Nitrogen fertilizer (15N) leaching in a central pivot fertigated coffee crop

Nitrogen has a complex dynamics in the soil-plant-atmosphere system. N fertilizers are subject to chemical and microbial transformations in soils that can result in significant losses. Considering the cost of fertilizers, the adoption of good management practices like fertigation could improve the N use efficiency by crops. Water balances (WB) were applied to evaluate fertilizer N leaching using 15N labeled urea in west Bahia, Brazil. Three scenarios (2008/2009) were established: i) rainfall + irrigation the full year, ii) rainfall only; and iii) rainfall + irrigation only in the dry season. The water excess was considered equal to the deep drainage for the very flat area (runoff = 0) with a water table located several meters below soil surface (capillary rise = 0). The control volume for water balance calculations was the 0 - 1 m soil layer, considering that it involves the active root system. The water drained below 1 m was used to estimate fertilizer N leaching losses. WB calculations used the mathematic model of Penman-Monteith for evapotranspiration, considering the crop coefficient equal to unity. The high N application rate associated to the high rainfall plus irrigation was found to be the main cause for leaching, which values were 14.7 and 104.5 kg ha-1 for the rates 400 and 800 kg ha-1 of N, corresponding to 3.7 and 13.1 % of the applied fertilizer, respectively.

QuMinS: fast and scalable querying, mining and summarizing multi-modal databases

Given a large image set, in which very few images have labels, how to guess labels for the remaining majority? How to spot images that need brand new labels different from the predefined ones? How to summarize these data to route the user’s attention to what really matters? Here we answer all these questions. Specifically, we propose QuMinS, a fast, scalable solution to two problems: (i) Low-labor labeling (LLL) – given an image set, very few images have labels, find the most appropriate labels for the rest; and (ii) Mining and attention routing – in the same setting, find clusters, the top-'N IND.O' outlier images, and the 'N IND.R' images that best represent the data. Experiments on satellite images spanning up to 2.25 GB show that, contrasting to the state-of-the-art labeling techniques, QuMinS scales linearly on the data size, being up to 40 times faster than top competitors (GCap), still achieving better or equal accuracy, it spots images that potentially require unpredicted labels, and it works even with tiny initial label sets, i.e., nearly five examples. We also report a case study of our method’s practical usage to show that QuMinS is a viable tool for automatic coffee crop detection from remote sensing images.

Coffee is highly tolerant to cadmium, nickel and zinc: Plant and soil nutritional status, metal distribution and bean yield

Sewage sludge has been used to fertilize coffee, increasing the risk of metal contamination in this crop. The aim of this work was to study the effects of Cd, Zn and Ni in adult coffee plants growing under field conditions. Seven-year-old coffee plants growing in the field received one of three;loses of Cd, Zn or Ni: 15,45 and 90 g Cd plant(-1); 35, 105 and 210 g Ni plant(-1); and 100, 300 and 600 g Zn plant(-1), with all three metals in the form of sulphate salts. After three months, we noticed good penetration of the three metals into the soil, especially in the first 50 cm, which is the region where most coffee plant roots are concentrated. Leaf concentrations of K, Ca, Mg, S, B, Cu, Fe and Mn were nor affected. N levels did not change with the application of Ni or Zn but were reduced with either 45 or 90 g Cd plant(-1). Foliar P concentrations decreased with the addition of 45 and 90 g Cd plant(-1) and 600 g Zn plant(-1). Zn levels in leaves were not affected by the application of Cd or Ni. The highest concentrations. of Zn were found in branches (30-230 mg kg(-1)), leaves (7-35 mg kg(-1)) and beam (4-6.5 mg kg(-1)); Ni was found in leaves (4-45 mg kg(-1)), branches (3-18 mg kg(-1)) and beans (1-5 mg kg(-1)); and Cd was found in branches (0-6.2 mg kg(-1)) and beans (0-1.5 mg kg(-1)) but was absent in leaves. The mean yield of two harvests was not affected by Ni, but it decreased at the highest dose of Zn (600 g plant(-1)) and the two higher doses of Cd (45 and 90 g plant(-1)). Plants died when treated with the highest dose of Cd and showed symptoms of toxicity with the highest dose of Zn. Nevertheless, based on the amounts of metal used and the results obtained, we conclude that coffee plants are highly tolerant to the three metals tested. Moreover, even at high doses, there was very little transport to the beans, which is the part consumed by humans. (C) 2011 Elsevier B.V. All rights reserved.