Mapping spatial and temporal potassium balances in Brazilian soils of south-west Goias.

Introduction: Brazil, is one of the main agricultural producers in the world ranking 1st in the production of sugarcane, coffee and oranges. It is also 2nd as world producer of soybeans and a leader in the harvested yields of many other crops. The annual consumption of mineral fertilizers exceeds 20 million mt, 30% of which corresponds to potash fertilizers (ANDA, 2006). From this statistic it may be supposed that fertilizer application in Brazil is rather high, compared with many other countries. However, even if it is assumed that only one fourth of this enormous 8.5 million km2 territory is used for agriculture, average levels of fertilizer application per hectare of arable land are not high enough for sustainable production. One of the major constraints is the relatively low natural fertility status of the soils which contain excessive Fe and Al oxides. Agriculture is also often practised on sandy soils so that the heavy rainfall causes large losses of nutrients through leaching. In general, nutrient removal by crops such as sugarcane and tropical fruits is much more than the average nutrient application via fertilization, especially in regions with a long history of agricultural production. In the recently developed areas, especially in the Cerrado (Brazilian savanna) where agriculture has expanded since 1980, soils are even poorer than in the "old" agricultural regions, and high costs of mineral fertilizers have become a significant input factor in determining soybean, maize and cotton planting. The consumption of mineral fertilizers throughout Brazil is very uneven. According to the 1995/96 Agricultural Census, only in eight of the total of 26 Brazilian states, were 50 per cent or more of the farms treated "systematically" with mineral fertilizers; in many states it was less than 25 per cent, and in five states even less than 12 per cent (Brazilian Institute for Geography and Statistics; Censo Agropecuario1995/96, Instituto Brazileiro de Geografia e Estadistica; IBGE, www.ibge.gov.br). The geographical application distribution pattern of mineral fertilizers may be considered as an important field of research. Understanding geographical disparities in fertilization level requires a complex approach. This includes evaluation of the availability of nutrients in the soil (and related soil properties e.g. CEC and texture), the input of nutrients with fertilizer application, and the removal of nutrients by harvested yields. When all these data are compiled, it is possible to evaluate the balance of particular nutrients for certain areas, and make conclusions as to where agricultural practices should be optimized. This kind of research is somewhat complicated, because it relies on completely different sources of data, usually from incomparable data sources, e.g. soil characteristics attributed to soil type areas, in contrast to yields by administrative regions, or farms. A priority tool in this case is the Geographical Information System (GIS), which enables attribution of data from different fields to the same territorial units, and makes possible integration of these data in an "inputoutput" model, where "input" is the natural availability of a nutrient in the soil plus fertilization, and "output" export of the same nutrient with the removed harvested yield.

Drought impact on rainfed common bean production areas in Brazil.

Common bean production in Goiás, Brazil is concentrated in the same geographic area, but spread acrossthree distinct growing seasons, namely, wet, dry and winter. In the wet and dry seasons, common beansare grown under rainfed conditions, whereas the winter sowing is fully irrigated. The conventional breed-ing program performs all varietal selection stages solely in the winter season, with rainfed environmentsbeing incorporated in the breeding scheme only through the multi environment trials (METs) wherebasically only yield is recorded. As yield is the result of many interacting processes, it is challengingto determine the events (abiotic or biotic) associated with yield reduction in the rainfed environments(wet and dry seasons). To improve our understanding of rainfed dry bean production so as to produceinformation that can assist breeders in their efforts to develop stress-tolerant, high-yielding germplasm,we characterized environments by integrating weather, soil, crop and management factors using cropsimulation models. Crop simulations based on two commonly grown cultivars (Pérola and BRS Radi-ante) and statistical analyses of simulated yield suggest that both rainfed seasons, wet and dry, can bedivided in two groups of environments: highly favorable environment and favorable environment. Forthe wet and dry seasons, the highly favorable environment represents 44% and 58% of production area,respectively. Across all rainfed environment groups, terminal and/or reproductive drought stress occursin roughly one fourth of the seasons (23.9% for Pérola and 24.7% for Radiante), with drought being mostlimiting in the favorable environment group in the dry TPE. Based on our results, we argue that eventhough drought-tailoring might not be warranted, the common bean breeding program should adapttheir selection practices to the range of stresses occurring in the rainfed TPEs to select genotypes moresuitable for these environments.

Parametros de solo em funcao da umidade na capacidade de campo em areas irrigaveis do tropico Semi-Arido brasileiro.

