SULPHUR AVAILABILITY IN BRAZILIAN SOILS: JOINT EVALUATION OF DRY MATTER YIELD AND SULPHUR UPTAKE AS A FUNCTION OF SOIL AND PLANT ANALYSES

• Autor(a) principal: Kliemann, Huberto José
• Data de Publicação: 2007
• Outros Autores: Malavolta, Eurípedes
• Tipo de documento: Artigo
• Idioma: por
• Título da fonte: Pesquisa Agropecuária Tropical (Online)
• Texto Completo: https://revistas.ufg.br/pat/article/view/2648
• topic: Sulphur; soil-plant; multiple regression; joint analysis; Enxofre; solo/planta; regressão múltipla; análise conjunta

A greenhouse experiment was carried out with surface samples from States of São Paulo, Minas Gerais and Goiás (Brazil). Carbon contents varied from 0.5 to 3.03%, sulphur from 0.0104 to 0.0310% and clay from 8 to 60%. Corn (Zea mays L.) was grown in two successive crops, with three rates of gypsum (0, 20 and 40 ppm S), previously incubated in absence and presence of lime. Soil analyses were made before and after the first cropping. Plant material was analysed after each harvest of the top. By using data from both soil and plant analyses multiple regression equations were calculated. Close relationships were found in the following cases: sandy soils: dry matter production as a function of total S and N/S ratio in the plants and extractable S-SO4= in the soil; accumulation of S in the plants as related to the same variables and soil pH; clayey soils: dry matter as a function of total S and N/S ratio in plants and pH, Ca++, Mg++, H+ + Al+++ and carbon in soils; accumulation of S in the plants as against their N/S and P/S ratios, and calcium phosphate extractable S-SO4=, pH, H+ + Al+++ and carbon in the soils. In sandy soils, sulphur availability depends only on extractable sulphur contents, being unaffected by liming in clayey soils, it is influenced by liming (pH and CTC) and carbon contents. Independently of texture and carbon content of the soils, dry matter yield and sulphur uptake show inverse relationships with corn leaf tissue N/S and P/S ratios. KEY-WORDS: Sulphur; soil-plant; multiple regression; joint analysis.