Primary and secondary yield losses caused by pests and diseases: Assessment and modeling in coffee

Cerda Rolando, Avelino Jacques, Gary Christian, Tixier Philippe, Lechevallier Esther, Allinne Clémentine. 2017. Primary and secondary yield losses caused by pests and diseases: Assessment and modeling in coffee. PloS One, 12 (1):e0169133, 17 p.

https://doi.org/10.1371/journal.pone.0169133

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Quartile : Q1, Sujet : MULTIDISCIPLINARY SCIENCES

Liste HCERES des revues (en SHS) : oui

Thème(s) HCERES des revues (en SHS) : Psychologie-éthologie-ergonomie; Staps

Résumé : 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.

Mots-clés Agrovoc : Coffea arabica, perte de récolte, rendement des cultures, ravageur des plantes, maladie des plantes, modèle mathématique

Mots-clés géographiques Agrovoc : Costa Rica

Classification Agris : H10 - Ravageurs des plantes
H20 - Maladies des plantes
F01 - Culture des plantes
U10 - Informatique, mathématiques et statistiques

Champ stratégique Cirad : Axe 4 (2014-2018) - Santé des animaux et des plantes

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