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Almond plantations are expanding worldwide, specifically in Spain; the new orchards are often designed under more intensive systems in comparison to the traditional rainfed orchards frequently found in the Mediterranean Sea basin. In these new areas, water is the main limiting factor, and therefore, the present research is aimed at quantitatively analyzing previous findings obtained in irrigation field trials carried out in Spain with mature almond trees. The goal was to derive applied water-production functions and compare sustained and regulated deficit irrigation strategies to provide robust information on the marginal water productivity and the preferred irrigation option to be applied under water scarcity conditions. This quantitative analysis reported a yield increase as water application increased, with the highest potential yield of about 2500 kg/ha achieved with around 1000 mm of irrigation water applied. Under severe water restrictions, similar responses were observed regardless of the deficit irrigation technique employed. In contrast, under moderate water stress, it seems more advantageous to apply a regulated deficit irrigation strategy rather than a sustained deficit strategy. The reported results are useful for deriving more sustainable irrigation protocols and highlight the need to optimize other inputs in addition to water to take full advantage of the irrigation intensification to be carried out in the new almond plantations. Published

In recent years, the area dedicated to modern irrigated almond plantations has increased significantly in Spain. However, the legal irrigation allocations are lower than the maximum water requirements of the crop in most cases. Therefore, almond growers are forced to implement regulated deficit irrigation strategies on their farms, applying water stress in certain resistant phenological periods and avoiding it in sensitive periods. Given the need to monitor the water status of the crop, especially in the most sensitive periods to water stress, the objective of this work was to evaluate the sensitivity of two UAV-based crop water status indicators to detect early water stress conditions in four almond cultivars. The field trial was conducted during 2020 in an experimental almond orchard, where two irrigation strategies were established: full irrigation (FI), which received 100% of irrigation requirements (IR), and regulated deficit irrigation (RDI), which received 70% of IR during the whole irrigation period except during the kernel-filling stage when received 40% IR. The UAV flights were performed on four selected dates of the irrigation season. The Crop Water Status Index (CWSI) and the Normalized Difference Vegetation Index (NDVI) were derived from thermal and multispectral images, respectively, and compared to classical water status indicators, i.e., stem water potential (Ψstem), stomatal conductance (gs), and photosynthetic rate (AN). Of the four flights performed, three corresponded to mild water stress conditions and a single flight was performed under moderate water stress conditions. Under mild water stress, CWSI was not able to capture the differences between FI and RDI trees that were observed with Ψstem. Under moderate stress conditions, CWSI was sensitive to the water deficit reached in the trees and showed significant differences among both irrigation treatments. No differences were observed in the CWSI and NVDI response to water stress among cultivars. Although NDVI and CWSI were sensitive to water stress, the low signal intensity observed in NDVI makes this index less robust than CWSI to monitor crop water stress. It can be concluded that UAV-based CWSI measurements are reliable to monitor almond water status, although for early (mild) levels of water stress, Ψstem seems to be the preferred option.

Vertical greening systems for buildings are becoming common in many cities. There are different options in the market but some types of living walls require the use of inorganic substrates as plants are grown in a hydroponics culture. In this study, three different substrates (expanded clay, perlite and pumice) are tested in six living wall modules (two per substrate) with two lighting conditions. The substrates were physically and chemically characterized and both water consumption and plant development (green cover, flowering, biomass production and photosynthetic activity) were monitored in two different tests. The three substrates showed to be suitable for living walls and most of the differences observed were due to the lighting conditions. Among the three substrates assessed, pumice exhibited a slightly better performance in terms of green cover and biomass production.

Remote and non-destructive estimation of leaf area index (LAI) has been a challenge in the last few decades as the direct and indirect methods available are laborious and time-consuming. The recent emergence of high-throughput plant phenotyping platforms has increased the need to develop new phenotyping tools for better decision-making by breeders. In this paper, a novel model based on artificial intelligence algorithms and nadir-view red green blue (RGB) images taken from a terrestrial high throughput phenotyping platform is presented. The model mixes numerical data collected in a wheat breeding field and visual features extracted from the images to make rapid and accurate LAI estimations. Model-based LAI estimations were validated against LAI measurements determined non-destructively using an allometric relationship obtained in this study. The model performance was also compared with LAI estimates obtained by other classical indirect methods based on bottom-up hemispherical images and gaps fraction theory. Model-based LAI estimations were highly correlated with ground-truth LAI. The model performance was slightly better than that of the hemispherical image-based method, which tended to underestimate LAI. These results show the great potential of the developed model for near real-time LAI estimation, which can be further improved in the future by increasing the dataset used to train the model.

Irrigation plays a very important role in a Mediterranean garden. In spite of this, there are not many studies assessing irrigation water management of landscapes. Moreover, historic gardens represent a special challenge due to their unique characteristics. The aim of this work is the characterization and evaluation of water management in a historic garden. For that, the gardens of The Real Alcazar of Seville were used as a case study. They comprise a total of 20 gardens of different styles with a total area of nearly 7 ha. Landscape water requirements and irrigation volume applied were estimated and used in conjunction with other descriptive and financial variables to calculate 6 performance indicators. Only 20% of gardens showed adequate irrigation in the spring-autumn period, being 10% during summer. However, the two well-watered gardens represent 30% of the total irrigated area. Management, operation and maintenance costs are 0.63 €·m−2 representing 0.58 € per volume of irrigation water used (m−3). Results obtained support the need of improving irrigation management. For that, simple solutions such as installing metering devices, calculating actual water requirements or optimizing irrigation schedules can be implemented. Other more complex actions such as modifying the irrigation network or creating hydrozones might also be explored.

