Cerda Rolando, Avelino Jacques, Gary Christian, Tixier Philippe, Lechevallier Esther, Allinne Clémentine
CIRAD, UMR System, 2 place Pierre Viala, Montpellier, France.
CATIE, Program of Sustainable Agriculture and Agroforestry, Turrialba, Costa Rica.
PLoS One. 2017 Jan 3;12(1):e0169133. doi: 10.1371/journal.pone.0169133. eCollection 2017.
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.
为了改善有助于农村家庭收入和全球粮食安全的生产系统，需要对作物产量损失进行评估。然而，量化产量损失并确定其原因的工作仍然有限，特别是对于多年生作物。我们的目标是量化咖啡因病虫害造成的初级产量损失（在当年生产中产生）和次级产量损失（由上一年的负面影响导致），并确定咖啡产量和产量损失的最重要预测因素。我们建立了一个全日照的实验性咖啡地块，包括六种处理方式，这些处理方式被定义为不同的农药施用顺序。试验持续了三年（2013 - 2015年），产量构成因素、枯死的生产性枝条以及叶部病虫害被作为产量的预测因素进行评估。首先，我们通过将特定处理的实际产量与在始终进行化学保护的地块中获得的估计可达到产量进行比较来计算产量损失。其次，我们使用结构方程模型来确定最重要的预测因素。结果表明，病虫害导致了较高的初级产量损失（26%）和甚至更高的次级产量损失（38%）。我们确定结果节点和枯死的生产性枝条是产量和产量损失最重要且有用的预测因素。这些预测因素可以添加到现有的咖啡机理模型中，或者可用于开发新的线性混合模型来估计产量损失。然后可以将估计的产量损失与生产因素相关联，以确定农民可以采取的减少损失的纠正措施。本研究的实验和建模方法也可应用于其他多年生作物以评估产量损失。
