GIS-FA: an approach to integrating thematic maps, factor-analytic, and envirotyping for cultivar targeting

Theoretical and Applied Genetics, Journal Year: 2024, Volume and Issue: 137(4)

Published: March 12, 2024

Language: Английский

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Leveraging data from the Genomes-to-Fields Initiative to investigate genotype-by-environment interactions in maize in North America

Nature Communications, Journal Year: 2023, Volume and Issue: 14(1)

Published: Oct. 30, 2023

Abstract Genotype-by-environment (G×E) interactions can significantly affect crop performance and stability. Investigating G×E requires extensive data sets with diverse cultivars tested over multiple locations years. The Genomes-to-Fields (G2F) Initiative has maize hybrids in more than 130 year-locations North America since 2014. Here, we curate expand this set by generating environmental covariates (using a model) for each of the trials. resulting includes DNA genotypes linked to 70,000 phenotypic records grain yield flowering traits 4000 hybrids. We show how valuable serve as benchmark agricultural modeling prediction, paving way countless investigations maize. use multivariate analyses characterize set’s genetic structure, study association key factors traits, provide benchmarks using genomic prediction models.

Language: Английский

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Using agro-ecological zones to improve the representation of a multi-environment trial of soybean varieties

Frontiers in Plant Science, Journal Year: 2024, Volume and Issue: 15

Published: March 25, 2024

This research introduces a novel framework for enhancing soybean cultivation in North America by categorizing growing environments into distinct ecological and maturity-based zones. Using an integrated analysis of long-term climatic data records varietal trials, this generates zonal environmental characterization which captures major components the environment affect range adaptation varieties. These findings have immediate applications optimizing multi-environment trials. allows breeders to assess representation multi-environmental trial varieties, strategize distribution testing placement test sites accordingly. application is demonstrated with historical scenario trial, using two resource allocation models: one targeted towards improving general focuses on widely cultivated areas, specific adaptation, diverse conditions. Ultimately, study aims improve efficiency impact breeding programs, leading development cultivars resilient variable changing climates.

Language: Английский

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Stability models regulate the adaptation of male sterility-based chilli hybrids for agro-ecologically diverse regions

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 10, 2025

The Chilli (Capsicum annuum var. annuum L.) cultivars are highly sensitive to diverse agroclimatic conditions. research presents a significant contribution by identifying high-yielding and stable hybrids for wider adaptability using genetic male sterility (GMS). study was conducted in seven environments following conventional farming under field conditions five locations of North-western Himalaya along with naturally ventilated polyhouse natural practices 12 GMS based 4 check varieties identify the phenotypic stability yield its related attributes. experiment randomized block design replicated thrice during summer season 2021 respective environments. Joint regression analysis revealed Genotype (G) × Environment (E) interaction E + (G E) all traits. Eberhart Russel model green fruit DPCHYB 10 (627.68 g/plant) 5 (583.50 got top ranks. G GE biplot extrudes that Berthin (E5) most representative discriminating environment suitable selecting generally adapted hybrids. Mean vs indicated superiority yield. ‘Which won where’ polygon view GGE showed high yielding hybrid except Palampur (E1) where responsive adaptive.

Language: Английский

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Detecting environmental trends to rethink soybean variety testing programs

Crop Science, Journal Year: 2025, Volume and Issue: 65(1)

Published: Jan. 1, 2025

Abstract Variety testing programs (VTPs) use multi‐environment trials (MET) to evaluate and report the performance of commercially available pre‐commercial soybean ( Glycine max L. Merr.) varieties targeting a specific set environments. Adequate modeling environmental variability genotype–environment interactions (G × E) within VTP would help farmers seed companies decide which variety choose or recommend. We propose an approach characterize environments using data from University Missouri VTP. modeled trend (EnvT) based on phenotypic mean observed phenotype in each environment. The were classified into four different EnvT environment types, soil climate used as predictors through eXtreme Gradient Boosting (XGBoost) model. Temperature late vegetative flowering, soil‐saturated hydraulic conductivity, silt content key drivers EnvT. identified overrepresented (62%) increased ratio between G E variance. A simulation case study verified that random removal sites dataset quickly degraded analysis, implying increasing number underrepresented is recommended. Our results demonstrate characterization essential for optimizing resource allocation VTP, thereby supporting end goal aiding utilize best their production

Language: Английский

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Envirotyping to drive spring barley adaptation in Northwestern Europe

Field Crops Research, Journal Year: 2025, Volume and Issue: 326, P. 109793 - 109793

