Abstract: Water and soil salinization are major constraints to agricultural productions because plant adaptation to hyperosmotic environments is generally associated to reduced growth and ultimately yield loss. Understanding the physiological/molecular mechanisms that link adaptation and growth is one of the greatest challenges in plant stress research since it would allow us to better define strategies to improve crop salt tolerance. In this study we attempted to establish a functional link between morphological and physiological traits in strawberry in order to identify margins to “uncouple” plant growth and stress adaptation. Two strawberry cultivars, Elsanta and Elsinore, were grown under 0, 10.20 and 40 mM NaCl. Upon salinization Elsanta plants maintained a larger and more functional leaf area compared to Elsinore plants, which were irreversibly damaged at 40 mM NaCl. The tolerance of Elsanta was correlated with a constitutive reduced transpirational flux due to low stomata! density (173 vs. 234 stomata mm(-2) in Elsanta and Elsinore, respectively), which turned out to be critical to pre-adapt plants to the oncoming stress. The reduced transpiration rate of Elsanta (14.7 g H2O plant(-1) h(-1)) respect to Elsinore (17.7 g H2O plant(-1) h(-1)) most likely delayed the accumulation of toxic ions into the leaves, preserved tissues dehydration and consented to adjust more effectively to the hyperosmotic environment. Although we cannot rule out the contribution of other physiological and molecular mechanisms to the relatively higher tolerance of Elsanta, here we demonstrate that low stomatal density may be beneficial for cultivars prescribed to be used in marginal environments in terms of salinity and/or drought. (C) 2012 Elsevier B.V. All rights reserved.

Abstract: An environmental dynamic system is usually modeled as a nonlinear system described by a set of nonlinear ODEs. A central challenge in computational modeling of environmental systems is the determination of the model parameters. In these cases, estimating these variables or parameters from other easily obtained measurements can be extremely useful. This work addresses the problem of monitoring and modeling a leaf area index and soil moisture model (LSM) using state estimation. The performances of various conventional and state-of-the-art state estimation techniques are compared when they are utilized to achieve this objective. These techniques include the extended Kalman filter (EKF), particle filter (PF), and the more recently developed technique variational filter (VF). Specifically, two comparative studies are performed. In the first comparative study, the state variables (the leaf-area index LAI, the volumetric water content of the soil layer 1, HUR1 and the volumetric water content of the soil layer 2, HUR2) are estimated from noisy measurements of these variables, and the various estimation techniques are compared by computing the estimation root mean square error (RMSE) with respect to the noise-free data. In the second comparative study, the state variables as well as the model parameters are simultaneously estimated. In this case, in addition to comparing the performances of the various state estimation techniques, the effect of number of estimated model parameters on the accuracy and convergence of these techniques are also assessed. The results of both comparative studies show that the PF provides a higher accuracy than the EKF, which is due to the limited ability of the EKF to handle highly nonlinear processes. The results also show that the VF provides a significant improvement over the PF because, unlike the PF which depends on the choice of sampling distribution used to estimate the posterior distribution, the VF yields an optimum choice of the sampling distribution, which also accounts for the observed data. The results of the second comparative study show that, for all techniques, estimating more model parameters affects the estimation accuracy as well as the convergence of the estimated states and parameters. However, the VF can still provide both convergence as well as accuracy related advantages over other estimation methods. (C) 2013 Elsevier B.V. All rights reserved.

Abstract: Uncertainty in weather conditions for the forthcoming growing season influences farmers’ decisions, based on their experience of the past climate, regarding the reduction of agricultural risk. Early within-season predictions of grain yield can represent a great opportunity for farmers to improve their management decisions and potentially increase yield and reduce potential risk. This study assessed 3 methods of within-season predictions of durum wheat yield at 10 sites across the Mediterranean Basin. To assess the value of within-season predictions, the model SiriusQuality2 was used to calculate wheat yields over a 9 yr period. Initially, the model was run with observed daily weather to obtain the reference yields. Then, yield predictions were calculated at a monthly time step, starting from 6 mo before harvest, by feeding the model with observed weather from the beginning of the growing season until a specific date and then with synthetic weather constructed using the 3 methods, historical, analogue or empirical, until the end of the growing season. The results showed that it is possible to predict durum wheat yield over the Mediterranean Basin with an accuracy of normalized root means squared error of <20%, from 5 to 6 mo earlier for the historical and empirical methods and 3 mo earlier for the analogue method. Overall, the historical method performed better than the others. Nonetheless, the analogue and empirical methods provided better estimations for low-yielding and high-yielding years, thus indicating great potential to provide more accurate predictions for years that deviate from average conditions.