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Crop and Pasture Science Crop and Pasture Science Society
Plant sciences, sustainable farming systems and food quality
Crop & Pasture Science

Crop & Pasture Science

Volume 75 Number 3 2024

CP23135Multivariate assumptions and effect of model parameters in path analysis in oat crop

Jaqueline Sgarbossa 0000-0001-7541-090X, Alessandro Dal’Cól Lúcio 0000-0003-0761-4200, José Antonio Gonzalez da Silva 0000-0002-9335-2421, Braulio Otomar Caron 0000-0002-6557-3294, Maria Inês Diel 0000-0002-7905-2166, Tiago Olivoto 0000-0002-0241-9636, Claiton Nardini 0000-0001-5791-6720, Odenis Alessi 0000-0002-3509-6984 and Darlei Michalski Lambrecht 0000-0002-1376-3504

Path analysis (PA) is a multivariate statistical technique, widely used, however, when carrying out PA, the parameters of the mathematical model referring to the experimental design and the effects of the study factors are not considered. Therefore, this study aims to evaluate these possible impacts on PA results. Removing parameters from the mathematical model promotes changes in the direction and magnitude of the path coefficients, regardless of the type of PA performed.

CP23046Evaluating the Agricultural Production Systems sIMulator (APSIM) wheat module for California

Nicholas Alexander George 0000-0003-1687-7360, Helio de Jesus Pedro Cuamba, Mark E. Lundy and Sarita Jane Bennett 0000-0001-8487-7560

Globally, crop simulation models are important for increasing efficiency and broadening the scope of agricultural research and management. The APSIM crop model is widely used to simulate crop production but has not been widely tested for wheat systems in the western United States. In this study, we leverage existing state-wide field trials to evaluate the model and inform further research on model validation and calibration.

CP23120Elucidating genotype × environment interactions for grain iron and zinc content in a subset of pearl millet (Pennisetum glaucum) recombinant inbred lines

Tripti Singhal 0000-0002-5766-4823, C. Tara Satyavathi 0000-0001-6501-8736, S. P. Singh 0000-0002-2476-9530, Mukesh Sankar 0000-0001-5459-392X, Mallik M. 0000-0001-6872-5313, Thribhuvan R., Sunaina Yadav and C. Bharadwaj 0000-0002-1651-7878

Best pearl millet genotypes for recommendation to breeders, and use in breeding, are usually identified by evaluation in field trials in diverse environments. The main objective of this study was to assess genotypes based on mean performance across a multitude of environments. High iron and zinc lines with consistent performance across environments were identified and can be used in the development of biofortified hybrids.

Genetic variability is crucial for improving crops and breeding programs. This study was focused on evaluating genetic diversity and differentiation of 93 maize lines in Iran, through use of advanced sequencing techniques. The findings provide valuable insights into the potential for future maize breeding programs, offering exciting possibilities for enhancing main traits and productivity.

CP23343Development of high-amylose maize (Zea mays L.) genotypes adapted to Indian conditions through molecular breeding

Arushi Arora, Deepak Bhamare, Abhijit Kumar Das 0000-0002-5816-2470, Shubhank Dixit, Sreya Venadan, Yathish K. R., Ramesh Kumar, Dharam Paul, J. C. Sekhar, Sunil Neelam, Sudip Nandi, M. C. Kamboj and Sujay Rakshit 0000-0001-6139-7943

Amylose is a type of resistant starch with numerous health benefits and industrial applications. Amylose content of normal maize starch is ~25%. We report the development of high-amylose maize (~50%) suited to Indian conditions through marker-assisted backcross breeding, using a high-amylose donor and high-yielding parental lines.

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