Breeding Trials Research Articles

Yield prediction through UAV-based multispectral imaging and deep learning in rice breeding trials

ContextPredicting crop yields with high precision and timeliness is essential for crop breeding, enabling the optimization of planting strategies and efficients resource allocation while ensuring food security. Current research in this field typically does not address the problem of yield prediction in the diverse context of breeding experiments involving numerous varieties. However, evaluating the performance of prediction models across multiple varieties is vital for further model refining and enhancing model robustness and adaptability. ObjectiveThis study aims to evaluate the performance of feature- and image-based yield prediction models for yields with multiple varieties to compare their capabilities and determine an appropriate timing for early yield prediction. MethodsThis study combines unmanned aerial vehicle (UAV)-based multispectral remote sensing imagery with machine learning and deep learning-based algorithms to develop rice yield prediction models across multiple varieties. The performances of both feature- and image-based models are evaluated. The feature-based models considered in this study include random forest (RF), deep neural network (DNN), and long short-term memory (LSTM) algorithms, and the image-based models are convolutional neural network (CNN) architectures, including both two-dimensional (2D) and three-dimensional (3D) CNN models. To assess the performance of the multi-variety crop yield prediction models thoroughly, this study considers two sampling scenarios: stratified sampling and group sampling. Results and conclusionsThe results show that the image-based deep learning models outperform the feature-based machine learning models, which indicates their superior robustness in multi-variety scenarios and highlights their significant potential of directly extracting spatiotemporal features from images for yield prediction. The results indicate that the multi-temporal 2D CNN model (i.e., the CNN-M2D model) can achieve the best yield prediction performance among all models, achieving RRMSE = 8.13 % and R2 = 0.73. The prediction results also demonstrate good consistency with the observed data, indicating an efficient capturing of spatial pattern variations in yield across different varieties. Based on the results, with the crops progressing along the growth stages, the accuracy of the yield prediction models improves gradually, achieving the best prediction performance during the flowering to grain-filling stage. Finally, according to the results, the optimal lead time for predicting rice yield is approximately one month before harvest. SignificanceOur study can provide a reference for the research community in yield prediction and high-yield variety selection in breeding trials.

Spatial analysis with unoccupied aircraft systems data in wheat breeding yield trials

AbstractAn important aspect of reliable cultivar development is good field trial evaluations. Unoccupied aircraft systems (UAS also known as drones or UAVs) are a popular high‐throughput phenotyping tool that has been used to successfully evaluate plant stress and other canopy characteristics in a field. In precision agriculture applications, UAS imagery has been used to identify spatial variability in field settings. Here, we use UAS spectral imagery to improve field trial spatial analysis, better control spatial variability, and reduce errors for more reliable selections. UAS imagery data were collected across 47 breeding trials planted in an augmented complete block design (ACBD) or alpha‐lattice replicated designs from 2020 through 2023. Trials were evaluated using three spatial analysis strategies: linear models incorporating block effect, row‐column effect, or 2D splines. UAS‐derived spectral reflectance indices (SRI) were combined with each model as covariates. Modeling strategies were used across all trials and evaluated for autocorrelation, model fitness, and coefficient of variation (CV). Akaike information criterion (AIC) was used to assess model fitness. For spatial analysis trials, SRIs significantly lowered model AIC by an average of 38.4 for alpha‐lattice trials and 69.1 for ACBD trials. CV scores were also lowered when SRIs were utilized, with average CV values being 2.6 lower for alpha‐lattice and 2.1 for ACBD trials. This study highlights the potential for SRIs to improve the analyses of field breeding trials despite extreme environmental variables and climate conditions, improving experiment reliability and changing the way breeders plan and implement breeding experiments.

Assessing the efficiency and heritability of blocked tree breeding trials

Progeny trials in tree breeding are often laid out using blocked experimental designs, in which families are randomly assigned to plots and several trees are planted per plot. Such designs are optimized for the assessment of family effects. However, tree breeders are primarily interested in assessing breeding values of individual trees. This paper considers the assessment of heritability at both the family and tree levels. We assess heritability based on pairwise comparisons among individual trees. The approach shows that there is considerable heterogeneity in pairwise heritabilities, primarily due to the differences in both genetic as well as error variances among within- and between-family comparisons. Our results further show that efficient blocking positively affects all types of comparison except those among trees within the same plot.

