Low-intensity, diversified agricultural land use is needed to counteract the current decline in agrobiodiversity. Landscape ecology tools can support agrobiodiversity assessment efforts by investigating biodiversity-related ecological functions (pattern–process paradigm). In this study, we test a toolkit of landscape ecology analyses to compare different farm management models: poly-culture agroforestry (POLY) vs. conventional monoculture crop management (CV). Farm-scale anal-yses are applied on temperate alluvial sites (Po Plain, Northern Italy), as part of a broader multi-scale analytical approach. We analyze the landscape ecological quality through landscape matrix composition, patch shape complexity, diversity, metastability, and connectivity indices. We assess farm differences through multivariate analyses and t-tests and test a farm classification tool, namely, a scoring system based on the relative contributions of POLY farms, considering their deviation from a local CV baseline. The results showed a separate ecological behavior of the two models. The POLY model showed better performance, with significant positive contributions to the forest and semi-natural component equipment and diversity; agricultural component diversity, metastability; total farm diversity, metastability, connectivity, and circuitry. A reference matrix for the ecological inter-pretation of the results is provided. Farm classification provides a quick synthesis of such contribu-tions, facilitating farm comparisons. The methodology has a low cost and quickly provides infor-mation on ongoing ecological processes resulting from specific farm management practices; it is intended to complement field-scale assessments and could help to meet the need for a partially outcome-based assessment of good farm practice.
The Landscape Ecological Quality of Two Different Farm Management Models: Polyculture Agroforestry vs. Conventional / G. Chiaffarelli, N. Sgalippa, I. Vagge. - In: LAND. - ISSN 2073-445X. - 13:10(2024 Sep 30), pp. 1598.1-1598.28. [10.3390/land13101598]
The Landscape Ecological Quality of Two Different Farm Management Models: Polyculture Agroforestry vs. Conventional
G. Chiaffarelli
Primo
;N. SgalippaSecondo
;I. VaggeUltimo
2024
Abstract
Low-intensity, diversified agricultural land use is needed to counteract the current decline in agrobiodiversity. Landscape ecology tools can support agrobiodiversity assessment efforts by investigating biodiversity-related ecological functions (pattern–process paradigm). In this study, we test a toolkit of landscape ecology analyses to compare different farm management models: poly-culture agroforestry (POLY) vs. conventional monoculture crop management (CV). Farm-scale anal-yses are applied on temperate alluvial sites (Po Plain, Northern Italy), as part of a broader multi-scale analytical approach. We analyze the landscape ecological quality through landscape matrix composition, patch shape complexity, diversity, metastability, and connectivity indices. We assess farm differences through multivariate analyses and t-tests and test a farm classification tool, namely, a scoring system based on the relative contributions of POLY farms, considering their deviation from a local CV baseline. The results showed a separate ecological behavior of the two models. The POLY model showed better performance, with significant positive contributions to the forest and semi-natural component equipment and diversity; agricultural component diversity, metastability; total farm diversity, metastability, connectivity, and circuitry. A reference matrix for the ecological inter-pretation of the results is provided. Farm classification provides a quick synthesis of such contribu-tions, facilitating farm comparisons. The methodology has a low cost and quickly provides infor-mation on ongoing ecological processes resulting from specific farm management practices; it is intended to complement field-scale assessments and could help to meet the need for a partially outcome-based assessment of good farm practice.File | Dimensione | Formato | |
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