Primary and Secondary Yield Losses Caused by Pests and Diseases: Assessment and Modeling in Coffee

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.

Interactive effects of altitude, microclimate and shading system on coffee leaf rust

“…Beneficial shading effects on coffee production through the mitigation of microclimatic extremes have been quantified and are generally well-established (Barradas and Fanjul 1986;Lin 2007). It has also been acknowledged that the extent to which shaded systems are advantageous depends on the biophysical context (Cerda et al 2017;Rahn et al 2018).…”

“…coffee physiology and productivity, soil, water, as well as biodiversity, which in turn may also be related amongst themselves and with pests and diseases (Muschler 2004). Moreover, these ecological mechanisms of shade are altered by greater spatial factors, such as macroclimatic variations along altitudinal or latitudinal gradients (Staver et al 2001;Avelino et al 2011;Cerda et al 2017).…”

Mitochondrial Genomes of Lepidopteran Insects Considered Crop Pests

“…Even so, there is no doubt that crop losses due to pests and diseases are a major threat to incomes of rural families and to food security worldwide [2]. Although there are a large number of different living organisms that affect agricultural crops (biotic factors) and therefore they are called pests or pathogens, organisms from Lepidoptera order within Insecta class are considered one of the most economically important pests due to huge crop losses caused by them, crop losses, in terms of quantity and quality that can occur in the field (pre-harvest) or in the storage (post-harvest) [3].…”

Coffee agroforestry systems capable of reducing disease-induced yield and economic losses while providing multiple ecosystem services

“…, we present a summary of the 324 characteristics of the six CAFs related to their agroforestry structure, yield losses, yields and management Primary and secondary yield losses were positively related to coffee leaf rust and dieback that leads to the 329 death of productive branches, considered as the main yield-reducing factor(Cerda et al, 2017b). However,330we highlight as an important finding that the coffee plots with the lower primary and secondary losses were 331 not necessarily associated with lower disease levels.…”

“…In each coffee plot, we marked a subplot composed of eight coffee rows with 15 plants each and selected 143 eight coffee plants (one per row). In these plants, we measured the number of fruiting nodes (yield 144 component) and the number of dead productive branches (yield-reducing factor) as predictors of yield and 145 yield losses (Cerda et al, 2017b). In the same plants, we measured the presence of foliar diseases in the 146 leaves of three lateral branches (one branch in the lower part of the coffee plant, another in the middle and a 147 third in the upper part) five times during the year, and then we calculated the standardized area under the 148 disease progress curve (sAUDPC) for each disease.…”