The use of reliable information and data that are rapidly and easily acquired is essential for farm water management and appropriate irrigation strategies. Over the past decade, new advances have been made in irrigation technology, such as platforms that continuously transmit data between irrigation controllers and field sensors, mobile apps, and equipment for variable rate irrigation. In this study, images captured with a thermal imaging camera mounted on an unmanned aerial vehicle (UAV) were used to evaluate the water status of sugar beet plants in a plot with large spatial variability in terms of soil properties. The results were compared with those of soil moisture measurements. No direct relationship was observed between the water status of the soil and that of the crops. However, the fresh root mass and sugar content tended to decrease when higher levels of water stress were detected in the crop using thermal imaging, with coefficients of determination of 0.28 and 0.94 for fresh root mass and sugar content, respectively. Differences were observed between different soil types, and therefore different irrigation strategies are needed in highly heterogeneous plots.

15 páginas.- 9 figuras.- 4 tablas.- 67 referencias, In most fruit tree orchards under localised irrigation, only a fraction of the rhizosphere is wetted by irrigation. As this may reduce growth and production, such that there is a demand for information on the optimum number and location of emitters. In a mature ‘Arbequina’ olive orchard with 1667 trees ha-1, we analysed the impact of applying the same amount of water with one vs. two drip irrigation pipes (laterals) per tree row. We had trees under full irrigation, for which irrigation supplies amounted to 80–100% of the crop water needs (ETc), and two deficit irrigation treatments supplying ca. 40% of ETc. We used the HYDRUS 2D/3D (v. 1.11) model to simulate the shape and volume of the fraction of the rhizosphere wetted by irrigation (irrigation bulbs), as well as drainage and soil evaporation. For both full irrigation and deficit irrigation, differences in the soil volume and the soil surface wetted by irrigation were not sufficient to affect drainage and soil evaporation. The greatest values of root length density and root surface per unit volume were found in trees under deficit irrigation with two laterals, and the lowest in fully irrigated trees with one lateral. This, however, was counterbalanced by both the greater size of the irrigation bulbs and the greater soil water contents in the bulbs of the fully irrigated trees, such that water uptake was not enhanced by the second lateral. For 2 consecutive years we found no impact of irrigating with one or two laterals per tree row on water potential and stomatal conductance, shoot and fruit growth, leaf area and production, either for full irrigation or deficit irrigation conditions. Therefore, our findings suggest no benefits of irrigating super high density olive orchards with two laterals instead of one., This work was funded by the Spanish Ministry of Science and Innovation (research project AGL2015–71585-R.).

Canopy temperature has been proposed as a relevant variable for crop water stress monitoring. Since crop temperature is highly influenced by the prevailing climatic conditions, it is usually normalized with indices such as the crop water stress index (CWSI). The index requires the use of two baselines that relate canopy temperature under maximum stress and non-water stress conditions with vapor pressure deficit (VPD). These reference baselines are specific to each crop and climatic region. In maize, they have been extensively studied for certain climatic regions but very little is known on their suitability to be used under Mediterranean-type conditions nor their temporal stability, both diurnally and between seasons. Thus, the objective of this work was to determine the reference baselines for maize grown under Mediterranean conditions, as well as its diurnal and long-term stability. An experiment was conducted for 3 years in a maize breeding field, under well-watered and water-stressed irrigation treatments. The determined reference baselines for computing CWSI in maize have shown to be stable in the long term but markedly influenced by the meteorological variations between 10–17 h UTC (Coordinated Universal Time). These results indicate that several reference baselines should be used for CWSI computing throughout the abovementioned time interval. The CWSI values calculated for well-watered and water-stressed maize breeding plots using the reference baselines derived in this study were successfully correlated with other physiological indicators of plant water stress.

12 páginas.-- 10 figuras.-- 3 tablas.-- 43 referencias, Characterization of the spatio-temporal variability of tree water status is a prerequisite to conducting precise irrigation management in fruit tree orchards. This study assessed the suitability of a crop water stress index (CWSI) derived from high-resolution aerial thermal imagery for estimating tree water status variability in super high density (SHD) olive orchards. The experiment was conducted at a commercial SHD olive orchard near Seville (southwestern Spain), with drip irrigated trees under three irrigation treatments (four plots per treatment in a randomized block design): a full irrigation treatment to replace the crop water needs (ETc) and two regulated deficit irrigation treatments to replace ca. 45% of ETc. Meteorological variables, soil moisture content, leaf water potential, stem water potential and leaf gas exchange measurements were performed along the irrigation season. Infrared temperature sensors (IRTs) installed approximately 1 m above the canopies were used to derive the required Non-Water-Stressed Baselines (NWSBs) for CWSI calculation. NWSBs were not common during the growing season, although the seasonal effect could be partly explained with solar angle variations. A thermal camera installed on a mini Remotely Piloted Aircraft System (RPAS) allowed for the recording of high-resolution thermal images on 5 representative dates during the irrigation season. The CWSI values derived from aerial thermal imagery were sensitive to the imposed variations in tree water status within the SHD olive orchard. Among the recorded variables, maximum stomatal conductance showed the tightest correlation with CWSI. We concluded that high-resolution thermal imagery captured from a mini RPAS is a suitable tool for defining tree water status variability within SHD olive orchards, This work was funded by the Spanish Ministry of Economy and Competitiveness (research project AGL2012-34544, predoctoral contract grant BES-2013-065380) and by the Junta de Andalucía (research project P12-AGR-1227).

