Sugarcane root length density and distribution from root intersection counting on a trench-profile

Root length density (RLD) is a critical feature in determining crops potential to uptake water and nutrients, but it is difficult to be measured. No standard method is currently available for assessing RLD in the soil. In this study, an in situ method used for other crops for studying root length density and distribution was tested for sugarcane (Saccharum spp.). This method involved root intersection counting (RIC) on a Rhodic Eutrudox profile using grids with 0.05 x 0.05 m and modeling RLD from RIC. The results were compared to a conventional soil core-sampled method (COR) (volume 0.00043 m³). At four dates of the cropping season in three tillage treatments (plowing soil, minimum tillage and direct planting), with eight soil depths divided in 0.1 m soil layer (between 0-0.6 and 1.6-1.8 m) and three horizontal distances from the row (0-0.23, 0.23-0.46 and 0.46-0.69 m), COR and RIC methods presented similar RLD results. A positive relationship between COR and RIC was found (R² = 0.76). The RLD profiles considering the average of the three row distances per depth obtained using COR and RIC (mean of four dates and 12 replications) were close and did not differ at each depth of 0.1 m within a total depth of 0.6 m. Total RLD between 0 and 0.6 m was 7.300 and 7.100 m m-2 for COR and RIC respectively. For time consumption, the RIC method was tenfold less time-consuming than COR and RIC can be carried out in the field with no need to remove soil samples. The RLD distribution in depth and row distance (2-D variability) by RIC can be assessed in relation to the soil properties in the same soil profiles. The RIC method was suitable for studying these 2-D (depth and row distance in the soil profile) relationships between soil, tillage and root distribution in the field.A densidade de comprimento de raízes (DCR) é uma característica importante para determinar o potencial de absorção de água e nutrientes das plantas, mas é difícil de ser medida. Nenhum método padrão está atualmente disponível para avaliar a DCR no solo. Neste estudo, um método in situ usado em outras culturas para estudo da densidade de comprimento e distribuições das raízes foi testado para a cana-de-açúcar (Saccharum spp.). O método envolveu contagem de intersecções de raízes (CIR) no perfil de um Latossolo Vermelho eutroférrico, usando grade com quadrículas de 0.05 x 0.05 m, modelizando a DCR a partir da CIR. Os resultados foram comparados com o método do trado cilíndrico (TRA) (volume de 0.00043 m-3). Em quatro épocas durante o ciclo em três manejos do solo (plantio convencional, cultivo mínimo e plantio direto), em oito profundidades divididas a cada 0.1 m (entre 0 - 0.6 e 1.6 - 1.8 m) e três distâncias horizontais em relação à linha de plantio (0 - 0.23, 0.23 - 0.46 e 0.46 - 0.69 m), os métodos TRA e CIR apresentaram resultados de DCR similares. Encontrou-se positiva entre TRA e CIR (R² = 0,76). As DCRs nos perfis, considerando as médias das três distâncias da linha por profundidade, obtida utilizando-se de TRA e CIR (média de quatro datas e 12 repetições), foram próximas e não diferiram a cada 0.1 m de profundidade até 0.6 m de profundidade. A DCR total entre 0 e 0.6 m foi de 7.300 e 7.100 m m-2 para TRA e CIR, respectivamente. Para o tempo de realização, o método CIR foi 10 vezes mais rápido do que TRA e o método CIR pode ser realizado no campo, sem necessidade de remover amostras de solo. A distribuição da DCR em profundidade e distância da linha (variabilidade 2D) pelo método CIR pode ser avaliada em relação às propriedades do solo nos mesmos perfis do solo. O método CIR foi apropriado para estudos dessas relações 2D (profundidade e distância da linha no perfil do solo) entre solo, manejo e distribuição de raízes no campo

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

TRY plant trait database, enhanced coverage and open access

Plant traits-the morphological, ahawnatomical, 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