Long-term trends in yield variance of temperate managed grassland.

The management of climate-resilient grassland systems is important for stable livestock fodder production. In the face of climate change, maintaining productivity while minimizing yield variance of grassland systems is increasingly challenging. To achieve climate-resilient and stable productivity of grasslands, a better understanding of the climatic drivers of long-term trends in yield variance and its dependence on agronomic inputs is required. Based on the Park Grass Experiment at Rothamsted (UK), we report for the first time the long-term trends in yield variance of grassland (1965-2018) in plots given different fertilizer and lime applications, with contrasting productivity and plant species diversity. We implemented a statistical model that allowed yield variance to be determined independently of yield level. Environmental abiotic covariates were included in a novel criss-cross regression approach to determine climatic drivers of yield variance and its dependence on agronomic management. Our findings highlight that sufficient liming and moderate fertilization can reduce yield variance while maintaining productivity and limiting loss of plant species diversity. Plots receiving the highest rate of nitrogen fertilizer or farmyard manure had the highest yield but were also more responsive to environmental variability and had less plant species diversity. We identified the days of water stress from March to October and temperature from July to August as the two main climatic drivers, explaining approximately one-third of the observed yield variance. These drivers helped explain consistent unimodal trends in yield variance-with a peak in approximately 1995, after which variance declined. Here, for the first time, we provide a novel statistical framework and a unique long-term dataset for understanding the trends in yield variance of managed grassland. The application of the criss-cross regression approach in other long-term agro-ecological trials could help identify climatic drivers of production risk and to derive agronomic strategies for improving the climate resilience of cropping systems. Supplementary Information: The online version contains supplementary material available at 10.1007/s13593-023-00885-w.

50 years of rice breeding in Bangladesh: genetic yield trends.

To assess the efficiency of genetic improvement programs, it is essential to assess the genetic trend in long-term data. The present study estimates the genetic trends for grain yield of rice varieties released between 1970 and 2020 by the Bangladesh Rice Research Institute. The yield of the varieties was assessed from 2001-2002 to 2020-2021 in multi-locations trials. In such a series of trials, yield may increase over time due to (i) genetic improvement (genetic trend) and (ii) improved management or favorable climate change (agronomic/non-genetic trend). In both the winter and monsoon seasons, we observed positive genetic and non-genetic trends. The annual genetic trend for grain yield in both winter and monsoon rice varieties was 0.01 t ha-1, while the non-genetic trend for both seasons was 0.02 t ha-1, corresponding to yearly genetic gains of 0.28% and 0.18% in winter and monsoon seasons, respectively. The overall percentage yield change from 1970 until 2020 for winter rice was 40.96%, of which 13.91% was genetic trend and 27.05% was non-genetic. For the monsoon season, the overall percentage change from 1973 until 2020 was 38.39%, of which genetic and non-genetic increases were 8.36% and 30.03%, respectively. Overall, the contribution of non-genetic trend is larger than genetic trend both for winter and monsoon seasons. These results suggest that limited progress has been made in improving yield in Bangladeshi rice breeding programs over the last 50 years. Breeding programs need to be modernized to deliver sufficient genetic gains in the future to sustain Bangladeshi food security.

Comparing the Predictive Abilities of Phenotypic and Marker-Assisted Selection Methods in a Biparental Lettuce Population.

Breeding for traits with polygenic inheritance is a challenging task that can be done by phenotypic selection, marker-assisted selection (MAS) or genome-wide selection. We comparatively evaluated the predictive abilities of four selection models on a biparental lettuce ( L.) population genotyped with 95 single nucleotide polymorphisms and 205 amplified fragment length polymorphism markers. These models were based on (i) phenotypic selection, (ii) MAS (with quantitative trait locus (QTL)-linked markers), (iii) genomic prediction using all the available molecular markers, and (iv) genomic prediction using molecular markers plus QTL-linked markers as fixed covariates. Each model's performance was assessed using data on the field resistance to downy mildew (DMR, mean heritability ∼0.71) and the quality of shelf life (SL, mean heritability ∼0.91) of lettuce in multiple environments. The predictive ability of each selection model was computed under three cross-validation (CV) schemes based on sampling genotypes, environments, or both. For the DMR dataset, the predictive ability of the MAS model was significantly lower than that of the genomic prediction model. For the SL dataset, the predictive ability of the genomic prediction model was significantly lower than that for the model using QTL-linked markers under two of the three CV schemes. Our results show that the predictive ability of the selection models depends strongly on the CV scheme used for prediction and the heritability of the target trait. Our study also shows that molecular markers can be used to predict DMR and SL for individuals from this cross that were genotyped but not phenotyped.