J. Vera

Stem water potential estimation of drip-irrigated early-maturing peach trees under Mediterranean conditions

Seasonal Ψstem is a useful diagnostic tool for peach tree irrigation management.The autumn rainfall events point to the resilient behaviour of the peach cultivar studied.The soil water content was the main contributor to Ψstem estimation.Ψstem was estimated by regression equation of soil water content, GDH and VPDm data. In the last decade deficit irrigation strategies allowed growers to deal with water shortages, while monitoring stem water potential (Ψstem) is deemed essential for maximising fruit yield and quality. However, because of the intensive labour involved in measuring Ψstem, alternative methods are desirable. The experiment described was conducted in Murcia (Spain) with adult peach trees (Prunus persica (L.) Batsch cv. Flordastar) submitted to different drip irrigation treatments, measuring Ψstem with a pressure chamber and the soil water content with a neutron probe. Agro-meteorological variables were recorded. Seasonal patterns of stem water potential provide a useful diagnostic tool for irrigation management in peach trees. Rainfall events and the meteorological conditions prevailing in autumn pointed to the resilient nature of the peach cultivar studied. Fitting Ψstem by linear regression analysis as a function of soil and atmosphere yielded a significant correlation, with the soil water content being the main contributor to estimating Ψstem. Linear regression analysis highlighted the importance of considering plant water status as a function of the peach tree cultivar, the atmospheric conditions in which it develops and the soil water conditions resulting from irrigation. A multiple linear regression equation based on soil water content in the soil profile, mean daily air vapour pressure deficit (VPDm) and growing degree hours (GDH) data explained 72% of the variance in Ψstem, and is proposed as an alternative to the field measurement of Ψstem.

Soft computing applied to stem water potential estimation

Soft computing was applied to agro-meteorological and soil moisture data.Soil moisture at 0.3m, day of year and air temperature were the most relevant inputs.Fuzzy rules-based model with only 5 rules estimated Ψ st with 86% variance explained.Supplied agro-linguistic terms improved the interpretability of the fuzzy rules.The model was almost as accurate as artificial neural networks, while being simpler. Measuring the stem water potential ( Ψ st ), which is an essential parameter for assessing plant water status, is a tedious and labor-consuming task. In this work, hybrid soft computing techniques were applied to design a model able to estimate Ψ st based on agro-meteorological and soil water content data. A Takagi-Sugeno-Kang fuzzy inference system (TSK-FIS) was obtained. This kind of model approximates non-linear systems by combining a set of functions local to fuzzy regions described by fuzzy rules. Such models have approximative power and are sufficiently descriptive. Starting from a set of input-output data, inputs relevant to Ψ st were automatically selected and fuzzy rules were identified based on the fuzzy clusters found in the data. The rule parameters were optimized by means of a neuro-fuzzy approach. The result was an accurate (86% variance explained) and simple model with five rules that considered soil water content at 0.3m depth, the day of the year and mean daily air temperature as input variables, confirming the suitability of such approach. In addition, a rule simplification method allowed a consistent agro-linguistic interpretation of the fuzzy sets of the rules: DRY, MOIST and WET for the soil water content, BLOOM, FRUIT GROWTH, EARLY POSTHARVEST and LATE POSTHARVEST for the day of the year, and COLD, MILD and WARM for mean daily air temperature.