Sugarcane Straw Blanket Management Effects on Plant Growth, Development, and Yield in Southeastern Brazil

In Brazilian sugarcane (Saccharum spp.) production systems, the practice of moving harvesting residue from row to inter-row positions (i.e., raking) has increased in response to producer concerns over the potential negative effects of sugarcane straw on crop establishment and stalk yield. Despite increasing adoption among sugarcane farmers, the impacts of straw raking practices on plant growth and yield remain unclear. A 2-yr experiment that included both dry and wet seasons was conducted at two sites in southeastern Brazil to evaluate straw management strategy effects on plant tillering, phytomass accumulation, plant nutritional status, and stalk yield. The experiments were established at the Bom Retiro mill and the Univalem mill. Experimental treatments included raking straw to inter-rows (raked), total straw removal (bare soil), and no straw removal (straw cover). Raked and bare soil treatments improved plant tillering but did not influence final plant population. Straw management had a slight effect on phytomass accumulation. Reduction of phytomass yield was observed from the first to the second ratoon during both seasons at both sites. At Bom Retiro, phytomass yield decreased 37% for stands established during the dry season and 19% for stands established during the wet season. At Univalem, phytomass yield decreased 20% for stands established during the dry season and 30% for stands established during the wet season. Retaining straw in the field (regardless of treatment) increased leaf tissue P content but not stalk yield. Raking straw from row to interrow positions at these locations in southeastern Brazil had no benefit on sugarcane yield but may result in soil compaction and higher production costs over time

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, and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space. While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes, vast areas of the tropics remain understudied. In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity, but it remains among the least known forests in America and is often underrepresented in biodiversity databases. To worsen this situation, human-induced modifications 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, 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

Measurements of Zγ+jets differential cross sections in pp collisions at s√ = 13 TeV with the ATLAS detector

Differential cross-section measurements of Zγ production in association with hadronic jets are presented, using the full 139 fb−1 dataset of s√ = 13 TeV proton–proton collisions collected by the ATLAS detector during Run 2 of the LHC. Distributions are measured using events in which the Z boson decays leptonically and the photon is usually radiated from an initial-state quark. Measurements are made in both one and two observables, including those sensitive to the hard scattering in the event and others which probe additional soft and collinear radiation. Different Standard Model predictions, from both parton-shower Monte Carlo simulation and fixed-order QCD calculations, are compared with the measurements. In general, good agreement is observed between data and predictions from MATRIX and MiNNLOPS, as well as next-to-leading-order predictions from MADGRAPH5_AMC@NLO and SHERPA

Evidence for the charge asymmetry in pp → tt¯ production at s√ = 13 TeV with the ATLAS detector

Inclusive and differential measurements of the top–antitop (tt¯) charge asymmetry Att¯C and the leptonic asymmetry Aℓℓ¯C are presented in proton–proton collisions at s√ = 13 TeV recorded by the ATLAS experiment at the CERN Large Hadron Collider. The measurement uses the complete Run 2 dataset, corresponding to an integrated luminosity of 139 fb−1, combines data in the single-lepton and dilepton channels, and employs reconstruction techniques adapted to both the resolved and boosted topologies. A Bayesian unfolding procedure is performed to correct for detector resolution and acceptance effects. The combined inclusive tt¯ charge asymmetry is measured to be Att¯C = 0.0068 ± 0.0015, which differs from zero by 4.7 standard deviations. Differential measurements are performed as a function of the invariant mass, transverse momentum and longitudinal boost of the tt¯ system. Both the inclusive and differential measurements are found to be compatible with the Standard Model predictions, at next-to-next-to-leading order in quantum chromodynamics perturbation theory with next-to-leading-order electroweak corrections. The measurements are interpreted in the framework of the Standard Model effective field theory, placing competitive bounds on several Wilson coefficients