A Case Study on The Evaluation of Maturity Class in Potato Breeding Trials Using UAV Imagery

In potato breeding, maturity class (MC) is a crucial selection criterion because this is a critical aspect of commercial potato production. Currently, the classification of potato genotypes into MCs is done visually, which is time- and labor-consuming. The objective of this research was to use vegetation indices (VIs) derived from unmanned aerial vehicle (UAV) imagery to remotely assign MCs to potato plants grown in trials, representing three different early stages within a multi-year breeding program. The relationships between VIs (GOSAVI – Green Optimized Soil Adjusted Vegetation Index, MCARI2 – Modified Chlorophyll Absorption Index-Improved, NDRE – Normalized Difference Red Edge, NDVI – Normalized Difference Vegetation Index, and OSAVI – Optimized Soil Adjusted Vegetation Index and WDVI – Weighted Difference Vegetation Index) and visual potato canopy status were determined. Further, this study aimed to identify factors that could improve the accuracy (decrease Mean Absolute Error – MAE) of potato MC estimation remotely. Results show that VIs derived from UAV imagery can be effectively used to remotely assign MCs to potato breeding lines, with higher accuracy for the potato B-clones (20 plants per plot) than the A-clones (6 plants per plot). Among the tested VIs, the NDRE allowed for potato MC evaluation with the lowest MAE. Applying NDRE for remote MC estimation using a validation dataset of potato B-clones (100 plants per plot), resulted in an MC estimate with a 0.81 MAE. However, the accuracy of potato MC estimation using UAV image-based methods should be improved by reducing the potato canopy’s variability (increasing uniformity) within the plot. This could be achieved by minimizing 1) potato vines bending over the neighboring row, causing vine overlap between plots, and 2) plants damaged by tractor wheels during field operations.

Leveraging soil mapping and machine learning to improve spatial adjustments in plant breeding trials

AbstractSpatial adjustments are used to improve the estimate of plot seed yield across crops and geographies. Moving means (MM) and P‐Spline are examples of spatial adjustment methods used in plant breeding trials to deal with field heterogeneity. Within the trial, spatial variability primarily comes from soil feature gradients, such as nutrients, but a study of the importance of various soil factors including nutrients is lacking. We analyzed plant breeding progeny row (PR) and preliminary yield trial (PYT) data of a public soybean breeding program across 3 years consisting of 43,545 plots. We compared several spatial adjustment methods: unadjusted (as a control), MM adjustment, P‐spline adjustment, and a machine learning‐based method called XGBoost. XGBoost modeled soil features at: (a) the local field scale for each generation and per year, and (b) all inclusive field scale spanning all generations and years. We report the usefulness of spatial adjustments at both PR and PYT stages of field testing and additionally provide ways to utilize interpretability insights of soil features in spatial adjustments. Our work shows that using soil features for spatial adjustments increased the relative efficiency by 81%, reduced the similarity of selection by 30%, and reduced the Moran's I from 0.13 to 0.01 on average across all experiments. These results empower breeders to further refine selection criteria to make more accurate selections and select for macro‐ and micro‐nutrients stress tolerance.

Ethological Study and Breeding Trials of Aulacodes (Thryonomys swinderianus, Temminck, 1827) Captured in the Forest Area of Sika-Komenankro (Ivory Coast)

Ethological Study and Breeding Trials of Aulacodes (Thryonomys swinderianus, Temminck, 1827) Captured in the Forest Area of Sika-Komenankro (Ivory Coast)

Effects of Intermittent Harem Mating on Parturition-induced Stress and Success Rate of Weaning in C57BL/6JNarl Mice.

Improving the effectiveness of mating schemes for large-scale production of mice is an ongoing challenge in animal facilities. Continuous mating, which requires fewer breeding cages than intermittent mating, has traditionally been used to take advantage of postpartum estrus for efficient production. However, the continuous mating scheme lacks flexibility because it cannot immediately accommodate the reduced needs of mice when production levels are high. In this study, we compared reproductive performance, fecal corticosterone metabolite (FCM) level as a stress indicator, and mouse mortality between the continuous trio (CT) and intermittent quad (IQ) mating schemes. The weaning rates in the IQ scheme were higher than those in the CT scheme (98.8% compared with 85.3%). The FCM levels in IQ female breeders were lower during the first 5 d after parturition than those in CT female breeders. The FCM levels in postpartum females housed with 2 adult mice were significantly higher on days 1, 3, and 5 after giving birth than those of females housed alone. This suggests that the presence of cage mates may induce stress responses in postpartum females. Increasing the individual cage area did not reduce the FCM levels of female breeders when accompanied by cage mates after parturition. In addition, the incidence of dystocia and mortality was lower in IQ breeders than in CT breeders. In summary, this breeding trial suggests that compared with the continuous mating scheme, the intermittent mating scheme improves the welfare of postpartum females with normal breeding performance in the C57BL/6JNarl production colony.

