Contribution of Pueraria phaseoloides L. in the cycling of macronutrients in oil palm plantations

O uso de culturas de cobertura tornou-se uma prática comum em plantações de dendê, garantindo maior sustentabilidade para os agroecosistemas, aumentando o ciclismo de nutrientes e proporcionando maior economia no uso de fertilizantes minerais. Nesse sentido, o objetivo deste estudo foi avaliar o acúmulo de macronutrientes (N, P, K, Ca, Mg e S) em plantas tropicais kudzu (Pueraria phaseoloides L.) em plantações de dendê. O experimento foi realizado no município de Tailândia, Pará, utilizando um projeto experimental completamente randomizado, quatro replicações e sete tratamentos (idades de dendê). O acúmulo de macronutrientes foi obtido através do produto de concentrações de nutrientes e a atéria seca da antena parte dos vivos, mortos e cobertura total de kudzu tropical. A ordem de acumulação total de nutrientes cicluidos por leguminosas foi ca, N, K, Mg, P e S. Tropical kudzu, como um cobrir planta, contribui para o ciclismo de nutrientes em plantações de dendê, mas esse benefício diminuiu com a era das palmeiras.The use of cover crops has become a common practice in oil palm plantations, ensuring greater sustainability to agroecosystems, increasing nutrient cycling and providing greater savings in the use of mineral fertilizers. In this sense, the objective of this study was to evaluate the accumulation of macronutrients (N, P, K, Ca, Mg and S) in tropical kudzu plants (Pueraria phaseoloides L.) in oil palm plantations. The experiment was carried out in the municipality of Tailândia, Pará State, using a completely randomized experimental design, four replications and seven treatments (ages of oil palm). The accumulation of macronutrients was obtained through the product of nutrient concentrations and the dry matter of the aerial part of the living, dead and total cover of tropical kudzu. The order of total accumulation of cycled nutrients by legumes was Ca, N, K, Mg, P and S. Tropical kudzu cultivation, as a cover plant, contributes to nutrient cycling in oil palm plantations, but this benefit decreased with the age of palm oil trees

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost