# Technologies for Forecasting Tree Fruit Load and Harvest Timing--From Ground, Sky and Time
## 2 Measurement of Fruit Number
### Manual Fruit Count
#### 2.3.3 Examples  
Some commercial sampling practices fail to adequately represent spatial variability within the orchard and/or risk large measurement errors by counting large numbers of fruit. For example, a typical practice in Chilean wine grape production is to count bunches on 10% of vines in the orchard, within 30 m long row segments in systematically chosen rows. In Chilean cherry production, a common practice is to count all fruit on each of 100 trees that appear 'average' for the orchard. In Australian mango production, the current best commercial practice for orchard fruit load estimation involves the visual count of fruit per tree of 5% of trees of the orchard (typically with about 1000 trees on 2 ha) [16], through a fruit count of trees on transects through the orchard. In a Brazilian mango production example, 5% of trees in the block are counted along a transect line, with the count increased if SD is >10% of the mean. In critique of the above practices, note that decisions on sample supplementation should not be made based solely on the observed SD (or CVobs = SD/mean). The observed variability of a sample is lower bound by the true or biological variability of the population Equation (5), which means that it cannot by itself indicate the accuracy of an estimate. A decision to supplement a sample can be made using the sample size calculation (Equations (3) and (4)) for random samples and the \(SE\) of the estimator. As an example, consider site 1 from Table 3, with an RS (without replacement) estimator. An estimator of the variance of the sample mean is given by substituting \(SD_{\text{obs}}\) for \(\sigma\) in Equation (1). As a starting point, a single realization of \(n=13\) trees was taken. The resulting sample provided an estimate of the mean as 69.5 fruits per tree (i.e., an error of \(-36.7\%\)), a sample SD of 90.5 fruits, and an estimated sampling variance Equation (1) of 612 (for an estimated coefficient of error, CE = 35.6%). Substituting these values in Equation (5) and then rearranging to solve for \(SD_{bio}\), the biological variability was estimated to be:  
\[SD_{bio}\approx\sqrt{90.5^{2}-890}=87.1\]compared to the true value of 82 fruit/tree. Supplementing this first sample with an additional random sample of 13 trees for a sample size of 26 trees provided an estimated mean per tree of 110.3 (i.e., an error of only \(-4.6\%\)) and an \(SD_{\text{obs}}\) of 104.8. The estimated CE was 18.1%, which compares well with the true CE of 16.5%, reported in the previous subsection) but the estimated \(SD_{\text{bio}}\) was 102.8 (an overestimate by 25%). The more precise estimator based on 26 trees provided an even higher SD than the smaller 13 tree sample! Increasing the sample size will reduce the sampling error (a property of an unbiased estimator), but in this case, the observed SD will always be large because the biological SD is high. The prediction of sampling variance for systematic sampling is more complicated. [PERSON] [17] reviews models and methods. [PERSON] and [PERSON] [27] evaluated several models for use under multistage systematic sampling of trees and concluded that a repeated sampling procedure provided a promising approach. The use of a transect is driven by the convenience of locating units in walking through the orchard. Counts of trees in transects represent a form of systematic sampling and so could be more efficient than RS, if the variability of tree fruit load in the orchard was captured. For example, a transect can be oriented with respect to an observable trend for tree loading across the orchard, with a random starting position. However, a single transect line is unlikely to capture all variation patterns within an orchard, and so is not recommended. Guidelines for the manual count-based estimation of fruit crop load are available for some crops, notably for citrus, grape and nut crops. For example, the use of a frame for citrus yield estimation was first documented by [PERSON] [28].