Fertility protection during chemotherapy treatment by boosting the NAD(P)+ metabolome.

Chemotherapy induced ovarian failure and infertility is an important concern in female cancer patients of reproductive age or younger, and non-invasive, pharmacological approaches to maintain ovarian function are urgently needed. Given the role of reduced nicotinamide adenine dinucleotide phosphate (NADPH) as an essential cofactor for drug detoxification, we sought to test whether boosting the NAD(P)+ metabolome could protect ovarian function. We show that pharmacological or transgenic strategies to replenish the NAD+ metabolome ameliorates chemotherapy induced female infertility in mice, as measured by oocyte yield, follicle health, and functional breeding trials. Importantly, treatment of a triple-negative breast cancer mouse model with the NAD+ precursor nicotinamide mononucleotide (NMN) reduced tumour growth and did not impair the efficacy of chemotherapy drugs in vivo or in diverse cancer cell lines. Overall, these findings raise the possibility that NAD+ precursors could be a non-invasive strategy for maintaining ovarian function in cancer patients, with potential benefits in cancer therapy.

Conditional loss of Brca1 in oocytes causes reduced litter size, ovarian reserve depletion and impaired oocyte in vitro maturation with advanced reproductive age in mice

An estimated 1 in 350 women carry germline BRCA1/2 mutations, which confer an increased risk of developing breast and ovarian cancer, and may also contribute to subfertility. All mature, sex steroid-producing ovarian follicles are drawn from the pool of non-renewable primordial follicles, termed the 'ovarian reserve'. The clinical implications of early ovarian reserve exhaustion extend beyond infertility, to include the long-term adverse health consequences of loss of endocrine function and premature menopause. We aimed to determine whether conditional loss of Brca1 in oocytes impacts ovarian follicle numbers, oocyte quality and fertility in mice with advancing maternal age. We also aimed to determine the utility of AMH as a marker of ovarian function, by assessing circulating AMH levels in mice and women with BRCA1/2 mutations, and correlating this with ovarian follicle counts. In this study, we addressed a longstanding question in the field regarding the functional consequences of BRCA1 inactivation in oocytes. To recapitulate loss of BRCA1 protein function in oocytes, we generated mice with conditional gene deletion of Brca1 in oocytes using Gdf9-Cre recombinase (WT: Brca1fl/flGdf9+/+; cKO: Brca1fl/flGdf9cre/+). While the length of the fertile lifespan was not altered between groups after a comprehensive breeding trial, conditional loss of Brca1 in oocytes led to reduced litter size in female mice. Brca1 cKO animals had a reduced ovarian reserve and oocyte maturation was impaired with advanced maternal age at postnatal day (PN)300, compared to WT animals. Serum anti-Müllerian hormone (AMH) concentrations (the gold-standard indirect marker of the ovarian reserve used in clinical practice) were not predictive of reduced primordial follicle number in Brca1 cKO mice versus WT. Furthermore, we found no correlation between follicle number or density and serum AMH concentrations in matched samples from a small cohort of premenopausal women with BRCA1/2 mutations. Together, our data demonstrate that BRCA1 is a key regulator of oocyte number and quality in females and suggest that caution should be used in relying on AMH as a reliable marker of the ovarian reserve in this context. This work was made possible through Victorian State Government Operational Infrastructure Support and Australian Government NHMRC IRIISS. This work was supported by funding from the Australian Research Council (ALW - DE21010037 and KJH - FT190100265), as well as the National Breast Cancer Foundation (IIRS-22-092) awarded to ALW and KJH. LRA, YML, LT, EOKS and MG were supported by Australian Government Research Training Program Scholarships. LRA, YML and LT were also supported by a Monash Graduate Excellence Scholarship. YC, SG and XC were supported by Monash Biomedicine Discovery Institute PhD Scholarships. LRA was also supported by a Monash University ECPF24-6809920940 Fellowship. JMS was supported by NHMRC funding (2011299). MH was supported by an NHMRC Investigator Grant (1193838).