Quantification of soil respiration (R s ) is an essential step in modelling soil carbon fluxes associated with stand structure and management. This study aims to characterise the spatial variations of R s at tree scale in adult (AO) and young (YO) drip-irrigated orange orchards, with special focus on abiotic (soil moisture, θ s ) and biotic (stand age and root traits) factors determining the variability of R s . R s , soil temperature (T s ), θ s , soil organic carbon content (OC) and root traits were sampled along three transects parallel to the drip-line. Measurements were carried out in autumn, with mean T s over the sampling area close to 18 °C. Heterotrophic respiration (R h ) was determined in undisturbed non-rooted soil, and root-derived (or rhizosphere) respiration (R r ) was estimated from the difference R s − R h . Root distribution and root density were closely correlated with θ s . Fine-root density appeared to play a major role in the control of R s , explaining a great part of the spatial variability of R s . The ratio R h /R s , estimated by means of a simple partitioning model, appeared to decrease with stand age (~ 0.68 and ~ 0.32 in YO and AO, respectively), indicating the predominance of R h in the young orchard and of R r in the old one. The study highlights that stand structure plays a determinant role in driving the spatial variability of R s and its partitioning between heterotrophic and root-derived respiration in irrigated orchards.

The water management of golf courses is an issue of concern in drought-prone areas that demand irrigation strategies aimed at reducing turfgrass water consumption. Deficit irrigation and su...

Estimar con exactitud la cosecha de un cultivo representa una información muy relevante para agricultores y cooperativas/agentes encargados de gestionar y vender el producto. De esta estimación depende la organización y logística necesarias para la recolección, planificación del almacenaje, stock y abastecimiento de los mercados. Actualmente la estimación de la cosecha se realiza en campo con personal experimentado realizando una inspección visual y en base a datos históricos, proceso que tiene riesgo de presentar errores humanos. Las redes neuronales convolucionales (CNN) basadas en el Deep Learning (DL), constituyen una herramienta prometedora para hacer estimaciones de rendimiento basadas en el reconocimiento y conteo de frutos. El objetivo del este trabajo ha sido crear un modelo basado en CNNs a partir de una arquitectura de red existente y entrenada para contar el número de frutos y estimar la producción de la parcela. Una vez entrenados los modelos, se testaron sobre imágenes tomadas con un dron multirrotor sobre 20 árboles seleccionados al azar de una parcela de cítricos Citrus sinensis (L.) cv. Navelina. Durante las tres campañas anuales en las que se han realizado las estimaciones, el error medio absoluto obtenido con DL fue entre 4-6% y el del técnico especialista en el aforo entorno al 8-11% frente a la producción real de la parcela. Estos resultados vislumbran un gran potencial de la metodología para la predicción del rendimiento de árboles de cítricos.

Los mejoradores vegetales demandan gran cantidad de información de los cultivos que les ayude a tomar decisiones a la hora de seleccionar variedades. El fenotipado de alto rendimiento tanto en plataformas terrestres como áreas, que permite la evaluación precisa con sensores remotos de las respuestas a estreses abióticos y bióticos de los cultivos, han emergido como una herramienta prometedora. Entre la gran cantidad de parámetros fisiológicos y estructurales que se pueden monitorear o estimar a nivel de planta, el Índice de Área Foliar (IAF) es un parámetro biofísico del cultivo que tiene gran importancia agronómica y es un indicador de la capacidad fotosintética del cultivo, estando estrechamente relacionado con la producción final. Su valor, se puede obtener a partir de dividir el área de las hojas de un cultivo expresado en m2 y el área de suelo sobre el cual se encuentra establecido. Para el cálculo del IAF, tradicionalmente se han utilizado métodos directos (destructivos) e indirectos (no destructivos). Los métodos directos, además de ser destructivos requieren mucho tiempo y son costosos. Es por lo que el IAF se suele estimar más frecuentemente mediante métodos indirectos (fotografías hemisféricas). Sin embargo, a pesar de que los métodos indirectos son bastante precisos, estos necesitan un post-procesamiento que consume mucho tiempo. El desarrollo de redes neuronales basadas en aprendizaje profundo o Deep Learning (DL), un subconjunto del aprendizaje automático o Machine Learning (ML), y concretamente las redes convolucionales (CNN o ConvNet) se presentan como alternativa para hacer estimaciones de IAF. Este tipo de redes permiten transferir lo aprendido por la red en un escenario en el que dispone de datasets o conjuntos datos de gran tamaño a otros escenarios con menores datos con el propósito de resolver un problema concreto, técnica conocida como transfer learning. El objetivo del presente trabajo ha sido crear un modelo basado en CNNs a partir de una arquitectura de red existente y entrenada para estimar el IAF a partir de imágenes RGB. Para crear los datasets necesarios para entrenar el modelo, se tomaron fotografías desde un trípode a una altura de 1 m sobre un ensayo de trigo de 10 variedades con tres repeticiones durante 9 fechas distintas. Al mismo tiempo, se realizaron medidas no destructivas, utilizando una relación alométrica, para calcular el IAF de cada unidad experimental. Una vez entrenado el modelo se testeó sobre uno de los días de ensayo. Los resultados preliminares muestran que el uso de DL es una herramienta prometedora para el cálculo del IAF.

