Olive tree system in Mediterranean basin: a mid-term survey on C sequestration dynamics and modelling

The contribution that olive orchards can provide in climate mitigation should be more deeply analyzed given that these systems can stock large amount of C in woody compartments and soils. These systems could play a fundamental role especially over Mediterranean basin that is one of the most sensitive areas to climate change and where they are widely cultivated. However some issues are still open: what do we really know about the C-sequestration capacity provided by these systems? Can these really contribute to climate change mitigation both for current and future periods? In order to solve these questions, a mid-term study (3 years) was carried out at Follonica (Tuscany, central Italy), where an eddy covariance tower was installed over a typical rainfed olive orchard. Data from eddy covariance were then used for calibrating and validating two different models able at simulating C-exchange and biomass production from this system. Our work firstly allowed to assess the C-fluxes dynamics from this system and their relation with the main meteorological parameters and agricultural practices, thus indicating the magnitude of C-sequestration capacity offered by a typical Mediterranean olive orchard; as second the implementation of new tools that can be used for assessing the efficiency of mitigation strategies or to predict changes in mitigation capacity that these systems will probably encounter over the next decades.</br

Use of Sentinel-2 Derived Vegetation Indices for Estimating fPAR in Olive Groves

Olive tree cultivation is currently a dominant agriculture activity in the Mediterranean basin, where the increasing impact of climate change coupled with the inefficient management of olive groves is negatively affecting olive oil production and quality in some marginal areas. In this context, satellite imagery may help to monitor crop growth under different environmental conditions, thus providing useful information for optimizing olive grove management and final production. However, the spatial resolution of freely-available satellite products is not yet adequate to estimate plant biophysical parameters in complex agroecosystems such as olive groves, where both olive trees and grass cover contribute to the vegetation indices (VIs) signal at pixel scale. The aim of this study is therefore to test a disentangling procedure to partition the VIs signal among the different components of the agroecosystem to use this information for the monitoring of olive growth processes during the season. Specifically, five VIs (GEMI, MCARI2, NDVI, OSAVI, MCARI2/OSAVI) as recorded by Sentinel-2 at a spatial resolution of 10 m over five olive groves in the Montalbano area (Tuscany, Central Italy), were tested as a proxy for olive tree intercepted radiation. The olive tree volume per pixel was initially used to linearly rescale the VIs signal into the relevant value for the grass cover and olive trees. The models, describing the relationship between rescaled VIs and observed fraction of Photosynthetically Active Radiation (fPAR), were fitted and then validated against independent datasets. While in the calibration phase, a greater robustness at predicting fPAR was obtained using NDVI (r = 0.96 and RRMSE = 9.86), the validation results demonstrating that GEMI and MCARI2/OSAVI provided the highest performances (GEMI: r = 0.89 and RRMSE = 21.71; MCARI2/OSAVI: r = 0.87 and RRMSE = 25.50), in contrast to MCARI2 that provided the lowest (r = 0.67 and RRMSE = 36.78). These results may be related to the VIs’ intrinsic features (e.g., lower sensitivity to atmosphere and background effects), which make some of these indices, compared to others, less sensitive to saturation effects by improving fPAR estimation (e.g., GEMI vs. NDVI). On this basis, this study evidenced the need to improve the current methodologies to reduce inter-row effects and select appropriate VIs for fPAR estimation, especially in complex agroecosystems where inter-row grass growth may affect remote sensed-derived VIs signal at an inadequate pixel resolution

Monitoring the incidence of Xylella fastidiosa infection in olive orchards using ground-based evaluations, airborne imaging spectroscopy and Sentinel-2 time series through 3-D radiative transfer modelling