Simula-se, atraves de um modelo matematico sequencial os principais parametros do solo: estimativas de umidade no ponto de murcha permanente (PMP) a 1,5 MPa, e curvas de retencao de agua por meio da correspondencia da umidade do solo no ponto de capacidade de campo (CC). Usando-se tecnicas de superficies de resposta, obtiveram-se as classificacoes texturais em funcao da disponibilidade total de agua no solo (DTA), em m3/ha por cm, que facilitou a aplicacao pratica dos resultados. O modelo, com base na analise de 4.288 amostras, permite estimar as variaveis relacionadas com um grau de significancia superior a 0,01. A validacao do campo mostrou ser aplicavel aos agricultores de areas irrigaveis que ainda nao tem acesso as informacoes obtidas em laboratorio.

Carbon in humic fractions of organic matter in soil treated with organic composts under mango cultivation.

Soil organic matter (SOM) plays a key role in maintaining the productivity of tropical soils, providing energy and substrate for the biological activity and modifying the physical and chemical characteristics that ensure the maintenance of soil quality and the sustainability of ecosystems. This study assessed the medium-term effect (six years) of the application of five organic composts, produced by combining different agro-industrial residues, on accumulation and chemical characteristics of soil organic matter. Treatments were applied in a long-term experiment of organic management of mango (OMM) initiated in 2005 with a randomized block design with four replications. Two external areas, one with conventional mango cultivation (CMM) and the other a fragment of regenerating Caatinga vegetation (RCF), were used as reference areas. Soil samples were collected in the three management systems from the 0.00-0.05, 0.05-0.10, and 0.10-0.20 m layers, and the total organic carbon content and chemical fractions of organic matter were evaluated by determining the C contents of humin and humic and fulvic acids. Organic compost application significantly increased the contents of total C and C in humic substances in the experimental plots, mainly in the surface layer. However, compost 3 (50 % coconut bagasse, 40 % goat manure, 10 % castor bean residues) significantly increased the level of the non-humic fraction, probably due to the higher contents of recalcitrant material in the initial composition. The highest increases from application of the composts were in the humin, followed by the fulvic fraction. Compost application increased the proportion of higher molecular weight components, indicating higher stability of the organic matter.

Physical soil attributes of Conilon coffee (Coffea canephora) under organic and conventional management systems.

Coffea sp. is cultivated in large areas, using both conventional and organic management. However, information about the sustainability of these two management systems is still deficient. The objective of the present study was to evaluate the physical properties of soil cultivated with Conilon coffee (C. canephora) under organic and conventional management. Two areas cultivated with Conilon coffee (under organic and conventional management) and a fragment of Atlantic forest, used as a reference, were selected for the experiment. Soil granulometry, hydraulic conductivity, water retention curve, resistance to penetration, porosity, optimal hydric interval, and other physical characteristics were measured at depths of 0 to 10 and 10 to 20 cm. The data was submitted to multivariate and descriptive statistical analyses. Higher similarity was observed between the soil cultivated with Conilon coffee under organic management and the Atlantic forest soil. Soil resistance to penetration at 10, 30, 100, 500 and 1500 kPa, macro porosity, density and total porosity were the main physical properties that differentiated both management systems studied. The non-use of agricultural machinery and the addition of organic matter may be the main reasons for higher soil sustainability observed under organic management when compared with the conventional system.

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

Energy balance model applied to pasture experimental areas in São Paulo State, Brazil.

The Simple Algorithm for Evapotranspiration Retrieving (SAFER) was used to estimate biophysical parameters and theenergy balance components in two different pasture experimental areas, in the São Paulo state, Brazil. The experimentalpastures consist in six rotational (RGS) and three continuous grazing systems (CGS) paddocks. Landsat-8 images from2013 and 2015 dry and rainy seasons were used, as these presented similar hydrological cycle, with 1,600 mm and 1,613mm of annual precipitation, resulting in 19 cloud-free images. Bands 1 to 7 and thermal bands 10 and 11 were used withweather data from a station located nearthe experimental area. NDVI, biomass, evapotranspiration and latent heat flux(λE) temporal values statistically differ CGS from RGS areas. Grazing systems influences the energy partition and theseresults indicate that RGS benefits biomass production, evapotranspiration and the microclimate, due higher LE values.SAFER is a feasible tool to estimate biophysical parameters and energy balance components in pasture and has potentialto discriminate continuous and rotation grazing systems in a temporal analysis.