Stability Analysis of Crop Yield under Different Cultivation Systems

Stability for yield and seed quality across environments are desirable traits for varieties used for the support of livestock, and such specific varieties of common vetch (Vicia sativa L.) and peas (Pisum sativum L.) are highly demanded from farmers. The objective of this study was to investigate the stability performance of seed quality attributes on six common vetch genotypes and five pea genotypes. The genotypes’ stability traits were based on seed quality characteristics of peas and common vetch under low-input vs. conventional cultivation systems. Significantly positive or negative correlations between the main traits in all cultivation schemes were found. Based on these findings, improving certain traits that exhibit qualitative inheritance is expected to be an efficient indirect way to improve seed quality stability, more easily in the case of peas. It was evident from comparisons that even in low-input farming systems, varieties showed stable performance. Analysis of variance (ANOVA), GGE biplot on main traits, and AMMI analysis all resulted in statistically significant variations between genotypes, environments, and farming practices. This analysis resulted in specific pea varieties and vetch cultivars that were stable for various regions and farming systems on seed quality traits. Full article

One of the main obstacles to finding cultivars with consistent performance across locations and years is the genotype × environment (GE) interaction effect. A new approach to stability analysis for qualitative characteristics in maize was conducted utilizing G × E interactions and further analysis via AMMI and GGE biplots. The study aimed to identify the type of trait inheritance through estimations of the stability index, to evaluate multiple locations and multiple genotypes to determine how different ecosystems and maize genotypes relate to one another, and, finally, to suggest the ideal climatic conditions and genotypes, carefully chosen for their stability. Fifteen F1 commercial maize hybrids comprised the genetic materials tested, along with 15 open-pollination lines created by 4-cycle Honeycomb assessment, at four different environments, Giannitsa, Florina, Trikala, and Kalambaka in Greece. The experiments were conducted in Randomized Complete Block Designs (RCB) with four replications. The tested characteristics were protein content (%), fat (%), ash (%), starch (%), crude fiber (%), moisture (%), seed length, seed thickness, and seed width. All genotypes showed statistically significant differences for all characteristics measured, especially for protein content and size of the kernel. G × E interaction was present only for moisture content and size of the kernel. Environments significantly affected fat, starch content, moisture content, and the kernel’s size (under a multiple G × E interaction). Protein, ash, and fiber content showed no G × E interaction. Further analysis via AMMI and GGE biplots was applied to explore the genotypic stability across all experimental environments for the traits that showed noteworthy G × E interaction. According to our results and approach, protein content is less qualitative than other characteristics like moisture and starch content. Correlations showed that negative selection for the last two characteristics, as well as for ash content, in combination with longer seeds, may lead indirectly to improved stability performance for protein content. Three environments, Giannitsa, Trikala and Kalambaka, exhibited higher stability index values for almost all characteristics measured. Therefore, those environments are perfect for ensuring the stability of the quality characteristics and could be recommended. The best maize hybrids were Mitic, 6818 and 6040, exhibiting high stability indices of quality characteristics and Kermes displaying stability for protein content. Therefore, those should be further tested in multiple environments to confirm the consistency of their high-stability performance. Full article

An increase in world population requires growth in food production. Wheat is one of the major food crops, covering 21% of global food needs. The food supply issue necessitates reliable mathematical methods for predicting wheat yields. Crop yield information is necessary for agricultural management and strategic planning. Our mathematical model was developed based on a three-year field experiment in a semi-arid climate zone. Wheat yields ranged from 4310 to 6020 kg/ha. The novelty of this model is the inclusion of some stochastic data (weather and technological). The proposed method for wheat yield modeling is based on the theory of random sequence analysis. The model does not impose any restrictions on the number of production parameters and environmental indicators. A significant advantage of the proposed model is the absence of limits on the yield function. Consideration of the stochastic features of wheat production (technological and weather parameters) allows researchers to achieve the best accuracy. The numerical experiment confirmed the high accuracy of the proposed mathematical model for the prediction of wheat yield. The mean relative error (for the third-order polynomial model) varied from 1.79% to 2.75% depending on the preceding crop. Full article