Captive Breeding of Pufferfish (Leiodon cutcutia) from Assam, India

Background: Leiodon cutcutia has been exploited from its natural habitats which leads to a drastic decline of its natural socks ever since it has been reported its ornamental value. The development of effective breeding protocols, optimal management of environmental conditions, and the use of appropriate hormonal treatments can help stock improvement of the species. Methods: Live specimens were collected from the Meleng River across the Jorhat districts. The maturity cycle was properly studied to determine the breeding season of the species. Acclimatization of the brooder and feeding were done following standard protocol. Breeding trials were conducted by habitat manipulation and also by administering hormonal injections. Findings: Spawning was triggered by simulating habitat conditions of the breeding enclosure and extending daylight hours. The species exhibits sexual dimorphism; the females have white spots on the body and are always larger than the males. Males typically display courtship behaviour such as chasing and circling females. Territorial and aggressive behaviours complicate breeding efforts, requiring careful management of tank mates and breeding pairs. Adhesive eggs were attached to the substratum and males were found to guard the eggs until hatching. The breeding experiment had moderate success. Conclusion: Captive breeding of Leiodon cutcutia presents unique challenges but offers significant benefits for conservation and the aquarium trade.

Feature engineering and parameter tuning: improving phenomic prediction ability in multi-environmental durum wheat breeding trials

Key Message Optimized phenomic selection in durum wheat uses near-infrared spectra, feature engineering and parameter tuning. Our study reports improvements in predictive ability and emphasizes customized preprocessing for different traits and models.The success of plant breeding programs depends on efficient selection decisions. Phenomic selection has been proposed as a tool to predict phenotype performance based on near-infrared spectra (NIRS) to support selection decisions. In this study, we test the performance of phenomic selection in multi-environmental trials from our durum wheat breeding program for three breeding scenarios and use feature engineering as well as parameter tuning to improve the phenomic prediction ability. In addition, we investigate the influence of genotype and environment on the phenomic prediction ability for agronomic and quality traits. Preprocessing, based on a grid search over the Savitzky–Golay filter parameters based on 756,000 genotype best linear unbiased estimate (BLUE) computations, improved the phenomic prediction ability by up to 1500% (0.02–0.3). Furthermore, we show that preprocessing should be optimized depending on the dataset, trait, and model used for prediction. The phenomic prediction scenarios in our durum breeding program resulted in low-to-moderate prediction abilities with the highest and most stable prediction results when predicting new genotypes in the same environment as used for model training. This is consistent with the finding that NIRS capture both the genotype and genotype-by-environment (G×E)\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$(G\ imes E)$$\\end{document} interaction variance.

Accuracy of prediction from multi-environment trials for new locations using pedigree information and environmental covariates: the case of sorghum (Sorghum bicolor (L.) Moench) breeding

Key messagesWe investigate a method of extracting and fitting synthetic environmental covariates and pedigree information in multilocation trial data analysis to predict genotype performances in untested locations.Plant breeding trials are usually conducted across multiple testing locations to predict genotype performances in the targeted population of environments. The predictive accuracy can be increased by the use of adequate statistical models. We compared linear mixed models with and without synthetic covariates (SCs) and pedigree information under the identity, the diagonal and the factor-analytic variance-covariance structures of the genotype-by-location interactions. A comparison was made to evaluate the accuracy of different models in predicting genotype performances in untested locations using the mean squared error of predicted differences (MSEPD) and the Spearman rank correlation between predicted and adjusted means. A multi-environmental trial (MET) dataset evaluated for yield performance in the dry lowland sorghum (Sorghum bicolor (L.) Moench) breeding program of Ethiopia was used. For validating our models, we followed a leave-one-location-out cross-validation strategy. A total of 65 environmental covariates (ECs) obtained from the sorghum test locations were considered. The SCs were extracted from the ECs using multivariate partial least squares analysis and subsequently fitted in the linear mixed model. Then, the model was extended accounting for pedigree information. According to the MSEPD, models accounting for SC improve predictive accuracy of genotype performances in the three of the variance-covariance structures compared to others without SC. The rank correlation was also higher for the model with the SC. When the SC was fitted, the rank correlation was 0.58 for the factor analytic, 0.51 for the diagonal and 0.46 for the identity variance-covariance structures. Our approach indicates improvement in predictive accuracy with SC in the context of genotype-by-location interactions of a sorghum breeding in Ethiopia.

Impact of recurrent intermittent flooding on the yield and growth of Elaeis guineensis

AbstractIncreased frequency and severity of flooding are linked to global warming. Flooding stress can severely affect crop yields and plant survival, and therefore the livelihoods of farmers. An oil palm breeding trial plot consisting of six progenies in Terengganu, Malaysia, happened to be inundated with flooding within a year after planting and annually for 2 years thereafter. Flooding recurred at the 8th year of planting and several times after that within the 14‐year period of monitoring. Due to the uneven terrain in this trial, palms were exposed to various flood levels and were categorized into two flooding groups. Yields and survivability of the palms were not affected by low‐flood levels, in contrast to the palms in lower areas that experienced higher flood levels. Compared to neighbouring non‐flooded trials, palms exposed to high floodwaters generally experienced lower fruit yields, and reduced vertical growth with poorer canopy cover. However, one of the progenies that exhibited a moderate survival rate in high‐flooded areas outperformed the other progenies in terms of yield in these areas. Generally, young oil palms exposed to low floods were able to survive and produce yields that were comparable to palms in non‐flooded areas, though 10%–20% of the low‐flooded palms produced poor yields in the first 3–5 years. In high‐flooded areas however, oil palm survivability ranged from 45% to 73% during the early 3 years and 46%–89% thereafter. Hence, young palms exposed to recurrent flooding of approximately more than 1 m during their early years are likely to become poor yielders with poor survival rates. Observations from this study may assist oil palm farmers in the management of this crop in the event of unexpected flooding.

Fast, Nondestructive and Precise Biomass Measurements Are Possible Using Lidar-Based Convex Hull and Voxelization Algorithms

Light detection and ranging (lidar) scanning tools are available that can make rapid digital estimations of biomass. Voxelization and convex hull are two algorithms used to calculate the volume of the scanned plant canopy, which is correlated with biomass, often the primary trait of interest. Voxelization splits the scans into regular-sized cubes, or voxels, whereas the convex hull algorithm creates a polygon mesh around the outermost points of the point cloud and calculates the volume within that mesh. In this study, digital estimates of biomass were correlated against hand-harvested biomass for field-grown corn, broom corn, and energy sorghum. Voxelization (r = 0.92) and convex hull (r = 0.95) both correlated well with plant dry biomass. Lidar data were also collected in a large breeding trial with nearly 900 genotypes of energy sorghum. In contrast to the manual harvest studies, digital biomass estimations correlated poorly with yield collected from a forage harvester for both voxel count (r = 0.32) and convex hull volume (r = 0.39). However, further analysis showed that the coefficient of variation (CV, a measure of variability) for harvester-based estimates of biomass was greater than the CV of the voxel and convex-hull-based biomass estimates, indicating that poor correlation was due to harvester imprecision, not digital estimations. Overall, results indicate that the lidar-based digital biomass estimates presented here are comparable or more precise than current approaches.

Natural breeding performance in different saline water pens, crablets rearing and cannibalism in juvenile phase of mangrove mud crab (Scylla olivacea) in earthen mangrove pens

The current study focused on natural breeding techniques using locally available mud crab broods during breeding season. The breeding trial was tested in artificially created earthen pens that designed with mangrove plants and salt tolerance grass. Mature brood crabs were collected from local rivers and equally distributed in 20 L black color plastic containers in three different salinities (T1 = 24ppt, T2 = 26ppt, and T3 = 28ppt) and covered with black cloth. The river salinity was 26ppt during breeding season that is considered as control(T2), and treatment T1 and T3 were prepared artificially mixing with water and sea salt respectively. Broods are reared in containers until hatching and zoea was transferred to earthen pens immediately after hatching. Zoea were reared in earthen pens until turns to crablet(0.027 ± 0.005 g). The broods as well as crablet were identified through molecular barcoding and confirmed species as Scylla olivicea. Besides, morphometric characters like weight(126.22 ± 0.19 g), carapace length(6.36 ± 0.08 cm), carapace width(9.08 ± 0.08 cm), carapace color(brownish to brownish green), Anterolateral margin and teeth (3.79 ± 0.05 cm), abdomen length(3.04 ± 0.02 cm), hand (5.50 ± 0.03 cm), carpus(2.54 ± 0.04), antenna (2.69 ± 0.07), frontal margin(2.90 ± 0.07 cm), dactylus(3.13 ± 0.06 cm), thoracic sternites(4.17 ± 0.10), merus(4.16 ± 0.11 cm) etc. are also assessed during the experiment. The hatching rate, survival %, incubation period and other parameters are significantly different among the treatments. The highest hatching rate was observed in T2(91.34 ± 4.61 %) and lowest was in T1(57.98 ± 1.38) whereas the highest survival rate was recorded in T2(10.53 ± 1.56) and lowest observed in T1(2.87 ± 0.68). The order of incubation was T1(12.3 days)>T3(11 days)>T2(8.67days) observed during experiment. The cannibalism rate was also estimated in this experiment and observed cannibalism rate was 71.20 ± 2.08. Natural mud crab breeding is easy and can be applicable in fields at low cost with significant survival rate.