La monitorizacion cercana requiere de equipamiento montado a bordo de vehiculos agricolas, implementos o cualquier plataforma que permitan obtener rasgos agronomicamente relevantes. Las plataformas moviles para el fenotipado de caracteristicas biofisicas de los cultivos permiten obtener una alta repetibilidad de las mediciones, al tiempo de no ser invasivas sobre las labores normales de cultivo. De la misma forma que el diseno de estas plataformas esta condicionado por la variedad de sensores que deben alojar, debera ser adecuado al tipo de cultivo del que se pretende obtener informacion precisa, y que su uso no requiera de una intensiva participacion humana. Los objetivos se centraron en (i) desarrollar una plataforma movil electrica, sencilla y modular, disenada para alojar una gran variedad de sensores que permitan la caracterizacion fenotipica de cultivos, y (ii) probar su rendimiento en campo, la reproducibilidad de los resultados y su capacidad para obtener mediciones sobre el volumen de copa de los naranjos de una parcela comercial. Para este segundo objetivo, y tras unas primeras comprobaciones en laboratorio sobre aspectos como la velocidad, la carga optima, y la estabilidad, se llevaron a cabo pruebas de campo empleando un sensor LiDAR escaneando de forma lateral y una camara de profundidad Kinect de Microsoft. En este trabajo se presentan los primeros resultados obtenidos del desarrollo tecnico de la plataforma para la caracterizacion electronica del volumen de copa de citricos, con los que se pretende en el futuro generar informacion de alto valor para el agricultor que le permitan ajustar aspectos como las aplicaciones de agroquimicos o labores como la poda.

Reduced stomatal conductance (gs) during soil drought in angiosperms may result from effects of leaf turgor on stomata and/or factors that do not directly depend on leaf turgor, including root-derived abscisic acid (ABA) signals. To quantify the roles of leaf turgor-mediated and leaf turgor-independent mechanisms in gs decline during drought, we measured drought responses of gs and water relations in three woody species (almond, grapevine and olive) under a range of conditions designed to generate independent variation in leaf and root turgor, including diurnal variation in evaporative demand and changes in plant hydraulic conductance and leaf osmotic pressure. We then applied these data to a process-based gs model and used a novel method to partition observed declines in gs during drought into contributions from each parameter in the model. Soil drought reduced gs by 63–84% across species, and the model reproduced these changes well (r2 = 0.91, P

The crop water stress index (CWSI) is usually used to normalize crop temperature for efficient irrigation management. For CWSI computation, the temperature of a non-transpiring crop (Tdry) and that of a crop transpiring at its potential rate (Twet) are required. Alternatively, the use of artificial reference surfaces (ARS) that mimic Tdry and Twet has been proposed. However, to date, little effort has been made on the development of such surfaces, and there is still much uncertainty concerning what is measured. The aim of this research was to design, develop and evaluate the feasibility of new artificial reference surfaces for real time CWSI computation in maize. Hemispherical cellulose paper-based surfaces were constructed using three shades of green. The paper-based hemispheric surfaces were placed in a 3D-printed plastic structure that allows water storage and the placement of an upward-looking infrared thermometer at the bottom of the ARS. An experimental array comprising 18 randomly located ARS was designed, of which 9 were wet ARS (3 replicates per colour) and 9 were dry ARS (3 replicates per colour). The prototypic ARS were able to accurately determine Tdry and Twet in maize canopies. The CWSI values determined using the ARS developed in this research were validated in a commercial maize breeding trial against leaf gas exchange variables. The shade of green that provided the best results in both cases was the RGB colour R32, G66, B34. While Tdry could be obtained directly from a dry ARS temperature, a solar radiation-based transformation is required to determine Twet from the wet ARS temperature.

