Selection of forage grasses for cultivation under water-limited conditions using Manhattan distance and TOPSIS.

Extreme weather events, such as severe droughts, pose a threat to the sustainability of beef cattle by limiting the growth and development of forage plants and reducing the available pasture for animals. Thus, the search for forage species that are more tolerant and adapted to soil water deficit conditions is an important strategy to improve food supply. In this study, we propose utilizing the mathematical concept of the Manhattan distance to assess the variations in the morphological variables of tropical forage grasses under water-limited conditions.

Magnesium supplementation alleviates drought damage during vegetative stage of soybean plants.

Our working hypothesis was that magnesium (Mg) supplementation modulates plant performance under low water availability and improves drought tolerance in soybean genotypes. Plants of Bônus 8579, M8808 and TMG1180 genotypes were grown under field conditions and subjected to three water stress treatments (control, moderate and severe stress) and three Mg levels [0.9 (low), 1.

Soil and foliar Si fertilization alters elemental stoichiometry and increases yield of sugarcane cultivars.

Silicon (Si) fertilization is widely recognized to improve the development of crops, especially in tropical soils and cultivation under dryland management. Herein, our working hypothesis was that Si stoichiometry favors the efficient use of carbon (C), nitrogen (N), and phosphorus (P) in sugarcane plants. Therefore, a field experiment was carried out using a 3 × 3 factorial scheme consisting of three cultivars (RB92579, RB021754 and RB036066) and three forms of Si application (control without Si; sodium silicate spray at 40 mmol L in soil during planting; sodium silicate spray at 40 mmol L on leaves at 75 days after emergence).

Yield of soybean genotypes identified through GGE biplot and path analysis.

Genotype × environment (G×E) interaction is an important source of variation in soybean yield, which can significantly influence selection in breeding programs. This study aimed to select superior soybean genotypes for performance and yield stability, from data from multi-environment trials (METs), through GGE biplot analysis that combines the main effects of the genotype (G) plus the genotype-by-environment (G×E) interaction. As well as, through path analysis, determine the direct and indirect influences of yield components on soybean grain yield, as a genotype selection strategy.