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Crop improvement programmes began with traditional breeding practices since the inception of agriculture. Farmers and plant breeders continue to use these strategies for crop improvement due to their broad application in modifying crop genetic compositions. Nonetheless, conventional breeding has significant downsides in regard to effort and time. Crop productivity seems to be hitting a plateau as a consequence of environmental issues and the scarcity of agricultural land. Therefore, continuous pursuit of advancement in crop improvement is essential. Recent technical innovations have resulted in a revolutionary shift in the pattern of breeding methods, leaning further towards molecular approaches. Among the promising approaches, marker-assisted selection, QTL mapping, omics-assisted breeding, genome-wide association studies and genome editing have lately gained prominence. Several governments have progressively relaxed their restrictions relating to genome editing. The present review highlights the evolutionary and revolutionary approaches that have been utilized for crop improvement in a bid to produce climate-resilient crops observing the consequence of climate change. Additionally, it will contribute to the comprehension of plant breeding succession so far. Investing in advanced sequencing technologies and bioinformatics will deepen our understanding of genetic variations and their functional implications, contributing to breakthroughs in crop improvement and biodiversity conservation. [ABSTRACT FROM AUTHOR]

Cool season grain legumes occupy an important place among the agricultural crops and essentially provide multiple benefits including food supply, nutrition security, soil fertility improvement and revenue for farmers all over the world. However, owing to climate change, the average temperature is steadily rising, which negatively affects crop performance and limits their yield. Terminal heat stress that mainly occurred during grain development phases severely harms grain quality and weight in legumes adapted to the cool season, such as lentils, faba beans, chickpeas, field peas, etc. Although, traditional breeding approaches with advanced screening procedures have been employed to identify heat tolerant legume cultivars. Unfortunately, traditional breeding pipelines alone are no longer enough to meet global demands. Genomics-assisted interventions including new-generation sequencing technologies and genotyping platforms have facilitated the development of high-resolution molecular maps, QTL/gene discovery and marker-assisted introgression, thereby improving the efficiency in legumes breeding to develop stress-resilient varieties. Based on the current scenario, we attempted to review the intervention of genomics to decipher different components of tolerance to heat stress and future possibilities of using newly developed genomics-based interventions in cool season adapted grain legumes. [ABSTRACT FROM AUTHOR]

Due to the increase in greenhouse gases from burning fossil fuels, there is increased attention on renewable energy sources from specialized crops. These crops should not compete with food security, and it is important to select plant resources which can produce methane-rich biogas efficiently. The most commonly used energy crops are planted and managed intensively with high inputs in productive land, and this negatively affects land use and sustainable use of resources. The main purposes of this study are to: (a) determine the best cropping system for optimal biogas and methane production from sole crops of winter pea, triticale and out and their mixtures at two different maturity stages (first stage: full-flowering stage of winter pea and beginning of milky stage of cereals; second stage: emergence of firsts pods for pea and milky/waxy stage of cereals); and (b) to develop and use a surface model to determine the best combinations of various mixtures that result in highest biogas and methane. The used pure or mixtures of pea, oat and triticale in two seed weight ratios (50%:50% and 75%:25%) produced different green mass, dry matter, solids, biogas and methane yields. The experiments showed that maximum green mass was produced by the mixture of pea and oat at the seed ratio 75%:25% and when crop was harvested at the full-flowering stage of winter pea and beginning of the milky stage of cereals. After quadratic model analyses, the combination ratios of the oat and triticale were, respectively, 30% and 8%, with a maximum green biomass yield of 61.48 t ha−1, while the corresponding values were 28% and 38%, with maximum solids yields of 25.64 t ha−1. As the model was set at 100 for all three independent variables (oat, triticale and pea), the pea should be at 62% (100-30-8) and 34% (100-28-38), respectively, for green mass and organic solids yields. The results of surface analysis and multivariate analysis of variance showed that the mixture of oat and triticale had great potentiality for biogas and methane yields. The optimal mixture of oat with triticale was 27~35% with 73~65% for producing biogas and (or) methane. [ABSTRACT FROM AUTHOR]

Crop nitrogen (N) fertilization diagnoses via the N nutrition index (NNI)-yield relationship have been tested for several crop species, but a cross-species comparison of that relationship has not been performed yet. This study aimed to perform a cross-species comparison of the relationship between NNI and yield with emphasis on the yield sensitivity to N deficiency, slope of the models. Additionally, we conducted an evaluation to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops. Based on a recently published global dataset to parametrize critical dilution curves , we calculated integrated NNI, instantaneous NNI, relative yield, and relative shoot biomass for annual ryegrass, tall fescue, maize, potato, rice, and wheat. We obtained 238 observations to fit integrated NNI-relative yield linear mixed-effects models and 1606 observations to fit instantaneous NNI-relative yield models. Subsequently, we performed a sensitivity analysis to determine the best NNI sampling moment to predict relative yield, with focus on major grain crops (maize, rice, and wheat). Our results show that there was low inter-species variation of sensitivity to N deficiency, i.e., the slope of the relationship between relative yield and integrated NNI. For grain crops, instantaneous NNI around anthesis demonstrated a better predictive capability for relative yield, outperforming other vegetative stages. This finding contributed to improving the understanding of the association between relative yield and NNI with implications for breeding programs, nutrient management practices, and crop modelling. Most importantly, this study is a contribution to improving the N nutrition diagnosis for several crop species, by using an integral, comparative approach. • The relationship between relative yield and integrated NNI was similar across species. • The slope of relative yield-integrated NNI shown limited variation across species. • The relative yield-integrated NNI slopes of maize and potato were the highest. • For grain crops, NNI around anthesis was the strongest predictor of relative yield. [ABSTRACT FROM AUTHOR]