Abstract
In orchards, the variations of fruit quality and its determinants are crucial for resource effective measures. In the present study, a drip-irrigated plum production (Prunus domestica L. “Tophit plus”/Wavit) located in a semi-humid climate was studied. Analysis of the apparent electrical conductivity (ECa) of soil showed spatial patterns of sand lenses in the orchard. Water status of sample trees was measured instantaneously by means of leaf water potential, Ψleaf [MPa], and for all trees by thermal imaging of canopies and calculation of the crop water stress index (CWSI). Methods for determining CWSI were evaluated. A CWSI approach calculating canopy and reference temperatures from the histogram of pixels from each image itself was found to suit the experimental conditions. Soil ECa showed no correlation with specific leaf area ratio and cumulative water use efficiency (WUEc) derived from the crop load. The fruit quality, however, was influenced by physiological drought stress in trees with high crop load and, resulting (too) high WUEc, when fruit driven water demand was not met. As indicated by analysis of variance, neither ECa nor the instantaneous CWSI could be used as predictors of fruit quality, while the interaction of CWSI and WUEc did succeed in indicating significant differences. Consequently, both WUEc and CWSI should be integrated in irrigation scheduling for positive impact on fruit quality.
Highlights
Following the concept of precision agriculture, correlation of spatial variation of soil and yield data has been analyzed in field crops, vegetable production, vineyards, and orchards
The first analyzes the spatial correlation between soil properties influencing the water supply as one main growth factor and yield as the Abbreviations: ρ, density of dry air [kg m−3]; t, time course, diurnal variation [h], leaf, leaf water potential [MPa]; π, osmotic potential [MPa]; u, wind speed [m s−1]; T, temperature [◦C]; TK, absolute temperature [K]; Tw, temperature of wet reference [◦C]; Td, temperature of dry reference [◦C]; Tc, current canopy temperature [◦C]; Tair, air temperature [◦C]; CWSI, crop water stress index [0; 1]; ECa, soil apparent electrical conductivity [mS m−1]; VPD, water vapor pressure deficit [kPa]; WUEi, instantaneous water use efficiency [μMol mMol−1]; WUEc, cumulative water use efficiency [g/]
We can certainly make no a-priori assumption on stable CWSI patterns, since crop load, stage of fruit development, and vegetative growth are all expected to influence water demand. This said, neither ECa nor crop load in the current study showed a correlation with CWSIR
Summary
Following the concept of precision agriculture, correlation of spatial variation of soil and yield data has been analyzed in field crops, vegetable production, vineyards, and orchards. ECa, CWSI, and WUE in Plum Production target variable This is consistent with findings in precision viticulture, where soil maps have provided a basis for delineating management zones (Williams and Araujo, 2002). The second approach is more driven by the endogenous growth factors of the plant It uses the correlation of plant data such as canopy volume representing the growth capacity, tree water status, and fruit quality at harvest (Zaman and Schumann, 2006). This latter approach may be more appropriate for orchards where fruit quality is crucial for marketing. The analysis of spatiallyresolved soil and plant data and its influence on fruit quality has rarely been studied
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