Articles
Forecasting yield in temperate fruit trees from winter chill accumulation
Article number
1327_53
Pages
397 – 404
Language
English
Abstract
The potential yields of deciduous fruit and nut trees are strongly affected by the climatic conditions that regulate dormancy and subsequent flowering.
However, the realized yield at the end of the growing season depends on several additional factors including environmental conditions and management measures.
These modulating factors dilute the direct effect of chill accumulation and frustrate efforts to forecast yields.
Such forecasts are also limited by a lack of data, which complicates the assessment of the relationship between chill and yield.
However, information on how diminishing chill impacts crop yield is crucial for farmers and other decision-makers, particularly in warm fruit and nut growing regions.
We address this challenge by adopting a probabilistic approach to yield forecasting.
This approach incorporates uncertainty and generates predictions that do not consist of precise numbers, but yield expectations expressed as probability distributions.
We demonstrate a set of functions that we developed in the R programming language to generate forecasts of possible yields with given chill.
We apply these methods to data sets of two sweet cherry (Prunus avium L.) cultivars Lapins and Brooks.
However, the realized yield at the end of the growing season depends on several additional factors including environmental conditions and management measures.
These modulating factors dilute the direct effect of chill accumulation and frustrate efforts to forecast yields.
Such forecasts are also limited by a lack of data, which complicates the assessment of the relationship between chill and yield.
However, information on how diminishing chill impacts crop yield is crucial for farmers and other decision-makers, particularly in warm fruit and nut growing regions.
We address this challenge by adopting a probabilistic approach to yield forecasting.
This approach incorporates uncertainty and generates predictions that do not consist of precise numbers, but yield expectations expressed as probability distributions.
We demonstrate a set of functions that we developed in the R programming language to generate forecasts of possible yields with given chill.
We apply these methods to data sets of two sweet cherry (Prunus avium L.) cultivars Lapins and Brooks.
Authors
C. Whitney, E. Fernandez, K. Schiffers, I.F. Cuneo, E. Luedeling
Keywords
flowering, dormancy, climate change, uncertainty, ripening, nut trees
Groups involved
- Division Plant Genetic Resources, Breeding and Biotechnology
- Division Tropical and Subtropical Fruit and Nuts
- Division Temperate Tree Fruits
- Division Vine and Berry Fruits
- Division Ornamental Plants
- Division Vegetables, Roots and Tubers
- Division Plant-Environment Interactions in Field Systems
- Division Greenhouse and Indoor Production Horticulture
- Division Postharvest and Quality Assurance
- Division Precision Horticulture and Engineering
- Division Horticulture for Human Health
- Division Horticulture for Development
- Division Landscape and Urban Horticulture
- Commission Banana
- Commission Agroecology and Organic Farming Systems
- Commission Cultivar Registration
- Division Temperate Tree Nuts
- Working Group Cannabaceae
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