Root and aerial growth in early-maturing peach trees under two crop load treatments

Abstract

The objectives of the paper were to study the pattern of root growth (measured by minirhizotrons) in relation to trunk, fruit and shoot growth and the effects of crop load on tree growth and yield in peach trees. Two crop load (commercial and low) treatments were applied in a mature early-maturing peach tree orchard growing in Mediterranean conditions. Root growth dynamics were measured using minirhizotrons during one growing season. Shoot, trunk and fruit growth were also measured. At harvest, all fruits were weighed, counted and sized. Roots grew throughout the year but at lower rates during the active fruit growth phase. Root growth was asynchronous with shoot growth, while root and trunk growth rates were highest after harvest, when the canopy was big enough to allocate the photo-assimilates to organs that would ensure the following season's yield. Shoot and fruit growth was greater in the low crop load treatment and was accompanied by a non-significant increase in root growth. High level of fruit thinning decreased the current yield but the fruits were more marketable because of their greater size.Additional keywords: fruit growth; minirhizotrons; phenology; Prunus persica L. Batsch; root length density; vegetative growth

Irrigation Factor Approach Based on Soil Water Content: A Nectarine Orchard Case Study

“…The SWC values registered during the studied period, depicted in Figure 1, correspond to the maximum root density area of Prunus trees (0-50 cm), as indicated in previous research in similar edaphic-climatic conditions [34]. SWC values varied slightly and between the imposed threshold values to automatically start and end irrigation, demonstrating the suitability of the applied automated irrigation schedule.…”

“…Probes were normalized and calibrated for a clay-loam soil [3,30,33]. Average SWC values in the 0-50 cm soil profile were calculated, since this depth corresponds to the maximum root density area [34]. Drip gauges were installed below the emitter with the capacitance probe to monitor real-time irrigation amounts in order to detect possible mishaps during the irrigation event.…”

Effects of Postharvest Water Deficits on the Physiological Behavior of Early-Maturing Nectarine Trees

“…Regarding plant water status, the T–C treatment showed an average Ψ stem value of about −0.80 MPa ( Figure 2 A), which is symptomatic of non-limiting soil water conditions in clay–loam soils [ 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 ]. In this sense, Abrisqueta et al [ 30 ] affirmed that a Ψ stem value above −0.9 MPa during the summer is an indicator of water stress initiation in early-maturing peach trees.…”

“…When both water-use efficiencies were correlated with the corresponding mean daily VPD values, a higher coefficient of determination was found for A CO2 /E (r 2 = 0.32 p ≤ 0.001) ( Figure 5 A) than for A CO2 /g s (r 2 = 0.05 ns) ( Figure 5 B). This could be explained by the fact that A CO2 /E is more influenced by environmental conditions, since E depends on the degree of stomatal opening and the vapor pressure deficit (VPD) of the atmosphere surrounding the leaf [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 , 55 , 56 , 57 , 58 ], whereas A CO2 /g s excludes the effects of changing evaporative demand on water flux out of the leaf, and it depends only on the stomatal opening [ 59 ].…”

“…Previous studies have shown that continuous real-time measurements based on soil water content (θ v ) with capacitance probes indicated accurately the impact of different irrigation events on the stored soil water. This provided useful information concerning the advance of the wetted front, the depth of the root system activity, and the fate of the applied water [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. These θ v -based data allowed an optimal soil moisture range to be set for the crop.…”