Outbreaks of Xylella fastidiosa (Xf) in Europe generate considerable economic and environmental damage, and this plant pest continues to spread. Detecting and monitoring the spatio-temporal dynamics of the disease symptoms caused by Xf at a large scale is key to curtailing its expansion and mitigating its impacts. Here, we combined 3-D radiative transfer modelling (3D-RTM), which accounts for the seasonal background variations, with passive optical satellite data to assess the spatio-temporal dynamics of Xf infections in olive orchards. We developed a 3D-RTM approach to predict Xf infection incidence in olive orchards, integrating airborne hyperspectral imagery and freely available Sentinel-2 satellite data with radiative transfer modelling and field observations. Sentinel-2A time series data collected over a two-year period were used to assess the temporal trends in Xf-infected olive orchards in the Apulia region of southern Italy. Hyperspectral images spanning the same two-year period were used for validation, along with field surveys; their high resolution also enabled the extraction of soil spectrum variations required by the 3D-RTM to account for canopy background effect. Temporal changes were validated with more than 3000 trees from 16 orchards covering a range of disease severity (DS) and disease incidence (DI) levels. Among the wide range of structural and physiological vegetation indices evaluated from Sentinel-2 imagery, the temporal variation of the Atmospherically Resistant Vegetation Index (ARVI) and Optimized Soil-Adjusted Vegetation Index (OSAVI) showed superior performance for DS and DI estimation (r2VALUES>0.7, p < 0.001). When seasonal understory changes were accounted for using modelling methods, the error of DI prediction was reduced 3-fold. Thus, we conclude that the retrieval of DI through model inversion and Sentinel-2 imagery can form the basis for operational vegetation damage monitoring worldwide. Our study highlight the value of interpreting temporal variations in model retrievals to detect anomalies in vegetation health.Data collection was partially supported by the European Union's Horizon 2020 research and innovation programme through grant agreements POnTE (635646) and XF-ACTORS (727987). A. Hornero was supported by research fellowship DTC GEO 29 “Detection of global photosynthesis and forest health from space” from the Science Doctoral Training Centre (Swansea University, UK). The authors would also like to thank QuantaLab-IAS-CSIC (Spain) for laboratory assistance and the support provided during the airborne campaigns and image processing. B. Landa, C. Camino, M. Montes-Borrego, M. Morelli, M. Saponari and L. Susca are acknowledged for their support during the field campaigns, as well as IPSP-CNR and Dipartimento di Scienze del Suolo (Università di Bari, Italy) as host institutions

Quantitative estimation of vegetation traits and temporal dynamics using 3-D radiative transfer models, high-resolution hyperspectral images and satellite imagery

Large-scale monitoring of vegetation dynamics by remote sensing is key to detecting early signs of vegetation decline. Spectral-based indicators of phys-iological plant traits (PTs) have the potential to quantify variations in pho-tosynthetic pigments, chlorophyll fluorescence emission, and structural changes of vegetation as a function of stress. However, the specific response of PTs to disease-induced decline in heterogeneous canopies remains largely unknown, which is critical for the early detection of irreversible damage at different scales. Four specific objectives were defined in this research: i) to assess the feasibility of modelling the incidence and severity of Phytophthora cinnamomi and Xylella fastidiosa based on PTs and biophysical properties of vegetation; ii) to assess non-visual early indicators, iii) to retrieve PT using radiative transfer models (RTM), high-resolution imagery and satellite observations; and iv) to establish the basis for scaling up PTs at different spatial resolutions using RTM for their retrieval in different vegetation co-vers. This thesis integrates different approaches combining field data, air- and space-borne imagery, and physical and empirical models that allow the retrieval of indicators and the evaluation of each component’s contribution to understanding temporal variations of disease-induced symptoms in heter-ogeneous canopies. Furthermore, the effects associated with the understory are introduced, showing not only their impact but also providing a compre-hensive model to account for it. Consequently, a new methodology has been established to detect vegetation health processes and the influence of biotic and abiotic factors, considering different components of the canopy and their impact on the aggregated signal. It is expected that, using the presented methods, existing remote sensors and future developments, the ability to detect and assess vegetation health globally will have a substantial impact not only on socio-economic factors, but also on the preservation of our eco-system as a whole

Assessing crop water requirements and irrigation scheduling at different spatial scales in Mediterranean orchards using models, proximal and remotely sensed data