Chitosan nanoconjugation of GnRH analogue augments reproductive performance in Clarias magur (Hamilton, 1822)

A breeding trial was conducted to investigate the impact of a chitosan nanoconjugated formulation of Salmon gonadotropin-releasing hormone analogue (ChN-sGnRH-a) on the induced breeding performance of female Clarias magur (magur). A total of 72 magur breeders were divided into six treatment groups: Cn, the Negative Control (injected with bare chitosan nanoparticles); Cp, the Positive Control (injected with Gonopro-FH®, a commercially available inducing hormone, at a dose of 1 mL/kg body weight of fish); T1, injected with ChN-sGnRH-a at a dose of 1 mL/kg; T2, T1 + 10 mg/kg domperidone; T3, injected with ChN-sGnRH-a at a dose of 0.5 mL/kg; T4, T3 + 10 mg/kg domperidone. Blood was collected at 0 (before injection), 6, 12 and 18 h post-injection to determine the serum steroid profile. In the Cp treatment group, the serum steroid levels of testosterone, estradiol, and 17α, 20β-dihydroxy progesterone in the fish, displayed a sharp increase, peaking at the 6-h post-injection, followed by a significant decrease. However, the fish in the T2 and T4 treatment groups exhibited serum steroid peaks at the 12-h post-injection and maintained those levels up to the 18-h post-injection. The reproductive performance, as measured by spawning fecundity, fertilization rate, and hatching rate, was significantly higher in the T2 treatment group (p < 0.05). No significant differences (p > 0.05) were observed between the Cp and T4 treatment groups. Compared to the Cn treatment group, the mRNA expression levels of pituitary follicle stimulating hormone subunit beta and luteinizing hormone subunit beta, as well as ovarian 17β-hydroxysteroid dehydrogenase type 3, were significantly higher in the T2 treatment group, followed by Cp and T4 treatment groups. Fish in the T2 treatment group displayed fewer mature oocytes and numerous post-ovulatory follicles in their ovaries. Additionally, the ovarian histology of fish in the T4 treatment group closely resembled that of the Cp treatment group. The results indicate that the chitosan nanoconjugated formulation of Salmon GnRH analogue might contribute to maintaining elevated levels of gonadotropic and steroid concentrations, resulting in improved reproductive output in female magur breeders. It is also revealed that the induced breeding of magur cannot be achieved without using dopamine inhibitors in GnRH analogue injection. Using chitosan nanoparticles as a nano-delivery system could reduce the Salmon GnRH analogue dose without compromising the reproductive performance of female magur breeders.

Growing Small Grains in Sub‐Arctic Alaska

Abstract The largest state in the U.S., Alaska has only 1% of its land devoted to agriculture. Yet, there’s a strong need for and interest in increasing local production to help fortify food security in the state. The Alaska Collaborative Food Production Systems Project develops research projects and supports agricultural producers in the state. At the 2023 ASA, CSSA, and SSSA Annual Meeting, researchers from the team talked about the project’s objectives and progress. With one growing season under their belts, they’ve made headway on breeding trials, gathered novel data on soil health and quality, finished a year of cover crop trials, and interviewed producers and consumers about food agency in the state.

Genetic and molecular regulation of chilling requirements in pear: breeding for climate change resilience.