Accurate and early estimation of citrus yields is important for both producers and agricultural cooperatives to be competitive and make informed decisions when selling their products. Yield estimation is key for predicting stock volumes, avoiding stock ruptures and planning harvesting operations. Visual yield estimations have traditionally been employed, resulting in inaccurate and misleading information. The main goal of this study was to develop an automated image processing methodology to detect, count and estimate the size of citrus fruits on individual trees using deep learning techniques. During 3 consecutive annual campaigns, a total of 20 trees from a commercial citrus grove were monitored using images captured from an unmanned aerial vehicle (UAV). These trees were harvested manually, and fruit sizes were measured. A Faster R-CNN Deep Learning model was trained using a custom dataset to detect oranges in the obtained images. An average standard error (SE) of 6.59 % was obtained between visual counting and the model’s fruit detection. Using the detected fruits, fruit size estimation was also performed. The promising results obtained indicate that this size estimation method can be employed for size discrimination prior to harvest. A model based on Long Short-term Memory (LSTM) was trained for yield estimation per tree and for a total yield estimation. The actual and estimated yields per tree were compared, resulting in an approximate error of SE = 4.53 % and a standard deviation of SD = 0.97 Kg. The actual total yield, the estimated total yield and the total yield estimated by an expert technician were compared. The error in the estimation by the technician was SE = 13.74 %, while the errors in the model were SE = 7.22 % and SD = 4083.58 Kg. These promising results demonstrate the potential of the present technique to provide yield estimates for citrus fruits or even other types of fruit.

The use of high-throughput phenotyping systems in crop research offers a powerful alternative to traditional methods for understanding plant behaviours. These systems provide a rapid, consistent, repeatable, non-destructive and objective sampling method to quantify complex and previously unobtainable traits at relatively fine resolutions. In this study, a field-based high-throughput phenotyping solution for wheat was developed using a sensor suite mounted on a self-propelled electric platform. A 2D LiDAR was used to scan wheat plots from overhead, while an odometry system was used as a local navigation system to determine the precise plot/plant/scan location. Accurate 3D models of the scanned wheat plots were reconstructed based on the recorded LiDAR and odometry data. Seven plots of different wheat cultivars were scanned to calculate the canopy height using LiDAR data, and these results were compared with manual ground truth measurements. Additionally, in each of these seven plots, the NDVI and PRI spectral indices were calculated using low-cost spectral reflectance sensors (SRSs) and an expensive visible/near-infrared (VIS/NIR) spectral analysis system used for reference purposes. The results of the validation showed good agreement between the LiDAR and manual wheat plant height measurements with an R2 of 0.73 and RMSE = 2.63 cm for three days of campaign measurements. A statistically significant linear correlation was observed between the NDVI values obtained with the reference spectrometer and the low-cost SRS; the coefficients of determination were R2 = 0.69 for day 1 and R2 = 0.81 for day 2, suggesting a similar degree of accuracy among both sensing systems. The developed platform and the obtained wheat phenotyping results demonstrated the suitability of the system for acquiring reliable data under field conditions while maintaining a constant low speed and stability during field deployment. The adaptability of the platform to the structure of the crop and the repeatability of data collection throughout the growing season make the system suitable for integration into commercial breeding programmes.

Both biotic and abiotic factors are involved in the seasonal variability of ecosystem respiration (Re) and its aboveground (Rag) and belowground (Rs) components. Knowledge of these factors is crutial to predict the respiration processes of structurally-distinct orchards under varying environmental conditions. This paper aims to characterize those factors in and across adult (AO) and young (YO) drip-irrigated citrus orchards over a 2-year period. Two methods for estimating Re were used and compared. In the first one (C-method) Re was calculated as the sum of Rag and Rs components, with each being estimated using organ-specific and soil respiration models previously validated and calibrated from chamber-based respiration and biometric canopy measurements. The second method was based on the determination of nighttime Re from data of early morning Net Ecosystem Exchange (NEE-method) provided by eddy covariance sensors located above the canopy. Estimates of Re by the two methods compared reasonably well. The C-method indicated that Rs was the predominant component of Re in both orchards, with a main peak in early spring (ratio Rs/Re ∼0.75 and 0.65 in AO and YO, respectively) during the period with no fruit load and minimum Rag. Data obtained with the NEE-method were used to test the performance of functional relationships between Re and abiotic factors (i.e., temperature through a Q10 function, and soil water content). It was found that a model only based on abiotic factors was unable to explain the differences in Re across sites; whereas accounting for canopy productivity and structure by introducing the leaf area index (LAI) as an additional driving variable notably increased the predictive power of the models in describing changes in Re, both seasonally and across sites. The study suggested that biotic factors explained a large part of the differences in Re between orchards, and that LAI could be an appropriate driving variable for predicting the impact of tree age and structural heterogeneity on Re.

