The growing use of commercial unmanned aerial vehicles (UAV) and the need to adjust N fertilization rates in maize (Zea mays L.) currently constitute a key research issue. In this study, different multispectral vegetation indices (green-band and red-band based indices), SPAD and crop height (derived from a multispectral compact camera mounted on a UAV) were analysed to predict grain yield and determine whether an additional sidedress application of N fertilizer was required just before flowering. Seven different inorganic N rates (0, 100, 150, 200, 250, 300, 400 kg·N·ha⁻¹), two different pig slurry manure rates (Ps) (150 or 250 kg·N·ha⁻¹) and four different inorganic-organic N combinations (N100Ps150, N100Ps250, N200Ps150, N200Ps250) were applied to maize experimental plots. The spectral index that best explained final grain yield for the N treatments was the Wide Dynamic Range Vegetation Index (WDRVI). It identified a key threshold above/below 250–300 kg·N·ha⁻¹. WDRVI, NDVI and crop height showed no significant response to extra N application at the economic optimum rate of fertilization (239.8 kg·N·ha⁻¹), for which a grain yield of 16.12 Mg·ha⁻¹ was obtained. This demonstrates their potential as yield predictors at V12 stage. Finally, a ranking of different vegetation indices and crop height is proposed to overcome the uncertainty associated with basing decisions on a single index.

Precision Viticulture (PV) is a concept that is beginning to have an impact on the wine-growing sector. Its practical implementation is dependant on various technological developments: crop sensors and yield monitors, local and remote sensors, Global Positioning Systems (GPS), VRA (Variable-Rate Application) equipment and machinery, Geographic Information Systems (GIS) and systems for data analysis and interpretation. This paper reviews a number of research lines related to PV. These areas of research have focused on four very specific fields: 1) quantification and evaluation of within-field variability, 2) delineation of zones of differential treatment at parcel level, based on the analysis and interpretation of this variability, 3) development of Variable-Rate Technologies (VRT) and, finally, 4) evaluation of the opportunities for site-specific vineyard management. Research in these fields should allow winegrowers and enologists to know and understand why yield variability exists within the same parcel, what the causes of this variability are, how the yield and its quality are interrelated and, if spatial variability exists, whether site-specific vineyard management is justifiable on a technical and economic basis.

This study examined the structure and trends of climate parameters important to winegrape production from 1952 to 2006 in the Alt Penedès, Priorat, and Segrià regions of NE Spain. Average and extreme temperature and precipitation characteristics from 3 stations in the regions were organized into annual, growing season, and phenological growth stage periods and used to assess potential impacts on vineyard and wine quality, and changes in varietal suitability. Results show an overall growing season warming of 1.0 to 2.2°C, with significant increases in heat accumulation indices that are driven mostly by increases in maximum temperature (average Tmax, number of days with Tmax > 90th percentile, and number of days with Tmax > 30°C). Changes in many temperature parameters show moderate to strong relationships with vine and wine parameters in the 3 regions, including earlier phenological events concomitant with warmer growing seasons, higher wine quality with higher ripening diurnal temperature ranges, and reduced production in the warmest vintages. While trends in annual and growing season precipitation were not evident, precipitation during the bloom to véraison period declined significantly for all 3 sites, indicating potential soil moisture stress during this critical growth stage. Shorter-term analysis of crop evapotranspiration (ETc) reveals that the current impact per 1°C of growing season (Apr to Oct) warming is an increase in water demands in the region by 6 to 14%. These observations, combined with climate projections, indicate potential disruption of climate–variety balance, increasing water stress, and challenges in producing quality wines without the adoption of appropriate adaptive measures.This research was developed in the framework of the Project AGL2005-00091/AGR financed by the CICYT (Programa Nacional de Tecnologías y Recursos Agroalimentarios)

LiDAR sensors are widely used in many areas and, in recent years, that includes agricultural tasks. In this work, a self-developed mobile terrestrial laser scanner based on a 2D light detection and ranging (LiDAR) sensor was used to scan an intensive olive orchard, and different algorithms were developed to estimate canopy volume. Canopy volume estimations derived from LiDAR sensor readings were compared to conventional estimations used in fruticulture/horticulture research and the results prove that they are equivalent with coefficients of correlation ranging from r = 0.56 to r = 0.82 depending on the algorithms used. Additionally, tools related to analysis of point cloud data from the LiDAR-based system are proposed to extract further geometrical and structural information from tree row crop canopies to be offered to farmers and technical advisors as digital raster maps. Having high spatial resolution information on canopy geometry (i.e., height, width and volume) and on canopy structure (i.e., light penetrability, leafiness and porosity) may result in better orchard management decisions. Easily obtainable, reliable information on canopy geometry and structure may favour the development of decision support systems either for irrigation, fertilization or canopy management, as well as for variable rate application of agricultural inputs in the framework of precision fruticulture/horticulture.