project - EIP-AGRI Operational Group
Control of Monilinia spp. in stone fruit: use of prediction models and cultural practices
Control de Monilinia spp. en fruta de hueso: utilización de modelos de predicción métodos profilácticos
Context
Brown fruit rot caused by Monilinia spp. (M. laxa, M. fructigena and M fructicola) is the main disease affecting stone fruits in our area. It causes serious losses both in the field and during post-harvest and it creates marketing complications. This project aims to improve its control in stone fruit through the use of predictive models, in order to make the treatments only when necessary, to select the best products for each moment (depending also on the existence of resistant strains) and to assess the feasibility of introducing preventive practices.
Objectives
The aim is to improve the control of Monilinia spp. in stone fruits through the use of predictive models and preventive practices. The specific objectives are: i) to validate a predictive model and to establish the level of resistance to the active ingredients used; ii) to study the feasibility of using the network of meteorological stations of DARP; iii) to assess the feasibility of creating an own network of meteorological stations; iv) to assess the effectiveness of treatments before or after rains; v) to determine the feasibility of removing secondary inoculum; vi) to develop a simple system that will allow to determine the risk of Monilinia spp. during postharvest.
Objectives
El objetivo es mejorar el control de Monilinia spp. en fruta de hueso mediante la utilización de modelos de predicción y de medidas profilácticas. Los objetivos específicos son: i) validar un modelo de predicción y determinar el nivel de resistencia a las materias activas utilizadas; ii) estudiar la viabilidad de utilizar la red de estaciones agrometeorológicas del DARP; iii) evaluar la viabilidad de crear una red propia de estaciones; iv) estudiar la eficacia de los tratamientos antes o después de lluvias; v) determinar la viabilidad de eliminar el inóculo secundario; vi) desarrollar un sistema sencillo que permita determinar el riesgo de aparición de Monilinia spp. durante la postcosecha.
Activities
1.Obtention of a predictive model of Monilinia spp. with applications of pesticides and assessment of residues levels and the development of resistances.
2. Studyinwhether meteorological stations of DARP are suitable for correlating the model or whether it is better to establih an own network, allowing to place stations in representative areas.
3. Applying fungicide treatmen depending on weather conditions (before and after the rain) and developing a strategy for removing rotten fruit before harvest.
4. Establishing methods to determine the risk of Monilinia spp. during postharvest.
5. Publishina manual of good practices.
6. Training courses.
Activities
1. Obtener un modelo predictivo de Monilinia spp. con aplicaciones de productos fitosanitarios y evaluación del número de residuos y la aparición de resistencias.
2. Estudiar si las estaciones agrometeorológicas del DARP son adecuadas para correlacionar con el modelo o si es mejor establecer una red propia.
3. Realizar los tratamientos fungicidas en función de la predicción (antes y después de lluvias) y desarrollar una estrategia de eliminación de frutos podridos antes de la cosecha.
4. Establecer metodologías para determinar el riesgo de aparición de Monilinia spp. en postcosecha.
5. Redacción de una guía de buenas prácticas
6. Realización de cursos de formación.
Project details
- Main funding source
- Rural development 2014-2020 for Operational Groups
- Rural Development Programme
- 2014ES06RDRP009 Spain - Rural Development Programme (Regional) - Cataluña
Location
- Main geographical location
- Lleida
EUR 184 300.00
Total budget
Total contributions from EAFRD, national co-financing, additional national financing and other financing.
Contacts
Project coordinator
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Rosa Altisent
Project coordinator
Project partners
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AGROPECUARIA I SC DE SOSES, SCCL
Project partner
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FRUITS DE PONENT, SCCL
Project partner
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ACTEL, SCCL
Project partner