For Tunisian olive tree orchards, nitrogen deficiency is an important nutritional problem, in addition to the availability of water. Establishment of relationships between nutrients such as nitrogen and ecophysiological parameters is a promising method to manage fertilisation at orchard level. Therefore, a nitrogen stress experiment with one-year-old olive trees (Olea europaea L. 'Koroneiki' and 'Meski') was conducted with trees respectively subjected to four nitrogen supply regimes (23.96 meq l-1, 9.58 meq l-1, 4.79 meq l-1 and 0 meq l-1 NO3-).
The current paper focuses on the use of the SPAD-502 portable chlorophyll (Chl) meter, a nondestructive method for fertilisation management under nitrogen stress conditions of olive trees. Maximum net photosynthetic assimilation rates, chlorophyll fluorescence parameters and the SPAD Chl index were therefore measured simultaneously and the Chl and nitrogen content of the leaves were analysed. Significant correlations were established in the olive tree leaves between SPAD-502 readings on the one hand and Chl content, nitrogen content, photosynthetic assimilation rate, and Chl fluorescence parameters (ΦPSII and ETR) on the other hand. and O. Boussadia ... [et al.].
Leaf area of a plant is essential to understand the interaction between plant growth and environment. This useful variable can be determined by using direct (some expensive instruments) and indirect (prediction models) methods. Leaf area of a plant can be predicted by accurate and simple leaf area models without damaging the plant, thus, provide researchers with many advantages in horticultural experiments. Several leaf-area prediction models have been produced for some plant species in optimum conditions, but not for a plant grown under stress conditions. This study was conducted to develop leaf area estimation models by using linear measurements such as lamina length and width by multiple regression analysis for green pepper grown under different stress conditions. For this purpose, two experiments were conducted in a greenhouse. The first experiment focused to determine leaf area of green pepper grown under six different levels of irrigation water salinity (0.65, 2.0, 3.0, 4.0, 5.0, and 7.0 dS m-1) and the other under four different irrigation regime (amount of applied water was 1.43, 1.0, 0.75, and 0.50 times of required water). In addition to general models for each experiment, prediction models of green pepper for each treatment of irrigation water salinity and of irrigation regime experiments were obtained. Validations of the models for both experiments were realized by using the measurements belong to leaf samples allocated for validation purposes. As a result, the determined equations can simply and readily be used in prediction of leaf area of green pepper grown under salinity and water stress conditions. The use of such models enable researchers to measure leaf area on the same plants during the plant growth period and, at the same time, may reduce variability in experiments. and B. Cemek ... [et al.].
Nondestructive approach of modeling leaf area could be useful for plant growth estimation especially when number of available plants is limited and/or experiment demands repeated estimation of leaf area over a time scale. A total of 1,280 leaves were selected randomly from eight different morphotypes of som (Persea bombycina) established at randomized complete block design under recommended cultural regimes in field. Maximum leaf laminar width (B), length (L) and their squares B2, L2; leaf area (LA), and lamina length × width (L×B) were determined over two successive seasons. Leaf parameters were significantly affected by morphotypes; but seasons had nonsignificant impacts on tested features. Therefore, pooled seasonal morphotype means of each parameter were used to establish relationship with LA. L and its square L2 did not provide accurate models for LA predictions. Considerably better models were obtained by using B (y = 2.984 + 7.9664 x, R2 = 0.615, P≥0.001, n = 119) and B2 (y = 12.784+ 0.9604 x, R2 = 0.605, P≥0.001, n = 119) as independent variables. However, maximum accuracy of prediction of LA could be achieved through a simple linear relationship of L×B (y = 8.2203 + 0.4224 x, R2 = 0.843, P≥0.0001, n = 119). The model (LA:L×B) was validated with randomly selected leaf samples (n = 360) of som morphotypes and highly significant (P≤0.001) linear function was found between actual and predicted LAs. Therefore, the last model may consider adequate to predict leaf area of all cultivars of som with sufficient fidelity. and S. Chattopadhyay ... [et al.].