16 páginas.-- 5 figuras.-- 2 tablas.-- 46 referencias.-- Comunicación presentada en el VIII International Olive Symposium. Split, (Croatia), October 10-14, 2016.-- Fernández Luque, José Enrique et al..., This work focuses on the suitability of precise irrigation (PI) in hedgerow olive orchards with high plant density. The success of PI relies on a wise choice of irrigation system, the irrigation strategy and irrigation scheduling method, as well as the production target. After 6 years of testing of different irrigation strategies and a variety of methods to schedule irrigation in an 'Arbequina' olive orchard with 1667 trees ha-1, we address here the potential of PI for this type of orchard in an area of southern Spain where water for irrigation is scarce. We conclude that PI is a suitable option for fruit tree orchards nowadays, thanks to increasing knowledge on the crop response to water conditions and to technological advances that increase the applicability of PI to commercial orchards. Our results suggest careful choice of the production target, and illustrate the importance of a detailed economic analysis to properly decide on the main aspects related to PI., This work was made in the frame of the research projects AGL2009-11310/AGR (Spanish Ministry of Science and innovation), AGL2012-34544 (Spanish Ministry of Economy and Competitiveness) and AGR-6456-2010 (Junta de Andalucía), and with funds by the FEDER programme. Thanks to the owners of Internacional Olivarera, S.A.U. (Interoliva), for allowing us to make the experiments in the Sanabria orchard. We also thank Silvia Seller, agronomist, and Juan Francisco Bernabé, foreman, for their technical assistance. The author I. Garcia-Tejero has a contract co-financed by the Operational Programme of the European Social Fund (ESF) 2007-2013 "Andalusía is moving with Europe".

Irrigated agriculture is crucial for rural development, especially in semiarid areas with few alternative economic opportunities. Climatic conditions in southern Europe are characterized by the scarcity and irregularity of rainfall. Moreover, the latest forecasts in terms of water requirements for irrigation anticipate an imbalance between the irrigation-water demand and the available resources. In irrigated areas, this situation will require the introduction of alternative crops able to maximize the profitability of agroecosystems as well as innovative strategies to improve the agricultural water management. The almond tree is grown under highly variable conditions (from rain-fed to fully irrigated), evidencing its drought tolerance. This chapter offers an overview concerning the present and future of this crop in areas characterized by water scarcity, the implementation of different deficit-irrigation strategies, and tools to assess the crop water status under water-stress situations, providing different options and approaches to solve current as well as future constraints.

Urban development is causing environmental and social concerns that are compromising human welfare and the sustainability of cities. New urban greening concepts are being developed to mitigate these problems in a sustainable and natural way. Vertical greening systems can be defined as structures that allow vegetation to spread over a building facade or interior wall. These systems are becoming popular though they are still evolving and more knowledge on some of their particular impacts is required. In the last five years, the number of studies published in the scientific literature on this topic, especially involving living walls, has significantly increased. This scientific interest has corresponded with an increased and parallel attention by the general public. This work offers a broad description of the different systems and a comprehensive review of the particular benefits of these green infrastructures. Knowledge gaps and shortcomings have also been identified and discussed.

Living walls (LW) are vertical greening systems that are becoming popular due to their multiple social and environmental benefits. When LW are installed indoors, a lightening system is often required to ensure an appropriate plant development. This work assesses the performance of three artificial lighting systems on six indoor LW [0.7 m (wide) × 0.7 m (high)] placed at two distances from the light source. The plant species selected for the tests were Soleirolia soleirolii and Spathiphyllum wallisii, which are frequently used in indoor LW. Three different lamps were used in the experiment: incandescent (IL), fluorescent (FL) and metal halide (MHL) lamps, all of them with an input electric power of ≈250 W. Differences in plant growth were only observed when the LW were close to the light source (about 1 m) but not at greater distances (≈1.5 m). IL had the poorest performance. Despite the lower photosynthetic photon flux density efficiency of FL compared with MHL, FL light enabled plants placed in the upper LW (closer to light source) reached similar size to those grown under MHL. Plant quality attributes were generally not affected by light type or the distance to light source. IL and FL generated higher total water losses (i.e. transpiration plus evaporation) than MHL on a LW basis. When expressed per unit of LW area covered by vegetation, FL and MHL reduced water consumption by 34% and 56%, respectively, as compared to IL. Overall, our results indicate that both FL and MHL outperform IL and have a similar ornamental performance, whereas MHL are more advantageous than FL in terms of water consumption and annual cost.

15 páginas.-- 9 figuras.-- 6 tablas.-- 72 referencias, A study was conducted in a hedgerow olive orchard (SW Spain) to assess the capability of the HYDRUS 2D/3D model in predicting seasonal soil water dynamics in a well-watered (full irrigation, FI) and two regulated deficit irrigated plots (60RDI and 30RDI) differing in the timing and level of water stress imposed. The simulated soil water balance components were used to assess the suitability of the irrigation management accomplished in the experimental orchard and that of the irrigation treatments for different soil types and management scenarios. Soil water content (θ) was measured in all four plots per irrigation treatment with two access tubes per plot installed at 0.1 m and 0.4 m away from the dripper. Comparison of simulated against observed θ showed mean absolute errors ranging from 0.03 to 0.045 cm3 cm−3, root mean square errors from 0.035 cm3 cm−3 to 0.056 cm3 cm−3 and Nash–Sutcliffe efficiency coefficients from 0.438 to 0.834 across all treatments and probe locations. The modelled soil water balance components showed that drainage water losses represented 9–12% only of the applied irrigation water across all irrigation treatments. Scenario analysis revealed that daytime irrigation led to higher drainage water losses in FI than nighttime irrigation. For the same irrigation volumes, rootzone soil pressure head decreased (i.e. became more negative) when the irrigation frequency of the RDI treatments increased, thus supporting the lower irrigation frequencies scheduled in RDI as compared to FI in the experimental orchard. Scenario analysis also revealed the importance of adjusting irrigation schedules to soil type, irrespective of whether FI or RDI treatments were to be implemented., This work was funded by the Spanish Ministry of Innovation and Science (project AGL2009-11310/AGR) and by the Junta de Andalucía (research project AGR-6456).

