The design of a Wireless Sensor Network (WSN) to monitor microclimatic variability was optimized. The design fitted to specific conditions of a vineyard located in Albacete. One of the key tasks was the development of an Information System integrating all the collected data. Results pointed out the validation and easy applications of these technologies in the farmer's decision making processes, and the establishment of tactical design recommendations. This paper shows the results of a research project carried out in a vineyard of Castilla-La Mancha, Spain, where an experimental WSN was set up. The nodes can communicate with a gateway unit, which can transmit the information to other computers via Internet. The results achieved in this project could help farmers to use this technology in modern grapevine growing. One key milestone was the development of a computer-based information system adapted to the grapevine grower requirements enabling easy data analysis. A better choice of grapes, leading to better wines, is the first step that wine-producers should consider.

This paper deals with how to set up and use a Geographic Information System to help decision-making aiming at improving the use of land growing vines, integrating information derived from different sources: infrastructures, environment, fanning area distribution, crop yields, weather parameters, geomorphology, land use, hydrology, and social and economic indicators. Valdepenas is a "Designation of Origin "area of Castilla-La Mancha, Spain, in which wine is granted with a genetic name given to a specific agricultural food product which meets certain requirements as to its origin, the way it is processed and its quality. The growing area spreads over 30000 ha in 10 municipal districts. There are 70 wineries, 34 of which bottle wine. The altitude is 700 similar to 800 in above sea level and the climate continental semiarid. The system was laid for a monitoring program through the analysis of the distribution, production and outcome of time rates related to agronomy, economy and social affairs. Once the GIS ready, grapevine growing areas were preliminary ranged according to physiography, fanning capability and grape variety which best fit to the different agricultural situations. This paper is part of the research work that is being developed by initiative of the vine growers of the region.

The importance of rapid, nondestructive, and accurate measurements of leaf area (LA) in agronomic and physiological studies is well known, but a search of the literature revealed little information available for grape ( Vitis vinifera L.). The results described herein include a comparison of 12 different mathematical models for estimating leaf area in `Cencibel'. The simplest, most accurate regression equations were: LA i = 0.587 LW ( R 2 = 0.987) and LA i = 0.588 LW ( R 2 = 0.994), where LA i is leaf area measured using image analysis and LW is leaf length × maximum width. Use of maximum width (W), leaf length (L), petiole length (L p ), and dry weight of leaves (DML) as single variables in the regression equations were not as closely associated with total leaf area, although their R 2 values were also highly significant.

The relevance of growing vines under semiarid conditions is universally accepted because of its impacts on social, economic and environmental aspects. Improving the knowledge of the soil–plant–atmosphere system related to the expression of vine growth allows the study of vine cover in wide areas. Several aspects of vine growing under semiarid conditions, related to weather, soil, and plant cover are analysed in this paper. Once the ground truth is achieved, multitemporal studies by remote sensing are especially useful for vine growth monitoring. The purpose of this work is focussed on determining changes of vine cover development according to available water resources in relation to present remote sensing methods. The method is based on using multitemporal masking classification techniques based on the ground truth knowledge achieved during previous research.

A method that permits determination of actual evapotranspiration, ET, in heterET=ETm-B(T-s-T-sm),which combines meteorological, National Oceanic and Atmospheric Administration advanced very high resolution radiometer (NOAA-AVHRR), and Landsat thematic mapper (TM) data. Thus, the maximum evapotranspiration for each crop, ETm, is obtained from in situ measurements carried out in a meteorological station; the temperature difference between each pixel and the pixel that has the maximum evapotranspiration, T-s-T-sm, is calculated for each crop from NOAA-AVHRR data; and the crop distribution in the area is known through a Landsat TM image. The semi-empirical coefficient B is derived for each crop in two ways: (1) experimentally and (2) by the identification of crop structure parameters. We have applied the proposed method to the Barrax region, Albacete, whose main crops, maize (Zea mays L.) and barley (Ordeum vulgare L.), cover a 10 km x 10 km area. With Penman's method as the standard, a method validation was conducted and an acceptable precision was obtained (+/-0.8 mm day(-1) and +/-1.0 mm day(-1) for barley and maize, respectively). Finally, actual evapotranspiration images are supplied. (C) Elsevier Science Inc., 1998.

During June 1991 more than 30 scientific teams worked in Castilla-La Mancha, Spain, studying the energy and water transfer processes between soil, vegetation and the atmosphere in semi-arid conditions within the co-ordinated European research project EFEDA (European Field Experiment in Desertification-threatened Areas). Measurements were made from the microscale (e.g. measurements on single plants) up to a scale compatible with the grid size of global models. For this purpose three sites were selected 70 km apart and heavily instrumented at a scale in the order of 30 km2. Aircraft missions, satellite data and movable equipment were deployed to provide a bridge to the larger scale. This paper gives a description of the experimental design along with some of the preliminary results of this successful experiment.