Accurate estimations of crop water requirements are necessary to improve water use in agriculture and to optimize the use of available freshwater resource. To this aim, the Agro-Hydrological models represent useful tools to quantify the crop actual evapotranspiration. To define the upper boundary condition of the Agro-Hydrological models it is essential to assess the atmospheric water demand, expressed as crop reference evapotranspiration, ETo. In literature several methods, different in terms of input data requirement and climate variables combinations, have been developed to estimate ETo. Among these methods it is commonly used the well-known FAO56 Penman-Monteith (FAO56-PM) thermodynamic approach. Implementing this method requires access to climate data usually measured by ground weather stations. Unfortunately, these instruments are not always available, in this case recent climate reanalysis databases are useful solution to overcome this limitation. Direct measurements of actual evapotranspiration, ETa, are important to validate the results of the model’s application. These measurements, especially for large scale use, can be time consuming and economically expensive. Moreover, improper installation of the sensors or incorrect calibrations could cause outliers in time series or compromise the continuity of the data time series. Recently Machine Learning (ML) algorithm have been developed to predict and fill the gaps in time series of ETa. The joint use of Agro-Hydrological models with proximity and remotely sensed data is one of the possible ways to accurately estimate crop water requirements. The remote observations of the land surface represent a reliable strategy to identify the spatial distribution of vegetation biophysical parameters, such as, crop coefficient Kc under actual field conditions. The general objective of the research was to assess the crop water requirements in two typical crops (citrus and olive) of the Mediterranean region, using FAO56 Agro-Hydrological model based on functional relationships Kc(VIs) between crop coefficient, Kc, and Vegetation Indices (VIs) calibrate using in situ measurements and VIs obtained by multispectral remotely sensed data. Moreover, it was evaluated the reliability of the reanalysis climate variables provided by ERA5-Land database to assess ETo in Sicily (Italy). The performance of the ERA5-Land reanalysis weather data to estimate ETo, was assessed considering 39 ground weather station distributed in Sicily region. The ETo values estimated on the basis of climate variables from ERA5-L database encourage the use of reanalysis database to assess ETo. In general, the results were in agreement with those obtained from ground measurement, with average Root Mean Square Error (RMSE) equal to 0.73 mm d-1 and corresponding Mean Bias Error (MBE) equal to -0.36 mm d-1. The research activities were carried out in two experimental fields. The first experimental field is a citrus orchard located near the Villabate town whereas the second one was the irrigation district 1/A, managed by “Consorzio di Bonifica della Sicilia” ex “Consorzio di Bonifica Agrigento 3”, Castelvetrano, Sicily (Italy), characterized mainly by olives orchards. The time series of ETa, acquired by the Eddy Covariance (EC) tower installed in the citrus experimental field was processed using the Gaussian Process Regression (GPR) algorithm in order to fill the gaps. The performances were evaluated in terms of Nash Sutcliffe Efficiency (NSE) coefficient and RMSE. The values of NSE ranging between 0.74 and 0.88, whereas the RMSE values lower or equal to 0.55 mm d-1 confirm the suitability of the GPR model, to predict time ETa series. FAO56 Agro-Hydrological model was applied for the irrigation seasons 2018, 2019 and 2020 (Villabate) and for the irrigation seasons 2018 and 2019 (Castelvetrano). For each study areas, using VIs obtained from Sentinel-2 Multi Spectral Images (MSI) level 2A, a Kc(VIs) relationship was developed and then implemented in the model. The model was used to estimates spatial and temporal variability of the actual evapotranspiration, soil water content (SWC), in the root zone, crop coefficient and stress coefficient, as well as, to irrigation scheduling. For the citrus orchard a non-linear Kc(VIs) relationship was identified after assuming that the sum of two VIs, such as Normalized Difference Vegetation Index (NDVI), and Normalized Difference Water Index (NDWI), is suitable to represent the spatio-temporal dynamics of the investigated environment. The application of the FAO56 Agro-Hydrological model indicated that the estimated ETa was characterized by RMSE, and MBE, of 0.48 and -0.13 mm d−1 respectively, while the estimated SWC, were characterized by RMSE = 0.01 cm3 cm−3 and the absence of bias, then confirming that the suggested procedure can produce highly accurate results in terms of dynamics of SWC and ETa under the investigated field conditions. In the Castelvetrano irrigation district 1/A, a linear Kc(VI) relationship was identified following the Allen and Pereira (A&P) procedure which was based on the height of the canopy and the fraction of vegetation cover, the last was estimated by the NDVI. The differences between simulated and measured seasonal values was encouraging for the 2018, with value equal to 3%, while for the 2019 it was equal to 17%. These results highlight that the proposed model, with further improvements, and more accurate information such as the effective depth of root zone and the real volumes delivered by the hydrants, can be a useful tool for supporting the decision in the management of the irrigation demands in the irrigation district