Reduced stomatal conductance (gs ) during soil drought in angiosperms may result from effects of leaf turgor on stomata and/or factors that do not directly depend on leaf turgor, including root-derived abscisic acid (ABA) signals. To quantify the roles of leaf turgor-mediated and leaf turgor-independent mechanisms in gs decline during drought, we measured drought responses of gs and water relations in three woody species (almond, grapevine and olive) under a range of conditions designed to generate independent variation in leaf and root turgor, including diurnal variation in evaporative demand and changes in plant hydraulic conductance and leaf osmotic pressure. We then applied these data to a process-based gs model and used a novel method to partition observed declines in gs during drought into contributions from each parameter in the model. Soil drought reduced gs by 63-84% across species, and the model reproduced these changes well (r(2) = 0.91, P 0.0001, n = 44) despite having only a single fitted parameter. Our analysis concluded that responses mediated by leaf turgor could explain over 87% of the observed decline in gs across species, adding to a growing body of evidence that challenges the root ABA-centric model of stomatal responses to drought.

Para un manejo adecuado del riego es necesario tener información disponible de una manera rápida y sencilla. Gracias a los avances tecnológicos, esto es posible. En este trabajo se han utilizado imágenes captadas mediante una cámara térmica montada sobre un vehículo aéreo para evaluar el estado hídrico de un cultivo de remolacha azucarera en una parcela con una gran variabilidad espacial en las propiedades del suelo. Los resultados se han comparado con mediciones puntuales de la humedad del suelo. No se ha percibido una relación directa entre el estado hídrico del suelo y del cultivo. Por otro lado, sí se advierte una tendencia decreciente en la producción en relación al estrés hídrico detectado en el cultivo gracias a las imágenes térmicas. Se observan claramente las diferencias entre los distintos tipos de suelo, de modo que queda patente la conveniencia de llevar a cabo estrategias de riego diferenciales en función de la distribución de los mismos, especialmente en parcelas muy heterogéneas.

Living walls are systems that allow the development of vegetation in a vertical surface attached to building facades or indoor walls. Traditionally, they have behaved as ‘passive’ bio-filters, but new approaches and technologies are moving towards their integration within the building's air conditioning and ventilation systems. In an Active Living Wall (ALW), air is forced to pass through the vegetated wall to take advantage of their evaporative cooling potential as well as the capacity of these biological systems to purify air. In the case of indoor ALWs, air is cooled, bio-filtered and humidified thus potentially reducing ventilation requirements. This work describes a prototypic indoor ALWs installed at the University of Seville (Spain). Preliminary results of its performance on indoor air conditions (temperature and humidity) are presented and discussed. Drops in temperature between 0.8 and 4.8 °C have been observed at different distances from the ALW. The cooling process was more efficient when the initial conditions of the room were drier and warmer.

This study assesses the long-term suitability of regulated (RDI) and sustained deficit irrigation (SDI) implemented over the first six growing seasons of an almond [Prunus dulcis (Mill.) D.A. Webb] orchard grown in a semiarid area in SE Spain. Four irrigation treatments were assessed: (i) full irrigation (FI), irrigated to satisfy maximum crop evapotranspiration (100% ETc); (ii) RDI, as FI but receiving 40% ETc during kernel-filling; (iii) mild-to-moderate SDI (SDImm), irrigated at 75–60% ETc over the entire growing season; and (iv) moderate-to-severe SDI (SDIms), irrigated at 60–30% ETc over the whole season. Application of water stress from orchard establishment did not amplify the negative effects of deficit irrigation on almond yield. Irrigation water productivity (IWP) increased proportionally to the mean relative water shortage. SDIms increased IWP by 92.5%, reduced yield by 29% and applied 63% less irrigation water. RDI and SDImm showed similar productive performances, but RDI was more efficient than SDImm to increase fruiting density and production efficiency (PE). We conclude that SDIms appears to be a promising DI option for arid regions with severe water scarcity, whereas for less water-scarce areas RDI and SDImm behaved similarly, except for the ability of RDI to more severely restrict vegetative development while increasing PE.

Coupled photosynthesis–stomatal conductance (A–gs) models are commonly used in ecosystem models to represent the exchange rate of CO2 and H2O between vegetation and the atmosphere. The ways these models account for water stress differ greatly among modelling schemes. This study provides insight into the impact of contrasting model configurations of water stress on the simulated leaf-level values of net photosynthesis (A), stomatal conductance (gs), the functional relationship among them and their ratio, the intrinsic water use efficiency (A/gs), as soil dries. A simple, yet versatile, normalized soil moisture dependent function was used to account for the effects of water stress on gs, on mesophyll conductance (gm) and on the biochemical capacity. Model output was compared to leaf-level values obtained from the literature. The sensitivity analyses emphasized the necessity to combine both stomatal and non-stomatal limitations of A in coupled A–gs models to accurately capture the observed functional relationships A vs. gs and A/gsvs. gs in response to drought. Accounting for water stress in coupled A–gs models by imposing either stomatal or biochemical limitations of A, as commonly practiced in most ecosystem models, failed to reproduce the observed functional relationship between key leaf gas exchange attributes. A quantitative limitation analysis revealed that the general pattern of C3 photosynthetic response to water stress may be well represented in coupled A–gs models by imposing the highest limitation strength to gm, then to gs and finally to the biochemical capacity.

Publication Inra prise en compte dans l'analyse bibliométrique des publications scientifiques mondiales sur les Fruits, les Légumes et la Pomme de terre. Période 2000-2012. http://prodinra.inra.fr/record/256699; International audience; In this paper, QualiTree, a fruit tree model designed to study the management of fruit quality, and developed and described in a companion paper (Lescourret et al. in Trees Struct Funct, 2010), was combined with a simple light-interception sub-model, and then parameterised and tested on peach in different situations. Simulation outputs displayed fairly good agreement with the observed data concerning mean fruit and vegetative growth. The variability over time of fruit and vegetative growth was well predicted. QualiTree was able to reproduce the observed response of trees to heterogeneous thinning treatments in terms of fruit growth. A sensitivity analysis showed that the average seasonal growth rates of the different organs were sensitive to changes to the values of their respective initial relative growth rates and that stem wood was the tree organ the most affected by a change in the initial relative growth rates of other organs. QualiTree was able to react to simulated scenarios that combined thinning and pest attacks. As expected, thinning intensity and the percentage damage caused by pests significantly affected fruit yield and quality traits at harvest. These simulations showed that QualiTree could be a useful tool to design innovative horticultural practices

We investigated the long-term effects of different deficit irrigation (DI) options on tree growth, shoot and leaf attributes, yield determinants and water productivity of almond trees (Prunus dulcis, cv. Marta) grown in a semiarid climate in SE Spain. Three partial root-zone drying (PRD) irrigation treatments encompassing a wide range of water restriction (30%, 50% and 70% of full crop requirements, ETc) and a regulated deficit irrigation treatment (RDI, at 50% ETc during kernel-filling) were compared over three consecutive growth seasons (2004�2006) to full irrigation (FI). The results showed that all deficit irrigation treatments have a negative impact on trunk growth parameters. The magnitude of the reduction in trunk growth rate was strongly correlated through a linear relationship with the annual volume of water applied (WA) per tree. Similarly, a significant relationship was found between WA and the increase in crown volume. In contrast, leaf-related attributes and some yield-related parameters (e.g., kernel fraction) were not significantly affected by the irrigation treatments. Except in PRD70, individual kernel weight was significantly reduced in the deficit irrigated treatments. Kernel yield, expressed in percent of the maximum yield observed in the FI treatment, showed a linear decrease with decreasing WA and a slope of 0.43, which implies that a 1% decrease in water application would lead to a reduction of 0.43% in yield. Water productivity increased drastically with the reduction of water application, reaching 123% in the case of PRD30. Overall, our results demonstrate the prevalence of direct and strong links between the intensity of the water restriction under PRD � i.e., the total water supply during the growing season � and the main parameters related to tree growth, yield and water productivity. Noteworthy, the treatments that received similar annual water volumes under contrasted deficit irrigation strategies (i.e., PRD70 and RDI) presented a similar tree performance.

Quantifying sap fluxes and ABA concentrations of xylem saps collected from different parts of the root system may assist in modelling leaf xylem ABA concentration ([X-ABA](leaf)) of plants grown with heterogeneous soil moisture distribution. To investigate the contribution of different parts of the root system to total sap flow and [X-ABA](leaf), individual shoots were grafted onto the root systems of two plants grown in two separate compartments and sap flow sensors placed on each hypocotyl below the graft union. Two different irrigation regimes were imposed: conventional deficit irrigation (DI) where both pots received the same volumes of water, and partial rootzone drying (PRD) where only one pot (designated "wet") was watered and other (designated "dry") dried the substrate. In plants grown in 3 L pots that were vertically divided into two watertight compartments, soil water content (theta) and sap flow were continuously monitored. In plants grown in 2 appressed 0.43 L pots, xylem sap was collected independently from each root system (after severing the shoot below the graft union) to measure root xylem ABA concentration. During PRD, sap flow from roots in drying soil decreased: this relationship was described by a threshold theta(dry) (at which sap flow from roots in drying soil started to decrease) and sensitivity (the rate of decline in sap flow). The threshold theta(dry) varied with both theta(wet) and soil volume. As theta(wet) increased, the threshold theta(dry) increased but the sensitivity of sap flow to theta(dry) was independent of theta(wet). At a similar theta(wet), as soil volume increased, the threshold theta(dry) decreased. Root xylem ABA concentration increased as theta declined in both DI and PRD plants. A simple model that weighted the ABA contributions of wet and dry root systems to [X-ABA](leaf) according to the sap flow from each, showed that with moderate soil drying, simulated [X-ABA](leaf) was higher under PRD than DI.