EFFECT OF CHARCOAL -ENRICHED SUBSTRATE ON SEEDLINGS OF RHIZOBIUM-INOCULATED LEGUME TREES

ABSTRACT Native legume trees are planted in agroforestry systems for their hardiness and symbiosis with soil bacteria of the genus rhizobium, efficient in N2 fixation. The enrichment of the substrate composition with fine charcoal for seedling production of these trees is interesting for increasing soil porosity, water retention and the proliferation of beneficial microorganisms. Experiments were carried out to analyze the effect of substrate enrichment with charcoal on the quality of Clitoria fairchildiana, Enterolobium schomburgkii and Inga edulis seedlings. The treatments consisted of a 3:2:0.5 (v:v) mixture of clay soil, sand and bovine manure and a 3:2 (v:v) mixture of clay soil and sand combined with charcoal rates of 0, 10, 19 and 29%. After mixing the components, substrate samples were collected and chemically analyzed. The experiment was arranged in a completely randomized design with 5 treatments and 10 replications. The seedlings were inoculated with homologues rhizobia and growth controlled monthly. The plants were collected to determine the number of nodules and dry biomass of roots, shoots and nodules. Seedling growth was similar on substrates containing charcoal or manure, except for E. schomburkii, which increased by more than 100% on the charcoal-containing substrates. The number and dry biomass of nodules in the charcoal-containing substrates was up to 100% and 300% higher than in the manure-containing treatment, respectively. The results indicated that the substitution of manure by charcoal favors the seedling quality of the studied species

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