Articles
Dynamic prediction of fruit quality traits as a function of environmental and genetic factors
Article number
1353_19
Pages
145 – 152
Language
English
Abstract
Fruit quality is a complex trait affected by interactions among horticultural management, genotype, and environment.
Growers and the horticulture value-chain are challenged by the increasing importance of quality aspects driven by consumer demand.
Process-based crop growth models and other data-driven decision support tools are actively used to increase and improve fruit production, but further work is required to include fruit quality predictions in this process.
This work presents an overview of a proposed methodologies to assess and model the variability of fruit quality and the possible integration with process-based crop models.
Four case studies provided here show the application on fruit quality data sets for strawberry (Fragaria × ananassa), blueberry (Vaccinium spp.), grapevine (Vitis vinifera) and grapefruit (Citrus paradisii MacFad.), production in the USA and Pakistan.
Statistical relations between measured fruit quality traits and both observed and simulated preharvest conditions were revealed by regression analysis.
A quality prediction model was built by identifying and integrating the key correlations into a quality prediction module.
For strawberry, the module can predict soluble solid content and titratable acidity based on average temperature during key phenological phases, e.g., individual fruit growth from end of flowering to harvest maturity.
The quality prediction model was integrated with the CROPGRO-Strawberry model of the Decision Support System for Agrotechnology Transfer (DSSAT) as a module to predict both quality and quantity dynamics of strawberry production.
A strategic analysis with historic weather data was conducted to reveal the impact of seasonal climate variability on strawberry yield and quality.
For grapefruit, a similar correlation between temperature and the ratio of soluble solids to titratable acidity was modeled and extended to include postharvest quality development for combined harvest and storage recommendations.
Overall, the proposed quality prediction methodology is an important advancement toward the objective assessment of genotype by environment by management effects on fruit quality, particularly when analyzing data from multiple sites or years.
Future work will include more refined, e.g., multivariate, statistical methods or inclusion of process-based approaches based on the availability of suitable data.
Growers and the horticulture value-chain are challenged by the increasing importance of quality aspects driven by consumer demand.
Process-based crop growth models and other data-driven decision support tools are actively used to increase and improve fruit production, but further work is required to include fruit quality predictions in this process.
This work presents an overview of a proposed methodologies to assess and model the variability of fruit quality and the possible integration with process-based crop models.
Four case studies provided here show the application on fruit quality data sets for strawberry (Fragaria × ananassa), blueberry (Vaccinium spp.), grapevine (Vitis vinifera) and grapefruit (Citrus paradisii MacFad.), production in the USA and Pakistan.
Statistical relations between measured fruit quality traits and both observed and simulated preharvest conditions were revealed by regression analysis.
A quality prediction model was built by identifying and integrating the key correlations into a quality prediction module.
For strawberry, the module can predict soluble solid content and titratable acidity based on average temperature during key phenological phases, e.g., individual fruit growth from end of flowering to harvest maturity.
The quality prediction model was integrated with the CROPGRO-Strawberry model of the Decision Support System for Agrotechnology Transfer (DSSAT) as a module to predict both quality and quantity dynamics of strawberry production.
A strategic analysis with historic weather data was conducted to reveal the impact of seasonal climate variability on strawberry yield and quality.
For grapefruit, a similar correlation between temperature and the ratio of soluble solids to titratable acidity was modeled and extended to include postharvest quality development for combined harvest and storage recommendations.
Overall, the proposed quality prediction methodology is an important advancement toward the objective assessment of genotype by environment by management effects on fruit quality, particularly when analyzing data from multiple sites or years.
Future work will include more refined, e.g., multivariate, statistical methods or inclusion of process-based approaches based on the availability of suitable data.
Authors
A. Hopf, A. Plotto, R. Rizwan, C. Zhang, K.J. Boote, V. Shelia, G. Hoogenboom
Keywords
strawberry (Fragaria × ananassa), blueberry (Vaccinium spp.), grapevine (Vitis vinifera), grapefruit (Citrus paradisii), quality modeling, crop modeling, regression, CROPGRO, DSSAT, soluble solids, SSC, acidity, TA
Groups involved
- Division Temperate Tree Fruits
- Division Temperate Tree Nuts
- Division Vegetables, Roots and Tubers
- Division Plant-Environment Interactions in Field Systems
- Division Horticulture for Human Health
- Division Postharvest and Quality Assurance
- Division Horticulture for Development
- Division Tropical and Subtropical Fruit and Nuts
Online Articles (43)