Evaluation of a Non-Destructive Sampling Method and a Statistical Model for Predicting Fruit Load on Individual Coffee ( Coffea Arabica ) Trees

Abstract Destructive sampling schemes are the most direct and accurate methods to estimate yields in agro-ecosystem studies. However, in many situations these resource-intensive schemes are not feasible and/or sustainable. The objective of this research was to develop and compare non-destructive visual censuses and analytical methods for estimating fruit loads on Coffea arabica var. Caturra and Catuai trees using different components of yield. Fruit load data were collected in coffee farms found in the Los Santos Region of Costa Rica. Two components of yield where estimated: number of productive lateral branches per tree and fruit load per lateral branch. OLS regression was used to develop empirical models relating these components of yield with total fruit load per plant. Productive laterals at medium relative distance from the apical meristem had higher fruit loads than those found at the top or bottom of the orthotropic stem in C. arabica plants. In addition, by sampling eight to nine productive laterals per plant, the maximum observed error of the estimated fruit load per lateral was reduced by half. Regression coefficients of the empirical models relating total fruit load with yield components ranged between 0.73 and 0.92. Sampling schemes which grant equal probability of selection to productive laterals at different relative distances from the apical meristem should be chosen when estimating fruit load per lateral in commonly cultivated varieties of C. arabica plants. Furthermore, a non-destructive sampling protocol of the key components of yield provides accurate estimates of total fruit load per tree. Additional research is required to relate fruit loads with total biomass of fresh fruit and dry biomass in this perennial crop.