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Scatterplots of |r| of six physiological parameters to Pi,yield versus |r| to yield from three selection methods (“random”, “even” and “top 20”) of 100 genotypes (Ngen = 100, SPG = 100) from 9000 environments (Nenv = 9000, SPE = 1). Each point represents a SPG and colour of the point represents the mean values (normalized to one by the parameter values of calibrated cultivar Hartog) of physiological parameters from 100 genotypes in one SPG. The “even” sampling method divides population into ten groups based on mean yield value, with ten virtual genotypes randomly selected in each yield group. The “top 20” method randomly selects virtual genotypes with mean yield above 80th percentile of the whole population. Grey rectangle area stands for the region of |r|> 0.33. Black dashed line stands for 1:1 line. Meaning of the physiological parameters (in italic) follow the order (left to right) in the figure: radiation use efficiency (y_rue), potential leaf specific area (y_sla), efficiency of roots to extract soil water (ll_modifier), potential grain growth rate at grain filling (potential_grain_filling_rate), number of growing leaves in the sheath (node_no_correction) and temperature effect on biomass accumulation (tfac_slope)
Source publication
Key message
Using in silico experiment in crop model, we identified different physiological regulations of yield and yield stability, as well as quantify the genotype and environment numbers required for analysing yield stability convincingly.
Abstract
Identifying target traits for breeding stable and high-yielded cultivars simultaneously is diffi...
Citations
... The yield stability of a genotype is determined by its slope relative to the population average, with lower-than-average slopes indicating higher-than-average stability. This method assumes that yields are measured across a range of environments that adequately represents the variability of the target environment, which can limit the practicality and ubiquity of stability as a selection parameter (Russell and Eberhart, 1968;Wang et al., 2023). ...
... Yet, without the rNDVI phenotype, identifying that these genotypes achieved similar productivity outcomes through such contrasting phenological characteristics would be difficult. More broadly, yield stability is resource-intensive to measure directly (Wang et al., 2023) and inconsistently quantified as a selection parameter (Reckling et al., 2021). Thus, an NDVI-proxy for stability that can be carried out consistently in the context of variety evaluations provides an additive, continuous trait(s) for inclusion in more extensive analyses of genotype-by-environment interactions to inform genomic selection (Araus et al., 2023). ...
... To achieve this, the Pearson correlation coefficient (r) between two agronomic traits (referred to as trait-trait correlation) in the field was compared with the trait-trait correlation simulated by the well-calibrated crop simulation model APSIM-wheat (doi: 10.5281/zenodo.7569104) [21][22][23] . As examples, we selected two locations (Hannover and Kiel) from one management scenario (high nitrogen and with fungicide under rain-fed condition; HN_WF_RF), where the maximum number of directly comparable traits to APSIM-wheat can be found. ...
... Field dataset from three consecutive years (2015-2017) under high nitrogen and fungicide application in rain-fed treatment (HN_WF_RF) from (A) Hannover and (B) Kiel was used. Simulation dataset comes from previous publications (doi: 10.5281/zenodo.7569104)21,22 . Each point represents the Pearson correlation coefficient (r) between two traits observed in the field experiment (x-axis) and in the simulations of APSIM-wheat (y-axis). ...
Multi-environmental trials (MET) with temporal and spatial variance are crucial for understanding genotype-environment-management (GxExM) interactions in crops. Here, we present a MET dataset for winter wheat in Germany. The dataset encompasses MET spanning six years (2015–2020), six locations and nine crop management scenarios (consisting of combinations for three treatments, unbalanced in each location and year) comparing 228 cultivars released between 1963 and 2016, amounting to a total of 526,751 data points covering 24 traits. Beside grain yield, ten agronomic traits, four baking quality traits, plant height, heading date, maturity date and six fungal disease infection indices are included. Additionally, we provide management records, including fertilizer use, plant protection measures, irrigation, and weather data. We demonstrate how this dataset can address four agronomic questions related to GxExM interactions. Further potential applications of the dataset include empirical analyses, genomic and enviromic analyses for breeding targets, or development of decision-supporting models for agricultural management and policy decisions.
... The envirotyping approach can also lead to an estimation of a similarity matrix of a MET locations, according to their limiting factors pattern. In silico experiments will be useful to test a wide range of genotypes and environmental conditions such as described by Wang et al. (2023) to validate our results. This can be a clue to identify accurate match between dedicated genotypes and environments (Resende et al. 2021) leading, notably, to better product placement for the seed industry. ...
Key message
A comprehensive environmental characterization allowed identifying stable and interactive QTL for seed yield: QA09 and QC09a were detected across environments; whereas QA07a was specifically detected on the most stressed environments.
Abstract
A main challenge for rapeseed consists in maintaining seed yield while adapting to climate changes and contributing to environmental-friendly cropping systems. Breeding for cultivar adaptation is one of the keys to meet this challenge. Therefore, we propose to identify the genetic determinant of seed yield stability for winter oilseed rape using GWAS coupled with a multi-environmental trial and to interpret them in the light of environmental characteristics. Due to a comprehensive characterization of a multi-environmental trial using 79 indicators, four contrasting envirotypes were defined and used to identify interactive and stable seed yield QTL. A total of four QTLs were detected, among which, QA09 and QC09a, were stable (detected at the multi-environmental trial scale or for different envirotypes and environments); and one, QA07a, was specifically detected into the most stressed envirotype. The analysis of the molecular diversity at QA07a showed a lack of genetic diversity within modern lines compared to older cultivars bred before the selection for low glucosinolate content. The results were discussed in comparison with other studies and methods as well as in the context of breeding programs.
... sparsity and breeding structure) that need to be addressed in order to make efficient use of the available databases. How best to handle the level of sparsity is an ongoing area of research given that genotypes are not missing at random in the dataset because poorer performing genotypes are dropped every year (Aguate et al. 2019;Hartung and Piepho 2021;Wang et al. 2023). Other relevant limitations for the use of large datasets are due to the complexity of the data, the statistical models, and the requirement of great computational power (Atanda et al. 2021b). ...
Context
Long-term multi-environment trials (METs) could improve genomic prediction models for plant breeding programs by better representing the target population of environments (TPE). However, METs are generally highly unbalanced because genotypes are routinely dropped from trials after a few years. Furthermore, in the presence of genotype × environment interaction (GEI), selection of the environments to include in a prediction set becomes critical to represent specific TPEs.
Aims
The goals of this study were to compare strategies for modelling GEI in genomic prediction, using large METs from oat (Avena sativa L.) breeding programs in the Midwest United States, and to develop a variety decision tool for farmers and plant breeders.
Methods
The performance of genotypes in TPEs was predicted by using different strategies for handling GEI in genomic prediction models including systematic and/or random GEI components. These strategies were also used to build the variety decision tool for farmers.
Key results
Genomic prediction for unknown genotypes, locations and years within TPEs had moderate to high predictive ability, accuracy and reliability. Modelling GEI was beneficial in small, but not in large, mega-environments. The latest 3 years were highly predictive of performance in an upcoming year for most years but not for years with unusual weather patterns. High predictive ability, accuracy and reliability were obtained when large datasets were used in TPEs.
Conclusions
Deployment of historical datasets can be accomplished through meaningful delineation and prediction for TPEs.
Implications
We have shown the performance of a simple modelling strategy for handling prediction for TPEs when deploying large historical datasets.
... These variables, encompassing the target variable, served as the input data for both the ML prediction model and model testing. Factors influencing crop growth include changes in growth environment, human management, and crop genotype variation [44]. Changes in growth environment and human management have specific, detailed parameter data, whereas crop genotype variation is influenced by multiple factors, including advancements in biological and cultivation techniques over the years and the genetic evolution of crops due to environmental adaptation, leading to an increasing yield trend. ...
One of the crucial research areas in agricultural decision-making processes is crop yield prediction. This study leverages the advantages of hybrid models to address the complex interplay of genetic, environmental, and management factors to achieve more accurate crop yield forecasts. Therefore, this study used the data of wheat growth environment, crop management, and historical yield in experimental fields in Anding District, Dingxi City, Gansu Province from 1984 to 2021 to construct eight machine learning models and ensemble models. Furthermore, Agricultural Production Systems sIMulator (APSIM), machine learning (ML), and APSIM combined with machine learning (APSIM-ML) were employed to predict wheat yields in 2012, 2016, and 2021. The results show that the APSIM-ML weighted ensemble prediction model, optimized to minimize the MSE, performed the best. Compared to the optimized ML and APSIM models, the average improvements in the RMSE, RRMSE, and MBE for the test years were 43.54 kg/ha, 3.55%, and 15.54 kg/ha, and 93.96 kg/ha, 7.55%, and 104.21 kg/ha, respectively. At the same time, we found that the dynamic flow of water and nitrogen between the soil and crops had the greatest impact on wheat yield prediction. This study improved the accuracy of dryland wheat yield prediction in Gansu Province and provides technical support for the intelligent production of dryland wheat in the loess hilly area.
... Cultivar superiority and the environmental variance were calculated using the "GEstability" procedure of GenStat version 20.1 [33] as well as with the R package toolStability [41,42]. The values of various stability indices will be presented together with the ranks of the seven entries (from 1 = most stable to 7 = least stable). ...
Barley is an important feed crop in Iran and is threatened by an increased frequency of drought. Increasing diversity in the form of evolutionary populations (EPs) and mixtures is one strategy to increase the resilience of crops. Four barley EPs, which have evolved in different locations over 7 to 10 years from the same original population, were evaluated for agronomic trait and stability together with two landraces, and one improved variety for three cropping seasons in four locations. Modest but significant differences were found only for plant height with a range of less than 4 cm. Stability, measured with cultivar superiority, as well as environmental variance and reliability measures generally indicated a superior stability of EPs-with two of them ranking first and second for grain yield reliability-but also differences between the EPs. The effect of recurrent droughts on the diversity within EPs is discussed as a possible explanation for the lack of divergent evolution. The seed management of Eps, including seed exchange between farmers, is suggested as a possible strategy to avoid the reduction in diversity within populations. Future research will address the nutritional value of the EPs, which is often quoted by sheep owners as superior to commonly grown varieties.
Salt-affected soils constrain wheat production in many countries. It is estimated that more than 831 million hectares (Mha) are salt affected globally, with 10 Mha of cultivated land lost annually. Salinity adversely affects wheat grain yield and grain quality traits. Wheat genotypes show wide variation for salinity stress tolerance. The main objective of this study was to evaluate the performance and stability of spike traits in 14 winter wheat varieties with different origin, in order to identify the varieties that can exploit the growing conditions of saline soils. The wheat material was tested in nine environments as a combination of three years and three fertilization treatments (urea, calcium nitrate, ammonium nitrate), using a split-plot design with three replications. At maturity plant height, spike length, grains number per spike, and spike yield were determined. The significant effect of genotype × environment interaction on the phenotypic expression of different traits indicates differential response of the genotypes across the testing environments, thus offering the possibility of identifying some genotypes of interest. The Romanian varieties ‘Alex’ and ‘Glosa’ present the highest productivity of spike associated with an average stability, being specifically adapted to favourable rainfall conditions, achieving above-average yields in less favourable conditions. The varieties ‘Cerere’, ‘Genesi’, ‘Alex’ and ‘Glosa’ can be selected for crossing and included in wheat breeding programs for the development of stable cultivars adapted to saline soils.
Abstrakt: W artykule omówiono wszystkie doniesienia ustne prezentowane na konferencji CBB7, poświęconej biotechnologii i hodowli zbóż, która odbyła się w pierwszej dekadzie listopada 2023 w Wernigerode, w Niemczech. Konferencji przewodniczył Profesor Andreas Börner, Instytut im. Leibniza (IPK) w Gaterslaben, Niemcy, Prezes EUCARPIA a współprzewodniczącymi byli Węgrzy, János Pauk, Cereal Research Nonprofit Ltd. oraz Profesor Gábor Galiba, Agricultural Institute Centre for Agricultural Research (ELKH). Konferencja obejmowała sześć bloków tematycznych: (1) Zasoby genetyczne dla ulepszania roślin uprawnych, (2) Adaptacja środowiskowa, (3) Biotyczna reakcja na stres i interakcje roślina-mikrobiom, (4) Poprawa wydajności i jakości plonu, (5) Bioinformatyka, genomika i edycja genomu, (6) Technologie fenotypowania, ogólnie oraz w ramach Wheat Initiative a także grupy roboczej ds. fenotypowania roślin w warunkach kontrolowanych (CEPPG-The Controlled Environment Plant Phenotyping working Group).W artykule zebrano najnowszą bibliografię zespołów badawczych z których wywodzili się kolejni wykładowcy, w ramach poruszanych tematów (https://static.akcongress.com/downloads/cbb/cbb7-ewac18-boa.pdf).
Context: Owing to the interaction between genotype and environment (G x E), identifying traits to increase wheat yield and grain protein concentration simultaneously or increasing one without affecting the other remains a challenge. Phenotypic plasticity is an insightful perspective to understand G x E. Objective: To explore the relations of wheat yield and grain protein concentration in response to N input and its physiological basis from the perspective of phenotypic plasticity. Method: We established a factorial experiment combining 14 winter wheat cultivars and four N fertilization rates (0, 45, 90 and 135 kg ha − 1) in eight environments. We analyzed the interaction of cultivar and N combining a phenotypic plasticity framework and a three-phase model of grain yield and protein response to N input. The phases are: phase I, N supply limits both yield and grain protein; phase II, N supply limits grain protein but not yield; and phase III, N supply does not limit yield or grain protein concentration Results: Grain yield plasticity was positively associated to yield in high-yielding environments without N limitations (phase II) with no cost in low yielding environments, and associated to harvest index. Grain protein plasticity was positively associated to protein in high protein environments without N limitations (phase III). Plasticity of grain protein concentration was negatively associated to grain number m − 2 , resulting in moderate negative association of protein plasticity and yield. Grain C:N ratio associated weakly and positively with yield plasticity and strongly and negatively for grain protein plasticity. Conclusion: This work proposes a yield-protein plasticity framework combined with a three-phase model that allows to disclose G × N interactions. Under our experimental conditions, we identified physiological mechanisms associated to yield and protein plasticity. Implications: Yield and protein plasticity can contribute guiding grower's cultivar selection towards high yield plasticity cultivars when aiming to high yield with acceptable protein levels or high protein plasticity cultivars to ensure high protein at the expense of lower yields. Yield plasticity brings opportunity to breed for high yielding cultivars while maintaining grain protein concentration. Accuracy of N recommendations models and mecha-nistic crop models can be improved by accounting for G × N interactions through plasticity of yield and grain protein.
