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Fluid Journal : Fluid Journal 2008-2009
disagreement is clear: by targeting a field mean of 2 percent, the grower is ensuring that all trees in the orchard are above the critically required K concentration of 1.4 percent. Maintenance of the field at the UC recommended 1.4 percent mean value would result in 50 percent of all trees being deficient in K. In this instance, the growers' perception of the critical value was more appropriate for yield optimization than the researchers'. These results highlight a point that has been overlooked: for an individual plant the CV represents the minimum nutrient concentration in that individual plant that is required to attain 95 percent full yield. In a population of plants, however, the CV is the nutrient concentration of the population that results in 95 percent of all individual plants attaining full yield. This population CV will always be greater than the CV of the individual plant by an amount determined by the variability in the population. Estimating field variability is, therefore, essential if the true field CV is to be determined. None of the current texts or guidelines on nutrient management in tree species recognizes this issue and, as a consequence, many researchers have been misusing the single most trusted tool for nutrient management in tree crops. To further examine and illustrate the extent and nature of errors in the use of tissue sampling we have initiated a series of experiments in which the yields and nutrient-use in large numbers of trees have been examined. Estimates of spatial, temporal, environmental, and genetic components of nutrient variability are under way and will be used to develop new approaches to sampling methodology and nutrient management in high-value crops. Experimentation Field variability. In agronomic crops derived from genetically uniform material, field variability in yields and nutrient status is largely the result of changes in the local environment (soil, water, micro-climate). In perennial crops, field variability not only is a result of this local environmental effect but is also a consequence of significant variability in genetics of the rootstock, the life history of the plant (grafting, pruning, and harvesting effects) as well as prior yield and growth of neighboring trees. The resulting complexity is therefore far greater. To address this, an extensive grid sampling protocol was established at each of five separate sites transecting Californian almond production regions, using techniques developed for GIS. In each orchard at 54 grid points, uniformly distributed across a 10- to 15-acre block of trees, May and July leaf nutrient status, light interception, truck diameter, and tree yield were determined in each tree (Figure 2). At 30 of these grid points, the nutrient status and yield of two neighboring NP trees were also collected as independent data points. Initially, non-fruiting spur leaves in exposed positions were selected for these samples. However, depending on early results, sampling protocols may be adjusted. Two statistical techniques- -nugget sampling and modified Mantel- -were used. These approaches allow for partitioning of variance in nutrient status due to environment, genetic variability, and random variability, plus allow for determination of interactions and dependencies between nutrition and yield and the nature of spatial variability within an orchard. Yield collection. Individual tree yields were determined on 4,288 trees for six years in a single, highly productive orchard. Tree yields were gathered by a precision harvester. A pistachio yield monitor was developed by UC Davis in collaboration with Paramount Farming Company. To allow tree yields to be discretely determined, a standard commercial pistachio harvester was retrofitted with a weighing system. Tree location in the field was simultaneously determined with a number of redundant mechanisms, including differential GPS for row identification, physical markings, and an odometrical encoder wheel. Nutrient-use efficiency. Leaf and nut samples have been collected across all experimental sites at five stages of crop growth. Sampling intensity averaged 20 discrete samples from each acre across each 50 acres at each of five experimental sites over five dates. Data will be presented as histograms to illustrate field variability and surface maps. Overall, this experiment will collect far more samples (2,672 samples from 456 trees), analyze far more nutrients (N,K,P,S,Ca,Mg,B,Zn,Mn,Fe)than ever performed before, and will collect individual tree yields associated with each of these samples. This detailed approach is designed to provide the foundation for statistical information needed to guide fertilizer practices for the foreseeable future. Nutrient-use efficiency (NUE) is calculated as N-removed-in-crop/N-input- annually over an eight-year period. In these orchards, no significant N is present in the irrigation water, irrigation water does not move below the root zone, and all pruning residue and leaves remain in the orchard. Results We predict that the adoption by growers of fertilization regimes, aimed to ensure that 95 percent of all individual trees in an orchard are above the established critical value, will result in a field mean nutrient concentration at least two standard deviations above the established CV. Figure 3 illustrates that this is indeed the case. The grower in this example precisely targeted the optimal economic fertilization rate. While the results illustrated in Figure 3 verify that growers are fertilizing the majority of their orchards to ensure Figure 2. Field sampling strategy to partition components of yield and nutrient variability in almond. This experiment I repeated at 5 sites spanning the almond production areas of California.
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