Agricultural land systems : modelling past, present and future regional dynamics

This thesis arises from the understanding of how the integration of concepts, tools, techniques, and methods from geographic information science (GIS) can provide a formalised knowledge base for agricultural land systems in response to future agricultural and food system challenges. To that end, this thesis focuses on understanding the potential application of GIS-based approaches and available spatial data sources for modelling regional agricultural land-use and production dynamics in Portugal. The specific objectives of this thesis are addressed in seven chapters in Parts II through V, each corresponding to one scientific article that was either published or is being considered for publication in peer-reviewed international scientific journals. In Part II, Chapter 2 summarises the body of knowledge and provides the context for the contribution of this thesis within the scientific domain of agricultural land systems. In Part III, Chapters 3 and 4 explore remotely sensed and Volunteered Geographic Information (VGI) data, multitemporal and multisensory approaches, and a variety of statistical methods for mapping, quantifying, and assessing regional agricultural land dynamics in the Beja district. In Part IV, Chapters 5–7 explore the CA-Markov model, Markov chain model, machine learning, and model-agnostic approach, as well as a set of spatial metrics and statistical methods for modelling the factors and spatiotemporal changes of agricultural land use in the Beja district. In Part V, Chapter 8 explores an area-weighting GIS-based technique, a spatiotemporal data cube, and statistical methods to model the spatial distribution across time for regional agricultural production in Portugal. The case studies in the thesis contribute practical and theoretical knowledge by demonstrating the strengths and limitations of several GIS-based approaches. Together, the case studies demonstrate the underlying principles that underpin each approach in a way that allows us to infer their potentiality and appropriateness for modelling regional agricultural land-use and production dynamics, stimulating further research along this line. Generally, this thesis partly reflects the state-of-art of land-use modelling and contribute significantly to the introduction of advances in agricultural system modelling research and land-system science

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

Development of an Autonomous Aerial Toolset for Agricultural Applications

According to the United Nations, the world population is expected to grow from its current 7 billion to 9.7 billion by the year 2050. During this time, global food demand is also expected to increase by between 59% and 98% due to the population increase, accompanied by an increasing demand for protein due to a rising standard of living throughout developing countries. [1] Meeting this increase in required food production using present agricultural practices would necessitate a similar increase in farmland; a resource which does not exist in abundance. Therefore, in order to meet growing food demands, new methods will need to be developed to increase the efficiency of farming, thereby increasing yield from the present land. One way in which this problem can be solved is through the usage of autonomous aerial systems to scout for problems which could potentially affect the crop yield – such as nutrient deficiency, water stress, or diseases. Once located, this data can be used to determine the proper treatment for the field to alleviate the problem. Through this process, resources can be reduced to the required minimum, while problems affecting the crop yield will still be corrected, allowing greater production with a lower amount of resources. This project on the application of Unmanned Aerial Vehicles (UAV’s) to the field of agriculture consisted of two phases. First, a study was conducted on the required background to define the problem statement and what solutions were available for this application. This consisted of first defining the operations within agriculture where UAV’s could be used to increase efficiency, and then the sensors, hardware, and software these operations would require. The remainder of the project consisted of evaluating the tools which could be utilized to develop such a solution. Primarily, the project focused on software tools – programming software, simulation environments, and machine learning algorithms – which could be utilized by future students to develop a functional hardware and software toolchain for the research of autonomous systems for agricultural applications. After analyzing these development solutions, a set of tools was selected which showed promise in the creation of a functional solution. It was demonstrated that the core functions required for a UAV-based agricultural solution – navigation, perception, and feature detection – could be implemented within these systems, implying that they could be integrated into a full solution. As the tools were selected to ensure the developed algorithms would be transferable to physical platforms, this additionally supports a physical system could also be developed. The present work is part of the Autonomous Systems Lab which belongs to the WKU Center for Energy Systems. The author hopes that this project contributes to the advancement of the curriculum within the engineering department and serves as a foundation for future students developing autonomous systems, perception, and applied artificial intelligence at WKU

Innovation Issues in Water, Agriculture and Food

In a worldwide context of ever-growing competition for water and land, climate change, droughts and man-made water scarcity, and less-participatory water governance, agriculture faces the great challenge of producing enough food for a continually increasing population. In this line, this book provides a broad overview of innovation issues in the complex water–agriculture–food nexus, thus also relative to their interconnections and dependences. Issues refer to different spatial scales, from the field or the farm to the irrigation system or the river basin. Multidisciplinary approaches are used when analyzing the relationships between water, agriculture, and food security. The covered issues are quite diverse and include: innovation in crop evapotranspiration, crop coefficients and modeling; updates in research relative to crop water use and saving; irrigation scheduling and systems design; simulation models to support water and agricultural decisions; issues to cope with water scarcity and climate change; advances in water resource quality and sustainable uses; new tools for mapping and use of remote sensing information; and fostering a participative and inclusive governance of water for food security and population welfare. This book brings together a variety of contributions by leading international experts, professionals, and scholars in those diverse fields. It represents a major synthesis and state-of-the-art on various subjects, thus providing a valuable and updated resource for all researchers, professionals, policymakers, and post-graduate students interested in the complex world of the water–agriculture–food nexus