Evaluation of tropical forages managed for stockpiling purposes

Conduziu-se um experimento objetivando-se determinar espécies adequadas ao manejo para a produção de feno-em-pé e as melhores épocas para diferimento e utilização. Comparou-se sete espécies de gramíneas em parcelas sub-subdivididas, com duas repetições. As parcelas principais foram constituídas pelas gramíneas, as sub parcelas pelas épocas de vedação e as subsubparcelas pelas de utilização. Quanto à produção de matéria seca verde (MSV) destacam-se (P < 0,01) Brachiaria decumbens, B. humidicola e Cynodon plectostachuys. Essas três gramíneas, independente da época de vedação, apresentaram decréscimos (P < 0,05) nas percentagens de MSV, no conteúdo de proteína bruta e na digestibilidade in vitro da matéria seca, durante o período de utilização. Apesar do decréscimo na percentagem de MSV estas mantiveram disponibilidade de MSV superior a 2000 kg/ha, durante todo o período. A melhor época da vedação para a B. humidicola foi janeiro e para B. decumbens e C. plectostachuys janeiro e fevereiro. Entretanto, para as duas últimas, caso a utilização seja no final da estação seca, é recomendável vedá-las em marçoAn experiment was conducted with the objectives of determining species of grasses adapted to stockpiling as well as establishing the best dates for deferment and utilization of the forage accumulated. Seven grass species were studied in a randomized block with split-split-plot design, in two replications. The grass species constituted the main plots, the deferring months the sub-plots and the utilization months the sub-sub-plots. Brachiaria decumbens, B. humidicola and Cynodon plectostachuys were superiors (P < 0.01) to the others, in terms of green dry matter (GDM). All three grasses, irrespective of diferring dates, showed reductions (P < 0.05) in green matter percentage (GMP), crude protein content and dry matter in vitro digestibility towards the later utilization dates. However, despite the reductions in GMP they were able to maintain over 2000 kg/ha of available GDM, at all utilization dates. The most adequate month for diferring B. humidicola was January, and for 8. decumbens and C. plectostachuys both January and February. However, if the two latters are to be used in the late dry season, they should be deferred in March

TRY plant trait database – enhanced coverage and open access

Plant traits—the morphological, anatomical, physiological, biochemical and phenological characteristics of plants—determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits—almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives