Impact of rising temperatures on historical wheat yield, phenology, and grain size in Catalonia.

Introduction: Climate change poses significant challenges to agriculture, impacting crop yields and necessitating adaptive strategies in breeding programs. This study investigates the genetic yield progress of wheat varieties in Catalonia, Spain, from 2007 to 2021, and examines the relationship between genetic yield and climate-related factors, such as temperature. Understanding these dynamics is crucial for ensuring the resilience of wheat crops in the face of changing environmental conditions. Methods: Genetic yield progress was assessed using a linear regression function, comparing the average yield changes of newly released wheat varieties to benchmark varieties. Additionally, a quadratic function was employed to model genetic yield progress in winter wheat (WW). The study also analyzed correlations between genetic yield (GY) and normalized values of hectoliter weight (HLW) and the number of grains (NG) for both spring wheat (SW) and WW. Weather data were used to confirm climate change impacts on temperature and its effects on wheat growth and development. Results: The study found that genetic yield was stagnant for SW but increased linearly by 1.31% per year for WW. However, the quadratic function indicated a possible plateau in WW genetic yield progress in recent years. Positive correlations were observed between GY and normalized values of HLW and NG for both SW and WW. Climate change was evident in Catalonia, with temperatures increasing at a rate of 0.050 °C per year. This rise in temperature had detrimental effects on days to heading (DH) and HLW, with reductions observed in both SW and WW for each °C increase in annual minimum and average temperature. Discussion: The findings highlighted the urgent need to address the impact of climate change on wheat cultivation. The stagnation of genetic yield in SW and the potential plateau in WW genetic yield progress call for adaptive measures. Breeding programs should prioritize phenological adjustments, particularly sowing date optimization, to align with the most favorable months of the year. Moreover, efforts should be made to enhance HLW and the number of grains per unit area in new wheat varieties to counteract the negative effects of rising temperatures. This research underscores the importance of ongoing monitoring and adaptation in agricultural practices to ensure yield resilience in the context of a changing climate.

Improving in-season wheat yield prediction using remote sensing and additional agronomic traits as predictors.

The development of accurate grain yield (GY) multivariate models using normalized difference vegetation index (NDVI) assessments obtained from aerial vehicles and additional agronomic traits is a promising option to assist, or even substitute, laborious agronomic in-field evaluations for wheat variety trials. This study proposed improved GY prediction models for wheat experimental trials. Calibration models were developed using all possible combinations of aerial NDVI, plant height, phenology, and ear density from experimental trials of three crop seasons. First, models were developed using 20, 50 and 100 plots in training sets and GY predictions were only moderately improved by increasing the size of the training set. Then, the best models predicting GY were defined in terms of the lowest Bayesian information criterion (BIC) and the inclusion of days to heading, ear density or plant height together with NDVI in most cases were better (lower BIC) than NDVI alone. This was particularly evident when NDVI saturates (with yields above 8 t ha-1) with models including NDVI and days to heading providing a 50% increase in the prediction accuracy and a 10% decrease in the root mean square error. These results showed an improvement of NDVI prediction models by the addition of other agronomic traits. Moreover, NDVI and additional agronomic traits were unreliable predictors of grain yield in wheat landraces and conventional yield quantification methods must be used in this case. Saturation and underestimation of productivity may be explained by differences in other yield components that NDVI alone cannot detect (e.g. differences in grain size and number).

First genetic maps development and QTL mining in Ranunculus asiaticus L. through ddRADseq.

Persian Buttercup (Ranunculus asiaticus L.; 2x=2n=16; estimated genome size: 7.6Gb) is an ornamental and perennial crop native of Asia Minor and Mediterranean basin, marketed both as cut flower or potted plant. Currently new varieties are developed by selecting plants carrying desirable traits in segregating progenies obtained by controlled mating, which are propagated through rhizomes or micro-propagated in vitro. In order to escalate selection efficiency and respond to market requests, more knowledge of buttercup genetics would facilitate the identification of markers associated with loci and genes controlling key ornamental traits, opening the way for molecular assisted breeding programs. Reduced-representation sequencing (RRS) represents a powerful tool for plant genotyping, especially in case of large genomes such as the one of buttercup, and have been applied for the development of high-density genetic maps in several species. We report on the development of the first molecular-genetic maps in R. asiaticus based on of a two-way pseudo-testcross strategy. A double digest restriction-site associated DNA (ddRAD) approach was applied for genotyping two F1 mapping populations, whose female parents were a genotype of a so called 'ponpon' and of a 'double flower' varieties, while the common male parental ('Cipro') was a genotype producing a simple flower. The ddRAD generated a total of ~2Gb demultiplexed reads, resulting in an average of 8,3M reads per line. The sstacks pipeline was applied for the construction of a mock reference genome based on sequencing data, and SNP markers segregating in only one of the parents were retained for map construction by treating the F1 population as a backcross. The four parental maps (two of the female parents and two of the common male parent) were aligned with 106 common markers and 8 linkage groups were identified, corresponding to the haploid chromosome number of the species. An average of 586 markers were associated with each parental map, with a marker density ranging from 1 marker/cM to 4.4 markers/cM. The developed maps were used for QTL analysis for flower color, leading to the identification of major QTLs for purple pigmentation. These results contribute to dissect on the genetics of Persian buttercup, enabling the development of new approaches for future varietal development.

Genome-Wide Survey and Development of the First Microsatellite Markers Database (AnCorDB) in Anemone coronaria L.

Anemone coronaria L. (2n = 2x = 16) is a perennial, allogamous, highly heterozygous plant marketed as a cut flower or in gardens. Due to its large genome size, limited efforts have been made in order to develop species-specific molecular markers. We obtained the first draft genome of the species by Illumina sequencing an androgenetic haploid plant of the commercial line "MISTRAL® Magenta". The genome assembly was obtained by applying the MEGAHIT pipeline and consisted of 2 × 106 scaffolds. The SciRoKo SSR (Simple Sequence Repeats)-search module identified 401.822 perfect and 188.987 imperfect microsatellites motifs. Following, we developed a user-friendly "Anemone coronaria Microsatellite DataBase" (AnCorDB), which incorporates the Primer3 script, making it possible to design couples of primers for downstream application of the identified SSR markers. Eight genotypes belonging to eight cultivars were used to validate 62 SSRs and a subset of markers was applied for fingerprinting each cultivar, as well as to assess their intra-cultivar variability. The newly developed microsatellite markers will find application in Breeding Rights disputes, developing genetic maps, marker assisted breeding (MAS) strategies, as well as phylogenetic studies.