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Matteo Zampieri

Publications and source records attributed to Matteo Zampieri.

3 recordsLinked to original sources

Earth Observation based multi-scale analysis of crop diversity in the European Union: first insights for agro-environmental policies

To understand the resilience of farms and the agricultural sector, as well as the provision of ecosystem services, we need to characterize and quantify crop diversity. Using a 10m resolution satellite-derived product, we created datasets of crop diversity across spatial and administrative scales for 27 EU countries and the UK in 2018. We define local crop diversity, or $\alpha$-diversity, at a 1km scale, corresponding to large or clusters of small-to-medium-sized farms. $\alpha$ crop diversities range from 2.3 to 4.4, with higher levels in systems with many small farms (averaging less than 10 ha). $\gamma$-diversity, the number and area of crops grown independently of location, increases from 2.85 at 1km to 3.86 at 10km, and levels off at 4.27 at 100km. These levels are higher than those reported in the U.S., possibly due to differences in farm structure and practices. $\beta$-diversity, the ratio of $\gamma$ and $\alpha$ diversities, measures the difference between agroecosystems and ranges from 1.2 to 2.3 across EU countries. We classify countries' crop diversities into four groups based on the magnitude and change of $\gamma$-diversity across scales, with implications for regional to national agro-environmental policy recommendations. Continental Copernicus crop type maps will enable temporal comparisons, and exploring ecosystem co-variates will deepen our understanding of the link between crop diversity and agro-ecosystem services.

cs.CE

Analysing the resilience of the European commodity production system with PyResPro, the Python Production Resilience package

This paper presents a Python object-oriented software and code to compute the annual production resilience indicator. The annual production resilience indicator can be applied to different anthropic and natural systems such as agricultural production, natural vegetation and water resources. Here, we show an example of resilience analysis of the economic values of the agricultural production in Europe. The analysis is conducted for individual time-series in order to estimate the resilience of a single commodity and to groups of time-series in order to estimate the overall resilience of diversified production systems composed of different crops and/or different countries. The proposed software is powerful and easy to use with publicly available datasets such as the one used in this study.

q-fin.GN

Estimating resilience of annual crop production systems: theory and limitations

Agricultural production is affected by climate extremes, which are increasing because of global warming. This motivates the need of a proper evaluation of the agricultural production systems resilience to enhance food security, market stability, and the general ability of society to cope with the effects of climate change. Resilience is generally assessed through holistic approaches involving a large number of indicators for the environmental, social and economic factors that influence food availability, access and utilization. Here, we investigate the problem of measuring resilience in a simplified framework, focusing on the crop production component of the agricultural system. For an idealized production system composed of a single crop, using the original definition of resilience, we identify the best combination of the mean and the variance of annual crop production data to estimate crop resilience i.e. the crop resilience indicator. Through numerical experiments conducted with a conceptual crop model, we show the general properties of this indicator applied to production systems for different levels of adaptation to climate variability and in case of increasing frequencies of extreme events. Finally, we discuss the applicability of the proposed approach to real agricultural production systems and the expected effects of crop diversity on the resilience of crop production systems, following directly from the mathematical definition of the crop resilience indicator.

q-bio.PE