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Projeto de investigação
Modelo de otimização espacial do Uso do Solo Agrícola: Integração de Autómatos Celulares e algorítmos inteligentes na análise de dados quantitativos e qualitativos
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Agricultural land systems importance for supporting food security and sustainable development goals: a systematic review
Publication . Viana, Cláudia M.; Freire, Dulce; Abrantes, Patrícia; Rocha, Jorge; Pereira, Paulo
Agriculture provides the largest share of food supplies and ensures a critical number of ecosystem services (e.g., food provisioning). Therefore, agriculture is vital for food security and supports the Sustainable Development Goal (SDGs) 2 (SDG 2 - zero hunger) as others SDG's. Several studies have been published in different world areas with different research directions focused on increasing food and nutritional security from an agricultural land system perspective. The heterogeneity of the agricultural research studies calls for an interdisciplinary and comprehensive systematization of the different research directions and the plethora of approaches, scales of analysis, and reference data used. Thus, this work aims to systematically review the contributions of the different agricultural research studies by systematizing the main research fields and present a synthesis of the diversity and scope of research and knowledge. From an initial search of 1151 articles, 260 meet the criteria to be used in the review. Our analysis revealed that most articles were published between 2015 and 2019 (59%), and most of the case studies were carried out in Asia (36%) and Africa (20%). The number of studies carried out in the other continents was lower. In the last 30 years, most of the research was centred in six main research fields: land-use changes (28%), agricultural efficiency (27%), climate change (16%), farmer's motivation (12%), urban and peri-urban agriculture (11%), and land suitability (7%). Overall, the research fields identified are directly or indirectly linked to 11 of the 17 SDGs. There are essential differences in the number of articles among research fields, and future efforts are needed in the ones that are less represented to support food security and the SDGs.
Evaluating dominant land use/land cover changes and predicting future scenario in a rural region using a memoryless stochastic method
Publication . Viana, Cláudia M.; Rocha, Jorge
The present study used the o cial Portuguese land use/land cover (LULC) maps (Carta de
Uso e Ocupação do Solo, COS) from 1995, 2007, 2010, 2015, and 2018 to quantify, visualize, and predict
the spatiotemporal LULC transitions in the Beja district, a rural region in the southeast of Portugal,
which is experiencing marked landscape changes. Here, we computed the conventional transition
matrices for in-depth statistical analysis of the LULC changes that have occurred from 1995 to 2018,
providing supplementary statistics regarding the vulnerability of inter-class transitions by focusing
on the dominant signals of change. We also investigated how the LULC is going to move in the
future (2040) based on matrices of current states using the Discrete-Time Markov Chain (DTMC)
model. The results revealed that, between 1995 and 2018, about 28% of the Beja district landscape
changed. Particularly, croplands remain the predominant LULC class in more than half of the Beja
district (in 2018 about 64%). However, the behavior of the inter-class transitions was significantly
di erent between periods, and explicitly revealed that arable land, pastures, and forest were the most
dynamic LULC classes. Few dominant (systematic) signals of change during the 1995–2018 period
were observed, highlighting the transition of arable land to permanent crops (5%) and to pastures
(2.9%), and the transition of pastures to forest (3.5%) and to arable land (2.7%). Simulation results
showed that about 25% of the territory is predicted to experience major LULC changes from arable
land (3.81%), permanent crops (+2.93%), and forests (+2.60%) by 2040.
Agricultural land systems : modelling past, present and future regional dynamics
Publication . Viana, Cláudia M.; Rocha, Fernando Jorge Pedro da Silva Pinto da; Freire, Maria Dulce Alves; Abrantes, Patrícia Catarina dos Reis Macedo
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.
Evolution of agricultural production in Portugal during 1850–2018: a geographical and historical perspective
Publication . Viana, Cláudia; Freire, Dulce; Abrantes, Patrícia; Rocha, Jorge
Agricultural statistical data enable the detection and interpretation of the development of agriculture and the food supply situation over time, which is essential for food security evaluation in any country. Based on the historical agricultural statistics, this study produces a long spatial time-series with annual production values of three cereals relevant to global food security—wheat, maize, and rice, aiming to provide geographical and historical perspectives. Therefore, we reconstructed past and current production patterns and trends at the district level over 169 years, which supported a space–time cross-reading of the general characteristics of the regional agricultural production value distributions and relative densities in Portugal. Particularly, the production trends of wheat, maize, and rice showed three different situations: growth (maize), stability (rice), and decline (wheat). For decades, maize and wheat production alternated, depending on agricultural years and political aspects, such as the Wheat Campaign (1929–1938). The changes over time presented a pattern that, in the case of these three cereals, enabled a clear division of the country into major regions according to cereal production. Overall, maize and rice, both grown on irrigated croplands, presented a similar pattern in some regions of Portugal, mainly the central region. In this study, a preliminary analysis was presented and related to successive public policies; however, notably, there are more lessons to be learned from this long spatial time-series.
Climate change and its impacts on health, environment and economy
Publication . Rocha, Jorge; Oliveira, Sandra; Viana, Cláudia; Ribeiro, Ana Isabel
The welfare and stability of health systems depend on how they cope with
climatic changes (Anderson & Bows, 2011). Climate change denotes a
long-term change (normally for decades or longer) of the climate state that can
be acknowledged through statistical methods (e.g. variability and/or the average
of its properties) and have come to outline local, regional and global climates.
Climate change can be triggered by natural processes, or by continuous
anthropogenic modifications in land use/cover or in the atmosphere composition.
Changes witnessed in the last century are mainly motivated by human actions
(e.g. burning fossil fuel) that increased greenhouse gas concentration in
the atmosphere and lead to the increase of average surface temperature (Masson-
Delmotte et al., 2021; Overview: Weather, 2020).
Globally, an increase of 1°C in the average temperature since the beginning
of the 20th century, has resulted in additional risks (Haustein et al., 2017;
IPCC, 2018), such as emerging infectious diseases in areas unaffected before
(Legendre et al.; Watts et al., 2015), persistent drought and heatwaves, severe
storms and floods, and threats to food security.
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Fundação para a Ciência e a Tecnologia
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SFRH/BD/115497/2016