Combined effects of soil biotic and abiotic factors, influenced by sewage sludge incorporation, on the incidence of corn stalk rot.

Abstract: The objectives of this study were to evaluate the combined effects of soil bioticand abiotic factors on the incidence of Fusarium corn stalk rot, during four annual incorporations of two typesofsewagesludge intosoil ina 5-years field assay under tropical conditions and topredict the effectsof these variables on the disease. For each type of sewage sludge, the following treatments were included: control with mineral fertilization recommended for corn; control without fertilization; sewage sludge based on the nitrogen concentration that provided the same amount of nitrogen as in the mineral fertilizer treatment; and sewage sludge that provided two, four and eight times the nitrogen concentration recommended for corn. Increasing dosages of both types of sewage sludge incorporated into soil resulted in increased corn stalk rot incidence, being negatively correlated with corn yield. A global analysis highlighted the effect of the year of the experiment, followed by the sewage sludge dosages. The type of sewage sludge did not affect the disease incidence. Amultiple logistic model using a stepwise procedure was fitted based on the selection of a model that included the three explanatory parameters for disease incidence: electrical conductivity, magnesium and Fusarium population. In the selected model, the probability of higher disease incidence increased with an increase of these three explanatory parameters. When the explanatory parameters were compared, electrical conductivity presented a dominant effect and was the main variable to predict the probability distribution curves of Fusarium corn stalk rot, after sewage sludge application into the soil.

Soil chemical attributes and leaf nutrients of Pacovan banana under two cover crops.

Banana is one of the most consumed fruits in the world, which is grown in most tropical countries. The objective of this work was to evaluate the main attributes of soil fertility in a banana crop under two cover crops and two root development locations. The work was conducted in Curaçá, BA, Brazil, between October 2011 and May 2013, using a randomized block design in split plot with five repetitions. Two cover crops were assessed in the plots, the cover 1 consisting of Pueraria phaseoloid es, and the cover 2 consisting of a crop mix with Sorghum bicolor, Ricinus commun is L., Canavalia ensiform is, Mucuna aterrima and Zea mays, and two soil sampling locations in the subplots, between plants in the banana rows (location 1) and between the banana rows (location 2). There were significant and independent effects for the cover crop and sampling location factors for the variables organic matter, Ca and P, and significant effects for the interaction between cover crops and sampling locations for the variables potassium, magnesium and total exchangeable bases. The cover crop mix and the between-row location presented the highest organic matter content. Potassium was the nutrient with the highest negative variation from the initial content and its leaf content was below the reference value, however not reducing the crop yield. The banana crop associated with crop cover using the crop mix provided greater availability of nutrients in the soil compared to the coverage with tropical kudzu.

Annual crop rotation of tropical pastures with no-till soil as affectedby lime surface application.

Soil acidity and low natural fertility are the main limiting factors for grain production in tropical regionssuch as the Brazilian Cerrado. The application of lime to the surface of no-till soil can improve plant nutrition, dry matter production, crop yields and revenue. The present study, conducted at the Lageado Experimental Farm in Botucatu, State of São Paulo, Brazil, is part of an ongoing research project initi-ated in 2002 to evaluate the long-term effects of the surface application of lime on the soil?s chemical attributes, nutrition and kernel/grain yield of peanut (Arachis hypogaea), white oat (Avena sativa L.) and maize (Zea mays L.) inter cropped with palisade grass (Urochloa brizantha cv. Marandu), as well as the forage dry matter yield of palisade grass in winter/spring, its crude protein concentration, estimated meat production, and revenue in a tropical region with a dry winter during four growing seasons. The experiment was designed in randomized blocks with four replications. The treatments consisted of four rates of lime application (0, 1000, 2000 and 4000 kg ha−1), performed in November 2004. The surface application of limestone to the studied tropical no-till soil was efficient in reducing soil acidity from the surface down to a depth of 0.60 m and resulted in greater availability of P and K at the soil surface. Ca and Mg availability in the soil also increased with the lime application rate, up to a depth of 0.60 m. Nutrient absorption was enhanced with liming, especially regarding the nutrient uptake of K, Ca and Mg by plants.Significant increases in the yield components and kernel/grain yields of peanut, white oat and maize were obtained through the surface application of limestone. The lime rates estimated to achieve the maximum grain yield, especially in white oat and maize, were very close to the rates necessary to increase the base saturation of a soil sample collected at a depth of 0?0.20 m to 70%, indicating that the surface liming of 2000 kg ha−1is effective for the studied tropical no-till soil. This lime rate also increases the forage dry matter yield, crude protein concentration and estimated meat production during winter/spring in the maize-palisade grass inter cropping, provides the highest total and mean net profit during the four growing seasons, and can improve the long-term sustainability of tropical agriculture in the Brazilian Cerrado.

Digital soil mapping using reference area and artificial neural networks.

Digital soil mapping is an alternative for the recognition of soil classes in areas where pedological surveys are not available. The main aim of this study was to obtain a digital soil map using artificial neural networks (ANN) and environmental variables that express soillandscape relationships. This study was carried out in an area of 11,072 ha located in the Barra Bonita municipality, state of São Paulo, Brazil. A soil survey was obtained from a reference area of approximately 500 ha located in the center of the area studied. With the mapping units identified together with the environmental variables elevation, slope, slope plan, slope profile, convergence index, geology and geomorphic surfaces, a supervised classification by ANN was implemented. The neural network simulator used was the Java NNS with the learning algorithm "back propagation." Reference points were collected for evaluating the performance of the digital map produced. The occurrence of soils in the landscape obtained in the reference area was observed in the following digital classification: medium-textured soils at the highest positions of the landscape, originating from sandstone, and clayey loam soils in the end thirds of the hillsides due to the greater presence of basalt. The variables elevation and slope were the most important factors for discriminating soil class through the ANN. An accuracy level of 82% between the reference points and the digital classification was observed. The methodology proposed allowed for a preliminary soil classification of an area not previously mapped using mapping units obtained in a reference area