Crop resilience via inter-plant spacing brings to the fore the productive ideotype

Abstract

Natural selection favors the competitive ideotype, enabling native plants to survive in the face of intense competition. The productive ideotype is the goal of artificial selection to achieve high crop yields via the efficient use of resources in a self-competition regime. When breeding is established under inter-genotypic competition, the competitive ideotype dominates and may fictitiously become selectable. The productive ideotype becomes selectable at the nil-competition regime, where widely spaced individuals prevent plant-to-plant interference for any input. Principal reasons bring to the fore the productive ideotype that combines low competitiveness and improved plant yield efficiency. Crop spacing via the productive ideotype is mandated to alleviate the varying optimum density and ensure efficient use of resources inter-seasonally, cope with intra-field variation and optimize resource use, compensate for missing plants and promote stability, counteract unpredictable stresses and offer a buffer against environmental diversity, and adopt low-input agriculture to conserve natural resources and the environment. For breeding toward the productive ideotype, nil-competition is the due condition to overcome the confounding effects of competition, maximize phenotypic differentiation and facilitate selection from an early segregating generation, optimize heritability due to moderated environmental variance and experimental designs that sample spatial heterogeneity, apply high selection pressure focusing exclusively on the targeted genotype, and avoid the risk of bias selection or loss of desired genotypes due to proximity to empty hills. The view of a modern crop variety composed of genotype(s) belonging to the productive ideotype is a viable option to reach crop resilience serving sustainability in enormously fluctuating agroecosystems.

Digital Phenotyping in Plant Breeding: Evaluating Relative Maturity, Stand Count, and Plant Height in Dry Beans (Phaseolus vulgaris L.) via RGB Drone-Based Imagery and Deep Learning Approaches

“…Maintaining optimal plant spacing can lead to more e cient resource utilization, such as water and nutrients, and contribute to increased grain yield [133]. Furthermore, optimizing PD can minimize competition between plants, promoting uniform growth and development resulting in increasing crop yields [134]. In addition to its potential impact on yield, the SC pipeline's ability to analyze PD can also be used to improve crop models.…”

An In-Depth Presentation of the ‘rhoneycomb’ R Package to Construct and Analyze Field-Experimentation ‘Honeycomb Selection Designs’

“…The main feature of HSDs is that individual plants are placed either in the center or vertices of a honeycomb pattern, and the inter-plant distance excludes plant-to-plant interference for any input. The growth of plants at nil-competition allows their maximum phenotypic expression and differentiation, which according to the breeder's equation of expected response to selection [11], is a prerequisite for the successful selection of high-yielding and stable genotypes [12][13][14]. Furthermore, with a standardized, even, and systematic entry layout instead of the randomized configuration to implement the main principles met in other models, such as blocking, replication, and nearest neighbor adjustment on the same baseline, the HSD model is more efficient than the popular ones in reducing the experimental error [15].…”