Published: March 7, 2025

Language: Английский

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Tap Maize Yield Productivity in China: A Meta-Analysis of Agronomic Measures and Planting Density Optimization

Agronomy, Journal Year: 2025, Volume and Issue: 15(4), P. 861 - 861

Published: March 29, 2025

Maize is a staple crop in China, playing crucial role agriculture and food security. However, current planting densities are suboptimal, leading to lower yields unrealized potential. This study explores the potential maximize maize by optimizing density implementing region-specific agronomic measures across China’s diverse agro-ecological zones. We compiled dataset consisting of 1974 independent field trials from 720 publications main maize-growing areas, spanning period 2000 2023, assess impact optimal practices on production. Our findings reveal that increasing levels—49.34% higher than farmer practices—can significantly boost national 16.28%. Furthermore, adopting techniques like precision irrigation, soil tillage, plant growth regulators enhances this effect, raising 69.91% yield 27.26%. Notably, irrigated areas Northwest China showed highest potential, whereas southern hilly regions had lowest. underscores significance tailoring each region. Combining with adjusted can reduce disparity. Precision were particularly effective maximizing especially North Plain. In contrast, proved most Southwest Southern China. integrating optimized improve productivity, thereby supporting sustainable agriculture. It provides scientific basis for regionalized agricultural management.

Language: Английский

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Multi-environment trials data analysis: linear mixed model-based approaches using spatial and factor analytic models

Frontiers in Research Metrics and Analytics, Journal Year: 2025, Volume and Issue: 10

Published: April 11, 2025

The analysis of multi-environment trials (MET) data in plant breeding and agricultural research is inherently challenging, with conventional ANOVA-based methods exhibiting limitations as the complexity MET experiments grows. This study presents linear mixed model-based approaches for analysis. Ten grain yield datasets from national variety Ethiopia were used. Randomized complete block (RCB) design analysis, spatial spatial+genotype-by-environment (G × E) compared under model framework. Spatial detected significant local, global, extraneous variations, positive correlations. For + G E increasing order factor analytic (FA) models improved explanation variance, though optimal FA was dataset-dependent. Integrating variability through modeling approach substantially genetic parameter estimates minimized residual variability. improvement particularly notable larger datasets, where number size each trial played a crucial role presence strong GxE effects. Additionally, correlation heat maps dendrograms provided intuitive insights into relationships, revealing patterns positive, negative, weak correlations, well distinct clusters. results clearly demonstrate that approaches, especially excel capturing complex plot variation effects by effectively integrating models. These have important implications improving efficiency accuracy which gain estimation research, ultimately accelerating delivery high-performing crop varieties to farmers consumers.

Language: Английский

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Modelling plants across scales of biological organisation for guiding crop improvement

Functional Plant Biology, Journal Year: 2023, Volume and Issue: 50(6), P. 435 - 454

Published: April 28, 2023

Grain yield improvement in globally important staple crops is critical the coming decades if production to keep pace with growing demand; so there increasing interest understanding and manipulating plant growth developmental traits for better crop productivity. However, this confounded by complex cross-scale feedback regulations a limited ability evaluate consequences of manipulation on production. Plant/crop modelling could hold key deepening our dynamic trait-crop-environment interactions predictive capabilities supporting genetic manipulation. Using photosynthesis as an example, review summarises past present experimental work, bringing about model-guided thrust, encompassing research into: (1) advancing plant/crop that connects across biological scales organisation using trait dissection-integration principle; (2) improving reliability predicted molecular-trait-crop-environment system dynamics validation; (3) innovative model application synergy experimentation G×M×E predict outcomes intervention (or lack it) strategising further molecular breeding efforts. The possible future roles maximising are discussed.

Language: Английский

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Origin of yield gains in maize hybrids

Natural sciences education, Journal Year: 2024, Volume and Issue: 53(1)

Published: March 3, 2024

Abstract Increasing grain yields in maize ( Zea mays L.) have been widely witnessed over the lifespan of many aging farmers. This paper aims to capture and summarize overlapping explanations for significant increases yields. Changes management practices resulted higher planting densities; however, genetic alterations allowed varieties tolerate increased stress levels. Increased levels, such as light availability, prompted changes leaf area index, radiation use efficiency, angles. The narrowing anthesis–silking interval is more planted densities. Stress factors affecting can be managed by both water or pesticide application, breeding modifications, resistance abiotic, temperature, moisture, biotic, pest, disease, insect stressors. increase past century attributed a combination achievements crop alternations practices.

Language: Английский

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