Comparing Soil Compaction under Different Grazing Systems with a Virgin Forest Soil to Determine Optimal Stocking Rates

The understanding of how soil physical properties respond to differing grazing practices may help explain the main causes of pasture degradation. Soil compaction has been shown to be a main degradation form of soil and the knowledge of techniques to quantify and rectify this are necessary to maintain optimal yields. This research aims to measure the rupture lines of red yellow latossol under differing pasture grazing practices compared to cropping and a natural forest. With this information it is aimed to calculate the correction factor for stocking rates and traffic of tillage tools. The differing soil management practices examined was, pasture grazed by sheep, and dairy cattle, a maize crop in no tillage cover-crop system and a natural forest. To quantify the soil physical changes, the direct shear test was used, which calculated the resultant force of a load. The resultant forces of the natural forest were compared against pasture systems and crop system, and a correction factor for stocking rates was calculated. The samples of Red yellow Latossol were equilibrated in the matrix potential (ψ): -6 kPa. In the shear test, the normal stress used was the 450kPa. The correction factor (CF) indicates whether the soil has structural degradation compared to natural forest. Values less than 1 indicated soil degradation. The pastures grazed by sheep and dairy cattle had values observed to be less than 1, excessive loads at high soil moisture may be attributed to this soil structural deformation. For these systems, grazing management and stocking rates should be corrected. The correction factor gives an indication of the magnitude of management change that is required (i.e. the stocking rate decreased). The crop area was found to have no soil strength issues, using the stress test

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