Estudio de la evolución hidrogeológica de la masa de agua subterránea (MAS) "aluviales: Jarama-Tajuña" (030.007)

La masa de agua subterránea (MAS) Aluviales: Jarama-Tajuña es una de las más afectadas por la actividad antrópica de la Comunidad de Madrid. Impactos como la extracción de áridos del aluvial del río Jarama, los vertidos de aguas contaminadas procedentes de industrias y núcleos urbanos, así como los retornos de riego con concentraciones de pesticidas y fertilizantes son notables a lo largo de la zona superficial de la MAS. A esto está unido que las características hidrogeológicas del acuífero lo clasifican como de elevada vulnerabilidad con una zona no saturada altamente permeable y nivel freático a menos de 5m de profundidad.Máster Universitario en Hidrología y Gestión de Recursos Hídrico

Estudio de la evolución hidrogeológica de la masa de agua subterránea (MAS) "aluviales: Jarama-Tajuña" (030.007)

La masa de agua subterránea (MAS) Aluviales: Jarama-Tajuña es una de las más afectadas por la actividad antrópica de la Comunidad de Madrid. Impactos como la extracción de áridos del aluvial del río Jarama, los vertidos de aguas contaminadas procedentes de industrias y núcleos urbanos, así como los retornos de riego con concentraciones de pesticidas y fertilizantes son notables a lo largo de la zona superficial de la MAS. A esto está unido que las características hidrogeológicas del acuífero lo clasifican como de elevada vulnerabilidad con una zona no saturada altamente permeable y nivel freático a menos de 5m de profundidad.Máster Universitario en Hidrología y Gestión de Recursos Hídrico

The Combined Use of Remote Sensing and Wireless Sensor Network to Estimate Soil Moisture in Golf Course

This research was partially funded by project PDR18-XEROCESPED, under the PDR-CM 2014-2020, by the EU (European Agricultural Fund for Rural Development, EAFRD), Ministerio de Agricultura, Pesca y Alimentacion (MAPA) and Comunidad de Madrid regional government through IMIDRA, by the Programa Estatal de I+D+i Orientada a los Retos de la Sociedad, en el marco del Plan Estatal de Investigacion Cientifica y Tecnica y de Innovacion 20172020 (Project code: PID2020-114467RR-C33), and by Proyectos de innovacion de interes general por grupos operativos de la Asociacion Europea para la Innovacion en materia de productividad y sostenibilidad agricolas (AEI-Agri) in the framework Programa Nacional de Desarrollo Rural 20142020, GO TECNOGAR, and by Conselleria de Educacion, Cultura y Deporte, through Subvenciones para la contratacion de personal investigador en fase postdoctoral APOSTD/2019/04.Mauri, PV.; Parra, L.; Mostaza-Colado, D.; García-García, L.; Lloret, J.; Marin, JF. (2021). The Combined Use of Remote Sensing and Wireless Sensor Network to Estimate Soil Moisture in Golf Course. Applied Sciences. 11(24):1-17. https://doi.org/10.3390/app112411769117112

Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success

[EN] The use of drones in agriculture is becoming a valuable tool for crop monitoring. There are some critical moments for crop success; the establishment is one of those. In this paper, we present an initial approximation of a methodology that uses RGB images gathered from drones to evaluate the establishment success in legumes based on matrixes operations. Our aim is to provide a method that can be implemented in low-cost nodes with relatively low computational capacity. An index (B1/B2) is used for estimating the percentage of green biomass to evaluate the establishment success. In the study, we include three zones with different establishment success (high, regular, and low) and two species (chickpea and lentils). We evaluate data usability after applying aggregation techniques, which reduces the picture's size to improve long-term storage. We test cell sizes from 1 to 10 pixels. This technique is tested with images gathered in production fields with intercropping at 4, 8, and 12 m relative height to find the optimal aggregation for each flying height. Our results indicate that images captured at 4 m with a cell size of 5, at 8 m with a cell size of 3, and 12 m without aggregation can be used to determine the establishment success. Comparing the storage requirements, the combination that minimises the data size while maintaining its usability is the image at 8 m with a cell size of 3. Finally, we show the use of generated information with an artificial neural network to classify the data. The dataset was split into a training dataset and a verification dataset. The classification of the verification dataset offered 83% of the cases as well classified. The proposed tool can be used in the future to compare the establishment success of different legume varieties or species.This research and the contract of S.Y. were funded by project PDR18-XEROCESPED, under the PDR-CM 2014-2020, by the EU (European Agricultural Fund for Rural Development, EAFRD), Spanish Ministry of Agriculture, Fisheries and Food (MAPA) and Comunidad de Madrid regional government through IMIDRA and the contract of L.P. was funded by Conselleria de Educacion, Cultura y Deporte with the Subvenciones para la contratacion de personal investigador en fase postdoctoral, APOSTD/2019/04.Parra-Boronat, L.; Mostaza-Colado, D.; Yousfi, S.; Marin, JF.; Mauri, PV.; Lloret, J. (2021). Drone RGB Images as a Reliable Information Source to Determine Legumes Establishment Success. Drones. 5(3):1-18. https://doi.org/10.3390/drones5030079S1185

Seed and Straw Characterization of Nine New Varieties of <i>Camelina sativa</i> (L.) Crantz

Camelina sativa (L.) Crantz is a promising oilseed crop that has increased worldwide attention because of its agronomic characteristics and potential uses. From an agricultural point of view, this plant can grow in different environments, providing a good yield with low input requirements. In addition, camelina seeds contain a high percentage of oil (36–47%) and protein (24–31%), making them interesting for food or energy industries. Nevertheless, its cultivation is not widespread in Europe, particularly in Spain. In the present context of global change and the search for new sustainable crops, we are conducting two pilot projects aiming to confirm that camelina is a good option for oilseed crops in semi-arid climates (especially in central Spain, Madrid) and to find new profitable varieties for farmers. To reach our objective we have used nine new varieties, recently developed, to characterize and compare their seed oil content, and their seed and straw chemical composition. Finally, with our preliminary results, we determine which varieties present better properties to be used in future agricultural research or breeding programs. These results are part of a larger study that we are carrying out

Methodology to Differentiate Legume Species in Intercropping Agroecosystems Based on UAV with RGB Camera

Mixed crops are one of the fundamental pillars of agroecological practices. Row intercropping is one of the mixed cropping options based on the combination of two or more species to reduce their impacts. Nonetheless, from a monitoring perspective, the coexistence of different species with different characteristics complicates some processes, requiring a series of adaptations. This article presents the initial development of a procedure that differentiates between chickpea, lentil, and ervil in an intercropping agroecosystem. The images have been taken with a drone at the height of 12 and 16 m and include the three crops in the same photograph. The Vegetation Index and Soil Index are used and combined. After generating the index, aggregation techniques are used to minimize false positives and false negatives. Our results indicate that it is possible to differentiate between the three crops, with the difference between the chickpea and the other two legume species clearer than that between the lentil and the ervil in images gathered at 16 m. The accuracy of the proposed methodology is 95% for chickpea recognition, 86% for lentils, and 60% for ervil. This methodology can be adapted to be applied in other crop combinations to improve the detection of abnormal plant vigour in intercropping agroecosystems

Methodology to Differentiate Legume Species in Intercropping Agroecosystems Based on UAV with RGB Camera

Mixed crops are one of the fundamental pillars of agroecological practices. Row intercropping is one of the mixed cropping options based on the combination of two or more species to reduce their impacts. Nonetheless, from a monitoring perspective, the coexistence of different species with different characteristics complicates some processes, requiring a series of adaptations. This article presents the initial development of a procedure that differentiates between chickpea, lentil, and ervil in an intercropping agroecosystem. The images have been taken with a drone at the height of 12 and 16 m and include the three crops in the same photograph. The Vegetation Index and Soil Index are used and combined. After generating the index, aggregation techniques are used to minimize false positives and false negatives. Our results indicate that it is possible to differentiate between the three crops, with the difference between the chickpea and the other two legume species clearer than that between the lentil and the ervil in images gathered at 16 m. The accuracy of the proposed methodology is 95% for chickpea recognition, 86% for lentils, and 60% for ervil. This methodology can be adapted to be applied in other crop combinations to improve the detection of abnormal plant vigour in intercropping agroecosystems

A Forecast Model Applied to Monitor Crops Dynamics Using Vegetation Indices (NDVI)

Vegetation dynamics is very sensitive to environmental changes, particularly in arid zones where climate change is more prominent. Therefore, it is very important to investigate the response of this dynamics to those changes and understand its evolution according to different climatic factors. Remote sensing techniques provide an effective system to monitor vegetation dynamics on multiple scales using vegetation indices (VI), calculated from remote sensing reflectance measurements in the visible and infrared regions of the electromagnetic spectrum. In this study, we use the normalized difference vegetation index (NDVI), provided from the MOD13Q1 V006 at 250 m spatial resolution product derived from the MODIS sensor. NDVI is frequent in studies related to vegetation mapping, crop state indicator, biomass estimator, drought monitoring and evapotranspiration. In this paper, we use a combination of forecasts to perform time series models and predict NDVI time series derived from optical remote sensing data. The proposed ensemble is constructed using forecasting models based on time series analysis, such as Double Exponential Smoothing and autoregressive integrated moving average with explanatory variables for a better prediction performance. The method is validated using different maize plots and one olive plot. The results after combining different models show the positive influence of several weather measures, namely, temperature, precipitation, humidity and radiation

The Combined Use of Remote Sensing and Wireless Sensor Network to Estimate Soil Moisture in Golf Course

In gardening, particularly in golf courses, soil moisture management is critical for maximizing water efficiency. Remote sensing has been used to estimate soil moisture in recent years with relatively low accuracies. In this paper, we aim to use remote sensing and wireless sensor networks to generate soil moisture indexes for a golf course. In the golf course, we identified three types of soil, and data was gathered for three months. Mathematical models were obtained using data from Sentinel-2, bands with a resolution of 10 and 20 m, and sensed soil moisture. Models with acceptable accuracy were obtained only for one out of three soil types, the natural soil in which natural vegetation is grown. Two multiple regression models are presented with an R2 of 0.46 for bands at 10 m and 0.70 for bands at 20 m. Their mean absolute error was lower than 3% in both cases. For the modified soils, the greens, and the golf course fairway, it was not feasible to obtain regression models due to the temporal uniformity of the grass and the range of variation of soil moisture. The developed moisture indexes were compared with existing options. The attained accuracies improve the current models. The verification indicates that the model generated with band 4 and band 12 is the one with better accuracy

Evaluation of Biostimulatory Activity of Commercial Formulations on Three Varieties of Chickpea

Biostimulants are studied as a possible agricultural practice that anticipates the reproductive stages of chickpeas to avoid their coincidence with high temperatures and hydric stress periods. The effect of several types of biostimulants on different chickpea varieties was analyzed. The Blanco Sinaloa chickpea variety showed opposite patterns with respect to biostimulant effect on germination success and vegetative and radicular development when compared with two other chickpea varieties, namely Amelia, a well-known variety, and IMIDRA10, a recently developed variety. Blanco Sinaloa is cultured under water irrigation conditions, while Amelia and IMIDRA10 are used under rainfed conditions. Blanco Sinaloa and IMIDRA10 are Kabuli-type varieties, while Amelia is Desi-type. All varieties emerged 9 days after the sowing, but Amelia nascence was more abundant at the beginning, on day 9. On day 32, the picture was quite different, since Blanco Sinaloa had germinated 100% in practically all treatments, followed by Amelia and IMIDRA10. There were significant differences between plant lengths among the three varieties, since Blanco Sinaloa is much larger than Amelia and IMIDRA10. Blanco Sinaloa was the only variety in which the plant lengths of biostimulant-impregnated seeds were superior to those of untreated plants; that is, it was the only one that was positively affected by biostimulants. Chickpea seeds should be treated with biostimulants such that they are dry for sowing, because the mechanic seeder only works with dry seeds