Synchronized Genotype-Management Selection for Crop Yield
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Solution Overview
Problem
Current agricultural practices involve a sequential approach to breeding and agronomy, where genetic improvements and agronomic management are optimized separately, missing opportunities for synchronized genotype-by-management interactions that could enhance crop yield and stability, especially in early-stage breeding programs.
Innovation Solution
The development of systems and methods that integrate breeding and agronomic practices using deep learning networks and simulation models to predict and optimize genotype-by-management interactions, allowing for the selection of genotypes and management strategies tailored to specific environmental conditions and target populations, thereby improving crop productivity and yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If breeders create products (maize hybrids) without active selection for desired agronomic practices, then breeding development can proceed independently, but the genetic potential of hybrids cannot be fully expressed under specific agronomic management techniques
Solution Approach 1:
The patent merges breeding programs with agronomic management practices into an integrated selection process. Instead of treating genotype development and management optimization as separate sequential steps, the invention combines them into a unified selection framework where both genotype and management practices are selected simultaneously to maximize their interactive effect on crop yield.
Solution Approach 2:
The patent applies preliminary action by conducting selection for both genotype and management practices at early breeding stages rather than waiting until late-stage finished commercial varieties are available. This allows the breeding program to proactively identify and select genotype-by-management combinations that will perform optimally under specific agronomic conditions before commercial deployment.
2Productivity
If agronomists develop management practices for finished crop varieties with fixed genetic characteristics, then agronomic optimization can be applied, but opportunities for synchronized genotype-by-management improvement are missed
Solution Approach 1:
The patent implements preliminary action by integrating agronomic management selection into early breeding programs rather than waiting until varieties are finished and genetically fixed. This allows synchronized selection of both genotype and management practices to occur during the breeding development phase, capturing yield improvement opportunities that would otherwise be lost by sequential approaches.
Solution Approach 2:
The patent introduces a selection system that acts as an intermediary between breeders and agronomists, facilitating synchronized selection of genotype-by-management combinations. This intermediary framework enables both disciplines to work together during the breeding process rather than operating in isolation, allowing management practices to be selected concurrently with genetic material development.
3Adaptability or versatility
If sequential approaches are used to handle breeding and agronomy separately, then each discipline can operate independently, but synchronized genotype-by-management improvements cannot be achieved
Solution Approach 1:
The patent applies segmentation by dividing the integrated selection process into distinct but coordinated components: genotype selection, management practice selection, and interaction effect evaluation. This segmented approach allows the complex integrated system to be managed through modular selection criteria while still achieving synchronized genotype-by-management improvement.
Solution Approach 2:
The patent creates a universal selection framework that can simultaneously evaluate and select for multiple objectives: genetic performance, management practice effectiveness, and their interactive effects. This multi-functional selection system handles both breeding and agronomic considerations within a single integrated process, reducing overall system complexity despite the sophisticated interactions being managed.
Data Source
AI summary
Systems and methods that integrate breeding and agronomy by employing genotype (G) by environment (E) by management (M) practice to improve synchronized breeding for crop yield gain are provided. Methods to perform G×E×M through machine learning, simulation, crop models, quantitative models and other prediction techniques are provided.