computer vision and plant science, we utilize advanced deep learning methods based on convolutional neural networks to empower the workflow of maize phenotyping analysis. This paper presents Maize-IAS (Maize Image Analysis Software), an integrated application supporting one-click analysis of maize phenotype, embedding multiple functions: (I) Projection, (II) Color Analysis, (III) Internode length, (IV) Height, (V) Stem Diameter and (VI) Leaves Counting. Taking the RGB image of maize as input, the software provides a user-friendly graphical interaction interface and rapid calculation of multiple important phenotypic characteristics, including leaf sheath points detection and leaves segmentation. In function Leaves Counting, the mean and standard deviation of difference between prediction and ground truth are 1.60 and 1.625.ConclusionThe Maize-IAS is easy-to-use and demands neither professional knowledge of computer vision nor deep learning. All functions for batch processing are incorporated, enabling automated and labor-reduced tasks of recording, measurement and quantitative analysis of maize growth traits on a large dataset. We prove the efficiency and potential capability of our techniques and software to image-based plant research, which also demonstrates the feasibility and capability of AI technology implemented in agriculture and plant science.

Tall 3-dwarfs: oxymoron or opportunity to increase grain yield in sorghum?

Plant height was positively correlated with grain yield across a large set of 3-dwarf sorghum hybrids and production environments in north-eastern Australia. In industrialised countries, plant breeders tend to select for short plant stature in cereals like wheat, barley and rice, but also grain sorghum. This is mainly to prevent stalk lodging and to allow for machine harvesting. However, this counteracts an intrinsic positive relationship between plant height and yield potential often observed in cereals. We used data from multi-environment breeding trials comprising large sets of female sorghum lines from a range of pedigrees in hybrid combination with five different male testers. The hybrids were grown in 22 different rainfed environments in north-eastern Australia, which allowed us to thoroughly examine the relationship between plant height and yield across a range of productivity levels. Covariate analysis showed that in 38 out of the 90 tested relationships, grain yield was significantly (p < 0.05) positively and in only one case significantly negatively associated with plant height. This strong positive association between the traits was supported by the observation that 87% of the effects were either positive or zero. The effects of height on yield ranged from a decrease of 0.015tha-1 to an increase of 0.057tha-1cm-1. The majority of the negative effects were observed in low-yielding trials and the positive effect of height tended to increase with increasing mean trial yield. Opportunities to increase yield potential by selecting for slightly taller sorghum hybrids therefore need to be explored in context with the target environments and in combination with other means to control the risks of lodging.

Near infrared spectroscopy of plantation forest soil nutrients in Sabah, Malaysia, and the potential for microsite assessment

Knowledge of soil physical and chemical properties is vital to the optimal growing performance of agricultural crops, including plantation forest trees. Near infrared (NIR) spectroscopy has been shown to be a tool that enables rapid and low-cost assessment of soils, however its use in forest plantations has been slow to develop. This study shows the development of calibrations for total organic carbon, total nitrogen and soil pH using a handheld NIR spectrometer for soils at three sites in Sabah, Malaysia. Soil samples were collected, dried, milled and scanned after which they were analysed using standard chemical methods to obtain total organic carbon (TOC) and total nitrogen (TN). Partial least squares regression was used to develop calibrations between reference data and NIR spectra and validated using an independent sample set. The calibration of soil pH is made using a subset of samples across A- and B-horizons for samples from two of the three sites. The most effective spectral pre-treatment was the standard normal variate for TOC and TN while the Savitzky-Golay first derivative was the best pre-treatment for predicting soil pH. Principal component analysis was performed on the raw NIR spectra of all samples to confirm that the samples from different sites were able to be used in a single regression analysis. Kennard-Stone selection was used to create calibration sets and validation sets from the combined spectra from all sites and both soil horizons. Calibrations were also developed independently on the A- and B-horizon samples, but there were insufficient sample numbers to utilize an independent validation set. The coefficients of determination for the validation set (r2p) were 0.77 and 53 for TOC and TN respectively while the root mean square error of prediction (RMSEP) was 0.44 g 100 g−1for TOC and 0.051 g 100 g−1for TN. In addition, it showcases the application of these calibrations to provide spatial assessment of two differing micro-sites within a single Eucalyptus pellita progeny breeding trial. Combined with the potential to monitor foliar nutrients, the ability to obtain high spatial details of soil composition will assist tree plantation growers and also other agricultural producers, such as oil palm plantation managers, to better manage their soil and fertiliser regimes.