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Primary and secondary yield losses caused by pests and diseases: assessment and modeling in coffee
- Source :
- PLoS ONE, PLoS ONE, Public Library of Science, 2017, ⟨10.1371/journal.pone.0169133⟩, PloS One, PLoS ONE, Vol 12, Iss 1, p e0169133 (2017), Plos One, . (2017)
- Publication Year :
- 2017
- Publisher :
- HAL CCSD, 2017.
-
Abstract
- The assessment of crop yield losses is needed for the improvement of production systems that contribute to the incomes of rural families and food security worldwide. However, efforts to quantify yield losses and identify their causes are still limited, especially for perennial crops. Our objectives were to quantify primary yield losses (incurred in the current year of production) and secondary yield losses (resulting from negative impacts of the previous year) of coffee due to pests and diseases, and to identify the most important predictors of coffee yields and yield losses. We established an experimental coffee parcel with full-sun exposure that consisted of six treatments, which were defined as different sequences of pesticide applications. The trial lasted three years (2013–2015) and yield components, dead productive branches, and foliar pests and diseases were assessed as predictors of yield. First, we calculated yield losses by comparing actual yields of specific treatments with the estimated attainable yield obtained in plots which always had chemical protection. Second, we used structural equation modeling to identify the most important predictors. Results showed that pests and diseases led to high primary yield losses (26%) and even higher secondary yield losses (38%). We identified the fruiting nodes and the dead productive branches as the most important and useful predictors of yields and yield losses. These predictors could be added in existing mechanistic models of coffee, or can be used to develop new linear mixed models to estimate yield losses. Estimated yield losses can then be related to production factors to identify corrective actions that farmers can implement to reduce losses. The experimental and modeling approaches of this study could also be applied in other perennial crops to assess yield losses.
- Subjects :
- 0106 biological sciences
0301 basic medicine
[SDV.SA]Life Sciences [q-bio]/Agricultural sciences
Leaves
Rain
Chemical protection
lcsh:Medicine
Coffea
Plant Science
01 natural sciences
Food Supply
Database and Informatics Methods
F01 - Culture des plantes
Agricultural Soil Science
lcsh:Science
2. Zero hunger
Ravageur des plantes
Multidisciplinary
Food security
U10 - Informatique, mathématiques et statistiques
crop losses crop damage
réduction des pertes
Plant Anatomy
food and beverages
Agriculture
Coffea arabica
Plants
Agricultural sciences
Rendement des cultures
Experimental Organism Systems
Agricultural soil science
Agrochemicals
Sequence Analysis
Modèle mathématique
Research Article
Costa Rica
Crops, Agricultural
Bioinformatics
Yield (finance)
Soil Science
Factors of production
Crops
Research and Analysis Methods
Fruits
modelling
03 medical and health sciences
Plant and Algal Models
Production (economics)
Grasses
Pesticides
Plant Diseases
H20 - Maladies des plantes
modélisation
business.industry
Crop yield
Ecology and Environmental Sciences
lcsh:R
Organisms
Biology and Life Sciences
Models, Theoretical
Plant Pathology
15. Life on land
H10 - Ravageurs des plantes
maladie des plantes
030104 developmental biology
Agronomy
perte de récolte
Linear Models
Environmental science
lcsh:Q
Pest Control
Rice
business
Sciences agricoles
Crop Science
Cereal Crops
010606 plant biology & botany
Subjects
Details
- Language :
- English
- ISSN :
- 19326203
- Database :
- OpenAIRE
- Journal :
- PLoS ONE, PLoS ONE, Public Library of Science, 2017, ⟨10.1371/journal.pone.0169133⟩, PloS One, PLoS ONE, Vol 12, Iss 1, p e0169133 (2017), Plos One, . (2017)
- Accession number :
- edsair.doi.dedup.....a7d99396d5d350df02a20b49cb7e8d35
- Full Text :
- https://doi.org/10.1371/journal.pone.0169133⟩