Pear (Pyrus spp.) is a deciduous fruit tree that requires exposure to sufficient chilling hours during the winter to establish dormancy, followed by favorable heat conditions during the spring for normal vegetative and floral budbreak. In contrast to most temperate woody species, apples and pears of the Rosaceae family are insensitive to photoperiod, and low temperature is the major factor that induces growth cessation and dormancy. Most European pear (Pyrus Communis L.) cultivars need to be grown in regions with high chilling unit (CU) accumulation to ensure early vegetative budbreak. Adequate vegetative budbreak time will ensure suitable metabolite accumulation, such as sugars, to support fruit set and vegetative development, providing the necessary metabolites for optimal fruit set and development. Many regions that were suitable for pear production suffer from a reduction in CU accumulation. According to climate prediction models, many temperate regions currently suitable for pear cultivation will experience a similar accumulation of CUs as observed in Mediterranean regions. Consequently, the Mediterranean region can serve as a suitable location for conducting pear breeding trials aimed at developing cultivars that will thrive in temperate regions in the decades to come. Due to recent climatic changes, bud dormancy attracts more attention, and several studies have been carried out aiming to discover the genetic and physiological factors associated with dormancy in deciduous fruit trees, including pears, along with their related biosynthetic pathways. In this review, current knowledge of the genetic mechanisms associated with bud dormancy in European pear and other Pyrus species is summarized, along with metabolites and physiological factors affecting dormancy establishment and release and chilling requirement determination. The genetic and physiological insights gained into the factors regulating pear dormancy phase transition and determining chilling requirements can accelerate the development of new pear cultivars better suited to both current and predicted future climatic conditions.

Competition effects can mislead selection in eucalypt breeding trials

The assessment of plant performance and the accuracy of genetic selection can be significantly affected by genetic competition among individuals. In addition to genetic causes, competition is influenced by external factors such as environment and age. This research uses multi-location multi-age Eucalyptus dunii trials to answer four questions: i) Are there major changes when competition effects (both genetic and residual) are estimated in single-age and multi-age models? ii) What are the implications of considering competition effects on the early selection of eucalypt clones? iii) What are the impacts of considering the reliability of direct (DGE) and indirect genotypic effects (IGE) as a weight in a selection index?, and iv) Which clones hold the potential to form highly productive clonal plantations in Southern Brazil when deployed together as clonal composites? The dataset contained three trials established in different locations in Southern Brazil, where growth traits were measured at 3.5 and 7 years. We fitted single-age and multi-age spatial competition models for each trial. The multi-age spatial competition model was a valuable tool for selecting superior eucalypt clones. Neglecting the IGEs led to changes in clone ranking, reducing the effectiveness of early selection. Additionally, a selection index where DGEs and IGEs are weighted by their reliability was proposed. Therefore, our study contributes to understanding the magnitude of IGE's impact on the daily practice of eucalypt breeding, such as early selection and multi-age analyses, and proposes selection strategies that consider the quantity and quality of information provided by the models for each individual.

Mapping Maize Planting Densities Using Unmanned Aerial Vehicles, Multispectral Remote Sensing, and Deep Learning Technology

Maize is a globally important cereal and fodder crop. Accurate monitoring of maize planting densities is vital for informed decision-making by agricultural managers. Compared to traditional manual methods for collecting crop trait parameters, approaches using unmanned aerial vehicle (UAV) remote sensing can enhance the efficiency, minimize personnel costs and biases, and, more importantly, rapidly provide density maps of maize fields. This study involved the following steps: (1) Two UAV remote sensing-based methods were developed for monitoring maize planting densities. These methods are based on (a) ultrahigh-definition imagery combined with object detection (UHDI-OD) and (b) multispectral remote sensing combined with machine learning (Multi-ML) for the monitoring of maize planting densities. (2) The maize planting density measurements, UAV ultrahigh-definition imagery, and multispectral imagery collection were implemented at a maize breeding trial site. Experimental testing and validation were conducted using the proposed maize planting density monitoring methods. (3) An in-depth analysis of the applicability and limitations of both methods was conducted to explore the advantages and disadvantages of the two estimation models. The study revealed the following findings: (1) UHDI-OD can provide highly accurate estimation results for maize densities (R2 = 0.99, RMSE = 0.09 plants/m2). (2) Multi-ML provides accurate maize density estimation results by combining remote sensing vegetation indices (VIs) and gray-level co-occurrence matrix (GLCM) texture features (R2 = 0.76, RMSE = 0.67 plants/m2). (3) UHDI-OD exhibits a high sensitivity to image resolution, making it unsuitable for use with UAV remote sensing images with pixel sizes greater than 2 cm. In contrast, Multi-ML is insensitive to image resolution and the model accuracy gradually decreases as the resolution decreases.