Investigaciones Sobre Programación del Riego en Limonero y Olivo Mediante Medidas Directas y Continuas del Estado Hídrico.

En el presente trabajo se realiza una revisión del estado actual de conocimientos sobre aspectos relativos a la utilización de medidas continuas del estado hídrico del limonero y olivo para su utilización en la programación del riego, abarcando, esencialmente, aspectos tales como la sensibilidad de distintos indicadores del estado hídrico medidos de forma continua o discontinua en las plantas, su significado fisiológico, la obtención de niveles de referencia de los indicadores y primeros resultados sobre programación del riegoThe feasibility of irrigation scheduling in olive and lemon trees using continuously recorded plant-based water stress indicators was studied. For this, the characteristics of different plant-based water stress indicators (stem water potential, sap flow and trunk diameter fluctuations), their sensibility and limitations for water stress diagnosis purposes was discussed. The ways in which data can be interpreted, and protocols for irrigation scheduling was also described

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Principal component analysis as an efficient method for capturing multivariate brain signatures of complex disorders—ENIGMA study in people with bipolar disorders and obesity

Multivariate techniques better fit the anatomy of complex neuropsychiatric disorders which are characterized not by alterations in a single region, but rather by variations across distributed brain networks. Here, we used principal component analysis (PCA) to identify patterns of covariance across brain regions and relate them to clinical and demographic variables in a large generalizable dataset of individuals with bipolar disorders and controls. We then compared performance of PCA and clustering on identical sample to identify which methodology was better in capturing links between brain and clinical measures. Using data from the ENIGMA-BD working group, we investigated T1-weighted structural MRI data from 2436 participants with BD and healthy controls, and applied PCA to cortical thickness and surface area measures. We then studied the association of principal components with clinical and demographic variables using mixed regression models. We compared the PCA model with our prior clustering analyses of the same data and also tested it in a replication sample of 327 participants with BD or schizophrenia and healthy controls. The first principal component, which indexed a greater cortical thickness across all 68 cortical regions, was negatively associated with BD, BMI, antipsychotic medications, and age and was positively associated with Li treatment. PCA demonstrated superior goodness of fit to clustering when predicting diagnosis and BMI. Moreover, applying the PCA model to the replication sample yielded significant differences in cortical thickness between healthy controls and individuals with BD or schizophrenia. Cortical thickness in the same widespread regional network as determined by PCA was negatively associated with different clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. PCA outperformed clustering and provided an easy-to-use and interpret method to study multivariate associations between brain structure and system-level variables. Practitioner Points: In this study of 2770 Individuals, we confirmed that cortical thickness in widespread regional networks as determined by principal component analysis (PCA) was negatively associated with relevant clinical and demographic variables, including diagnosis, age, BMI, and treatment with antipsychotic medications or lithium. Significant associations of many different system-level variables with the same brain network suggest a lack of one-to-one mapping of individual clinical and demographic factors to specific patterns of brain changes. PCA outperformed clustering analysis in the same data set when predicting group or BMI, providing a superior method for studying multivariate associations between brain structure and system-level variables.</p

Influence of crop load on maximum daily trunk shrinkage reference equations for irrigation scheduling of early maturing peach trees

The effects of high crop load (unthinned trees, 22-23 fruits cm-2 of trunk cross-sectional area (TCSA)), commercial crop load (3-4 fruits cm-2 of TCSA), and no crop load (all fruitlets removed) on maximum daily trunk shrinkage (MDS), trunk growth rate (TGR) and stem water potential ([Psi]stem) were studied during the fruit growth period and 20 days following harvest in fully irrigated early maturing peach trees, Prunus persica (L.) Batsch, cv. Flordastar. Even though crop load did not affect plant water status, the MDS and TGR values increased and decreased, respectively, as a result of the crop load effect. In this sense, for the same [Psi]stem value, there was a linear increase in MDS with crop load, with a slope of 6.6[mu]mMPa-1 per unit of crop load increment. The effects of environmental conditions on daily MDS values were also dependent on crop load, suggesting that MDS reference values should be obtained by representing the relations between MDS and the climatic variables (daily mean air temperature, daily mean vapour pressure deficit and daily crop reference evapotranspiration) for a given crop load. The constancy of the relation between MDS and [Psi]stem across crop load underlined the constancy of the elastic properties of the bark tissues.Irrigation scheduling Reference equations Stem water potential Water relations

New approach for olive trees irrigation scheduling using trunk diameter sensors

Trunk diameter fluctuations (TDFs) have been suggested as an irrigation-scheduling tool for several fruit trees, but the works in olive trees has not obtained successful results with any of the indicators (maximum daily shrinkage (MDS) and trunk growth rate (TGR)) that are calculated from the daily TDF curves. No studies of olive trees have ever used reference trees to reduce the influence of the environment, as in work for other fruit trees. In this work, we compare different continuous and discrete water status measurements in a drought cycle. We suggest the calculation of a new and related indicator (DTGR), the difference between the TGR of stressed trees, and the TGR of reference trees. Negative DTGR values always indicate water stress conditions. The current work describes the variations of this new indicator (DTGR) in relation to water stress, and compares DTRG to the midday stem water potential, maximum leaf conductance and to the MDS. The midday stem water potential and the maximum leaf conductance describe the stress cycle clearer than the trunk diameter fluctuation indicators. No significant differences were found in the values of MDS between stressed and reference trees. On the other hand, the DTGR pattern values were near that of the stem water potential, though positive values were recorded in some dates during the water stress cycle. These variations indicate that DTGR is not a cumulative water stress indicators, as is water potential. Therefore, according to our data, water potential is a better indicator than the TDF parameters when no deficit irrigation scheduling is performed in olive trees. DTGR seems to be a good indicator of water stress from a threshold value around -1.4 MPa in olive trees. In addition, higher variability of DTGR than stem water potential may also be reduced with the increase in the number of sensors.Leaf conductance Maximum daily shrinkage Stem water potential Trunk diameter fluctuations Trunk growth rate Water relations

Could trunk diameter sensors be used in woody crops for irrigation scheduling? A review of current knowledge and future perspectives

The use of trunk diameter fluctuations and their derived parameters for irrigation scheduling in woody crops is reviewed. The strengths and weaknesses of these continuously measured plant-based water stress indicators compared with other discretely measured indicators for diagnosing plant water status in young and mature trees are discussed. Aspects such as sensor reading variability, signal intensity and the relationship between trunk diameter fluctuations and plant water status are analyzed in order to assess their usefulness as water stress indicators. The physiological significance of maximum and minimum daily trunk diameter and maximum daily trunk shrinkage (MDS) are also considered. Current knowledge of irrigation protocols and baselines for obtaining maximum daily trunk shrinkage reference values is discussed and new research objectives are proposed. We analyze the response of woody crops to continuous deficit irrigation scheduled by maintaining MDS signal intensity at threshold values to generate mild, moderate and severe water stress and assess the possibility of using linear variable displacement transducer (LVDT) sensors in trunk as a precision tool for regulated deficit irrigation scheduling. Finally, the possibility of using MDS signal intensity as a tool to match the irrigation regime to tree water requirements is also reviewed.Deficit irrigation Maximum daily trunk shrinkage Irrigation requirements Plant water relations Sensors Stem water potential Trunk growth rate